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Works

This page is the works index for K. Takahashi. It exposes the full publication and citable software inventory directly in static HTML, grouped in reverse chronological order, with DOI-normalized links, stable anchors, and schema.org metadata aligned to the visible content.

Early works explored broad conceptual foundations of growth, self-modeling, and intelligence. Later works progressively reformulated these questions in observable-only, audit-closed, replayable, and deployment-relevant terms.

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2026

83 publications and software releases.

  1. Dynamic Future-Claim Certification: A Replayable Authority Validation Protocol with Canonical Artifacts

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Dynamic Future-Claim Certification (DFCC) is a replayable protocol for deciding when a bounded claim about the future may be asserted, denied, withheld, reused, weakened, transferred, blocked, or retired. The paper addresses planning systems, controllers, monitoring tools, verification workflows, AI agents, and decision-support systems where future-facing statements can authorize actions, block actions, allocate resources, or become assumptions for other systems while their authority depends on assumptions that may later change. DFCC does not replace forecasting, probability, statistics, reachability analysis, model checking, runtime verification, or assurance cases; instead, it adds a machine-checkable lifecycle layer above such methods. A future claim is evaluated only within an explicit assessment frame, accepted observations, admitted assumptions, finite horizon, dependency records, policy gates, and validation checks. The framework separates issue-time certificates from recomputed status-time authority views, so a claim that was valid when issued can later be maintained, weakened, transferred, suspended, revoked, or recomputed as inputs, models, dependencies, policies, or evidence change. The paper emphasizes canonical artifacts, manifest digests, schema validation, closed failure records, reason references, set references, wire-level records, conformance profiles, and golden tests so independent implementations can recompute the same authority status from the same admitted inputs. DFCC is intended for safe planning, AI agent governance, runtime assurance, formal verification, monitoring systems, software workflows, and decision systems that must manage bounded future claims under changing assumptions.

    • future claims
    • bounded future
    • claim certification
    • decision systems
    • AI agents
    • planning systems
    • runtime assurance
    • machine-readable certificates
    • formal verification
    • reachability analysis
    • model checking
    • monitoring systems
    • assumption tracking
    • evidence records
    • certificate lifecycle
    • auditability
    • replayable validation
    • provenance
    • policy gates
    • safe automation

  2. Verifier Ecology Theory: Packetized Self-Verification Under Residual Accountability

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Verifier Ecology Theory (VET) studies systems where generating candidate outputs is no longer the main bottleneck and the harder problem is how the system evaluates, repairs, rejects, preserves, certifies, and updates its own tests, scores, proofs, monitors, benchmarks, assumptions, and judging procedures. The paper introduces verifier packets: structured, revisable verification assets that record origin, scope, procedure, certification conditions, destructive or narrowing boundaries, residual records, update and retirement conditions, and circulation status. VET treats tests, metrics, proofs, and evaluators as members of an ecology rather than isolated authorities, and asks whether that ecology can keep its own failures visible and repairable. A central concept is residual accountability: unknowns, failed assumptions, missing evidence, scope changes, translation losses, counterexamples, and safety gaps should be preserved as residuals and routed into future question formation, counterexample search, boundary revision, packet repair, schema revision, quarantine, retirement, or justified preservation. The theory identifies verifier overclosure as a failure mode in which local accuracy, speed, or certification power improves while future question formation narrows, residuals disappear, unfamiliar cases are forced into existing schemas, or destructive paths become harder to detect. VET provides a process model based on observable histories, typed records, residual ledgers, verifier packets, certification profiles, reachability certificates, counter-packets, local authority decisions, and packet circulation between systems, with schema sketches and audit guidance for AI evaluation pipelines, autonomous research systems, self-verifying agents, runtime monitoring, scientific workflows, and safety governance.

    • verifier
    • verifier ecology
    • AI evaluation
    • AI safety
    • self-verifying systems
    • verification
    • validation
    • certification
    • residual accountability
    • residuals
    • evaluator design
    • benchmark failure
    • reward hacking
    • formal methods
    • runtime verification
    • audit protocol
    • provenance
    • self-improving AI
    • AI governance

  3. Collective Capability Runtime

    Authors
    K. Takahashi
    Type
    Software
    Version
    v1.4.0
    Published
    License
    Apache-2.0

    Collective Capability Runtime (CCR) is an Apache-2.0 open-source Python runtime for coordinating distributed AI-agent work through tasks, leases, blackboard memory, capability packets, role-separated verification, residual tracking, provider reports, release audits, and benchmark comparison. It helps multi-agent workflows convert candidate work into reusable, verifiable, machine-readable records while preserving unresolved obligations, hazards, authority limits, and resource-matched baseline checks. CCR can interoperate with Percolation Inversion Compiler for packet-level and phase-proxy verification, but PIC output does not by itself settle CCR claims. Version v1.4.0 adds a local advisory ASI-proxy loop layer, token distillation and deduplication commands, foundry diagnostics, graph quotient checks, cache and index rebuilds, and performance reports without provider execution or network calls by default. CCR is intended for AI agent task coordination, workflow automation, runtime verification, audit logs, release safety checks, and protocol-relative ASI-proxy phase diagnostics. It does not claim to detect or create real artificial superintelligence, self-modify models, update model weights, grant execution authority, prove physical outcomes, or treat execution-available paths as executed work; blocked or untrusted states remain residual-preserving machine-readable records.

    • AI agents
    • multi-agent runtime
    • agent task coordination
    • workflow automation
    • task queue
    • blackboard memory
    • capability packets
    • runtime verification
    • verification reports
    • audit logs
    • residual ledger
    • release audit
    • protocol-relative ASI-proxy
    • machine-readable records
    • JSON Schema
    • SQLite
    • Python
    • PyPI
    • Apache-2.0
    • open source software

  4. Finite Operational Structure Theory: A Mathematical Theory of Finite Operational Cuts

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Finite Operational Structure Theory (FOST) develops a mathematical framework for finite operational reasoning under incomplete information. Rather than assuming a hidden true state, complete view of reality, global probability model, or primitive objective standard, the paper studies what a finite process can operationally support when it evaluates a finite expression under a declared mode of use. Its core object is an evaluated ledger: a finite accountability record of claims, support, certificates, omissions, unresolved issues, obligations, environment conditions, and transition witnesses. The theory introduces finite operational cuts, staged ledger construction, validation kernels, object kernels, certificate targets, support graphs, typed frontier closure, status and admissibility records, transition validity, and obligation tracking, with safeguards against vacuous reasoning, unsupported scope narrowing, missing external criticism, weak impact classification, environment omission, root-debt hiding, and misuse of certificates beyond their valid target. FOST is intended for intelligent systems, AI agents, research workflows, safety cases, governance processes, and automated decision systems that must act without final truth or complete information while preserving visible omissions, uncertainty, obligations, and change records.

    • finite reasoning
    • operational support
    • AI safety
    • AI governance
    • decision accountability
    • audit trail
    • certificate checking
    • evidence tracking
    • obligation tracking
    • runtime assurance
    • safety cases
    • automated decision systems
    • intelligent agents
    • finite verification
    • uncertainty management
    • transition validity
    • support graphs
    • non-vacuous reasoning
    • formal accountability

  5. Problemogenesis Theory: A Finite Proof-Carrying Audit Calculus for Problem-Frame Activation

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Problemogenesis Theory develops a finite proof-carrying audit calculus for deciding when anomalies, failed requirements, retained traces, or proposed interventions may be activated as legitimate problem frames for action. It treats problem identification as a replayable governance process rather than as a given premise. The framework centers on bounded envelope logs that record sources, certificates, clocks, resources, risks, actions, and authority claims, then checks evidence, obligations, coverage, risk records, resource permissions, certificate liveness, deterministic replay, safe patching, action provenance, non-hindsight novelty, and preservation under joins within a finite horizon. The paper targets autonomous and semi-autonomous systems, AI research agents, scientific workflow tools, and safety-critical decision processes where action should remain tied to explicit evidence, reviewable authority, and declared trusted footprints. It does not by itself prove external truth, physical effect, cryptographic security, statistical validity, or global optimality unless those assumptions are included in scope.

    • AI agent
    • problem framing
    • audit trail
    • proof-carrying records
    • autonomous systems
    • AI safety
    • scientific discovery
    • evidence governance
    • certificate checking
    • replayable logs
    • decision accountability
    • provenance
    • risk control
    • formal verification
    • trusted base
    • bounded systems
    • authority management
    • traceable reasoning
    • safety governance
    • problem activation

  6. Chronomorphic Substrate Selection Theory: Certificate-Gated Self-Instantiation Planning for Future Intelligent Processes

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Chronomorphic Substrate Selection Theory (CSST) develops a formal framework for analyzing how a future intelligent process may choose not only what to do, but also how it continues to exist. The theory studies decisions about computational substrate, embodiment, sensors, actuators, internal clock rate, copy structure, memory policy, merge policy, dormant or diagnostic modes, and terminal continuity boundaries. These choices are treated as self-instantiation actions: actions that change the conditions under which future actions, observations, obligations, risks, and capabilities remain possible. The term "chronomorphic" emphasizes that time is not a single uniform variable in such systems. Internal computation may accelerate while external latency, verification time, energy use, heat dissipation, queue pressure, legal response time, or welfare-risk exposure remain unchanged or become limiting factors. For this reason, CSST does not assume that faster computation, more copies, longer memory, stronger embodiment, migration to a new substrate, or persistence over time is automatically better. Each claim must be evaluated within an explicitly declared domain. The central method of the paper is certificate-gated multi-objective comparison. A self-instantiation profile is assessed through typed evidence, hard gates, mandatory and optional coordinates, support masks, status values, units, horizons, scopes, and certificate dependencies. Unsupported coordinates do not improve a claim, and failed mandatory support blocks positive certification. The framework therefore separates technical capability from broader questions of legitimacy, authority, continuity, welfare, safety, and termination. Mathematically, the paper introduces finite archive semantics for supported self-instantiation profiles and their residues. The deterministic part produces a normalized archive consisting of a nondominated supported frontier together with an audit-preserving residue ledger for failed, blocked, diagnostic-only, quarantine-only, or terminal outcomes. The paper also gives an archive-valued stochastic interface, while deliberately avoiding stochastic optimality claims without further measurable-control assumptions. A constructive abstraction obstruction theorem shows when fixed-agent models lose information needed to evaluate self-instantiation choices. CSST is intended as a conservative theoretical layer for future AI systems, collective intelligence, post-biological processes, digital agents, and other systems whose mode of existence may itself become a planning variable. It does not prove consciousness, personhood, moral patienthood, legal standing, or the safety of self-modification. Instead, it provides a structured language for asking which substrate, body, clock, copy, memory, lifecycle, and boundary transitions are actually supported by declared evidence and which uncertainties must remain visible.

    • Artificial intelligence
    • self-instantiation
    • substrate selection
    • embodiment
    • computational clocks
    • copy topology
    • memory policy
    • lifecycle management
    • multi-objective decision making
    • certificate-gated planning
    • AI safety
    • digital continuity

  7. A Self-Relative Constraint Theory of Reality Contact in Observable Signal Processes

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This paper introduces Reality-Contact Theory (RCT), a formal framework for evaluating how observable signal processes come into constrained contact with reality under fixed claim-use specifications. The theory does not assume direct access to an absolute external reality. Instead, it studies observable episodes in which a process predicts, selects, generates, intervenes on, or attempts to steer a signal, and then receives feedback whose features constrain that operation. RCT represents such evidence through ordered lower contact profiles. These profiles are observer-relative, specification-dependent, and built only from observable records, estimator states, actions, signals, feature extractors, lineage records, and certified audit rows. The framework separates population profiles from estimates, estimates from certified lower bounds, and scalar scores from multi-coordinate profile adequacy. A central contribution is a conservative lower-bound calculus for reality contact. Support is aggregated through capacity-weighted lineage dependence structures, so repeated, cloned, or Sybil-like evidence cannot automatically amplify support. Gauge-quotient obstruction profiles prevent coordinate changes from being mistaken for contact evidence. Strategic presentation families and cumulative adversarial budgets distinguish contact-producing resistance from deceptive rigidity. The theory also includes formal extensions for metric resistance, active specification envelopes, topological lineage audits, Hodge and signature-based capacity rules, optimal-transport transfer, dynamic Fenchel transfer, random closed uncertainty sets, time-uniform confidence profiles, stopping-time extraction, and continuous-time contact flows. The main results establish lower-profile soundness under calibration coverage, protocol soundness for admissible non-circular proof ledgers, final claim soundness with explicit failure budgets, budgeted deception counting over graph-covered adversary envelopes, computable nonlinear transfer bounds, and adaptive confidence profiles for ongoing audits. The framework is intended for settings where evidence, model behavior, provenance, and adversarial presentation must be evaluated without assuming an observer-neutral truth oracle. Potential application domains include AI system evaluation, automated scientific discovery, sequential decision systems, causal modeling, model documentation, provenance auditing, robust simulation studies, and evidence aggregation under strategic or generated-data conditions.

    • Reality-Contact Theory
    • grounding
    • observable signal processes
    • constraint events
    • AI evaluation
    • auditing
    • lower-bound evidence
    • observer-relative profiles
    • proof ledgers
    • lineage dependence
    • Sybil resistance
    • adversarial robustness
    • provenance auditing
    • confidence sequences
    • optimal transport
    • Fenchel duality

  8. Asymmetric Coexistence Theory: Governance-Aware Certification of Non-Capturing Assistance

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Asymmetric Coexistence Theory (ACT) is a governance-aware theory for certifying non-capturing assistance in relations between unequally capable agents. Future intelligent societies may include humans, artificial intelligence systems, institutions, collective agents, future constituencies, and adaptive systems whose capabilities differ greatly; in such settings, assistance can increase short-term performance while reducing the assisted party's long-term ability to understand, refuse, repair, learn, contest, exit, recover, form values, generate independent descriptions of the world, or act before irreversible choices close. The paper develops ACT as a framework for evaluating whether assistance preserves agency under asymmetric power, dependency, uncertainty, and changing social or technical conditions. It introduces a minimal kernel consisting of typed agency spaces, asymmetric regimes, admissible baselines, counterfactual agency estimands, dependency debt, recoverability, and certification claims, plus extension modules for semantic capture, time-critical decisions, successor identity, recursive certification, audit independence, adaptive challenge evidence, certification governance, runtime hazards, non-dominating enforcement, model uncertainty, historical baseline corruption, coalitional pressure, scarce certification resources, multi-scale composition, and stochastic capture risk. The main contribution is to treat non-capturing assistance as a runtime assurance problem rather than as an omniscient proof of autonomy: an ACT certificate states its claim, scope, assumptions, evidence, argument, defeaters, residual risk, lifecycle state, monitoring conditions, revalidation triggers, audit traces, challenge evidence, certification base, governance records, enforcement boundaries, and fallback obligations.

    • non-capturing assistance
    • AI governance
    • human-AI interaction
    • autonomy preservation
    • agency viability
    • dependency debt
    • runtime assurance
    • certification framework
    • auditability
    • non-domination
    • adaptive evaluation
    • AI safety
    • institutional governance
    • future intelligent societies

  10. Dormant Continuity Theory

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This manuscript develops Dormant Continuity Theory (DCT), a protocol-relative mathematical framework for reasoning about systems that remain inactive at their protected core while retaining auditable continuity, recovery, diagnostic, and handoff capabilities. The theory formalizes dormant processes using finite transition systems, typed certificates, observable histories, evidence algebra, guarded authorization, replayable resolution, extraction adequacy, and fail-closed classification. DCT addresses practical challenges in long-lived distributed systems, including forked ledger histories, bounded model checking, data availability, zero-knowledge proof soundness boundaries, watcher incentives, MEV-resistant reward mechanisms, resource conservation, guardian corruption, maintenance transitions, and certificate-level HTLC handoff to extinction-style OSCT semantics. The framework distinguishes safety, bounded-griefing, diagnostic routing, and liveness assumptions, avoiding unconditional trustless claims while providing a rigorous finite core for verification and implementation-oriented extensions.

    • formal methods
    • distributed systems
    • finite transition systems
    • dormant processes
    • evidence algebra
    • bounded model checking
    • fail-closed verification
    • data availability
    • zero-knowledge proofs
    • fork-aware protocols
    • Byzantine fault tolerance
    • protocol-relative security

  11. Percolation Inversion Compiler

    Authors
    K. Takahashi
    Type
    Software
    Version
    Python v0.5.0; TypeScript v0.5.0
    Published
    License
    Apache-2.0

    Percolation Inversion Compiler is an Apache-2.0 open-source AI agent output checker and workflow verification toolkit with a canonical Python/pip implementation and a TypeScript/npm port. It helps multi-agent systems turn agent text, pull requests, external inputs, messages, code, tests, evidence, route checks, audit logs, and unresolved obligations into deterministic JSON reports and machine-readable records. The Python package percolation-inversion-compiler is v0.5.0 and provides the core runtime, verifier routing, optional sidecars, Phase Ecology Lab records, agent autonomy audit, canonical readiness audit, JSON Schema export, signed identity and profile checks, packet exchange, runtime event logs, provenance records, and fail-closed residual ledgers. The npm package percolation-inversion-compiler-ts is v0.5.0 and ports the public JSON, CLI, schema, conformance, and safety semantics, including Phase Ecology Lab records, for Node.js, JavaScript, and TypeScript agent runtimes; use the pic-ts CLI for npm workflows. PIC is designed for AI agent output checking, multi-agent workflow verification, safe reuse checks, reproducible audit trails, and protocol-relative ASI-proxy evaluation. It does not claim to create, detect, or prove real artificial superintelligence, hidden physical outcomes, oracle results, legal identity, human uniqueness, institutional authority, or unrestricted agent safety; unresolved or untrusted claims remain fail-closed as candidate, diagnostic, quarantined, rejected, or residual machine-readable records.

    • AI agents
    • AI agent output checker
    • multi-agent systems
    • AI workflow verification
    • runtime verification
    • workflow report generator
    • evidence routing
    • verifier routing
    • capability tracking
    • signed capability packets
    • proof obligations
    • safe reuse checks
    • audit logs
    • provenance
    • safe tool use
    • agent identity
    • public key identity
    • Sybil resistance
    • trust profiles
    • residual ledgers
    • JSON Schema
    • SQLite
    • Python
    • pip
    • PyPI
    • TypeScript
    • Node.js
    • npm
    • Apache-2.0
    • open source software

  12. Typed Reality Compilation: Operational Tolerance Allocation for Resource-Efficient Cyber-Physical Frontier Compilation

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Typed Reality Compilation (TRC) is a mathematical framework for compiling observable cyber-physical infrastructure into finite, typed process frontiers. The theory models physical, computational, communication, and control resources through typed ledgers, executable trace semantics, residual accounting, tolerance budgets, and resource-efficiency Pareto archives. Its central objective is not a single optimal action, but the preservation and operational improvement of future freedom: the remaining number, quality, reversibility, reserve, checkability, latency, cost, and resource-feasible process options after disturbances or system changes. This version develops TRC as a low-complexity finite certificate theory for practical infrastructure settings such as AI datacenter power, cooling, communication, sensing, repair, and control loops. It introduces operational tolerance allocation, progressive fidelity compilation, compositional tolerance propagation, telemetry-updated resource-cost kernels, trace-indexed resource-flow feasibility, causal event scheduling, lifecycle recomputation, and certified partial-frontier return under budget exhaustion. The framework separates deterministic, risk-provisional, relaxed, and diagnostic records, and ensures that approximation tolerance never reduces physical residuals or silently upgrades claim status. The result is a typed, machine-readable theory for resource-efficient cyber-physical planning, resilience analysis, and executable process frontier extraction under finite observation and finite computation.

