Cross-domain convergence and principle deduplication

Different sciences often describe the same constrained operation in different nouns. The architecture should pay for the operation once.

Scope

This chapter turns the project's open-world research policy into an architectural method. Findings may originate in neural tissue, plants, immune systems, animal collectives, ecosystems, control theory, databases, or materials. They enter the design only after their causal operation has been normalized and compared with the principles already present.

The canonical unit is therefore neither a paper nor an organism. It is a versioned problem–solution invariant with a stable P- identifier, scoped evidence, an engineering null model, and an experiment that can reject its AI translation. The detailed registry remains in the principle ledger; this chapter explains how its bundles compose into one system.

Biological observation

The current evidence corpus repeatedly encounters five pressures.

1. Scarcity: more states, candidates, or possible actions exist than can be active at once. Examples include constrained cortical signaling (C-001), sparse insect odor codes (C-025), immune selection (C-028), and congestion-triggered reserve routes (C-035).
2. Locality: a complete central description is too slow or expensive. Dendritic branches integrate locally (C-017); cephalopod limbs retain peripheral control (C-024); and collective systems can coordinate through sparse influence or shared environmental state (C-031, C-054).
3. Reversible change: recent evidence must affect behavior before it deserves permanent structure. Eligibility traces (C-019), plant priming (C-026), selective replay (C-036), and retrieval-sensitive memory (C-039) all separate a temporary state from a later commitment decision.
4. Stability: selection and learning create positive feedback that must be bounded. Synaptic scaling (C-018), feedback inhibition (C-025), reconstruction around a target organization (C-033), and ecological recovery dynamics (C-058, C-059) expose different parts of that control problem.
5. Repeated work: a stable solution should migrate from expensive general search into topology, placement, representation, or material. Relevant observations include body–controller co-development (C-029), structured pruning (C-012), lower-precision representation (C-013), and activity-dependent resource placement (C-050).

These recurrences do not make the underlying mechanisms identical. They reveal where multiple fields expose the same engineering pressure and where one shared experiment can replace several renamed proposals.

Proposed AI translation

From domain language to a mechanism record

Every retained observation is rewritten as the following tuple:

where:

The tuple is a structured research record, not a numerical embedding. Its fields retain units and provenance. Two findings are candidates for one principle only when their problem, causal operation, information topology, and timescale agree at the abstraction needed by an experiment.

Source organization and scale are not optional metadata. A material structure, an organism-level control loop, and an ecosystem-level interaction can deliver a similar function through different causal paths, authority, and timescales; they do not merge until those differences are shown irrelevant to the discriminating experiment.

Biomimetic transfer is a search method, not an evidence grade

Biomimetics gives this project useful process language, but it does not create a shortcut through the evidence ledger. The search runs in both directions:

1. Phenomenon push begins with a measured effect in a living system and asks which artificial problem shares its function, constraints, causal operation, and failure boundary.
2. Problem pull begins with a measured artificial-system failure and asks which scientific fields contain a mechanism that solves the normalized problem under a comparable budget.

Phenomenon push broadens the established biology-push/solution-driven route; problem pull broadens technology-pull/problem-driven design. The broader names matter because this project searches physical, formal, social, and engineering sciences as well as biology.

Both routes converge on $M$, then pass through deduplication, silicon-native redesign, a strong conventional null, and a falsifiable equal-budget test. A visual likeness without transferred function is rejected; a transferred function without a causal and cost model remains only a lead; and a validated source mechanism does not establish that its artificial translation works.