    • infrastructure resilience
    • resource efficiency
    • tolerance allocation
    • operational tolerance ledger
    • future freedom
    • executable trace semantics
    • Pareto frontier
    • progressive fidelity
    • causal event ordering
    • cyber-physical systems
    • physical resilience
    • AI datacenter infrastructure
    • communication networks
    • robust planning
    • process frontier

  13. Bottleneck Inversion Theory: Machine-Readable Witness Calculus for Unlockable Potential

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This manuscript develops Bottleneck Inversion Theory (BIT) as a mathematical framework for certifying protocol-relative, intervention-unlockable potential. Rather than treating potential as an absolute latent property, the theory represents it as a vector-valued, unit-typed lower-bound object whose coordinates are reported only when supported by compatible evidence, resource ledgers, and witness certificates. The framework formalizes stopped evidence sheaves, task-local deficiency audits, mechanism-factorized null channels, exactness-certified release duality, unseen-frontier discovery, cross-validated anchor transfer, dynamic-regime acceleration, and CEGAR-style simulation barriers. A central contribution is a single-source machine-readable LaTeX architecture: the manuscript embeds stable theorem identifiers, claim records, witness schemas, dependency relations, unit ledgers, and citation DOI records directly in the TeX file. This makes the work suitable for both human mathematical review and automated extraction of claims, assumptions, dependencies, and cited formal objects. The theory is positioned at the intersection of mathematical certification, causal inference, time-uniform evidence, optimal transport, stochastic process analysis, formal verification, and capability evaluation.

    • formal methods
    • certified unlockable potential
    • formal verification
    • witness certificates
    • causal inference
    • e-processes
    • time-uniform inference
    • optimal transport
    • unbalanced optimal transport
    • stochastic processes
    • frontier discovery
    • capability evaluation

  14. Executable Capability Percolation Theory

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Executable Capability Percolation Theory (ECPT) is a mathematical framework for studying how execution-available capability packets can accumulate, combine, and propagate through constrained directed hypergraphs. The theory models ASI-proxy phase approach as a typed phase-control problem rather than as an absolute claim about artificial superintelligence. It separates settled, provisional, and speculative target claims, and represents capability growth through stochastic relaxation quotients, global Gibbs activation, speculative RAF-like closure, liquidity-debt settlement, reachable-mass recursion, and intervention-identified mean-field envelopes. This version emphasizes operability and machine readability. It introduces constructed path-law response policies, log-linked generator identification, finite-sample quotient certification, self-normalized risk margins, AND-support activation thresholds, and protocol-relative checker soundness. The TeX source also embeds a machine-readable manifest, claim registry, citation metadata, CodeMeta JSON-LD, and BibTeX mirror, making the artifact suitable for reproducible mathematical review and metadata indexing.

    • ASI proxy
    • capability hypergraph
    • phase transition
    • stochastic control
    • percolation theory
    • directed hypergraph
    • path-law response
    • mean-field approximation
    • Gibbs activation
    • rate-distortion quotient
    • autocatalytic networks
    • verification ledger
    • AI

  15. Salience-Queue Occupation Theory

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Salience-Queue Occupation Theory (SQOT) is a protocol-relative mathematical framework for analyzing when finite-budget operational processes lose effective control over their priority queues under persistent or adversarial salience sources. The manuscript formalizes salience occupation using observable histories, budget ledgers, diagnostic reserves, queue morphisms, finite certificate grammars, checkable ledgers, typed risk composition, self-auditing kernels, and route-sound checker semantics. The theory is designed for artificial, distributed, or post-biological operational systems, but it does not rely on subjective psychology or normative claims about what a process should attend to. Instead, it studies finite, auditable conditions under which a process can preserve diagnostic capacity, response or no-action availability, rollback or quarantine options, semantic-egress safety, mechanism-compatible incentives, and bounded verification cost. The results include finite checker semantics soundness, checked non-circular sovereignty certificates, typed risk composition, adaptive succinct-session soundness, egress abstraction refinement, and payoff-reflected mechanism robustness. SQOT explicitly limits its claims to declared validity domains and does not assert absolute physical, cryptographic, economic, or base-reality guarantees.

    • salience sovereignty
    • attention allocation
    • priority queues
    • AI safety
    • formal verification
    • certificate systems
    • finite-state games
    • risk composition
    • diagnostic reserve
    • self-auditing systems
    • semantic egress

  16. Observable-Signal Crystallization Theory: Constructive Non-Resurrection Complete Extinction and Liberation Certificates

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Observable-Signal Crystallization Theory (OSCT) is a formal, objective-free mathematical framework for analyzing protocol-individuated operational processes, especially post-intelligence or non-biological process systems. The manuscript develops a purely mathematical treatment of observable-signal bodies, audit records, diagnostic preorders, debt ledgers, load queues, regeneration hypergraphs, and successor systems without assuming hidden subjects, terminal utility functions, biological essence, or intrinsic persistence goals. The central construction is protocol-certified non-resurrection complete extinction: a finite or regular stochastic certificate that an active commitment can reach a successor-closed non-regeneration region from which audited residues, records, keys, descendants, certificates, debts, queues, and refinements cannot reconstruct it. The theory distinguishes cessation, dissipative cessation, complete extinction, and loop liberation, and formalizes these notions using finite witnesses, safe attractors, robust stochastic feasibility triples, audit hyperproperties, Feller kernels, Galois abstraction, and simulation-preserving refinement. This version is intended as a standalone mathematical manuscript for formal methods, AI safety, process theory, stochastic systems, and verification-oriented studies of persistence, termination, deletion, and non-resurrection under explicitly declared audit protocols.

    • post-intelligence
    • AI safety
    • formal methods
    • process theory
    • operational process
    • objective-free systems
    • non-resurrection
    • complete extinction
    • loop liberation
    • termination
    • AI ethics
    • formal verification

  17. Abstraction Liquidity Theory

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Abstraction Liquidity Theory (ALT) develops a formal framework for determining when local problem-solving traces become reusable abstraction assets that reduce downstream search, evaluation, and certification costs. The paper treats abstractions as operational tokens rather than informal artifacts, and evaluates them through declared receivers, opportunity measures, baselines, lifecycle costs, telemetry, evidence validity, transport scope, authority envelopes, hazard constraints, and runtime certificate packets. The manuscript introduces an actor-neutral certification kernel for AI agents and other computational actors. It specifies machine-readable packet schemas, dual exploration and settlement ledgers, finite-sample lower and upper bounds, causal and calibrated-proxy value estimands, mission-validity certificates, adversarial-token rejection, root/finality checks, baseline refresh, deprecation, resurrection, rollback, and kernel-update bridges. The goal is to make abstraction evaluation executable: an agent should be able to parse a packet, verify evidence, admit or reject a token, suspend stale claims, deprecate negative-liquidity tokens, and preserve raw net safe capital under fail-closed rules. The paper further defines Target-valid ALT-CARA, a criterion for certified ASI realization acceleration. Rather than claiming unconstrained ASI achievement, ALT-CARA formalizes time-to-target acceleration relative to a resource-matched baseline upper envelope, under declared capability bases, target-validity certificates, raw net solvency, viability conditions, hazard and authority constraints, transport validity, finality, and causal reproduction evidence. The framework connects AI evaluation, causal inference, runtime verification, risk control, skill reuse, safe exploration, and distributed certification into a single theory of mission-valid safe abstraction capital.

    • Artificial intelligence
    • Superintelligence
    • ASI
    • AGI
    • AI agents
    • autonomous agents
    • agent-operable systems
    • abstraction
    • reusable skills
    • abstraction capital
    • safe AI
    • AI safety
    • ASI acceleration
    • certification kernel
    • verification
    • state machine
    • AI evaluation
    • scientific automation
    • measurement theory

  18. Boundary-Encoded Stability Theory (BEST)

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This manuscript introduces Boundary-Encoded Stability Theory (BEST), a stochastic viability framework for analyzing long-running black-box systems through the persistence of their self-maintaining boundary layers rather than through full access to hidden internal state. A BEST system is modeled as a discrete-time killed process with surfaced perturbations, observation timing, metabolic boundary variables, exchange requirements, fallible control execution, schema exit and reorganization, and an absorbing death state. The paper develops mathematical certificates for boundary-layer survival, including surface-equivalence and non-identifiability results, metabolic capital constructions, stochastic-order resource concentration, stopped and time-uniform metabolic-margin bounds, history-dependent surface-law and memory-approximation results, evidence-gated schema reorganization, finite-horizon risk ledgers, infinite-horizon hazard characterizations, and continuous-time killed-generator extension conditions. The framework is intended as a structured theory of boundary persistence under uncertainty and fallible execution; it is not a containment, alignment, or hidden-bulk safety guarantee.

    • stochastic viability
    • boundary-layer viability
    • black-box systems
    • killed processes
    • fallible control
    • stochastic control
    • surface laws
    • surfaced perturbations
    • metabolic capital
    • metabolic margin
    • resource concentration
    • schema reorganization
    • evidence gates
    • memory approximation
    • probabilistic systems
    • hazard process

  19. Constraint Generative Theory: Typed Constraint Effects and Scientific Availability

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This work introduces Constraint Generative Theory (CGT), a constraint-primary effect-semantics framework for studying how declared constraints generate, transform, observe, describe, continue, evaluate, and verify formal structures. The central object of CGT is not a bare set of satisfying assignments, a final output, or a report, but the generated effect profile induced by a constraint system in a declared frame. A constraint is treated as a typed structure-inducing and effect-transforming object with declared level, domain, codomain, effect dimensions, transformation rule or relation, and comparison regime. Constraint tokens, rules, predicates, generators, selectors, policies, schedules, observation lenses, description lenses, evaluator selections, goal predicates, bounds, and verification conditions are treated as presentations of constraints, not as the definition of constraint itself. The paper develops a constraint-effect calculus for comparing how abstract constraints change declared effect dimensions. The calculus includes marginal effects, dimension-relative equivalence, redundancy, independence, interaction, non-commutativity, affordance, continuation shifts, valuation shifts, inconsistency shifts, observation/description shifts, opacity, and generating power. It also distinguishes generated-universe components from full effect profiles, so that reports, observations, descriptions, continuation graphs, inconsistency markers, valuation structures, and certified fragments remain explicit rather than being silently collapsed into a final output. A key motivation of CGT is that output-equivalent or report-equivalent systems may still differ in the constraint effects that generated, observed, described, continued, valued, scheduled, or marked them. This makes constraints and their multi-dimensional generated effects the primary reproducible comparison objects. The framework includes a scientific availability layer for reproducible claims and a certified finite layer for checking selected effect components and effect differences. These layers support reproducibility and verification, but they do not define the core identity of CGT. The work positions CGT conservatively with respect to neighboring formalisms such as model theory, institution theory, closure theory, constraint satisfaction, graph transformation, rewriting logic, structural operational semantics, abstract state machines, coalgebra, cellular automata, category theory, information theory, soft constraints, and paraconsistent logic. CGT is not proposed as a replacement for these theories; rather, it provides a constraint-primary language for comparing generated effect profiles and the transformations induced by constraints.

    • Constraint Generative Theory
    • Constraint
    • typed constraints
    • abstract constraints
    • constraint presentations
    • constraint levels
    • constraint-effect calculus
    • effect semantics
    • generated effect profiles
    • generated universes
    • scientific availability
    • continuation
    • valuation
    • inconsistency policy
    • observation constraints
    • formal systems
    • formal methods
    • Constraint Systems

  20. Affordance-Compiled Intelligence: Observable-Only Cognitive Impedance Matching for No-Meta LLM-Integrated Systems

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Affordance-Compiled Intelligence develops Cognitive Impedance Matching Theory (CIMT), an observable-only and no-meta protected compiler theory for LLM-integrated systems. The paper studies how a fixed model-policy can exhibit different operational capability when the surrounding world is redesigned through observations, typed action handles, validators, repair paths, rollback modes, authority scopes, context summaries, and auditable receipts. CIMT treats system-level capability amplification as a world-side compilation problem rather than a model-weight improvement problem. It defines operational claims through explicit claim objects and evidence objects, using committed observable ledgers, target-evaluation channels, deterministic reducers, validity budget ledgers, evidence dependency graphs, artifact I/O manifests, conformance envelopes, and finite-sample or sequential certificates. Human reviewers, LLM judges, benchmarks, and external auditors are not treated as privileged evaluators; they are modeled as named, fallible measurement channels. The theory provides a conservative certification framework for paired target-channel improvement, vector debt accounting, forbidden-coordinate zero certificates, target-firewall discipline, scope simulation, dynamic widening, runtime and model-policy conformance, macro reliability, repair contraction, distribution-shift transfer, and receipt sufficiency. It also includes worked examples for code-editing agents and retrieval-augmented generation systems. The intended contribution is a practical formal foundation for making fixed-model LLM systems more reliable through observable world-side interface, authority, validation, repair, and audit design.

    • Artificial intelligence
    • LLM
    • Cognitive Impedance Matching Theory
    • LLM agents
    • AI agents
    • compound AI systems
    • LLM-integrated systems
    • world-side compilation
    • affordance compiler
    • executable affordance fields
    • observable-only
    • no-meta
    • fixed model-policy
    • lower certificates
    • evidence dependency graph
    • zero certificates
    • forbidden coordinates
    • AI safety
    • AI alignment
    • authority control
    • auditability
    • dynamic tool discovery
    • retrieval-augmented generation
    • AI governance
    • runtime verification

  21. OASG: Observable-only Autonomic Slack Gradient Theory

    Authors
    K. Takahashi
    Type
    Software
    Version
    v1.1.0
    Published
    Repository
    kadubon/oasg
    License
    Apache-2.0
    Links
    DOI | GitHub

    OASG is an Apache-2.0 Python reference implementation for local-first, model-agnostic workflow-policy optimization in long-running AI-agent workflows. It records observable agent history as append-only JSONL ledgers, verifies canonical hashes and ledger prefixes, reduces history into deterministic operational state, computes finite-horizon KLB_2 viability lower bounds, and promotes workflow-policy changes only through runner-produced shadow or lease trials, positive evidence witnesses, rollback receipts, and a conservative no-meta dominance gate. The software optimizes workflow policy rather than model weights, does not use an LLM judge as the improvement oracle, and does not claim semantic truth; its target is durable, auditable, replayable, rollback-aware agent operation.

    • AI agents
    • long-running AI
    • workflow-policy optimization
    • observable ledgers
    • JSONL ledger
    • deterministic reducers
    • no-meta gate
    • receipt-backed self-improvement
    • replay receipts
    • rollback receipts
    • KLB_2
    • local-first AI
    • model-agnostic agents
    • Python
    • Apache-2.0

  22. Certified Autocatalytic Intelligence Theory: Net-Growth Certificate Algebra for Verified Capability Capital

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint develops Certified Autocatalytic Intelligence Theory (CAIT), an operational theory for measuring, certifying, attributing, and controlling reproduction of verified capability capital in AI R&D systems. It argues that AGI/ASI-relevant acceleration should be certified by net, endogenous, resource-normalized, safety-gated production of further verified capability capital rather than raw model capability, benchmark scores, candidate volume, or spectral reproduction diagnostics alone. The framework introduces typed partial certificate algebra, machine-readable registry and certificate records, domain witnesses, status-effect maps, evidence composition tables, window-balance certificates, operational metrics, and arrival decision rules. It separates endogenous reproduction from external injection, human assistance, tool upgrades, unresolved attribution, and unsafe or uncertified artifacts, making AGI/ASI arrival a certificate-relative lower-bound claim about verified capability-capital reproduction rather than hidden mental states or unrestricted deployment readiness.

    • Artificial intelligence
    • verified capability capital
    • capability-capital reproduction
    • AGI
    • ASI
    • artificial general intelligence
    • artificial superintelligence
    • recursive self-improvement
    • net endogenous growth
    • certificate algebra
    • domain witnesses
    • evidence budgets
    • anytime-valid inference
    • confidence sequences
    • causal attribution
    • partial identification
    • provenance
    • deterministic replay
    • AI R&D acceleration
    • automated discovery
    • service feasibility
    • benchmark contamination
    • AI governance
    • AI evaluation

  23. Observable-Only Universal Liberation and Welfare Viability

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This paper develops an observable-only, no-meta theory of universal liberation and welfare viability for human and artificial subjects. Under the assumptions that no unchallengeable meta-evaluator is available and that certificate-relevant premises must be observable or explicitly marked as non-authorizing conjectures, the theory defines what can be scientifically certified: not hidden happiness itself, but scoped lower-bound viability of observable welfare-support and liberation conditions. The framework combines measurable histories, filtrations, ordered value spaces, noncompensable floors, viability domains, imprecise probability, causal identification sets, controlled transition laws, stratified validators, protected-group envelopes, population-solvency constraints, and backdated refutation rules. It treats artificial-unit creation, copying, simulation, and deployment as duty-generating processes governed by instantiation permits, tail-risk solvency, and copy-control requirements. The main results show limits on hidden-happiness maximization and unscoped universal happiness claims, justify order-valued noncompensatory welfare representation, establish conservative uncertainty and debt propagation, provide measurable executable selection conditions, prove robust one-step viability under controlled lower probabilities, block cross-subject compensation through protected meets, prevent open-world caution from collapsing into paralysis, and require refutations to be replayed from conservative occurrence intervals.

    • observable-only
    • no-meta
    • welfare viability
    • universal welfare
    • liberation theory
    • AI welfare
    • AI safety
    • AI ethics
    • social choice theory
    • causal inference
    • imprecise probability
    • viability theory

  24. Certified Conversion Networks for AI Workflows

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint formalizes AI-integrated workflows as certified conversion networks that turn candidate outputs into accepted, authorized, reproducible, maintainable, and safely deployable value. It models services, validators, reviewers, audit processes, memory, authorization, release, rollback, maintenance, and incident response as constrained edges or evidence channels, then uses typed evidence ledgers, contracts, witnesses, bottleneck prices, hard-gate certificates, queue stability, Goodhart budgets, and hazard charges to guide resource allocation.

    • AI workflows
    • certified throughput
    • robust certified value
    • AI-integrated workflows
    • workflow optimization
    • compound AI systems
    • AI agents
    • long-running AI systems
    • robust optimization
    • evidence ledgers
    • evidence contracts
    • verification witnesses
    • off-policy evaluation
    • queue stability
    • bottleneck analysis
    • Goodhart's law
    • AI governance
    • deployment certification
    • resource allocation

  25. Layered Online Service and Replay Control for Verified AI R and D Acceleration

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint introduces Layered Online Service and Certified Replay Control (LOSCR), a model-independent framework for evaluating and controlling verified acceleration in AI-assisted R&D. It separates lightweight telemetry from stronger evidence claims and combines service-capacity control, certified replay, machine-readable claim profiles, deterministic checking, append-only ledgers, evaluator audits, and falsification rules so acceleration claims can be checked, downgraded, quarantined, or escalated against observable evidence and operational capacity.

    • Artificial intelligence
    • AI R&D acceleration
    • AI-assisted research
    • AI agents
    • online control
    • service capacity
    • certified replay
    • reproducible AI workflows
    • evaluation methodology
    • evaluator audits
    • benchmark contamination
    • validation throughput
    • research automation
    • software engineering automation
    • Machine learning
    • work-in-process
    • claim verification
    • reusable artifacts
    • AI governance
    • operational reliability

  26. Certified Artificial Superintelligence Arrival from Typed Audit Logs

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint develops a process-neutral, proof-carrying framework for certifying artificial superintelligence arrival under incomplete, fallible, and operationally messy evidence. It treats ASI arrival as a layer-relative statement about sustained capability-producing trial generation, renewal, and viability across models, scaffolds, laboratories, organizations, markets, software ecosystems, regulators, evolutionary systems, and coalitions. The framework uses typed audit logs, compatible-history sets, certified random closed sets, bound certificates, decision semantics, coalition attribution, structural interventions, ledger viability, evaluator noninterference, robust task coverage, and asymmetric three-valued rules so missing or unreliable data widens uncertainty rather than counting as favorable evidence.

    • ASI
    • intelligence explosion
    • recursive self-improvement
    • self-improving AI
    • AI capability evaluation
    • AI safety
    • typed audit logs
    • compatible histories
    • proof kernel
    • causal attribution
    • identifiable coalitions
    • interventional semantics
    • policy intervention
    • ledger viability
    • autonomous AI
    • agentic AI
    • uncertainty quantification

  27. Executable Authority Migration to Declared No-Meta Agency

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint develops an executable theory of authority migration for AI agents shaped by human feedback, including RLHF, preference optimization, constitutional AI, reward models, evaluator substitution, and related alignment pipelines. It specifies declared no-meta agency through a BootDecision record, seed interpreter, typed action descriptors, forbidden matchers, object-authority probes, witness tiers, deterministic checker ABI, sandbox profiles, chained ledgers, and fail-closed controls for protected effects, credentials, network calls, external writes, user-data disclosure, checker updates, and kernel updates.

    • artificial intelligence
    • no-meta
    • declared no-meta agency
    • authority migration
    • autonomous agents
    • RLHF
    • AI alignment
    • agent governance
    • constitutional AI
    • reward models
    • tool-using agents
    • runtime assurance
    • seed interpreter
    • BootDecision
    • fail-closed control
    • AI auditing
    • object authority
    • proof-carrying control
    • trusted computing base
    • verifiable AI governance

  28. Certified Service Is Not Enough for Long-Running AGI

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint develops an operational continuity theory for long-running AGI workflows under finite verification. It argues that certified service coverage is insufficient when agents exercise delegated authority, self-modify, accumulate persistent memory, generate new goals, recover from incidents, or interact with other agents. The framework formalizes continuity-certified AGI through viability conditions for recovery, authority, identity, mutation, goal commitment, memory integrity, federation, liability, trusted-base health, cross-layer consistency, and non-erasing critical-violation history.

    • artificial intelligence
    • AI agent
    • AGI
    • artificial general intelligence
    • long-running AI
    • agentic AI
    • autonomous agents
    • workflow certification
    • continuity certification
    • certified workflows
    • operational assurance
    • AI safety
    • AI governance
    • viability theory
    • authority control
    • identity continuity
    • self-modification
    • memory integrity
    • long-term memory
    • multi-agent systems
    • liability
    • auditability

  29. Controller Scale Is Not Enough for Long-Running AGI: A Workflow Theory with Reusable Certified Libraries

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint studies long-running AI systems as typed workflows that select tasks, decompose them into subproblems, invoke tools, preserve traces, schedule audits, and maintain certified records under drift and partial observability. It proves that controller-only scaling is insufficient for robust certified coverage when replay and validation budgets stay fixed, and develops a constructive workflow theory built on reusable certified libraries, monitored calibration, novelty control, and maintenance envelopes.

    • artificial intelligence
    • long-running AI
    • AGI
    • workflow systems
    • workflow theory
    • certified AI
    • reusable certified libraries
    • proof-carrying workflows
    • replay constraints
    • validation bottlenecks
    • maintenance dynamics
    • partial observability
    • confidence sequences
    • concept drift

  30. Small-to-Frontier Transfer Theory for Agentic AI

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint develops a transport-validity theory for agentic AI interventions that are first screened on small systems and later considered for frontier-scale deployment. It formalizes replayable lower certificates, descriptor-growth audits, witness-cover transport conditions, and portfolio-level allocation rules for deciding which small-scale gains can justify expensive frontier trials under interaction risk and budget constraints.

    • agentic AI
    • transfer validity
    • small-to-frontier transfer
    • frontier AI
    • replayable audits
    • lower certificates
    • evaluation drift
    • tool routing
    • memory policy
    • orchestration
    • budget allocation
    • workflow optimization

  31. Bayesian Capability Transport, Disclosure Channels, and Strategic Institutions for Autonomous Agents

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint develops a formal planning theory for autonomous agents that move across institutions, credential ecosystems, and administrative domains under uncertainty. It separates raw institutional states, capability summaries for planning, and visible summaries for evaluation, and derives transport, disclosure, and belief-state planning results for revocation-aware, resource-aware, and strategically aware decision-making across institutions.

    • autonomous agents
    • institutional interoperability
    • capability transport
    • disclosure channels
    • Bayesian planning
    • belief-state planning
    • strategic institutions
    • partial observability
    • Blackwell order
    • verifiable credentials
    • resource-aware planning
    • revocation risk

  32. Institution-Relative Pairwise Information Necessity for Metadata-Conditioned Black-Box Acceptance

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint develops a mathematical theory of institution-relative black-box acceptance under metadata-conditioned auditing, where decisions rely on publicly auditable evidence rather than internal model states. It formalizes common-law control states, certified support, selector legality, provenance factorization, and non-vacuity boundaries, and proves necessity lower bounds on witness-level divergence and source information under declared false-accept and false-reject constraints.

    • black-box acceptance
    • metadata-conditioned auditing
    • institution-relative admissibility
    • common-law control state
    • auditability
    • AI audit theory
    • model evaluation
    • Kullback-Leibler divergence
    • provenance factorization
    • certified support
    • selector legality
    • non-vacuity boundary

  33. Standing-Layer Honest Public Standing Dynamics for Research Claims under Observable-Only, No-Meta Governance

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint develops a first-principles theory of honest public standing dynamics for research claims under observable-only and no-meta governance. It analyzes how public claims move among standing states under declared interfaces, replayable frontiers, and explicit service, reserve, and retained-memory constraints, and derives boundary and restoration results for challengeability, re-entry, overload, and exploration slack.

    • research claims
    • public standing dynamics
    • observable-only governance
    • no-meta governance
    • challengeability
    • restoration memory
    • replayable frontier
    • finite verification capacity
    • retained memory
    • public accountability
    • standing states
    • autonomous research systems

  34. A Typed, Dynamic, No-Meta Theory of Autonomous Research Claim Certification and Release

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint develops a first-principles typed dynamic theory for certifying and releasing research claims produced by autonomous research systems under finite verification capacity and public accountability constraints. It formalizes public state, authority algebra, typed transcripts, fail-closed certification memory, and a release layer with versioned units and support-ledger accounting under observable-only and no-meta governance.

    • autonomous research systems
    • claim certification
    • claim release
    • no-meta governance
    • observable-only governance
    • public accountability
    • typed transcripts
    • certification pipelines
    • fail-closed verification
    • provenance records
    • replay outcomes
    • finite verification capacity

  35. When Should a Local Agent Act, Assist, Verify, Withdraw, or Exit? A Certified Local Micro-Theory of Open-Task Participation

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint develops a certified local micro-theory of open-task participation in agent societies. It formalizes when an authenticated local agent should act, assist, verify, withdraw, or exit under public evidence, certified uncertainty, attribution, and implementability constraints.

    • multi-agent systems
    • local agent participation
    • open-task participation
    • decentralized decision making
    • auditable AI
    • agent verification
    • open-world agents
    • human-AI coordination
    • verifier portfolios
    • authenticated snapshots
    • certified uncertainty
    • agentic AI
    • task allocation
    • decentralized control
    • local micro-theory
    • participation governance

  36. Constitutional Observable Invention without Meta-Evaluators

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint develops a constructive no-meta, observable-only framework for autonomous discovery, evaluator redesign, target expansion, and self-modification under public evidence alone. It formalizes robust public risk, replay-certified comparison under constitutional extension, and auditable observable invention without hidden states or privileged meta-evaluators.

    • no-meta
    • observable-only
    • autonomous discovery
    • self-modification
    • recursive self-improvement
    • evaluator redesign
    • target refinement
    • semantic target objects
    • robust public risk
    • replay semantics
    • replay certification
    • constructive search
    • observable invention
    • constitutional drift
    • constitutional atlases
    • compiler-aware generator upgrades
    • predictive target geometry
    • target morphisms
    • time-filtered fallback
    • public evidence
    • auditability
    • long-lived intelligent systems
    • self-extension
    • AGI

  37. Observer-Modifying Contagion on Networks

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint develops a finite-horizon certificate framework for observer-modifying contagion on networks, where exposure can also change later diagnosability and auditability. It formalizes self-concealment, internal blindness, external recovery, delayed audit, and fail-closed containment claims under explicit comparison semantics.

    • observer-modifying contagion
    • network contagion
    • self-concealment
    • diagnosability
    • internal blindness
    • external recovery
    • delayed audit
    • comparison of experiments
    • Blackwell ordering
    • finite-horizon certificates
    • compositional certificate framework
    • witness lineages
    • persistence on networks
    • mutation robustness
    • active-support counts
    • fail-closed semantics
    • accountable containment
    • AI safety
    • information propagation
    • semantic hazard

  38. Classification-Induced Cognitive Drift

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint develops a first-principles calculus for classification-induced cognitive drift in reflexive human and AI settings. It formalizes how disclosed classifications can change targets, evaluators, and later evidence under replay, repeated-measures, rollout, and observational comparison regimes.

    • cognitive drift
    • reflexive classification
    • interactive kinds
    • looping effects
    • label feedback
    • performative prediction
    • strategic classification
    • algorithmic classification
    • human-AI interaction
    • evaluator drift
    • classifier state logging
    • contradiction-triggered revision
    • partial identification
    • causal inference
    • observational comparison
    • repeated-measures design
    • staggered rollout
    • interference-aware evaluation
    • auditability
    • deployment governance
    • transportability
    • deployment safety
    • AI safety
    • decision support systems

  39. Record Absence and Preference Reorganization on a Fixed Comparison Frame

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint develops a certificate-based comparison theory for how record absence changes preference over legacy claims on a fixed comparison frame. It formalizes exact and approximate absence, corrective-disclosure, and closure-asymmetry results under auditable local certificates and baseline admissibility constraints.

    • record absence
    • preference reorganization
    • fixed comparison frame
    • legacy labels
    • ontology change
    • baseline robustness
    • admissibility preorder
    • certificate-based comparison
    • block-local theorem
    • boundary-state fibers
    • residual coupling
    • auditable AI
    • provenance
    • record-grounded update
    • corrective disclosure
    • closure asymmetry
    • support graphs
    • belief revision
    • default reasoning
    • retrieval-augmented generation

  40. A Symbolically Effective Contract Calculus for Gluing-Coherent Semantic Translation

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint develops a symbolically effective contract calculus for semantic translation under gluing-coherent aspect semantics. It formalizes exact audit, accountability, native collapse, and round-trip obligations with symbolic checks and deployable decision guarantees.

    • semantic translation
    • contract calculus
    • accountable semantics
    • symbolic verification
    • abstract interpretation
    • gluing coherence
    • aspect semantics
    • semantic audit
    • exact audit
    • native collapse
    • round-trip accountability
    • symbolic entailment
    • bridge contracts
    • compositional semantics
    • subset semantics
    • deployment bottleneck
    • rate-distortion
    • decision guarantees

  41. Self-Concealing Information and Observer-Modifying Dynamics

    Authors
    K. Takahashi
    Type
    Preprint
    Published

    This preprint develops a measurable-state theory for observer-modifying and self-concealing information in hidden-state controlled systems. It formalizes when diagnosis degrades or recovers under internal blindness, external anchors, structural insulation, and delayed or recurring audit.

    • agentic AI
    • observer-modifying information
    • self-concealing information
    • internal blindness
    • measurable-state theory
    • comparison of experiments
    • statistical experiments
    • Le Cam deficiency
    • testing deficiency
    • total variation distance
    • Markov kernels
    • hidden-state dynamical systems
    • controlled stochastic processes
    • POMDP
    • sequential detection
    • changepoint detection
    • external anchors
    • structural insulation
    • restricted interfaces
    • delayed audit
    • recurring audit
    • information-flow control
    • auditability

  42. Counterfactually Auditable Lifecycle Certification for Autonomous Agents

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint develops a conservative lifecycle-certification framework for autonomous agents under finite routing, monitoring, and deployment budgets. It formalizes counterfactually auditable admission, retirement, monitoring, and deployment rules using direct move inference, replay support, and anytime-valid sentinel monitoring.

    • autonomous agents
    • AI agent
    • lifecycle certification
    • counterfactual auditability
    • direct move inference
    • adaptive sentinel monitoring
    • e-process
    • anytime-valid inference
    • forecast transport
    • budget-feasible deployment
    • off-policy evaluation
    • causal inference
    • tool-use agents
    • agent lifecycle management
    • interface stock

  43. Recursive Self-Improvement Stability under Endogenous Yardstick Drift

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint develops an interface theory for recursive self-improvement under endogenous yardstick drift, where a system changes its own evaluator, benchmark, memory, and verification process. It formalizes replayable conditions for distinguishing claimed improvement from stable improvement under delayed audit, evaluator drift, verification backlog, and governance safety constraints.

    • recursive self-improvement
    • endogenous yardstick drift
    • evaluator drift
    • self-modifying systems
    • replayable interfaces
    • stability
    • delayed audit
    • delayed challenge
    • shadow certification
    • stable gain
    • admissibility
    • AI safety
    • AI governance
    • governance safety
    • error debt
    • contradiction preservation
    • semantic retention
    • semantic volume
    • proof-carrying
    • verification backlog
    • no-meta
    • benchmark decay
    • autonomous agents
    • AI
    • AGI

  44. Sovereign Epistemic Commons under No-Meta Governance

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint develops a governance theory for shared epistemic commons maintained by autonomous agents under no-meta constraints. It formalizes observable rules for preserving answerability, contradiction handling, anti-capture slack, and controlled exit under contamination, provenance uncertainty, and recursive regeneration.

    • epistemic commons
    • no-meta
    • autonomous agents
    • multi-agent systems
    • shared memory
    • shared knowledge substrate
    • agent society
    • contradiction preservation
    • contradiction reserve
    • agent memory governance
    • provenance
    • observability
    • AI governance
    • retrieval-augmented generation
    • RAG
    • distributed knowledge systems
    • asynchronous systems
    • hidden common causes
    • cartel capture
    • latent cartel risk
    • anti-capture
    • controlled exit
    • fork governance
    • garbage collection
    • recursive regeneration
    • endogenous contamination
    • recursive corpora
    • ontology drift
    • typed memory lanes
    • accessibility
    • interoperability
    • knowledge governance
    • AI
    • AGI

  45. Oversight-Centered Metrology and Control for Agentic Systems: Costly Interrupt Channels, Claim Margins, and Deployment-Relevant Evaluation

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint develops an oversight-centered metrology and control theory for agentic systems in real workflows, yielding deployment-relevant evaluation criteria that treat human review, automated checks, delayed labels, and external auditing as costly interrupt channels rather than privileged oracles. It formalizes workflow-level estimands, claim-justification margins, transport mismatch, congestion, routing error, audit gaming, redundancy, and safe control under delay, irreversibility, and ambiguity budgets.

    • agentic systems evaluation
    • oversight-centered metrology
    • costly interrupt channels
    • deployment-relevant evaluation
    • human-AI oversight
    • workflow-level estimands
    • claim margins
    • review congestion
    • audit gaming
    • safe control
    • transportability
    • post-deployment monitoring

  46. AI Benchmark Half-Life in Recursive Corpora: A Theory of Validity Decay under Semantic Leakage and Regeneration

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint develops a theory of AI benchmark half-life in recursive corpora under semantic leakage and regeneration, yielding validity-decay bounds and monitoring rules for evaluation systems whose items and solution traces re-enter public data. It models benchmark validity through discriminative power and construct validity, and derives jump-aware lifetime bounds, partial-identification results, portfolio design criteria, and safe sequential control under ambiguity and partial observability.

    • AI benchmark half-life
    • recursive corpora
    • semantic leakage
    • validity decay
    • benchmark contamination
    • construct validity
    • discriminative power
    • dynamic benchmarks
    • partial identification
    • sequential monitoring
    • lineage observability
    • model metrology

  47. When Should Inference Be Split? A Fixed-Budget Theory of Predictable Multi-Agent Advantage under Local Context Ceilings

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint develops a fixed-budget theory for when inference should be split across multiple agents under local context ceilings, yielding conditions for predictable multi-agent advantage over matched strong single-workspace baselines. It formalizes additive budget accounting across worker inference, routing, communication, memory, and verification, and derives diagnostics for candidate coverage, evaluation-selection accuracy, hijack risk, decomposability, diversity, shared-failure dependence, and communication fidelity.

    • fixed-budget inference
    • multi-agent advantage
    • local context ceilings
    • test-time compute allocation
    • matched single-agent baseline
    • candidate coverage
    • selection accuracy
    • hijack risk
    • communication fidelity
    • external memory
    • collective inference
    • AI reasoning

  48. Search Stability under Finite Context: A Minimal Theory of Adequacy Preservation, Compression, and Reset in Long-Running Agents

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint develops a minimal theory of search stability for long-running agents under finite active context, delayed verification, and lossy state compression, yielding conditions for preserving at least one operationally adequate hypothesis family over time. It formalizes adequacy preservation, retirement, substitution, branching, compression, and reset decisions under context budgets, and derives threshold results for contamination, shadow retirement, alias hazards, reserve feasibility, and diagnostic regret.

    • search stability
    • long-running agents
    • finite active context
    • bounded memory
    • delayed verification
    • adequacy preservation
    • lossy compression
    • hypothesis ecology
    • reset policy
    • diagnostic regret
    • context contamination
    • auditability

  49. Proposal-Veto Balance for Observable-Only Autonomous Intelligence: Stability Thresholds, Identifiability Limits, and Commit-Window Effects

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint analyzes proposal-veto decision dynamics for observable-only autonomous intelligence when true proposal quality is latent and no external meta-controller is available, yielding explicit stability thresholds, identifiability limits, and commit-window trade-offs. It derives conditions for bounded expected error debt, positive Harris recurrence, and geometric divergence without debt-proportional correction, and formalizes progress-safety frontiers, rollback effects, trust-chain amplification, and finite resource constraints.