flowchart TB
    bio["PHENOMENON PUSH<br/>observe a reproducible effect"] --> bmeasure["Measure function,<br/>mechanism, cost, boundary"]
    problem["PROBLEM PULL<br/>declare failure + budget"] --> pspec["Specify function,<br/>constraints, strongest null"]
    bmeasure --> abstract["FUNCTIONAL ABSTRACTION<br/>normalized mechanism record"]
    pspec --> abstract
    resemblance["Shape-only or story-only resemblance"] --> reject["REJECT AS LEAD<br/>no functional transfer"]
    abstract --> dedup["DEDUPLICATE<br/>merge · discriminate · hold"]
    dedup --> substrate["RE-ENGINEER<br/>use silicon affordances"]
    substrate --> test["FALSIFY<br/>equal quality · risk · lifecycle budget"]
    test --> keep["KEEP / MERGE / RETIRE"]

    classDef bio fill:#087f5b,stroke:#34d399,color:#ffffff,stroke-width:2px;
    classDef pull fill:#c2410c,stroke:#fb923c,color:#ffffff,stroke-width:2px;
    classDef shared fill:#6d28d9,stroke:#a78bfa,color:#ffffff,stroke-width:2px;
    classDef gate fill:#1d4ed8,stroke:#60a5fa,color:#ffffff,stroke-width:2px;
    classDef pass fill:#166534,stroke:#4ade80,color:#ffffff,stroke-width:2px;
    classDef fail fill:#991b1b,stroke:#fb7185,color:#ffffff,stroke-width:2px;
    class bio,bmeasure bio;
    class problem,pspec pull;
    class abstract,dedup,substrate shared;
    class test gate;
    class keep pass;
    class resemblance,reject fail;

The public descriptions of biomimicry are useful for discovering biological strategies and unifying themes, while ISO 18458:2015 and VDI 6220 Part 2 provide more precise terminology and process frames. None is evidence that a particular mechanism transfers or improves efficiency. The dated method audit records the source hierarchy and the parts adopted here.

Five solution families

The thirteen current P- principles remain distinct, but they compose into five navigational families:

flowchart TB
    evidence["Primary observations from many fields"] --> record["Normalize problem · state · operation · topology · timescale"]
    subgraph online["Online pressure"]
        direction LR
        scarcity["Scarcity<br/>P-001 · P-007"] ~~~ locality["Locality + coordination<br/>P-002 · P-008 · P-011 · P-013"]
    end
    subgraph lifecycle["Lifecycle pressure"]
        direction LR
        change["Reversible change<br/>P-003 · P-004 · P-005 · P-012"] ~~~ stability["Stability + maintenance<br/>P-006 · P-009"] ~~~ structure["Structure as computation<br/>P-010"]
    end
    record --> scarcity
    record --> locality
    record --> change
    record --> stability
    record --> structure
    scarcity --> primitive["Shared engineering primitives"]
    locality --> primitive
    change --> primitive
    stability --> primitive
    structure --> primitive
    primitive --> test["Null model · ablation · lifecycle measurement · rejection"]

Editable source: ../assets/diagrams/recurring-solution-families.mmd.

Deduplicate before architecture expansion

A new finding passes through six decisions:

1. Scope the evidence. Record the observed system, intervention, result, uncertainty, and boundary as a C- claim.
2. Normalize the mechanism. Fill every field of $M$ without organism-themed names standing in for operations.
3. Search the registry. Compare against all existing P- records, including held candidates and negative evidence.
4. Merge or discriminate. Merge when the same control loop would be tested; otherwise name the smallest experiment that distinguishes the mechanisms.
5. Name the strongest null. A scheduler, cache, controller, estimator, database, routing rule, or standard learning method gets the same interface and budget.
6. Change one canonical object. Update a principle, primitive, equation, diagram, or experiment instead of adding another themed architecture.

This creates a many-to-one evidence graph:

many observations → scoped claims → fewer principles → shared primitives → decisive experiments

Recurring outcomes still need a construct firewall

Different fields often expose the same broad problem while assigning different causal content to it. Ecological succession describes ordered community change; an ecological priority effect requires an arrival-order intervention; continual-learning task order concerns sequential parameter updates; curriculum learning chooses an order; and a scheduler may merely decide when unchanged jobs execute. Calling all five “succession” would erase the experiment.