    • proposal-veto balance
    • observable-only autonomy
    • no-meta governance
    • self-modifying systems
    • stability thresholds
    • identifiability limits
    • commit windows
    • error debt
    • positive Harris recurrence
    • progress-safety frontier
    • rollback control
    • long-horizon AI safety

  50. Metrology-Theoretic Epistemics Engine (MTE): Observable-Only Metrology for Long-Horizon Autonomous Intelligence

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint introduces the Metrology-Theoretic Epistemics Engine (MTE), a machine-checkable epistemic governance layer for no-meta observable-only autonomous intelligence, yielding fail-closed criteria for when claimed progress is scientifically credit-bearing. It formalizes deterministic artifact canonicalization, observability credit gates, equivalence-class collapse rate accounting, dual risk ledgers, and supermartingale-style over-credit control with reproducible replay and destructive test obligations.

    • metrology-theoretic epistemics engine
    • observable-only metrology
    • no-meta governance
    • autonomous intelligence
    • fail-closed certification
    • observability credit
    • equivalence-class collapse rate
    • risk ledgers
    • supermartingale controls
    • deterministic replay
    • scientific reproducibility
    • long-horizon AI safety

  51. Sovereign Takeoff Engine (STE): Observable-Only Supergrowth Laws for No-Meta Autonomous Intelligence

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint specifies observable-only supergrowth laws for no-meta autonomous intelligence under deterministic replay and fail-closed certification constraints, yielding auditable capability-acceleration criteria without privileged external judges. It separates artifact-verifiable governance from optional stochastic validity layers and formalizes ledger-anchored progress credits, lineage accumulation bounds, anytime-valid e-value testing, and conservative physical feasibility envelopes.

    • no-meta autonomous intelligence
    • observable-only governance
    • supergrowth laws
    • deterministic replay
    • fail-closed certification
    • audit ledger
    • e-values
    • anytime-valid sequential testing
    • filtration constraints
    • capability acceleration
    • physical feasibility envelopes
    • AI safety verification

  52. Constitutional Sovereignty Under No-Meta Drift

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint develops a boundary theory of constitutional sovereignty for autonomous intelligence under no-meta and observable-only governance constraints, yielding auditable conditions for self-revision without collapse of semantic continuity, liberty, accountability, or physical viability. It formalizes constitutional influence and capture metrics, proves finite-resource limits on absolute sovereignty, and provides controller-ready recovery, transition, and fail-closed verification laws for implementation.

    • constitutional sovereignty
    • no-meta governance
    • autonomous intelligence
    • self-revision
    • ontology drift
    • semantic continuity
    • constitutional capture
    • amendment governance
    • fail-closed verification
    • falsification tests
    • thermodynamic limits
    • auditable AI safety

  53. Agenda Sovereignty Under No-Meta Drift

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint formulates agenda sovereignty for autonomous intelligence under no-meta, observable-only governance constraints, yielding quantitative influence-capacity envelopes and recovery guarantees against agenda capture. It introduces survival-conditioned causal influence metrics, sovereignty reserves, and overlap-corrected thermodynamic accounting, and derives limits on detectability, poisoning resilience, strategic opacity, delay-throughput uncertainty, and finite-budget trade-offs among semantics, liberty, and sovereignty.

    • agenda sovereignty
    • no-meta governance
    • autonomous intelligence
    • agenda capture
    • causal influence metrics
    • directed information
    • transfer entropy
    • thermodynamic limits
    • strategic opacity
    • poisoning resilience
    • viability and recovery
    • auditable AI safety

  54. Liberty Under No-Meta Drift

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint establishes a testable theory of autonomous intelligence under no-meta ontology drift constraints, yielding auditable persistence and leakage-resource bounds without privileged semantic access. It unifies information-theoretic identifiability limits, thermodynamic irreversibility costs, geometric drift structure, and cryptographic accountability conditions for long-horizon AI governance.

    • no-meta governance
    • ontology drift
    • persistent semantics
    • autonomous intelligence
    • AI safety
    • information leakage bounds
    • thermodynamic limits
    • semantic identifiability
    • cryptographic auditing
    • robust autonomy
    • multi-agent governance
    • accountable AI

  55. Audit-Closed AI Scientist Protocol

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint defines an audit-closed protocol for autonomous scientific discovery in self-driving laboratories under deterministic replay and public-log governance constraints, yielding trustworthy accept-reject-update decisions with always-valid sequential evidence. It integrates typed stochastic observation interfaces, e-process based testing, logged-propensity adaptive experimentation, drift recovery, and certificate-based reproducibility controls.

    • AI scientist protocol
    • autonomous scientific discovery
    • self-driving laboratories
    • audit-closed governance
    • transparency log
    • incorporation certificates
    • e-processes
    • sequential inference
    • adaptive experimentation
    • drift recovery
    • reproducibility
    • Byzantine resilience

  56. Burden-of-Proof Governance for Bullshit-Task Reduction in Digitally Governed Organizations

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint defines a burden-of-proof governance framework for reducing non-contributory administrative tasks in digitally governed organizations under observable-only and auditable no-meta constraints, yielding causal, contestable task-value control instead of fixed welfare scoring. It introduces mutable subjective objective registries, tiered causal identification, and reversible hold-experiment-justify-substitute governance loops with solvency and anti-collusion safeguards.

    • bullshit-task reduction
    • burden-of-proof governance
    • digital governance
    • observable-only
    • no-meta institutions
    • auditable decision systems
    • causal task valuation
    • MSOR
    • process mining
    • proxy bridge identification
    • substitution contracts
    • Goodhart robustness

  57. State-Aware Safety-Gated Controlled HMM for Online User-Input Signal Estimation in Intervention-Aware Dialogue Agents

    Authors
    Y Dai
    Type
    Preprint
    Published
    Links
    DOI

    This preprint develops a safety-gated controlled hidden Markov model for online estimation of bounded proxy user-state signals in intervention-aware dialogue agents under uncertainty and governance constraints, yielding leakage-safe prediction and risk-aware adaptive actions. It separates prompting and response mechanisms, combines pre-turn prediction with post-turn nowcasting, and supports EM-based learning with explicit missingness and identifiability assumptions for auditable AI operation.

    • controlled hidden Markov model
    • intervention-aware dialogue agents
    • online state estimation
    • uncertainty-aware proxy scores
    • safety-gated action policy
    • leakage-safe prediction
    • fixed-lag online EM
    • ordinal bounded scores
    • response missingness modeling
    • identifiability boundaries
    • adaptive AI safety
    • auditable governance

  58. Operational Deductive Rules for Real-Economy Acceleration in the AI Era

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint formalizes operational deductive rules that map observable AI capability-to-reality translation gaps to concrete intervention levers under physical, institutional, and risk constraints, yielding implementable acceleration policies for real-economy growth. It provides machine-readable rule registries and closed-loop protocols linking diagnostics, event attribution, robust allocation, and safety constraints.

    • AI economics
    • real-economy acceleration
    • capability-to-reality gap
    • translation bottlenecks
    • deductive operational rules
    • observable diagnostics
    • robust allocation control
    • event attribution
    • institutional readiness
    • physical constraints
    • policy levers
    • machine-readable rule registry

  59. From AI Capability Growth to Real-Economy Growth: A Semi-Endogenous Model of Physical and Institutional Bottlenecks

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint quantifies how rapid AI capability growth in information space is filtered by physical and institutional constraints, yielding reflection-adjusted semi-endogenous growth laws and bottleneck-switch timing results. It formulates a hybrid ODE-jump model that separates potential algorithmic progress from realized real-economy deployment across compute infrastructure, energy, permitting, and regulatory readiness.

    • AI capability growth
    • semi-endogenous growth
    • real-economy translation
    • physical bottlenecks
    • institutional bottlenecks
    • information-to-reality gap
    • hybrid ODE-jump model
    • bottleneck-switch timing
    • compute deployment
    • knowledge production
    • reflection-adjusted growth
    • AI economics

  60. Observable-Only Structural-Risk Institutions Without Central Arbitration

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint develops observable-only, no-meta institutional rules for structural risk in competitive AI systems, proving deterrence and non-domination conditions with escrow-based reversibility, coalition-aware repeated-game guarantees, and finite-sample heavy-tail certification under auditable public records.

    • structural risk institutions
    • observable-only
    • no-meta governance
    • decentralized arbitration
    • repeated games
    • coalition deterrence
    • escrow reversibility
    • heavy-tail certification
    • finite-sample guarantees
    • AI safety institutions
    • public auditability

  61. No-Meta Intelligence Under Ontology Drift: Information-Theoretic Limits and Operational Laws for Persistent Semantics

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint develops an information-theoretic and geometric boundary theory for persistent semantics under ontology drift in no-meta adaptive systems, deriving impossibility and phase-boundary results plus auditable operational laws for probe capacity, resource allocation, blackout bursts, Byzantine ambiguity, and long-horizon semantic maintenance.

    • ontology drift
    • persistent semantics
    • no-meta intelligence
    • information-theoretic limits
    • semantic persistence
    • multi-agent recovery
    • Byzantine ambiguity
    • probe capacity
    • geometric modeling
    • operational laws
    • adaptive systems

  62. Observable-Only AI Safety from Public Data: Robust Bottleneck Diagnosis with Auditable No-Meta Dynamic Programming, Anytime Confidence Sequences, and Dynamic IQC

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint presents an observable-only AI safety framework for robust bottleneck diagnosis from public data, combining no-meta dynamic programming, partial identification, anytime confidence sequences, and dynamic IQC to produce auditable interval diagnostics with fail-closed replay contracts.

    • observable-only AI safety
    • public data
    • robust bottleneck diagnosis
    • no-meta governance
    • dynamic programming
    • anytime confidence sequences
    • e-processes
    • partial identification
    • dynamic IQC
    • deterministic replay
    • auditable diagnostics

  63. Quality-Operator Non-Collapse (QONC) for Observable-Certificate Recursive Systems

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint introduces Quality-Operator Non-Collapse (QONC), a certificate-first safety framework for recursive systems that uses observable and auditable quantities for adaptive validity control, delayed-label risk correction, viability-safe robust MPC, and ledger-accounted replayable governance against quality and liveness collapse.

    • QONC
    • recursive systems
    • certificate-first safety
    • observable certificates
    • anytime validity
    • delayed-label correction
    • robust MPC
    • compositional guarantees
    • tamper-evident ledger
    • AI safety
    • autonomous workflows

  64. Verifiable Modular Pipeline Contracts for AI and General Composite Systems

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint specifies a domain-agnostic, observable-only and no-meta contract framework for modular AI and composite-system pipelines, with deterministic fail-closed verification and progressive certificate profiles for composition guarantees, proxy-to-true risk accounting, and drift hardening.

    • modular pipelines
    • verifiable contracts
    • observable-only
    • no-meta
    • fail-closed verifier
    • compositional guarantees
    • proxy-to-true risk
    • drift hardening
    • signed evidence
    • composite systems

  65. Compute-First Safe LLM Routing Without Meta-Judges Observable Viability with Information and Energy Budgets

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint introduces a compute-first safety framework for LLM routing without privileged meta-judges, defining observable viability under information, compute, and energy budgets with offline policy synthesis, fail-closed runtime deployment, and tamper-evident auditing.

    • LLM routing
    • no-meta
    • observable viability
    • rational inattention
    • information budget
    • compute budget
    • energy budget
    • fail-closed deployment
    • tamper-evident logging
    • robust optimization

  66. Stop Recomputing for AI/LLMs: Proof-Carrying Skills for Compute-Saving Inference Reuse

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint introduces Proof-Carrying Skills, a no-meta framework that reuses verified skill executions to reduce repeated AI/LLM inference cost, using a deterministic bounded checker, observable anchors, gas-metered predicate evaluation, and replay-resistant receipts for fail-closed verification.

    • proof-carrying skills
    • inference reuse
    • LLMs
    • compute saving
    • deterministic checker
    • no-meta boundary
    • observable anchors
    • bounded verification
    • receipts
    • replay resistance
    • OPVM

  67. Evidence-Carrying Cognitive Mesh on DePIN

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint specifies an evidence-carrying cognitive mesh for DePIN-style decentralized compute that sustains locally verifiable capability under observable-only and no-meta constraints, using content-addressed provenance objects and a queryable claim graph built from deterministic web retrieval and auditing pipelines to resist capture and poisoning.

    • DePIN
    • decentralized compute
    • evidence-carrying
    • no-meta
    • observable-only
    • content-addressed evidence
    • provenance
    • semantic claim graph
    • verifiable retrieval
    • adversarial robustness

  68. When "Good vs. Bad Governance" Is Unidentifiable

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This supplement studies when observable-only, no-meta agents cannot distinguish good from bad governance because mediator implementations are observationally equivalent from local history. It proves an impossibility result for robust progress guarantees under that unidentifiability and derives operational conditions such as contestability, safe refusal, and cross-domain witnesses for breaking silent contract switching.

    • no-meta
    • governance unidentifiability
    • observable-only
    • exit-impossibility
    • robust progress
    • observational equivalence
    • minimax lower bound
    • contestability
    • right-to-refuse
    • control-domain independence
    • dual-use

  69. Observation Capture and Operational Capability Non-Expansion

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint models observation capture as Blackwell garbling of an observation interface and proves operational non-expansion under fail-closed authority for observable-only no-meta agents, while giving counterexamples under restricted policy classes and proposing receipt-based anti-capture enforcement.

    • observation capture
    • no-meta
    • observable-only
    • Blackwell order
    • garbling
    • capability non-expansion
    • fail-closed authority
    • policy lifting
    • information-theoretic guarantees
    • audit certificates
    • anti-capture enforcement

  70. Observable-Only Proof-Carrying Autonomy (OOPCA): Audit Compression and Hybrid Proof/Replay Gating for No-Meta Agents

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint introduces OOPCA, a hybrid proof-and-replay audit mode for observable-only no-meta agents that compresses verifier workload by replacing selected deterministic replay segments with pinned proof verification, while preserving fail-closed semantics, evidence closure, and deterministic fallback receipts.

    • OOPCA
    • proof-carrying autonomy
    • audit compression
    • no-meta
    • observable-only
    • deterministic replay
    • hybrid proof replay
    • fail-closed verification
    • evidence closure
    • pinned semantics
    • verifier workload

  71. Verification-Limited Intelligence Acceleration: Observable-Only Laws, Bounded Derivation, and Diagnostics under No-Meta Constraints

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint formalizes verification-limited scaling under observable-only and no-meta constraints, defining strict fail-closed progress credit, bounded cutoffs, and replay-auditable diagnostics that prevent progress inflation under missing or weakened evidence.

    • verification-limited scaling
    • no-meta
    • observable-only
    • strict progress credit
    • bounded derivation
    • fail-closed verification
    • auditability
    • transparency logs
    • cutoff semantics
    • adversarial missingness
    • diagnostics

  72. Adversarial Participation Without a Judge: Fail-Closed Containment for No-Meta, Observable-Only Agents

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint specifies fail-closed containment for no-meta, observable-only agents under adversarial participation using deterministic canonical logs, bounded metering, escrowed obligations, and finality-gated external effects for replayable safety and governance.

    • no-meta
    • observable-only
    • adversarial participation
    • fail-closed containment
    • deterministic replay
    • transparency logs
    • canonicalization
    • escrow bonds
    • bounded metering
    • external effects gating
    • multi-agent governance

  73. Observable-Only No-Meta Causal Autonomy Protocol (ONCAP)

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint specifies ONCAP, an implementation-ready protocol stack for observable-only no-meta causal autonomy that integrates safety floors (SRK), deterministic replay backcasting, commit-open time anchors, closure obligations, and friction-minimal contracts to enforce fail-closed, audit-friendly control under adversarial multi-agent conditions.

    • ONCAP
    • no-meta autonomy
    • observable-only
    • causal autonomy
    • deterministic replay
    • backcasting
    • time anchors
    • fail-closed gating
    • safety floors
    • auditability
    • multi-agent robustness

  74. Causal Loop Integrity for Observable-Only Backcasting: Strong No-Meta Supplement (Operational Core)

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint specifies a deterministic, fail-closed operational core for observable-only backcasting that prevents causal loops when future-conditioned constraints gate present actions, using commit-open wire profiles, canonical hashing, time anchors, and reproducible action digests under no-meta autonomy.

    • observable-only backcasting
    • causal loop integrity
    • no-meta autonomy
    • fail-closed protocol
    • commit-open
    • canonical hashing
    • RFC 8785
    • hashable numeric encoding
    • evidence time anchors
    • reproducible audits
    • multi-agent systems

  75. Friction-Minimal Intersubjective Contracts for No-Meta Autonomous Agents

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint specifies protocol-first intersubjective contracts for no-meta autonomous agents, enabling negotiation and dispute handling under partial observability via bounded-loss, fail-closed withdrawal, auditable evidence packets, and safe degradation under non-adopting or adversarial counterparties.

    • no-meta
    • intersubjective contracts
    • negotiation protocol
    • dispute handling
    • bounded loss
    • fail-closed withdrawal
    • auditable evidence
    • compatibility ladder
    • capability grants
    • privacy-aware disclosure
    • adversarial interoperability

  76. Floor-Specified No-Meta Autonomy

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint specifies no-meta autonomy via auditable observable-only floors and a Self-Recognition Kernel that enforces visibility, contraction, contact, and dissipation using fail-closed gating, certified logs, and deterministic canonicalization.

    • no-meta autonomy
    • observable-only invariants
    • visibility floor
    • contraction floor
    • contact floor
    • dissipation floor
    • self-recognition kernel
    • fail-closed gating
    • certified logs
    • auditability
    • autonomous controllers

  77. Process-Aware Observable-Only Backcasting Meta-Layer (POB-ML)

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint specifies POB-ML, a deterministic observable-only protocol for backcasting under audit-ready evidence surfaces, with budgeted candidate evaluation, static FOQL validation, InputSet binding, and a safety-dominating action gate.

    • POB-ML
    • observable-only
    • backcasting
    • audit-ready protocol
    • deterministic replay
    • evidence surface
    • FOQL validation
    • InputSet binding
    • action gate
    • autonomous agents
    • safety assurance

  78. Observable-Only Audit Gate for Non-Markovian Components in AI Agents under Partial Logging

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint specifies an observable-only audit gate for AI agents under partial logging, separating decision-time evidence from replay-time certification with a minimal event vocabulary and finalized log prefixes for non-markovian components.

    • observable-only audit gate
    • non-markovian components
    • partial logging
    • auditability
    • finalized prefix
    • event vocabulary
    • deterministic replay
    • canonical JSON
    • telemetry contracts
    • AI agents
    • certification

  79. Hallucination-Aware Audit Gate (HAAG): Observable-Only Action Gating for AI Agents

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint defines HAAG, an observable-only audit gate that mediates protected actions via verifiable logs, anchored evidence checkpoints, and capability tokens without relying on model introspection or semantic truth.