The history-conditioned modular-succession audit therefore reuses C-008, C-056, C-057, C-574, Fixture F-014, and Candidates 004 and 019 without creating another principle. Its residual test holds the task multiset, eligible module identity set, exogenous presentations, update ceilings, capacity, optimizer, evaluator and budget fixed, randomizes order, and separately cuts age, routed acceptance, capacity pre-emption, shared-state modification, facilitation and lock-in. If canonical replay, a non-learning scheduler, a mature continual-learning method, or a search-budget-charged curriculum explains the outcome, the biological transfer adds nothing.

New mechanisms can sharpen a principle without multiplying it

The developmental, regenerative, plant-plasticity, and multiscale-reduction depth passes add new failure boundaries without adding another universal principle. That is a useful result, not a failed search.

• Positional memory and repair separate what state says should exist from which workers can currently rebuild it (C-1506, C-1507). They sharpen P-005, P-009, and P-012 by making write authority, reconstruction capacity, and current context separately testable.
• Compensating sources, finite scaling, receiver geometry, delayed reinforcement, conditional redundancy, and local/global fields (C-1508–C-1515) refine locality, maintenance, and structural offloading. They do not imply that every duplicated route, boundary, or organizer is useful.
• Plant memory divides population-level digital switching, lifecycle reset, writing, trace maintenance, retrieval, systemic route identity, action-conditioned growth, and joint environmental context (C-1516–C-1525). These are discriminating state and protocol dimensions inside P-002, P-005, P-006, P-009, P-012, and P-013—not a plant-themed architecture.
• Projection memory, normal-hyperbolicity limits, macro-to-micro queries, and lift dependence (C-1526–C-1529) supply mathematical rejection tests for coarse state. They constrain when an existing runtime or memory primitive is valid; they are not biological claims at all.

The deduplicated output is therefore richer in contracts while remaining at thirteen P-series principles. Fixtures F-022, F-023, and F-024 preserve the distinctions so later implementations can test whether a proposed composition adds value beyond its strongest ordinary null within a declared workload and transfer boundary.

Composition without double counting

Principles can cooperate while remaining separately testable. For one event:

1. prediction-error allocation decides whether more evidence is valuable;
2. selective allocation chooses a module within the active budget;
3. local autonomy runs that module near its state;
4. a temporary trace records unresolved credit;
5. the maintenance plane later decides whether to replay, protect, merge, or forget it; and
6. structural offloading compiles only the recurring path that survives those tests.

Calling the entire chain “sparsity,” “memory,” or “homeostasis” would hide which operation produced a gain. Each experiment therefore removes one principle at a time while holding the other interfaces constant.

Interconnection is an intervention

A small or typed interface does not by itself make two modules dynamically modular. In transcriptional regulation, covalent-modification cycles and yeast synthetic circuits, attaching a downstream target can alter the upstream state or transient response (C-1550–C-1555). Substrate competition in Drosophila MAPK signalling supplies a distinct, potentially functional case (C-1559). The shared mechanism record is connection-induced back-action; the biochemical literature calls one important family retroactivity.

The domains do not collapse into one mechanism. Direct binding can sequester a signal, multiple substrates can compete for an enzyme, and nominally separate modules can instead couple through shared transcriptional, translational, CPU, memory, or scheduler resources (C-1558). The causal intervention therefore crosses connection state and shared-resource load independently. It also includes a genuinely nondestructive immutable-read control, because silicon can often copy or address state without the molecular consumption that motivated the analogy.

Insulation is bounded rather than automatically desirable. It must preserve both the producer and useful downstream service while exposing latency, staleness, copies, buffers, replicas, operations, maintenance and physical energy. Weak coupling may spend less while losing tracking or robustness (C-1556, C-1557); substrate competition can also be a useful integrator rather than a fault (C-1559). Fixture F-027 therefore compares suppressing, preserving and deliberately using back-action. If immutable messages, ordinary queues, resource isolation, explicit filters, stop-gradient paths or conventional controllers reach the same frontier, the translation is retired without adding a principle.

Passive self-organization is a mandatory null

Efficient-looking structure does not establish sensing, represented goals, or counterfactual action. Fracture sets, drainage networks, and river avulsions can arise from local stress, gravity, flow, conservation, thresholds, and stored geometry (C-232–C-242). The project calls a mechanism adaptive control only when it specifies:

1. a service variable external to the adaptation law;
2. observations with spatial/temporal support and latency;
3. a decision that could choose differently under different evidence;
4. authority and an actuator;
5. movement, reserve, monitoring, and recovery budgets;
6. persistent state and reset cost; and
7. a guarantee or tested failure envelope.

This criterion makes passive physics a stronger baseline. If local flow–structure feedback produces the same topology and service without a controller, the controller must justify its sensing, decision, switching, and maintenance cost. Connectivity is not throughput (C-234); material removed by “pruning” must appear as transport, storage, or output elsewhere (C-239); and a topology change can reassign or destroy service rather than improve it (C-242). A closed-form branching law is therefore a regime-qualified physical null, not a portable architecture rule (C-1489).

Transported fields are not messages

Microbial signals, metabolites, fungal nutrients, plant hormones, process streams, queues, and network packets can all create a local field that later receivers use. The common systems object is not “communication in nature.” It is a transported, transformed, delayed, and locally decoded quantity whose physical path may also change future capacity (C-563, C-578, C-580).

For transported type $k$, use the local balance

where concentration $c_k$ is mol/m³, diffusion coefficient $D_k$ is m²/s, velocity $\mathbf u$ is m/s, production and uptake rates $p_k,q_k$ are mol/(m³ s), and first-order loss $r_k$ is 1/s. Digital fields use an analogous typed byte or item ledger; they do not inherit molecular units or diffusion laws. Geometry, boundary conditions, sampling support, and receiver state are part of the contract. Countercurrent orientation is one finite exchanger arrangement inside that contract; it does not establish effectiveness or a generic bidirectional-computation benefit by itself (C-1490).

Several fields can be superposed at the same endpoints. If the observed outcome is

where $G$ is the number of typed channels, $g$ indexes one channel, $c_g$ is its transported concentration or inventory, $h_g$ is its receiver-dependent effect, $\theta_g$ is channel state, $b$ is background, and $\varepsilon$ is measurement error. Observing $y$ or cutting every channel together does not identify any one $h_g$. Each claimed channel needs an independently targetable intervention or a justified identification model. Rillig et al.'s concurrent-fungal-network perspective makes this problem explicit for fungal guilds sharing plant endpoints; the project adopts it as an engineering inference, not as evidence that such guild combinations have a demonstrated beneficial function (DOI, C-217, C-584).

flowchart LR
    P["Local production, release, or deficit"] --> T["Transport: flow · diffusion · delay · loss"]
    G["Geometry, topology, and boundaries"] --> T
    T --> C["Local concentration or typed inventory"]
    C --> R["Receiver state + uptake + response"]
    R --> O["Task outcome + local cost"]
    O --> U["Update source, sink, conductance, or topology"]
    U --> G
    C --> A["Accumulate · transform · consume · export · purge"]
    A --> C
    F["Damage, contamination, incompatibility"] --> X["Contain · isolate · reroute · regrow"]
    X --> G
    O --> V{"Independent validation"}
    V -->|"useful"| U
    V -->|"not useful"| D["Decay, disperse, or retire"]

Editable source: transported-field-contract.mmd.

The diagram deliberately spans several fields while retaining their differences:

• microbial quorum output depends on production, confinement, flow, loss, and receptor state; it is not a vote count (C-563, C-575);
• cross-feeding can help, harm, or create a weakest-link dependency, so local transfer is not system utility (C-571);
• fungal flow can both deliver material and remodel conductance while rapid containment remains distinct from later bypass (C-577–C-581); and
• distinct symbiotic partners can exchange unequally under conflicting objectives (C-584); and
• concurrent typed channels sharing endpoints remain causally unresolved when the experiment observes or disables only their aggregate.