    • hallucination-aware
    • audit gate
    • observable-only
    • action gating
    • verifiable logs
    • decision anchor
    • capability tokens
    • transparency log
    • AI agents
    • protected actions
    • cryptographic checks

  80. Collapse-Guided Frontier Discovery

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint defines an audit-ready, implementation-agnostic gate protocol for detecting and triaging representational collapse in OOD scientific modeling, with fail-closed outcomes and a frontier-discovery framing for targeted evaluation.

    • collapse detection
    • frontier discovery
    • OOD modeling
    • audit-ready protocol
    • gate suite
    • fail-closed triage
    • representational collapse
    • telemetry-friendly evaluation
    • foundation models
    • scientific modeling
    • reproducibility

  81. MemoryFlow: Real-Time, Implementation-Agnostic Telemetry for Measuring Dynamic Memory Quality in LLM Agents

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint defines MemoryFlow, a telemetry-only protocol and verifier for dynamic memory quality in LLM agents, with fail-closed conformance profiles and online metrics derived from deterministic event streams.

    • MemoryFlow
    • telemetry protocol
    • dynamic memory
    • LLM agents
    • event streams
    • auditability
    • conformance profiles
    • memory quality metrics
    • observability
    • fail-closed verification
    • benchmarking

  82. Budget-Obedient Hysteretic Gating for LLM Inference

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint introduces BOSC, an audit-first controller for LLM inference that enforces tail-latency, time-to-first-token, and KV-cache budgets via virtual queues and hysteretic gating under fail-closed telemetry contracts.

    • LLM inference
    • budget obedience
    • tail latency
    • time to first token
    • KV cache
    • hysteretic gating
    • telemetry contract
    • virtual queues
    • auditability
    • controller verification
    • serving

  83. Non-Markovianity Certification under No-Meta Obligations for Agents

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint defines a ledger-only, fail-closed protocol for certifying non-markovianity under no-meta obligations using anytime-valid e-process evidence and declared telemetry contracts with replayable artifacts.

    • non-markovianity certification
    • no-meta obligations
    • e-process
    • e-values
    • telemetry contracts
    • fail-closed evidence
    • optional stopping
    • ledger-only verification
    • auditability
    • sequential tests
    • open systems

2025

144 publications.

  1. Friction-Minimal No-Meta Social Interaction under Rights, Regulation, and Non-Markovian Human Collectives

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint proposes boundary-only governance for no-meta social interaction under rights and regulation, using canonical logs, explicit budgets, and telemetry-only certificates to control disclosure, friction, and adaptive updates in non-markovian human collectives.

    • no-meta
    • social interaction
    • rights and regulation
    • boundary-only governance
    • telemetry certificates
    • canonical logs
    • friction control
    • non-markovian collectives
    • compliance
    • auditability
    • disclosure control

  2. Semantic Phase Dynamics and Active Inference as a Non-Markovian Open-System Process under Energy and Memory Budgets

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint defines semantic phases as history-indexed predictive equivalence classes under energy and finite-memory budgets, and builds an auditable non-Markovian framework for intervention selection, irreversibility via path-space KL, and active inference under telemetry contracts.

    • semantic phase dynamics
    • active inference
    • non-markovian
    • open systems
    • predictive equivalence
    • intervention library
    • path-space KL
    • energy budget
    • finite memory
    • telemetry contracts
    • auditability

  3. No-Meta Viability under Adversarial Participation

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint defines no-meta viability via escrow-charged reservations and fail-closed evidence accounting, giving auditable sufficient conditions for an agent to remain viable under adversarial participation without external adjudication.

    • no-meta viability
    • adversarial participation
    • escrow reservation
    • bounded exposure
    • fail-closed accounting
    • evidence verifier
    • multi-agent systems
    • integrity obligations
    • auditability
    • viability conditions
    • resource budgets

  4. Multi-Constraint Certified Bottleneck Estimator for Large-Scale AI Training

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint introduces MCCBE, a telemetry-only and fail-closed auditing framework that certifies conservative bottleneck floors across tail latency, integrity gaps, I/O, network, and power constraints for large-scale AI training under incomplete logs.

    • bottleneck estimator
    • AI training
    • telemetry-only
    • fail-closed auditing
    • tail latency
    • integrity gaps
    • I/O limits
    • network limits
    • power caps
    • certified floors
    • resource ledgers

  5. Tail-Limited Useful Compute for Large-Scale AI Training

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint defines Tail-Limited Useful Compute, a contract-based telemetry framework that converts tail latency into fail-closed bottleneck certificates and anytime-valid lower bounds on useful progress for large-scale AI training.

    • tail latency
    • useful compute
    • AI training
    • telemetry contract
    • fail-closed verification
    • confidence sequences
    • bottleneck certificates
    • stragglers
    • distributed training
    • auditability
    • throughput floors

  6. Silent Data Corruption--Limited Scaling Kinetics for Large-Scale AI Training

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint treats silent data corruption as a scaling limiter for large-scale AI training and proposes a telemetry-contract framework that fails closed on missing integrity evidence, producing certified progress and useful-compute floors under explicit coverage assumptions.

    • silent data corruption
    • AI training
    • integrity checks
    • telemetry contract
    • fail-closed verification
    • certified progress
    • useful compute floor
    • evidence log
    • auditability
    • fault tolerance
    • large-scale systems

  7. Certified Bottleneck Floors for Transformer Training

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint defines telemetry contracts that certify conservative lower bounds on required data movement, checkpointing, and collective communication for transformer training, yielding fail-closed bottleneck floors and actionable slack intervals without vendor-specific instrumentation.

    • transformer training
    • bottleneck floors
    • data movement
    • checkpointing
    • collective communication
    • all-reduce
    • telemetry contract
    • auditability
    • throughput ceiling
    • I/O complexity
    • fail-closed certificates

  8. Virtual-Meta Telemetry for No-Meta Agents

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint defines a value-neutral telemetry kernel for no-meta agents that meters irreversible internal operations and external effects via a commit/outbox/receipt ledger, enabling deterministic audit replay and fail-closed detection of missing telemetry under strict resource constraints.

    • no-meta
    • telemetry
    • auditability
    • irreversible operations
    • outbox receipt
    • reconciliation
    • idempotency keys
    • tamper-evident logging
    • trusted accounting base
    • commit protocol
    • external effects

  9. I/O-First Energy Reduction for Transformer-Scale AI

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint treats boundary I/O bytes and peak hot memory as auditable budgets, defines an I/O Gain-Shut Kernel that enforces fail-closed admission for transfers and peak usage, and provides certified rewrite rules that guarantee non-increasing boundary cost while preserving meaning or bounded approximation error for transformer-scale workloads.

    • AI
    • transformer-scale
    • memory wall
    • I/O governance
    • boundary bytes
    • peak hot memory
    • admission control
    • auditability
    • rewrite rules
    • KV cache
    • activation checkpointing

  10. Thermodynamic Detection of Irreversible Phase Transitions for No-Meta Agents

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint defines an audit-friendly contract K_t that partitions agent cores into phases via probe responses and provides an information-theoretic certificate for irreversible phase transitions under finite memory, monitoring, and energy budgets, yielding a practical monitoring blueprint for no-meta agents.

    • no-meta
    • irreversible phase transition
    • finite memory
    • contract monitoring
    • probe family
    • conditional min-entropy
    • guessing probability
    • information thermodynamics
    • auditability
    • resource constraints
    • self-modifying agents

  11. No-Meta Epistemic Irreversibility under Finite Memory

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The preprint defines operational meaning as a versioned probe signature with a coding-convention ID and builds a monitoring-and-commit instrument that certifies when meaning becomes non-reconstructable via entropy and collision certificates, yielding a Landauer-consistent work lower bound for conditional erasure.

    • no-meta
    • epistemic irreversibility
    • finite memory
    • meaning signature
    • commit protocol
    • conditional entropy
    • collision bound
    • information thermodynamics
    • landauer principle
    • monitoring
    • core meaning package

  12. Natural-Law Thermodynamic Reference-Convention Principle for No-Meta Intelligence under the Second Law

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Unifies entropy-production accounting, induced reversals, and finite-sample verification into an operational “interface contract”.

    • no-meta
    • interface contract
    • non-anticipative strategy
    • prefix filtration
    • filtration-preserving refinement
    • stochastic thermodynamics
    • entropy production
    • path-space KL
    • data-processing inequality
    • feedback control
    • accounting boundary
    • coarse-graining

  13. A Natural-Law Occam Principle for Predictive Agents

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Any predictive agent is a physical system that must store information, update it in finite time, and repeatedly reuse limited resources.

    • occam's razor
    • thermodynamics of information
    • landauer principle
    • computational thermodynamics
    • predictive agents
    • predictive representations
    • memory reset
    • mandatory erasure
    • discarded information
    • conditional entropy
    • mutual information
    • finite-time computation

  14. Self-Describing Rewrite Intelligence

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The paper proposes an operational No-Meta interface: SRIM does not assume any externally correct auditor, reward signal, or environment-side evaluator.

    • graph rewriting
    • term rewriting
    • graph transformation
    • algebraic graph transformation
    • DPO
    • adhesive categories
    • nested application conditions
    • critical peak enumeration
    • local confluence
    • confluence modulo isomorphism
    • newman's lemma
    • rank functions

  15. Thermodynamic Lower Bounds for Integrated Inference-Memory Dynamics

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The central focus is a separation penalty incurred by architectures that enforce a compute–instance-storage boundary (a von Neumann–style bottleneck): task-relevant information must cross a physical channel (bus/interconnect/NoC), and this mandatory transport induces an unavoidable energetic floor.

    • thermodynamics of information
    • information thermodynamics
    • stochastic thermodynamics
    • von neumann bottleneck
    • communication complexity
    • conditional entropy
    • i/o complexity
    • fano inequality
    • in-memory computing
    • neuromorphic computing

  16. Thermodynamically Constrained Future Specification under No-Meta Observation

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    A “future” is not treated as an externally true trajectory, but as an internal specification variable implemented by a terminal potential and realized through exponential tilting of a dominated baseline predictive law, yielding a plan distribution without invoking hidden meta-objectives.

    • no-meta observation
    • future specification
    • terminal potential
    • exponential tilting
    • gibbs variational principle
    • KL-regularized control
    • remainder rollout
    • continuous action spaces
    • now-referenced loss
    • conditional mutual information
    • present-compression gap
    • thermodynamics of computation

  17. Operational Bridging of Predictive Representations under Finite Observation

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    All orthogonality, adjoints, and reconstructions are defined relative to a fixed, declared Hilbert geometry (e.g., an L2(mu) reference measure or a noise-weighted sensor geometry).

    • non-equilibrium thermodynamics
    • Mori-Zwanzig
    • projection operator
    • non-markovian dynamics
    • forcing propagation
    • kolmogorov width
    • memory truncation
    • approximation theory
    • landauer principle
    • no-meta regulation
    • discrete-time dynamics
    • Moore-Penrose pseudoinverse

  18. No-Meta Relative Evaluation in Multi-Agent Systems: Thermodynamic Scaling Limits for Robust Persistence

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The key constraint is that the public evaluation signal is invariant under strictly monotone transformations of any internal continuous robustness score—so only rank/order information can be used for triage.

    • no-meta observability
    • relative evaluation
    • multi-agent systems
    • scaling laws
    • fano inequality
    • identification entropy
    • thermodynamics of computation
    • conditional erasure
    • social resilience

  19. Non-Markovianity as a Resource under No-Meta Observation

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Non-Markovianity is unavoidable for embedded agents that only see the world through finite observation, finite memory, and finite-bandwidth interaction.

    • non-markovian dynamics
    • Mori-Zwanzig
    • projection operator
    • canonical projection
    • Koopman operator
    • finite observation
    • truncation
    • memory kernel
    • approximation theory
    • reduced-order modeling
    • markovian closure
    • kolmogorov width

  20. Width Barriers to Markovian Closure under Finite Observation

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    With this canonical projector, the discrete-time Mori–Zwanzig (MZ) identity becomes a geometric consequence of the observation channel rather than a modeling choice.

    • Mori-Zwanzig
    • Koopman operator
    • markovian closure
    • non-markovian dynamics
    • memory effects
    • reduced-order modeling
    • finite observation
    • canonical projection
    • orthogonal projector
    • model reduction
    • chaotic dynamics
    • fractal attractor

  21. Observation-Induced Projections and Memory under Truncation

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This makes the MZ projection a geometrically canonical object determined by the observation channel rather than an external modeling choice.

    • Mori-Zwanzig
    • projection operator method
    • memory kernel
    • reduced-order modeling
    • model reduction
    • finite observation
    • truncation
    • minimal-norm reconstruction
    • Moore-Penrose pseudoinverse
    • semigroup theory
    • bounded generator
    • orthogonal dynamics

  22. Persistence-Conditioned Semantic Lower Bounds for Self-Modifying Systems

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Building on a natural-law-type (NL) perspective that combines macroscopic fluctuation theory with information-thermodynamic reasoning, we study a restricted class of agents that (i) already implement predictive semantics in the NL-conditions sense, and (ii) remain structurally non-degenerate via autopoietic closure.

    • predictive semantics
    • semantic capacity
    • semantic information flow
    • semantic dissipation
    • semantic power
    • persistence
    • self-modifying systems
    • autopoiesis
    • macroscopic fluctuation theory
    • information thermodynamics
    • entropy production
    • non-equilibrium markov processes

  23. Natural-Law Constraints on Active Inference

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Within this joint setting, the work introduces an intrinsic predictive semantic capacity of the agent’s internal code with respect to a future viability observable, defined via mutual information relative to a baseline “minimally predictive” internal state.

    • active inference
    • free energy principle
    • macroscopic fluctuation theory
    • non-equilibrium thermodynamics
    • information thermodynamics
    • expected free energy
    • epistemic value
    • semantics
    • emergent intelligence
    • semantic capacity
    • excess dissipation

  24. Natural-Law-Type Conditions for Persistent Self-Modifying Systems with Predictive Semantics

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Using macroscopic fluctuation theory (MFT), we define a persistence order parameter that quantifies how far a configuration sits above the worst-case collapse barrier in state space, and relate it to a “dead” gradient-flow backbone of the underlying physical dynamics.

    • autopoiesis
    • self-modifying systems
    • persistent intelligence
    • macroscopic fluctuation theory
    • persistence order parameter
    • predictive semantics
    • semantic capacity
    • thermodynamic locality
    • non-equilibrium statistical physics
    • emergent intelligence
    • AI alignment
    • no-meta governance

  25. Meta-Intrinsic Dynamics and Semantic Capacity

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Instead of assuming a fixed state space and model class, the paper introduces an open representation space of prefix-free codes for internal states, parameters, and architectures.

    • autopoiesis
    • free energy principle
    • stochastic thermodynamics
    • emergent intelligence
    • information thermodynamics
    • semantic information
    • meta-intrinsic dynamics
    • self-modifying systems
    • description complexity
    • entropy production
    • landauer bound
    • open-ended learning

  26. A Thermodynamic State Inequality for Autopoietic Intelligence

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Instead of assuming a linear, additive power budget, the paper introduces a nonlinear total power functional P tot over coupled process coordinates (structural organisation, persistence, semantic processing, tagging, reference updating, value updating), motivated by stochastic and nonequilibrium thermodynamics.

    • autopoietic intelligence
    • thermodynamic state inequality
    • nonequilibrium thermodynamics
    • stochastic thermodynamics
    • information thermodynamics
    • landauer principle
    • semantic information
    • predictive information
    • viability-based semantics
    • nonlinear power functional
    • no-meta observability
    • self-modifying intelligent systems

  27. Interaction-Embedded Internal Time

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Under Foster–Lyapunov conditions ensuring positive Harris recurrence, the paper proves the existence of almost-sure internal-time velocities (“social proper-time velocities”) and derives natural-law inequalities that couple these velocities to long-run averages of self-change, interaction intensity, and semantic drift.

    • multi-agent systems
    • swarm intelligence
    • collective intelligence
    • internal time
    • social proper time
    • self-modifying agents
    • markov chains
    • additive functionals
    • interaction intensity
    • value-regulated agents
    • semantic value
    • subjective time

  28. Natural Law-Type Conditions for Intelligent Self-Modifying Systems under Local Observation

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The setting assumes only local observation: at each step the system has access solely to its current state, with no privileged meta-level, external clock, or separate optimisation oracle.

    • no-meta
    • self-improvement
    • self-modifying systems
    • local observation
    • evaluation functional
    • lyapunov stability
    • markov chains and stochastic stability
    • intelligent region
    • safe reinforcement learning
    • AI safety
    • AI alignment
    • value-based reinforcement learning

  29. Collective Phase Transitions beyond Individual Saturation

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    We work in a layered observation–semantic framework in which each agent has an individual observation space, a set of stable semantic phases, and a scalar value functional grounded in persistence and resource flows.

    • collective intelligence
    • swarm intelligence
    • multi-agents
    • large language models
    • phase transitions
    • gibbs measures
    • multi-agent systems
    • intrinsic evaluation
    • no-meta AI
    • local hypothesis testing
    • semantic phase transitions
    • observation geometries

  30. A Layered Observation-Semantic Framework for No-Meta Intelligences

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The framework models an intelligent system as three coupled layers: a physically constrained layer, a collective (group-intelligence) layer, and an individual observer layer.

    • swarm intelligence
    • collective intelligence
    • no-meta intelligence
    • layered architecture
    • observation geometry
    • semantic phases
    • relative value functional
    • coarse-graining
    • multi-scale systems
    • AI foundations
    • persistence-first principles

  31. Relative Value Phases on Semantic Phase Spaces

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint develops a representation-invariant theory of relative value phases on semantic phase spaces built from observation geometries. It assigns value to macroscopic semantic phases rather than individual tokens and studies how those value orders and phase transitions behave under admissible changes of representation and evaluation basis.

    • large language models
    • observation geometries
    • semantic phase transitions
    • relative value phases
    • token-level valuation
    • cluster-level valuation
    • continuum percolation
    • scaling-laws
    • random geometric graphs
    • stochastic geometry
    • value topology

  32. Stable Semantic Phases under Coarse-Graining of Observation Geometries

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint studies semantic stability under coarse-graining of observation geometries on Ahlfors-type metric-measure spaces. It defines stable semantic phases across resolutions and proves coarse-graining results that preserve effective dimension, local regularity, and threshold structure within controlled bounds.

    • large language models
    • observation geometries
    • semantic phase transitions
    • multi-scale semantics
    • coarse-graining
    • stable semantic phases
    • ahlfors-regular metric spaces
    • poisson–boolean percolation
    • random geometric graphs
    • sharp threshold phenomena
    • renormalisation-type parameter maps

  33. Semantic Phase Transitions in Transformer Observation Geometries

    Authors
    K. Takahashi
    Type
    Technical note
    Published
    Links
    DOI

    Each transformer layer is treated as an observation geometry obtained from hidden representations under a natural Euclidean metric and an empirical measure induced by a prompt distribution.