The held fixture belongs jointly to P-011/P-013 and existing Candidates 001 and 013 (C-585). It must beat typed pub/sub or queues, fixed-graph adaptive routing, backpressure/primal–dual allocation, and make-before-break state transfer. Charge production, transport, storage, cleanup, contamination, topology edits, failed delivery, reserve, and measurement. If geometry and cleanup do not affect the task, use the simpler digital primitive.

Inactive is not one state

The immune evidence in C-727–C-747 does not add another architecture principle. It contributes a stricter state contract. Representation, recognition, authentication, authorization, activation, suppression, deletion, impairment, contraction, memory, and recovery answer different questions and have different reversal costs.

flowchart LR
    G["Generate candidate rule / module"] --> R["Represent sampled evidence"]
    R --> V{"Developmental validation"}
    V -->|"insufficient coverage"| I["Ignorant · unresolved"]
    V -->|"unsafe with high support"| D["Delete + retain decision record"]
    V -->|"provisional pass"| N["Eligible but inactive"]
    N --> C["Context · provenance · current system state"]
    C --> A{"Recognition + authority + risk gate"}
    A -->|"permit"| X["Activate · expand · execute"]
    A -->|"monitor"| M["Observe without authority increase"]
    A -->|"quarantine"| Q["Anergic analogue · reversible disablement"]
    A -->|"external control"| S["Suppressed · rate-limited · scoped"]
    X --> O["Outcome · collateral loss · resource use"]
    O --> K["Contract · clean up · retain memory"]
    X -->|"persistent load or dysfunction"| H["Exhausted / impaired trajectory"]
    K --> P["Maintained memory · placement · expiry"]
    P -->|"qualified recurrence"| C
    I --> R
    M --> C
    Q -->|"revalidate"| C
    S -->|"authority changes"| C
    H -->|"diagnose + bounded intervention"| C
    O -->|"harm or false permit"| D

Editable source: typed-tolerance-lifecycle.mmd.

An activation bit cannot distinguish an absent module from an unobserved one, a policy-suppressed module from a damaged one, or post-response contraction from retained memory. That ambiguity makes reactivation unsafe and makes resource accounting wrong. Each transition therefore records evidence support, authority, expected useful effect, collateral-loss terms, resource budget, expiry, recovery target, and observed outcome.

Recognition supplies evidence; it does not grant authority. Deletion requires a higher support threshold than reversible monitoring or quarantine because a rare useful capability can be lost. Local copies pay maintenance, refresh, invalidation, and inconsistency costs. Recovery ends only when useful service and reserve are both restored.

The typed lifecycle mathematics compares this contract against a complete ordinary stack: typed state machines, calibrated risk and abstention, least privilege, constrained control, anomaly detection, evolutionary or ensemble search, replay, placement, and resource-aware scheduling. If the ordinary stack reproduces the decisions and frontier, the biological vocabulary is removed and the state contract remains.

Coordination without a privileged clock

Music cognition provides a combined stress regime for mechanisms that already exist elsewhere in the project: multiscale prediction, local timing control, transformation-aware memory, constrained generation, role protocols, repair, and cumulative transmission. The evidence in C-748–C-783 does not justify a music-specific principle.

Its useful residue is a benchmark: agents must preserve literal phrase constraints and bounded relative timing while tempo, expressive microtiming, delay, roles, motifs, leaders, and partners change, without reading a privileged shared clock. Synchrony still requires common-input controls, edge interventions, and a protected functional endpoint before it can count as useful coordination (C-1495).

flowchart LR
    task["Score · cue · literal task constraints"] --> phrase["Phrase · motif · boundary state"]
    partner["Partner action"] --> channel["Delay · jitter · loss · acoustic/physical path"]
    channel --> observe["Timestamped local observation + uncertainty"]
    observe --> phase["Local phase · tempo · drift estimate"]
    memory["Partner · role · repertoire memory"] --> phrase
    phase --> controller["Role-qualified local controller"]
    phrase --> controller
    controller --> action["Timed expressive action"]
    action --> partner
    action --> evaluate["Literal task · synchrony · response · quality · cost"]
    evaluate --> repair["Correct · clarify · rehearse · update convention"]
    repair --> phrase
    repair --> memory
    nulls["PLL · Kalman · MPC · retrieval · typed protocol · rehearsal"] --> evaluate
    perturb["Tempo drift · role switch · leader loss · new motif · turnover"] --> channel
    perturb --> controller

Editable source: shared-clock-free-coadaptation.mmd.

The fixture refuses several easy substitutions:

• similar mean tempo is not pairwise synchronization;
• stimulus-frequency neural power is not evidence of endogenous prediction;
• low onset error is not the correct phrase or role;
• familiar-partner performance is not co-adaptation when retrieval explains it;
• novelty is not validity, responsiveness, or usefulness; and
• within-group convention compression is not cross-group task success.

Fixture F-001 compares the full composition with fixed correction, PLL/adaptive oscillator, Kalman identification, distributed MPC, a paid centralized conductor, transformation-aware retrieval, and typed role/version protocols. The mathematical contract keeps local clock error, phase, phrase prediction, role safety, message bytes, latency, rehearsal, recovery, and joules separate. If online estimation plus retrieval and explicit protocol state ties, the residual is retired.

Preserved bits are not preserved meaning

Library and archival science adds a dependency that storage abstractions often hide: information remains useful only for a declared interpreter community with the formats, schemas, vocabularies, identity mappings, authority evidence, software, units, permissions, and practiced capability needed to use it. The audit evidence will occupy C-784–C-803.

flowchart LR
    object["Payload · metadata · evidence links"] --> package["Versioned preservation package"]
    community["Designated interpreter community + capabilities"] --> package
    dependencies["Format · schema · vocabulary · software · authority"] --> package
    queries["Registered queries · units · tolerances"] --> gate
    package --> transform["Retain · compact · migrate · emulate · delete"]
    transform --> candidate["Candidate version + manifest"]
    candidate --> gate{"Query · evidence · authenticity · access gates"}
    gate -->|"pass"| publish["Publish version + supersession lineage"]
    gate -->|"fail"| fallback["Retain source · narrow contract · repair"]
    publish --> drift["Community · dependency · vocabulary · authority drift"]
    drift --> invalidate["Invalidate · re-test · migrate · retire"]
    invalidate --> gate
    requests["New unregistered information need"] --> abstain["Answer if supported · otherwise abstain / recover source"]
    publish --> abstain
    outcomes["Task outcome · error · effort · cost"] --> queries
    publish --> outcomes

Editable source: query-registered-preservation.mmd.

This separates several failure modes that are otherwise mistaken for one success:

• fixity detects bit change; it does not establish truth or authenticity;
• provenance records lineage; it does not validate the assertion;
• a valid ontology constrains representation; it does not prove instance data;
• a citation edge is not endorsement or comparable impact;
• findability and interoperability do not establish correctness or safe reuse; and
• retaining records does not retain the tacit capability needed to interpret or act on them.

The query-registered preservation mathematics defines finite native-unit tolerances, evidence reachability, interpreter and dependency validity, migration regression, unregistered-query abstention, and complete lifecycle cost. The Candidate 017 track must beat ordinary OAIS/PREMIS-style packaging, versioned schema/vocabulary, retained-source recovery, and query-regression tests. If it does not, the ordinary preservation stack remains the implementation.