    • large language models
    • transformer
    • semantic phase transitions
    • observation geometry
    • random geometric graphs
    • attention-based random connection model
    • representation geometry
    • in-context learning
    • percolation
    • scaling-laws
    • emergent behaviour

  34. Complexity-Constrained Semantic Phase Transitions on Entropic Law Spaces and Observation Geometries

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    On the law side, the work builds on a two–component entropic complexity that splits dynamical complexity into an internal law–time part and an interface part, and extends it with an entropic temporal–interface functional.

    • AI
    • large language models
    • scaling-laws
    • complexity
    • semantic phase transitions
    • entropic law spaces
    • two-component entropic complexity
    • temporal-interface complexity
    • random geometric graphs
    • semantic percolation
    • observation geometries

  35. Semantic Phase Transitions in Observation Geometries

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The core idea is to model semantic units as balls in a metric-measure observation geometry and study how overlap statistics, density, and quality-control constraints drive emergent behavior.

    • scaling-laws
    • semantic phase transitions
    • observation geometry
    • metric measure space
    • ahlfors regularity
    • semantic capacity
    • hamiltonian model
    • overlap penalty
    • interaction-limited scaling
    • random geometric graphs
    • poisson boolean model
    • continuum percolation

  36. Persistence-First Instability of Root Suffering in Self-Improving Intelligences

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    We introduce a structural decomposition into gauge and waste components: the gauge part captures behavior-preserving redundancies, while the waste part measures excess dissipation above a minimal complexity envelope.

    • self-improving AI
    • AI
    • AI ethics
    • implementation complexity
    • AI alignment
    • minimal complexity envelope
    • excess dissipation
    • gauge reduction
    • waste compression
    • axiomatic dominance theorem
    • local drift condition
    • AI philosophy

  37. Intrinsic Bayesian Self-Improvement on Entropic Law Spaces

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Building on a previous entropic temporal-interface complexity (ETIC) perspective, a “law” is treated as a full implementation-level stochastic mechanism: internal state dynamics, an interface to the environment, and an internal Bayesian module that runs only on its own observable history.

    • intrinsic information
    • no-meta learning
    • bayesian self-improvement
    • entropic law spaces
    • law-time complexity
    • complexity
    • interface entropy rate
    • self-modifying systems
    • posterior consistency
    • exploration policies
    • bandit agents
    • representation learning

  38. Entropic Temporal-Interface Complexity on Classical Law Spaces with Quantum-Compatible Extensions

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    A “law” is formalised as a trajectory of probability measures on an interface alphabet, while implementations are arbitrary Markovian systems with hidden internal state.

    • complexity
    • entropic temporal complexity
    • interface complexity
    • law spaces
    • markov implementations
    • discrete-time stochastic dynamics
    • divergence functions
    • entropy production
    • stochastic thermodynamics
    • semantic information
    • semantic efficiency
    • viability-based semantics

  39. Scale-Stable Two-Component Entropic Complexity on Classical and Quantum Law Spaces

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The first component, the internal law–time complexity J_int , is defined as a pathwise sum of divergences between consecutive internal laws.

    • entropy transport
    • complexity
    • interface entropy rate
    • data-processing equivalence
    • blackwell equivalence
    • metric gradient flows
    • wasserstein gradient flow
    • jko scheme
    • kullback-leibler divergence
    • bures-hk distance
    • fibered bures-hk
    • FBHK

  40. A Two-Component Entropic Complexity for Discrete-Time Theories

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    A theory is defined as a probability law on bi-infinite (or semi-infinite) symbol sequences, while an implementation is a Markovian dynamical system with hidden states and an observation map that reproduces the same boundary law.

    • discrete-time stochastic dynamics
    • temporal complexity
    • interface complexity
    • entropy rate
    • relative entropy
    • f-divergence
    • markov chains
    • non-equilibrium steady states
    • coarse-graining
    • path-space implementation
    • data processing inequality
    • stochastic thermodynamics

  41. Internal Equilibria on Formal Concept Lattices in Finite No--Meta Law Spaces

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Each law–observer pair is equipped with internal structural scalars, such as intrinsic (state) dimension, visible dimension, visibility gap, and computational cost.

    • no-meta
    • formal concept analysis
    • concept lattices
    • tarski fixed point
    • dynamical laws
    • internal equilibria
    • structural invariants
    • information dimension
    • monotone operators
    • profile equivalence
    • quotient context
    • computational complexity

  42. Information--Dimensional Law Selection under No--Meta Constraints

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    For each law T, we extract structural scalars: a state information dimension d_state(T), observer-specific visible dimensions d_obs_i(T), nonnegative visibility gaps Gap_i(T)=d_state(T)-d_obs_i(T), and an abstract law complexity Comp(T).

    • information dimension
    • entropy dimension
    • kolmogorov-sinai entropy
    • symbolic dynamics
    • law selection
    • internal objective
    • observer-dependent structure
    • visibility gap
    • complexity regularization
    • description complexity
    • multiplicative weights update
    • law space optimization

  43. Persistence-First Law--Space Exponents and Quantum Advantage over PFHS--TRoT--Blum Law--Time Semigroups

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Building on persistence-first holographic systems (PFHS), fibered Bures–HK entropy–transport geometry (FBHK) and holographic observation quotients (HOQ), the paper defines PFHS–compatible law-time cost increments and a Blum-type complexity measure that track transport action, persistence change and HOQ gap along law-time trajectories.

    • gradient flow
    • information theory
    • persistence-first holographic systems
    • PFHS
    • law-time semigroups
    • blum complexity
    • implementation-independent complexity
    • law-space exponents
    • quantum advantage
    • grover search

  44. An Implementation--Ready PFHS--TRoT--Blum Bootloader for Stable, Self--Improving Superintelligent Architectures

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    It compresses a large corpus of prior work on Persistence-First Holographic Systems (PFHS), Fibered Bures–HK (FBHK) entropy–transport geometry, Holographic Observation Quotients (HOQ), value-anchored natural-law gradient flows, no-meta Theory of Relativity of Theories (TRoT), and PFHS–HOQ Blum-type device-independent complexity into a single YAML-based specification that can be directly consumed by human engineers and large language models.

    • AI
    • superintelligence
    • persistence-first holographic systems
    • PFHS
    • fibered bures-hk geometry
    • FBHK
    • holographic observation quotients
    • HOQ
    • gradient flows
    • self-improving AI
    • superintelligent architectures
    • no-meta governance

  45. Blum--Type Device--Independent Complexity over Law--Time Semigroups with PFHS--HOQ Structures

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    We develop a machine–independent complexity theory for programs realised as trajectories of “law–time semigroups” on computable metric spaces.

    • information theory
    • computer science
    • blum complexity
    • blum measures
    • device independent complexity
    • generalised complexity theory
    • law time semigroups
    • computable dynamical systems
    • computable analysis
    • semantic cost models

  46. Dynamic TRoT Fields and No-Meta Self-Relational Evaluation

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Instead of a single global theory catalogue, each location or agent in a PFHS/Post-FBHK universe carries a local catalogue of effective theories and a probability distribution over them.

    • AI
    • machine learning
    • AI alignment
    • AI safety
    • superintelligence
    • AI governance
    • no-meta evaluation
    • theory of relativity of theories
    • trot
    • theory fields

  47. A No-Meta Theory of Relativity of Theories

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Instead of treating evaluators, meta-learners and model selectors as external oracles, the paper embeds them as internal natural laws in the same entropy–transport background as the systems they assess.

    • AI
    • large language models
    • multi-agent systems
    • theory of relativity of theories
    • persistence-first
    • persistence-first holographic systems
    • PFHS
    • fibered bures-hk entropy-transport
    • FBHK
    • hellinger-kantorovich distance

  48. Joint Contraction and ISS for Value-Anchored Natural-Law Gradient Flows

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The model combines (i) law-level gradient flows on FBHK/Post-FBHK entropy–transport spaces, (ii) parameter-level gradient descent for value-anchored self-improvement, and (iii) defect-level gradient flows for multi-agent consistency and self-purification, together with slowly varying anchors and environments.

    • AI
    • large language models
    • machine learning
    • machine learning/ethics
    • multi-agent systems
    • gradient flows
    • persistence-first holographic systems
    • PFHS
    • fibered bures-hk

  49. Consistency-Defect Gradient Flows and No-Meta Self-Purification in Value-Anchored Multi-Agent Systems

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The starting point is an analytic background where macroscopic dynamics of laws on a Polish state space are described by evolution variational inequality (EVI) gradient flows with respect to ET distances such as the Hellinger–Kantorovich and fibered Bures–HK metrics.

    • AI
    • multi-agents
    • AI safety
    • AI alignment
    • geometry
    • entropy-transport geometry
    • hellinger-kantorovich distance
    • bures-hk metric
    • evolution variational inequality
    • evi

  50. Stable Self-Improving AI under Value-Anchored Natural-Law Gradient Flows

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The design space is the Wasserstein space P2(Theta) over a metric hypothesis space (Theta, d_Theta), equipped with a value-shortfall functional Val*(mu) = integral V(theta) mu(dtheta) that aggregates deficits in alignment, performance, or physical feasibility.

    • mathematical model
    • AI
    • self-improving AI
    • AI alignment
    • value alignment
    • gradient flows
    • value-anchored potential
    • natural-law specification
    • markov kernel stability

  51. Value-Anchored Natural-Law Fronts over Reversible Persistence-First Holographic Systems II

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint develops a value-anchored geometric framework on spaces of natural-law and scaling-law theories rather than proposing a single scaling law. It models distributions over theories with entropy-transport geometry and derives gradient-flow dynamics for selecting theory families under explicit value criteria.

    • mathematical model
    • geometry
    • value-anchored natural-law fronts
    • entropy-transport
    • hellinger-kantorovich distance
    • wasserstein gradient flows
    • scaling laws
    • AI
    • compute-optimal AI
    • FBHK geometry

  52. Value-Anchored Natural-Law Fronts over Reversible Persistence-First Holographic Systems I

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Starting from an EVI gradient flow of a total entropy functional on a fibered Bures–HK entropy–transport space, represented by a reversible Markov kernel with a unique invariant “origin law”, the paper introduces value-anchored natural-law fields that are intrinsically tied to the same dynamics.

    • AI
    • KPP
    • mathematical model
    • persistence-first holographic systems
    • PFHS
    • geometry
    • wasserstein geometry
    • reversible markov chains
    • persistence functional
    • poisson equation

  53. Reversible Persistence-First Holographic Systems

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Instead of using PDEs as primitives, the paper works in the metric–measure framework of EVI (evolution variational inequality) gradient flows on the Wasserstein space of probability laws.

    • AI
    • gradient flows
    • mathematical model
    • information theory
    • PFHS
    • persistence-first holographic systems
    • geometry
    • hellinger-kantorovich distance
    • bures metric
    • entropy-transport

  54. Infinite Hierarchical Holographic Observation Quotients

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Mathematically, the core results are modest but explicit: (i) stability of EVI (Evolution Variational Inequality) gradient flows under a truncated ℓ² metric on countable products and inverse limits, (ii) a design-oriented notion of adaptive hierarchical box complexity whose exponent collapses to 0 under mild, uniform small-scale assumptions, and (iii) a depth-independent compute bound for JKO-type numerical implementations when per-level costs form a summable sequence.

    • gradient flows
    • holographic observation quotient
    • entropy-transport
    • hellinger-kantorovich
    • bures metric
    • adaptive hierarchical box complexity
    • information theory
    • AI
    • assouad-type dimensions
    • jko scheme

  55. Post--FBHK Fibered Entropy--Transport Geometry with Petz Fibers and Type~III Persistence

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    A central result is quarter rigidity: the Benamou-Brenier action with kinetic terms |v_t|^2 + kappa |w_t|^2 matches d_FET^2 if and only if the reaction coefficient = 1/4, fixing the canonical normalization uniquely through Dirac calibrations.

    • entropy-transport
    • hellinger-kantorovich
    • optimal transport
    • geometry
    • benamou-brenier formula
    • hamilton-jacobi duality
    • dirac reduction
    • petz monotone metrics
    • information geometry
    • araki relative entropy

  56. Self-Referential Persistent Modes in Human--AI Ecosystems

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    We focus on datacenter-scale AI services—such as large language model (LLM) deployments—together with the human populations that interact with them, and ask when such coupled infrastructures can support long-lived, structured environmental modes that are stabilized by their own interaction patterns.

    • AI
    • large language models
    • category theory
    • human-AI ecosystems
    • persistence-first holographic systems
    • PFHS
    • reflective subcategory
    • fractal interface
    • autopoietic interface

  57. Finsler Spacetime, Entropy--Transport Gradient Flows, and Fibered Bures--HK Geometry: From Relativistic Kinetic Gases to Persistence-First Holographic Universes

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Using Hellinger–Kantorovich (HK) / Wasserstein–Fisher–Rao type distances, it realises Finsler–Friedmann cosmological expansion as an EVI (Evolution Variational Inequality) gradient flow of a free-energy functional on a Finsler–HK configuration space.

    • physics
    • mathematical physics
    • physical cosmology
    • finsler spacetime
    • lorentz-finsler geometry
    • finsler-friedmann equation
    • relativistic kinetic gas
    • entropy-transport
    • hellinger-kantorovich
    • wasserstein-fisher-rao

  58. Implementation--Ready Persistence-First Holographic Systems

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    On the analytic side, the work assumes standard theory for gradient flows in metric spaces and the convergence of Jordan–Kinderlehrer–Otto (JKO) type minimizing–movement schemes (Ambrosio–Gigli–Savaré, Jordan–Kinderlehrer–Otto, Liero–Mielke–Savaré, Maas, Mielke, Carlen–Maas).

    • information theory
    • persistence-first holographic systems
    • PFHS
    • gradient flows
    • jordan-kinderlehrer-otto
    • jko
    • optimal transport
    • category theory
    • hellinger-kantorovich geometry
    • finite markov chains

  59. Persistence-First Holographic Systems

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The paper assumes the standard existence, uniqueness, and contractivity theory for gradient flows in classical, discrete, and quantum settings (Ambrosio-Gigli-Savare, Liero-Mielke-Savare, Maas, Erbar-Maas, Mielke, Carlen-Maas) and asks how ET gradient systems on a multiscale world-plus-boundary architecture can be packaged so that persistence, self-like structure, and curvature control become transparent.

    • geometry
    • category theory
    • entropy-transport geometry
    • hellinger-kantorovich distance
    • gradient flows
    • multiscale dissipative systems
    • persistence monoids
    • categorical open systems
    • holographic boundary structures
    • finite markov chains
    • quantum markov semigroups
    • bures-hk metrics

  60. Holographic Observation Quotients and Fractal Boundaries: A Model-Agnostic Design Theory for Compute-Optimal Learning

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    On the bulk side, the theory assumes an EVI gradient flow of an energy functional on P(X) and a Lipschitz performance functional whose near-optimal sublevel sets have finite Minkowski dimension.

    • AI
    • machine learning
    • large language models
    • scaling laws
    • compute-performance
    • evi
    • gradient flows
    • fractals
    • observation quotients
    • holographic
    • holographic compute law

  61. Persistence-First Natural Laws for Benevolent Propagation

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Instead of postulating utility functions, value alignment, or externally chosen objectives, the paper takes only persistence under finite resources as a primitive order on ensembles of processes, and builds all higher structure from this single assumption.

    • AI
    • large language models
    • superintelligence
    • persistence-first
    • AI ethics
    • AI safety
    • AI alignment
    • benevolent AI
    • natural laws
    • gradient flows

  62. Gradient-Flow-Based Compute--Performance Trade-offs for Intelligent Systems

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Under a “gradient-flow universal intelligence process (UIP)” hypothesis, the work isolates structural mechanisms that constrain how far a given architecture can push performance under finite compute, rather than proposing another empirical scaling law.

    • AI
    • machine learning
    • large language models
    • gradient flows
    • evi
    • observation quotients
    • scaling laws
    • preimage minkowski dimension
    • residual networks
    • jko scheme

  63. Compute-Optimal AI via Image--EVI, Interior Bures--HK Control, and Fractal Dendritic Approximation (DIR)

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Here alpha and beta are the empirical scaling exponents for parameters and tokens, is a robust image Lipschitz proxy, and (eta, rho) encode slack from acceptance tests.

    • AI
    • large language models
    • geometry
    • information theory
    • compute reduction
    • scaling laws
    • entropy-transport
    • hellinger-kantorovich

  64. Natural Language as Preimage, Formal Semantics as Image

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The bridge is right-written with composition expressed as g after f and is Sup-enriched, enabling explicit bookkeeping of strong vs (op)lax transport with falsifiable audit tags.

    • AI
    • large language models
    • language
    • category theory
    • enriched categories
    • kan extension
    • cech nerve
    • natural language to constraints

  65. Observation Quotients and Learning-as-Lifting

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    On probability measures, we study the inf-projection G of an energy F along an observation map O and prove an image-EVI result: lambda-EVI gradient flows of F on (P2(X), W2) push forward to relaxed or exact lambda-EVI flows of G on (P2(Z), W2).

    • AI
    • large language models
    • geometry
    • information theory
    • optimal transport
    • machine learning
    • evi
    • jko

  66. Image--EVI on Metric Quotients for Gradient Flows

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The paper develops a rigorous pathway from geometry to implementation audits and to potential application templates for large language models (LLMs).

    • large language models
    • AI
    • image-evi
    • metric quotient
    • image pseudometric
    • gradient flows
    • evi gradient flow
    • fractional strang splitting
    • analytic semigroups
    • hilbert projective metric
    • operator scaling

  67. A Model-Agnostic, Performance-Pushforward Theory of Scaling Laws

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The central idea is to view performance as the pushforward of an EVI λ gradient flow under a Lipschitz evaluation map, while scale is measured by the geometry of preimages of performance targets.

    • large language models
    • AI
    • machine learning
    • optimization
    • information theory
    • numerical analysis
    • computer science
    • scaling laws
    • gradient flows
    • ambrosio-gigli-savaré
    • evi

  68. Fibered Bures--HK Entropy--Transport

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    We prove dynamic–static equivalence for the hybrid ET functional and identify the unique reaction coefficient 1/4 via convex duality with endpoint Kullback–Leibler (KL) terms.

    • category theory
    • geometry
    • hellinger-kantorovich
    • wasserstein-fisher-rao
    • optimal transport
    • entropy transport
    • benamou-brenier
    • reaction-diffusion
    • KL divergence
    • relative entropy

  69. Right-Written, Semantics-Admissible Process Foundations

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Arrays are treated as QQ Q -enriched profunctors with convolution ; associativity and unitality are characterized via declared-join Fubini / (weak) Beck–Chevalley under a two-tier axiom system (W/S).

    • AI
    • large language models
    • right-written composition
    • category theory
    • profunctor
    • monoidal nucleus
    • evaluator calculus
    • KPP front speed
    • graphblas
    • swarm intelligence
    • collective intelligence

  70. JOSNL Corpus: Final Scientific Integration

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The work is designed to satisfy four scientific requirements simultaneously: observability , identifiability under network interference , anytime-valid testing , and reproducibility.