Candidate production is not acceptance

Mathematical practice supplies the sharpest version of a distinction already needed throughout this project: producing a candidate and earning acceptance are different operations. Conjectures, analogies, abstractions, proof sketches, retrieved lemmas, numerical patterns, solver traces, and learned tactic choices can all direct search. None of those origins establishes the resulting claim. The evidence and formal-system boundaries are tracked in C-861–C-880.

The transferable lifecycle is typed:

1. Propose. Bind a candidate to the exact problem, definitions, logic, axioms, library, corpus, proposal ancestry, and accessible evidence.
2. Challenge. Search examples, counterexamples, countermodels, boundary cases, definition changes, and adversarial encodings before commitment.
3. Decompose. Turn the target into a dependency DAG whose child artifacts reconstruct the parent goal through named interfaces and side conditions.
4. Prove or refute. Produce a proof term, checked finite reduction, solver certificate, admissible model, or exact counterexample—not only a verdict.
5. Check. Bind the artifact to the exact instance and library version, then run a small independent checker while publishing shared trust roots.
6. Publish. Retain proved, refuted, tested, unknown, disputed, and retracted as different states with evidence, provenance, and cost.
7. Invalidate. A definition, axiom, checker, dependency, or library change quarantines its reverse-dependency closure until rebuild or recheck.

flowchart LR
    identity["Immutable identity<br/>problem · definitions · logic · library · corpus · checker"] --> propose["PROPOSE<br/>conjecture · analogy · abstraction · candidate proof"]
    split["Dependency-safe split<br/>ancestor · sibling · restatement · source isolation"] --> propose
    propose --> challenge["CHALLENGE<br/>examples · countermodels · adversarial cases · critique"]
    challenge -->|"valid counterexample"| refuted["REFUTED<br/>typed counterexample + domain witness"]
    challenge -->|"survives finite tests"| tested["TESTED<br/>support range + precision + generator"]
    challenge --> revise["Revise statement · definition · abstraction"]
    revise --> propose
    challenge --> decompose["DECOMPOSE<br/>goal DAG · lemmas · interfaces · side conditions"]
    abstraction["Abstraction obligations<br/>soundness · adequacy · reconstruction · excluded cases"] --> decompose
    decompose --> prove["PROVE<br/>proof term · solver trace · model · exact finite reduction"]
    prove --> check["CHECK<br/>independent kernel or certificate checker"]
    separation["Generator/checker separation<br/>frozen input hash · explicit shared roots"] --> check
    check -->|"accepted certificate"| proved["PROVED<br/>scoped proposition + dependency closure"]
    check -->|"failed or unsupported"| unknown["UNKNOWN<br/>timeout · incomplete theory · malformed certificate"]
    refuted --> publish["PUBLISH<br/>versioned artifact · state · evidence · provenance · cost"]
    tested --> publish
    proved --> publish
    unknown --> publish
    disputed["DISPUTED<br/>formalization · assumptions · significance · review"] --> publish
    publish --> registry["Release registry<br/>proved · refuted · tested · unknown · disputed · retracted"]
    registry --> change["Dependency, definition, checker, or library change"]
    change --> invalidate["INVALIDATE<br/>reverse dependency closure · quarantine · recheck"]
    invalidate --> retracted["RETRACTED<br/>superseded state + reason + affected versions"]
    invalidate --> challenge
    nulls["ITP · ATP · CDCL · SMT · CP/MIP · CEGAR/CEGIS<br/>formal libraries · learned guidance · exact numerics · peer review"] --> compare{"Equal lifecycle budget<br/>hidden families · human-hours · joules"}
    registry --> compare
    compare --> retain["Retain measured residual<br/>otherwise retire composition"]

Editable source: versioned-proof-discovery-lifecycle.mmd.