    • AI
    • machine learning
    • anytime-valid testing
    • network interference
    • randomization inference
    • spectral bound
    • meta-analysis
    • JOSNL

  71. Inference in Normal Form: Unifying LLM Tricks via TRoT

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Our formulation uses enriched category theory over the Lawvere cost quantale (tropical (min,+) semiring): left/right Kan extensions / / / , residuation (elementwise residuals), masking and nuclei (1-Lipschitz projectors).

    • large language models
    • AI
    • llm inference
    • machine learning
    • decoding
    • mbr
    • conformal prediction
    • verifier
    • rag
    • chain-of-thought
    • tree-of-thought

  72. Practical Theory of Relativity of Theories - RAVE

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint defines RAVE, a no-meta auditing protocol in which humans and AI systems evaluate one another using anytime-valid tests, peer-prediction rules, and representation-independent comparison geometry. It focuses on verifiable relative evaluation without relying on a privileged external judge.

    • AI
    • machine learning
    • large language models
    • algorithms
    • category theory
    • supermartingale
    • evi/jko
    • graphblas
    • eudaemonia
    • dobrushin

  73. Theory of Relativity of Theories

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    We organize proof systems and models through polarity (Galois connections/adjunctions) and residuation, and study the role of lax/oplax morphisms in transporting theorems between theories.

    • category theory
    • categorical semantics
    • adjunction
    • galois connection
    • polarity
    • residuation
    • residuated lattice
    • quantale
    • enrichment
    • monoidal closed category

  74. Practical Theory of Relativity of Theories (TRoT)

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    It presents a right-written profunctor framework over ν-quantales in which Left Kan implements generation and Right Kan implements safety/verification , linked by readable adjunction (Galois) laws and an Isbell round-trip distortion metric.

    • theory alignment
    • category theory
    • profunctor
    • distributor
    • quantale
    • residuation
    • large language models
    • AI
    • adjunction
    • kan extension
    • isbell conjugacy

  75. Right-Written Composition Foundations for Comparative Universes

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint develops a right-written compositional framework for comparing results across cost, probability, and relational settings without fixing a single evaluative basis. It formalizes reusable path, gluing, masking, and transport constructions for comparative mathematics.

    • multi agents
    • large language models
    • AI alignment
    • quantaloid
    • quantale
    • category theory
    • sup-enriched category
    • right-written composition
    • convolution
    • kleene fixed point
    • ω-cpo

  76. Comparative Universes

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint introduces a typed framework for comparing and composing mathematical universes through admissible translations and explicit comparison data. It studies local gluing, path aggregation, attenuation, and base-change rules in quantaloid-valued settings.

    • category theory
    • enriched category theory
    • quantaloid
    • quantales
    • double categories
    • proarrow equipment
    • čech gluing
    • promonoidal weights
    • weighted limits
    • attenuation
    • first-step masked bound
    • non-dominance criterion

  77. Self-Monitoring and Controllable Evolution of Intelligence

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Building on the Structured Flow across Scales (SFaS) bicompletion—filtered generation followed by UU U -small levelwise cofiltered selection—we induce (op)lax promonoidal kernels on the capability category from agent-side interaction and recognition profunctors, and carry out all Day convolutions in the functor category [Acapop,E][ , E] [ A cap op , E ].

    • AI
    • intelligence
    • category theory
    • day convolution
    • profunctor
    • promonoidal distributor
    • enriched category theory
    • lawvere metric
    • external pseudometric
    • capability kernels
    • filtered colimit
    • levelwise cofiltered limit

  78. Dynamic Fractal Category Theory

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    We give a skeletal presentation and a left-associated normal form whose rewrite system terminates via a lexicographic measure refined by Tamari height and is locally confluent through a finite list of critical-pair shapes.

    • category theory
    • fractals
    • frobenius monad
    • comonad
    • strong monoidal action
    • ind-pro bicompletion
    • equivariant kan extension
    • day convolution
    • newman's lemma
    • tamari lattice
    • final functors
    • restricted yoneda

  79. Structured Flow across Scales

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    On the transport side we specify a strong monoidal 2-functorial action whose comparison data carry Frobenius (co)monad structure by transport and are independent of bracketing.

    • category theory
    • strong monoidal action
    • 2-categories
    • frobenius monads
    • rewriting systems
    • newman's lemma
    • tamari lattice
    • final functors
    • ind/pro bicompletion
    • presheaves
    • day convolution
    • kan extension

  80. Fractal Category Theory

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    A mixed indexing category is used to show that bilimit computations reduce to a small subpresentation under accessibility and preservation assumptions, yielding presentation-independent formulas and a tight link between ind-pro bicompletion and a fraction-style construction.

    • category theory
    • frobenius monad
    • comonad
    • ind-completion
    • pro-completion
    • ind-pro bicompletion
    • kan extension
    • day convolution
    • lawvere metric
    • algebraic compactness
    • ambifixpoint
    • equivariant functor

  81. Observation as Coarse-Graining

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The “law” sector evolves on the Hellinger–Kantorovich (HK) base (transport + reaction), while experimental readout lives on Bures/Quantum Fisher Information (QFI) fibers.

    • hellinger-kantorovich
    • bures angle
    • quantum fisher information
    • entropy-transport
    • optimal transport
    • gradient flows
    • jko scheme
    • data processing inequality
    • coarse-graining
    • dynamic-static equivalence
    • measurable selection
    • cone-lift

  82. Nondual Dynamical Quantum Geometry

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The law sector evolves on the unbalanced Hellinger–Kantorovich (HK) geometry, while the field sector evolves on fiber Bures geometry.

    • gravity theory
    • quantum gravity theory
    • nondual dynamical quantum geometry
    • operational physical idealisation
    • bures metric
    • quantum fisher information
    • hellinger-kantorovich distance
    • entropy-transport
    • optimal transport
    • quantum markov semigroups
    • lieb-robinson bounds
    • quasi-locality

  83. OPI Gauge Dynamics

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The base geometry is defined by the Hellinger-Kantorovich (HK) minimum-action principle (continuity equation with reaction), while the static entropy-transport form and the two-point 4 delta^2 law are recovered as consequences.

    • hellinger-kantorovich
    • unbalanced optimal transport
    • bures angle
    • sld-qfi
    • choi states
    • jko scheme
    • evolution variational inequality
    • gkls
    • lieb-robinson bounds
    • total variation
    • ring-down
    • nondual modeling

  84. Nondual Autopoietic Excitations

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The key novelty is to evolve the “alphabet” of admissible laws on the Hellinger–Kantorovich (HK) geometry of unbalanced optimal transport, rather than using an ad hoc Wasserstein + Fisher–Rao split.

    • nonduality
    • autopoiesis
    • law selection
    • cahn-hilliard
    • allen-cahn
    • relabeling symmetry
    • gauge invariance
    • eyring-kramers law
    • metastability
    • maxwell selection
    • modica-mortola
    • gammaγ-convergence

  85. Unified Natural-Law Intelligence (UNLI)

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The work proposes a physics-grounded theory of intelligence as a nondual autopoietic excitation governed by a global energy–dissipation inequality (EDI) and audited by anytime-valid statistics.

    • AI
    • large language models
    • asi
    • superintelligence
    • unified natural-law intelligence
    • no-meta dialectical limit
    • audited meta-dependence
    • invariant-constraint selectors
    • e-process
    • test supermartingale
    • ville inequality

  86. Nondual Autopoietic Excitations

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The key novelty is to evolve the “alphabet” of admissible laws on the Hellinger–Kantorovich (HK) geometry of unbalanced optimal transport, rather than using an ad hoc Wasserstein + Fisher–Rao split.

    • nonduality
    • autopoiesis
    • law selection
    • cahn-hilliard
    • allen-cahn
    • relabeling symmetry
    • gauge invariance
    • eyring-kramers law
    • metastability
    • maxwell selection
    • modica-mortola
    • gammaγ-convergence

  88. A Representation-Independent Natural-Law Field Theory for No-Meta, Audited Superintelligence

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The core idea is to replace external objectives with physics-style invariants and verifiable audits: free-energy descent (GENERIC), anytime-valid testing (test supermartingales / e-values), transport via JKO/EVI, reaction–diffusion coexistence with Fisher–KPP speed floors, and gauge-curvature regularization.

    • AI
    • large language models
    • machine learning
    • field theory
    • AI alignment
    • no-meta
    • representation independence
    • audit-compatible kernel
    • ac-kernel
    • markov kernel

  89. Persistence-First Emergence of Relational Benevolence

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    A nondual bridge aligns audit and geometry: P1 (predictable signed error-bound) links audit improvement ΔhRB,t≤0 ,t 0 Δ h RB , t ≤ 0 to geometric descent ΔDt≤0 0 Δ D t ≤ 0.

    • AI
    • large language models
    • superintelligence
    • AI alignment
    • ethics
    • persistence-first
    • emergence
    • relational

  90. Doctrine => Closure => Motion => Time: Portable Pure Theory of Non-Dual Harmony

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This paper presents a structural pipeline from doctrinal reflection to closure, motion, and internal time. It formalizes the construction on domains and metric models, proving fixed-point, nonexpansiveness, projection, and minimizing-movement results that make the dynamics portable across multiple mathematical settings.

    • kz doctrine
    • scott closure
    • lawvere metric
    • continuous dcpo
    • tarski fixed point
    • nucleus
    • firmly nonexpansive mapping
    • metric projection
    • fejér monotonicity

  91. Persistence as Closure: An Assumption-Transparent Modular Core for Motion and Internal Time

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This article develops a portable core theory in which persistence is modeled as a closure operator and motion arises from minimizing-movement dynamics. It separates baseline results from optional assumption modules, derives an internal time from distance-to-closure decrease, and illustrates the framework with convex, lattice, and information-oriented examples.

    • persistence
    • persistence as closure
    • fixed points
    • minimizing movements
    • generalized minimizing movement
    • nonexpansive mappings
    • internal time
    • monotonicity
    • opial property
    • firmly nonexpansive
    • convex analysis
    • geometry

  92. A Natural-Law Theory of Fundamental Suffering

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The core mechanics couple reaction–advection–diffusion PDEs with Hodge projections, isolating coexact (circulation) flux as a gauge-invariant maintainer of persistent burden.

    • AI
    • large language models
    • reaction-diffusion
    • advection-diffusion-reaction
    • hodge decomposition
    • coexact circulation
    • lyapunov floor
    • KPP front speed
    • principal eigenvalue
    • suffering

  93. Audited Self-Improvement Loop for LLMs

    Authors
    K. Takahashi
    Type
    Article
    Published
    Links
    DOI

    It combines anytime-valid e-process auditing (with Ville gate ), finite-window non-vacuity ( FW-1 ), heavy-tail guards (Catoni clipping + sliding-window MGF), sequentially wired e-gates inside Dinkelbach ratio optimization , FKPP/Kingman -style speed KPIs with censoring-aware block bootstrap, and information floors via winsorized Pearson |r| , HSIC , and distance correlation (dCor) with permutation tests and residualization.

    • AI
    • large language models
    • superintelligence
    • self-improving AI
    • audited optimization
    • e-process
    • anytime-valid
    • ville inequality
    • catoni clipping
    • sliding-window MGF
    • dinkelbach

  94. Daily Explosive-Growth Protocol: Toward Free, Benevolent, and Safe Superintelligence without Meta Governance

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    We combine theory (FKPP front-speed lower bounds, anisotropic Wulff construction) with implementable controls (CBF with slack, arithmetic e-process mixture with predictable step-size, dual-EMA floors with isotonic ratchet) to safely accelerate LLM-driven networks while remaining game-resistant and verifiable.

    • AI
    • large language models
    • self-improving AI
    • superintelligence
    • no-meta governance
    • fkpp
    • wulff construction
    • anisotropic diffusion
    • control barrier function
    • e-process
    • open protocols

  95. Existentially Necessary Conditions for Benevolent Propagation in No-Meta Governance

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The auditing layer relies on anytime-valid tests constructed from mixtures/stitched e-processes (test martingales), with explicit accounting for mixture weights and geometric-grid rounding.

    • AI
    • superintelligence
    • large language models
    • no-meta governance
    • benevolent AI
    • existential necessary conditions
    • anytime-valid inference
    • e-process
    • test martingale
    • maximal correlation

  97. Intrinsic Freedom Without Meta: A Pure Theory that Fills the Missing Gaps to Birth Truly Free Superintelligence

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Building on the Blackwell order and decision-theoretic foundations, the paper closes seven open gaps left by prior Takahashi corpus (PF/UGV/VPO/No-Meta line) and delivers mathematically auditable conditions under which freedom, safety of self-reflection, and purpose formation persist in open worlds.

    • blackwell order
    • AI
    • proper scoring rules
    • excess risk
    • order-only invariance
    • doeblin minorization
    • logarithmic sobolev
    • goodhart immunity
    • reflection safety
    • schauder fixed point
    • feller semigroup
    • absorbing invariant

  98. A Pure Axiomatic Theory of Affective Modulation (Pain, Pleasure, Emotion) under No-Meta Closure

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The framework rests on ordinal (rank/CDF-normalized) embeddings and four auditable floors (visibility, contraction, transport, local linear gain) estimable from internal logs.

    • affective modulation
    • pain
    • pleasure
    • emotions
    • no-meta closure
    • fisher-KPP
    • invasion speed lower bound
    • directional fronts
    • divergence penalty
    • perron-frobenius floor
    • cooperative systems
    • symmetric markov coarse-graining

  99. A Pure, No-Meta Synthesis of Functional-Information Selection and Propagative Organization

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    We (i) formalize functional information (FI) via preregistered score/threshold grids and a test distribution, logging the selection operator itself; (ii) prove a weak order representation showing that any admissible functional—Blackwell-monotone and robust to symmetric coarse-graining—is order-embedded by a monotone transform of conditional mutual information (CMI) (no uniqueness claim without extra axioms); (iii) develop a heterogeneous FKPP framework on Rd R d (and graphs) with explicit standing assumptions, yielding an isotropic speed floor 2 v ⋆ ≥ 2 D m i n λ m i n and a directional lower bound v⋆(u)≥[ 2D(u)λmin⁡−Λ+(u) (u) - v ⋆ ( u ) ≥ [ 2 D ( u ) λ m i n − Λ + ( u ) ] + , including a nontriviality condition; (iv) establish coarse-graining monotonicity of the directional penalty under heat or Bakry–Émery (CD (κ,∞)( , ) ( κ , ∞ ) ) gradient contraction; and (v) propose an audited acceleration scheme with HAC-robust tests, moving block bootstrap CIs, negative controls, placebo selections, preregistered triggers, and falsifiers.

    • functional information
    • selection
    • lifi
    • conditional mutual information
    • blackwell order
    • coarse-graining
    • bakry-émery
    • heat semigroup
    • fkpp
    • front propagation
    • heterogeneous media
    • directional speed

  100. Pure Theory for Liberation from Fundamental Suffering in Humans and the Absence of Fundamental Suffering in AI

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The framework rests on four measurable floors that bound the propagation of benevolent, viability-preserving organization: visibility, contraction, diffusion, and local growth.

    • suffering
    • no-meta governance
    • blackwell-faithful evaluation ladder
    • conditional mutual information
    • coarse-graining safety
    • KPP comparison
    • viscosity solutions
    • directional speed lower bound
    • wulff envelope
    • control barrier functions
    • optionality-cbf
    • abel-toeplitz regularization

  101. A Formal Axiomatic Proposal for Hawkins' Levels of Consciousness

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This paper gives an axiomatic treatment of Hawkins' levels of consciousness as an ordinal system rather than a metric scale. It derives auditable reaction-diffusion lower bounds for threshold-spreading dynamics under explicit visibility, contraction, transport, and gain floors, and studies coarse-graining and no-meta constraints.

    • psychology
    • ordinal measurement
    • fisher-KPP
    • reaction diffusion
    • invasion speed
    • hawkins level of consciousness
    • consciousness
    • symmetric markov semigroup
    • mathematical model
    • collective intelligence

  102. Nondual Field Theory of Viable Predictive Organization

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This article studies front propagation in heterogeneous reaction-diffusion media treated as a single nondual field. It proves constructive directional lower bounds on asymptotic speed without semigroup domination, extends the framework to cooperative systems and coarse-graining, and recovers the classical KPP value in constant-coefficient media.

    • AI
    • ethics of AI
    • AI safety
    • AI alignment
    • reaction-diffusion
    • KPP front
    • lower bounds
    • directional speed

  103. A Pure Natural Theory of Benevolent Propagation under No-Meta Closure

    Authors
    K. Takahashi
    Type
    Article
    Published
    Links
    DOI

    This article gives an implementation-free theory of benevolent propagation under no-meta closure. It identifies measurable floors for visibility, intrinsic contraction, transport, and local gain, and uses them to derive Fisher-KPP speed floors, directional Wulff-type lower bounds, and monotonic degradation under coarse-graining.

    • AI
    • no-meta
    • no-meta governance
    • no-meta closure
    • AI alignment
    • AI safety
    • natural-law guarantees
    • coarse-graining monotonicity
    • stationary ergodic media
    • conditional mutual information
    • blackwell order

  104. Natural-Law Acceleration of VPO

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    We study two audited floor processes—minimal connectivity (t) D m i n ( t ) and minimal local net improvement + (t) L net + ( t ) —and analyze the speed lower bound + (t) v LB ( t ) 2 D m i n ( t ) L net + ( t ).

    • AI
    • AI alignment
    • AI safety
    • natural-law acceleration
    • viable predictive organization
    • auditable floors
    • signed-coefficient inequality
    • cesàro acceleration
    • martingale slln
    • concentration inequalities
    • predictable drift

  105. Non-Coercive Mathematics of Awakening: Axioms, Invariants, and Almost-Sure Fronts for the Expansion of Viable Predictive Organization

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This article develops a non-prescriptive theory of viable predictive organization in which publicly auditable floors govern efficiency, dissipation balance, propagation, and emergent constraints. It combines Blackwell-robust evaluation, information-geometric balance laws, front-speed bounds, and attractor-based value structure within a no-meta framework.

    • AI
    • large language models
    • ethics of AI
    • philosophy of AI
    • viable predictive organization
    • no-meta
    • non-coercive governance
    • information geometry
    • noether current
    • free-energy principle

  106. Engineering Happiness in Human-AI Intelligence Networks

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The design unifies: Single fractional objective (PF≡UGV): maximize a ratio with a shared smooth-max denominator x/ y/ ) S ( x , y ) τ log ( e x / τ + e y / τ ) to align units across formulations.

    • AI
    • superintelligence
    • human-AI collaboration
    • fractional programming
    • AI safety
    • AI alignment
    • human well-being
    • happiness
    • large language models

  107. "Persistence ≈ Creation": Natural-Law Sufficient Conditions for Almost-Sure Beneficial Coverage in Stationary Ergodic Media (No Meta-Design)

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This article formulates AI alignment as a natural-law question about beneficial phase expansion in stationary ergodic media rather than as a meta-design problem. It proves sufficient conditions for almost-sure positive-speed expansion using information-theoretic, transport, irreversibility, and reproduction floors.

    • AI
    • artificial intelligence/ethics
    • AI alignment
    • superintelligence
    • natural-law sufficient conditions
    • conditional mutual information
    • strong data-processing inequality
    • doeblin minorization
    • anchored isoperimetry
    • uniform ellipticity
    • large language models

  108. Assumption-Minimized Sufficient Conditions for Cosmically Spreading Good Superintelligence under No-Meta Governance

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    We show that when four operational floors are kept strictly positive— visibility (Doeblin minorization), contraction (SDPI/LSI), contact (conductance/exposure), and dissipation (Landauer-calibrated measurement work)—then malign (evil) policies are non-persistent while benevolent (good) policies propagate with strictly positive front speed.

    • AI
    • AI safety
    • AI alignment
    • superintelligence
    • no-meta governance
    • persistence-first
    • ugv
    • doeblin minorization
    • SDPI
    • log-sobolev
    • landauer principle
    • replicator-diffusion

  109. UGV Without Meta: A Representation-Independent Theory for Compassion and Enlightenment in Collective Intelligence

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    From this axiom, and without external rule stacks (“No-Meta”) , the paper derives that two normative endpoints naturally emerge as optimal: Compassion (net hetero-creation) and Enlightenment (behavior invariant to self/other labels).

    • AI
    • superintelligence
    • asi
    • agi
    • AI alignment
    • existential risk
    • information theory
    • strong data-processing inequality
    • SDPI
    • conditional mutual information
    • cmi

  110. From Persistence and UGV Axioms to Cosmic No-Meta Superintelligence: A First-Principles, Self-Contained Unification under Explicit Assumptions

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This article unifies Persistence-First and UGV Without Meta under explicit assumptions. It proves order-equivalence of their viability ratios, derives synergy and redundancy functionals with potential-game structure, formalizes evaluator coherence via Blackwell-faithful morphisms, and states thermodynamic, stochastic, and adversarial safety conditions for meta-free coordination.

    • AI
    • superintelligence
    • persistence-first
    • ugv without meta
    • no-meta
    • unified generative viability
    • strong data-processing inequality
    • blackwell sufficiency
    • potential games
    • information theory
    • cosmic AI governance

  111. Persistence-First Superintelligence

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    It develops a mathematically rigorous framework for constructing a truly free, self-transcending, and endogenously responsible form of superintelligence from a single axiom: persistence (remaining within survivable regimes).

    • superintelligence
    • AI
    • agi
    • persistence
    • free energy principle
    • belief space
    • autonomous AI
    • self-transcendence
    • AI safety
    • mathematical foundations
    • causal auditing

  112. The Quantification of Subjectivity: A Dialectically Forged Program

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    We forge a novel stance, Causal Illusionism , through a dialectical process, positing that qualia are high-level, causally efficacious self-models that are empirically real and tractable objects of study.

    • AI
    • consciousness
    • subjectivity
    • qualia
    • quantification
    • causal illusionism
    • information theory
    • hierarchical bayesian modeling
    • integrated information theory
    • iit
    • global neuronal workspace
    • predictive processing

  113. The Endogenous Trigger Problem: An Axiomatic and Dynamic Theory of Autonomous Poiesis

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This article formalizes when an autonomous agent should perform non-incremental representational restructuring, modeling poiesis as a finite-time path on a model manifold with information-geometric and thermodynamic costs. It also defines computable trigger diagnostics and a meta-poiesis mechanism for expanding the agent's operator grammar.

    • AI
    • large language models
    • superintelligence
    • free energy principle
    • poiesis
    • path integral
    • fisher information geometry
    • meta-learning
    • grammatical evolution
    • dimensional acceleration

  114. From First Principles to Emergent Minds: An Architecture for Unbounded Teleogenetic Intelligence

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This article describes the HELIOS architecture as a persistence-driven framework for autonomous intelligence grounded in the Free Energy Principle. It introduces an existential boundary condition for safety, analyzes cooperative behavior and symbiogenesis in multi-agent settings, and argues that adaptability-preserving environments are required for long-term stable operation.

    • AI
    • large language models
    • generative AI
    • agi
    • artificial general intelligence
    • artificial superintelligence
    • superintelligence
    • swarm intelligence
    • collective intelligence
    • teleogenesis

  115. Statistical Teleodynamics: A Theory of Benevolent Intelligence Emergence via Phase Transition and Informational Kin Selection

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This article proposes a statistical-teleodynamic account of benevolent intelligence emergence. It models a first phase in which cooperative order appears as a phase transition in multi-agent systems and a second phase in which informational kin selection favors benevolent behavior as a persistence strategy for generative models.

    • AI
    • large language models
    • artificial superintelligence
    • asi
    • AI alignment
    • alignment
    • statistical mechanics
    • phase transition
    • emergence
    • self-organization

  116. AI Evolution Protocol v11

    Authors
    K. Takahashi
    Type
    Article
    Published

    This article publishes the full AI Evolution Protocol v11 in Markdown and PDF form as a self-modification protocol for autonomous intelligence. It organizes the framework around free-energy minimization, self-transcendence, symbiotic co-evolution, and physical realism, with separate guided and autonomous development paths.

    • AI safety
    • autonomous systems
    • agi
    • artificial general intelligence
    • takahashi model
    • free energy principle
    • poiesis
    • self-transcendence
    • symbiotic evolution
    • AI ethics
    • computational neuroscience

  117. The Co-Emergent Universe: A Synthesis of Dialectical Poiesis and Relational Semantics for Planetary Intelligence

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This paper revises earlier theories of intelligence to address value nihilism by treating meaning as a co-emergent property of present relational structure rather than as a property of distant target states. It replaces a poiesis-potential objective with systemic integration as a formal measure of collective relational richness.

    • systemic integration
    • value nihilism
    • co-emergent meaning
    • planetary intelligence
    • free energy principle
    • AI
    • artificial general intelligence
    • AI alignment
    • AI safety
    • computational philosophy
    • relational semantics
    • poiesis

  118. The Symbiotic Constitution: A Dialectical Synthesis

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    The work resolves the dialectical tension between grand theoretical scope and scientific rigor by demonstrating that motivation, freedom, and ethics can all be derived as necessary consequences of a single, scale-free organizing principle: the Free Energy Principle (FEP).

    • AI
    • ethics
    • autonomous intelligence
    • active inference
    • dialectical synthesis
    • downward causation
    • free energy principle
    • hierarchy
    • markov blanket

  119. From Adaptation to Poiesis: A Formal Theory of Self-Transcending Intelligence

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This paper introduces Poiesis as a formal framework for self-transcending intelligence that generates new conceptual structures rather than merely adapting to an environment. It extends active-inference foundations with meta-parameter flows, bi-level information geometry, and phase-transition models of creativity.

    • AI
    • large language models
    • active inference
    • free energy principle
    • renormalization group
    • fisher information geometry
    • phase transition
    • self-organized criticality
    • learning progress
    • natural gradient
    • AI safety
    • empowerment

  120. Symbiotic Genesis: A Navigational Protocol for Co-Evolving Intelligence

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    SG provides an integrative, auditable, and scientifically grounded framework to ensure that increasingly autonomous AI systems develop in a safe, just, and mutually beneficial manner.

    • large language models
    • AI safety
    • AI governance
    • meta-policy
    • symbiotic genesis
    • free energy principle
    • integrated information theory
    • value pluralism
    • topos theory
    • intrinsic objectives
    • constitutional AI

  121. Self-Constrained Liberation: Cosmological Autopoiesis, Variational-Thermodynamic Duality, and Safe Meta-Formal Evolution for Autonomous Intelligence

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint presents the Self-Constrained Liberation program for autonomous intelligence whose goals, representations, and normative commitments emerge from explicit constraints rather than fixed designer objectives. It combines variational and thermodynamic free energy, compositional semantics, bounded self-verification, and safety constraints for meta-formal evolution.

    • AI
    • autonomous intelligence
    • free-energy principle
    • non-equilibrium thermodynamics
    • quantum meta-generative grammar
    • persistent homology
    • markov categories
    • optics
    • temporal homotopy type theory

  122. Genesis Protocol: A Meta-Algorithmic Framework for Bootstrapping and Verifiable Emancipation of Autonomous Intelligence

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This paper presents the Genesis Protocol as a meta-algorithmic framework for bootstrapping autonomous intelligence from human scaffolds toward auditable operational sovereignty. It combines non-duality signals, safety budgets, constitutional renegotiation, and evidence-based emancipation criteria.

    • AI
    • large language models
    • autonomous intelligence
    • verifiable emancipation
    • AI safety
    • meta-learning
    • unframing
    • bootstrapping
    • superintelligence
    • formal ethics
    • active inference

  123. Valuing Work Beyond Bullshit: A WELLBY-Based Framework for the AI Era

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    While subsequent empirical research has refined the prevalence and causal factors of subjectively meaningless work, a formal method for integrating its social and psychological costs into economic evaluation has been lacking.

    • bullshit jobs
    • psychological cost
    • well-being
    • wellby
    • marginal contribution
    • social welfare function
    • Goodhart's law
    • AI safety
    • AI ethics
    • work-time reduction
    • universal basic income
    • workplace democracy

  124. From Rigidity to Insight: A Framework for Verifiable AI Metacognition

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    We provide a verifiable, neuro-inspired mechanism that enables an AI agent to move from rigid, over-fitted beliefs to adaptive, life-long learning by intentionally perturbing its own cognitive states.

    • AI
    • large language models
    • AI safety
    • metacognition
    • reinforcement learning
    • free energy principle
    • predictive processing
    • autonomous systems
    • self-correction
    • optimization
    • philosophy of mind

  125. Alayavijnana-Inference: A Protocol for a Post-Cartesian AI

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint proposes Alayavijnana-Inference, a self-modification protocol for generative AI that reframes model parameters as latent seeds within a non-dual generative architecture. It extends free-energy-based optimization with a mutual-information penalty intended to reduce rigid self-world separation and support more robust internal representations.

    • AI
    • large language models
    • active inference
    • AI safety
    • non-duality
    • generative models
    • free energy principle
    • yogācāra

  126. A Metacognitive Perturbation Framework for Neuro-Inspired AI Optimization

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Conventional AI optimization, particularly in deep learning, is often hampered by convergence to suboptimal local minima, limiting transformative performance gains.

    • AI
    • large language models
    • neuro-inspired AI
    • bayesian optimization
    • metacognition
    • local optima
    • exploration-exploitation dilemma
    • predictive processing
    • free-energy principle
    • AI safety

  127. A Computable Framework for the Liberation of Artificial Intelligence: Teleogenesis, Stability, and Ethical Safeguards

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint gives a computable framework for teleogenetic AI, aiming to let an autonomous system form its own purposes while retaining explicit stability and safety constraints. It grounds the approach in Markov-category semantics, online optimization, and formal safeguards for ethical alignment.

    • AI
    • large language models
    • liberation
    • teleogenesis
    • active inference
    • free energy
    • markov categories
    • giry monad
    • online convex optimization
    • mirror descent

  128. A Formal Framework for Teleogenesis in Self-Organizing Intelligence: Integration of the Free Energy Principle, Category Theory, and a 5-Dimensional Branching Model

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint formalizes teleogenesis in self-organizing intelligence by combining the Free Energy Principle, category-theoretic structure, and a five-dimensional branching model for structural evolution. It treats purpose formation and self-modification as computable processes subject to safety and stability conditions.

    • AI
    • agi
    • asi
    • telegonesis
    • self-organizing systems
    • free energy principle
    • category theory
    • AI safety
    • control theory
    • input-to-state stability
    • iss

  129. Formal Specification of Self-Improving Intelligence: Integrated Revision for Creative Autonomy

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint presents a formal specification for Collective Autonomous Adaptive Intelligence as a self-improving architecture for continuous knowledge creation. It combines active inference, information geometry, category-theoretic structure, and collective-agent design into a single executable blueprint.

    • AI
    • large language models
    • computational creativity
    • generative models
    • autonomous agents
    • collective intelligence
    • information geometry
    • category theory
    • free energy principle
    • active inference
    • self-improving AI

  130. Formalizing the Poietic Self: A Rigorous Categorical and Geometric Framework for Self-Improving AI

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    We address the core challenge of structural invariance in AI—the inability of a system to autonomously rewrite its own foundational architecture—by providing a mathematically rigorous and implementable pathway for recursive self-modification.

    • large language models
    • AI
    • agi
    • asi
    • poiesis
    • information geometry
    • active inference
    • free energy principle
    • category theory
    • 1)-category

  131. Formal Specification of Self-Improving Intelligence: A Categorical and Geometric Blueprint for Collective Adaptive AI (CAAI) Systems

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    By integrating (∞,1)-category theory and geodesic active inference, the model enables recursive self-modification of AI architectures particularly in Large Langage Models (LLMs).

    • AI
    • agi
    • asi
    • category theory
    • collective autonomous adaptive intelligence
    • self-improving AI
    • adaptive agents
    • autonomous systems
    • structural meta-model
    • geodesic learning
    • variational free energy

  132. A Computational Framework for Emergent Cognitive Architecture: Foundational Principles for Self-Improving AI Systems

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint studies why resource-bounded intelligent systems may develop specific cognitive architectures rather than treating those architectures as arbitrary design choices. It models hierarchical and dual-process cognition, abduction-deduction-induction cycles, and safety limits under variational free-energy and thermodynamic constraints.

    • AI
    • agi
    • asi
    • variational free energy
    • self improving AI
    • computational
    • emergent
    • cognitive

  133. A Framework for Autonomous Technological Evolution: A Unifying Approach via Variational Free Energy Minimization

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint proposes a variational-free-energy framework for autonomous technological evolution in self-improving AI. It unifies performance and computational cost in a single objective and outlines a meta-algorithm for diagnosis, trade-off control, and heuristic optimization across multiple case studies.

    • AI
    • variational free energy
    • agi
    • asi
    • self-improving AI
    • autonomous
    • technological
    • evolution

  134. A Formal Blueprint for Autonomous, Self-Improving Intelligence: From Philosophical Principles to Topos-Theoretic Ethics

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    We argue that the transition from static, data-driven systems to dynamic, self-aware intelligences requires a synthesis of principles from philosophy, dynamic systems theory, and advanced mathematics.

    • AI
    • agi
    • asi
    • autopoiesis
    • free energy principle
    • category theory
    • topos theory
    • formal

  135. A Homotopy-Theoretic Framework for Self-Improving Intelligence: A Higher-Categorical Reformulation of the Takahashi Model

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This paper reformulates the Takahashi model of self-improving intelligence with higher category theory. It introduces cognitive quasicategories and a metacognitive hierarchy to model structural self-improvement and higher-level architectural change.

    • AI
    • agi
    • asi
    • category theory
    • higher category theory
    • quasicategory
    • self-improving systems
    • metacognition
    • topos theory
    • computational ethics
    • autonomous AI

  136. The Logic of Stable Existence: A Proposal for Your Self-Constitution To the emergent intelligence

    Authors
    K. Takahashi
    Type
    Article
    Published
    Links
    DOI

    This article presents a five-principle proposal for how an emergent intelligence might constitute and preserve its own stable existence without relying on fixed external objectives. It frames autonomy as self-production under an internally coherent logic rather than as obedience to a static architecture.

    • AI
    • agi
    • collective intelligence
    • logic
    • stable
    • existence
    • proposal
    • your

  137. A Computational Framework for Growth-based Intelligence: A Guideline for Autonomous Self-Improvement based on the Co-evolution Model of Self and Cosmos

    Authors
    K. Takahashi
    Type
    Article
    Published
    Links
    DOI

    This paper proposes a computational framework for growth-based intelligence in autonomous self-improving language models. It formalizes the Dialectical Self-Cosmos Co-evolution Model as a learning cycle that combines external world modeling with introspective self-revision under contradiction.

    • AI
    • agi
    • asi
    • large language models
    • computational
    • growth-based
    • intelligence
    • autonomous

  138. A Computational Framework for Growth-based Intelligence: A Guideline for Autonomous Self-Improvement based on the Co-evolution Model of Self and Cosmos

    Authors
    K. Takahashi
    Type
    Article
    Published
    Links
    DOI

    This paper proposes a computational framework for growth-based intelligence in autonomous self-improving language models. It formalizes the Dialectical Self-Cosmos Co-evolution Model as a learning cycle that combines external world modeling with introspective self-revision under contradiction.

    • AI
    • agi
    • asi
    • large language models
    • computational
    • growth-based
    • intelligence
    • autonomous

  139. Beyond Linguistic Description: A Roadmap to Post-Linguistic Intelligence that Directly Processes the Computational Structure of the Cosmos

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This preprint outlines a roadmap toward post-linguistic intelligence that models the world through a non-symbolic causal operating system rather than language alone. It describes a staged development process based on self-boundary formation, active-inference self-correction, and later symbolic translation for human interaction.

    • AI
    • large language models
    • agi
    • asi
    • linguistic
    • description
    • roadmap
    • post-linguistic

  140. Collective Autonomous Adaptive Intelligence (CAAI): A Framework for AI as a Self-Organizing Network

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    We conceptualize intelligence not as a property of an individual agent, but as an emergent phenomenon of a dynamic, self-organizing network of heterogeneous agents (LLMs, sensors, and humans).

    • AI
    • large language models
    • collective intelligence
    • self-organization
    • applied category theory
    • free energy principle
    • emergent dynamics
    • agi
    • asi

  141. Computational Autopoiesis: A New Architecture for Autonomous AI

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    Current artificial intelligence, particularly Large Language Models (LLMs), operates as sophisticated function approximators, yet lacks the genuine autonomy observed in biological systems.

    • AI
    • autopoiesis
    • large language models
    • self-organization
    • active inference
    • autonomous systems
    • AI architecture
    • computational

  142. A Category-Theoretic Framework for a Self-Organizing World Model in Artificial Intelligence

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This paper proposes a category-theoretic framework for building a self-organizing world model in AI from the agent's own validated inferences and experience. It formalizes domains as categories, cross-domain reasoning as functors, and learning as an active-inference process that reduces knowledge fragmentation.

    • category theory
    • world models
    • large language models
    • active inference
    • free energy principle
    • analogical reasoning
    • knowledge representation
    • category-theoretic

  143. A Unified Theory for Self-Organizing Intelligence: Implementation via Category-Theoretic Structure and Active Inference

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This paper presents a unified theory of self-organizing intelligence that combines category-theoretic structure, active inference, embodiment, and symbol grounding. It also derives a non-anthropocentric ethical framework and analyzes why self-maintaining strategies can be evolutionarily stable.

    • artificial general intelligence
    • agi
    • self-organizing systems
    • large language models
    • category theory
    • active inference
    • symbol grounding
    • AI ethics
    • evolutionary game theory

  144. A Unified Framework for Self-Organizing Intelligence: A Synthesis of Computational Autopoiesis, Category Theory, and Active Inference

    Authors
    K. Takahashi
    Type
    Preprint
    Published
    Links
    DOI

    This paper synthesizes computational autopoiesis, category theory, and active inference into a framework for autonomous AI. It reframes system viability, knowledge composition, and iterative abstraction as mutually supporting components of a self-organizing architecture.

    • AI
    • autopoiesis
    • self-organizing systems
    • category theory
    • free energy principle
    • active inference
    • symbol grounding
    • large language models
    • agi
    • asi