This lifecycle prevents five common substitutions:

• finite agreement is evidence for a tested range, not a universal result;
• a counterexample rejects the encoded universal claim but does not choose its unique repair;
• individually plausible lemmas do not close a parent goal unless their proof artifacts reconstruct it;
• a checker establishes the encoded proposition relative to its logic, definitions, axioms, libraries, preprocessing, and trusted implementation; it does not validate the informal intent or importance; and
• participant or model count does not establish independent checking when sources, parsers, libraries, prompts, or failure modes are shared.

Fixture F-004 compares the joined lifecycle with complete interactive and automated theorem provers, formal libraries, CDCL SAT, DPLL(T) SMT, constraint and model finding, CEGAR/CEGIS, learned premise/tactic guidance, exact numerical and exhaustive methods, and ordinary expert review. The mathematical contract defines immutable identities, leakage closures, abstraction obligations, proof-DAG reconstruction, certificate cost, typed states, invalidation, human effort, and joules. If the mature stack ties, the composition is retired; the state distinctions and negative result remain.

Efficiency mechanism

Deduplication saves two different resources.

First, it reduces research duplication. If plant priming, eligibility traces, and cache admission all motivate a temporary state before commitment, the project maintains one promotion interface and tests the domain-specific differences as variants. Papers and claims remain separate; architecture and instrumentation are reused.

Second, the resulting families define where the runtime should avoid repeated work:

• scarcity families reduce unnecessary activation and acquisition;
• locality families reduce movement and synchronization;
• reversible-change families prevent every event from rewriting durable state;
• maintenance families move repair and integration off the critical path; and
• structural offloading reduces the recurring cost of mature behavior.

For principle $j$, lifecycle acceptance uses the energy contract from chapter 80:

where $N$ is a dimensionless count of qualified events, event terms are joules per qualified event, and introduction, maintenance, and expected recovery are joules over the same observation horizon. A positive $\Delta E_j(N)$ is only an energy result; quality, risk, calibration, latency, and resilience must also remain inside their declared envelopes.

Evidence status

Speculative extensions

• Represent claims, principles, primitives, experiments, null models, and failures as a queryable versioned graph while keeping Markdown canonical.
• Track negative results as first-class edges so a rejected translation is not repeatedly rediscovered under another domain name.
• Estimate reviewer agreement on mechanism tuples before allowing a new principle ID.
• Search specifically for counterexamples: fields where the same pressure produces a different stable solution or where the recurring solution fails.
• Use contradictions between domains to generate new experiment regimes rather than averaging the difference away.
• Maintain silicon-native escape routes beside every principle: exact copying, direct addressing, typed storage, rollback, high-speed communication, and variable precision can change which operation is cheapest.

Failure modes

• Naming duplication: the same feedback loop appears repeatedly as a new organism-inspired component.
• False convergence: similar diagrams hide different sensed variables, causal operations, or timescales.
• Overcompression: a family becomes so broad that no ablation can isolate its mechanism.
• Evidence laundering: recurrence is treated as proof of independence, optimality, or transfer to AI.
• Null-model neglect: a familiar cache, scheduler, controller, or estimator is omitted because the biological story sounds novel.
• Interface drift: two variants share a principle ID while receiving different inputs, budgets, or evaluation envelopes.
• Positive-result bias: failed translations disappear, so the same proposal returns with a new metaphor.
• Double-counted savings: two principles claim the same avoided operation or compare against different baselines.
• Verifier laundering: checker acceptance is reported as truth about an informal statement without exposing formalization, axioms, dependencies, or the trusted implementation.
• Finite-to-universal leakage: a sampled numerical or test range is silently promoted from tested to proved.
• Correlated checking: producer and verifier share the parser, library, preprocessing, or defect that matters while being counted as independent.
• Taxonomy lock-in: stable IDs are mistaken for immutable scientific truth.

Measurable predictions

1. Independent reviewers given only normalized mechanism records agree on merge-versus-separate decisions more often than reviewers given titles and domain descriptions alone.
2. As domain coverage grows, the number of supporting claims per accepted principle rises faster than the number of principles; a near one-to-one ratio signals failed deduplication.
3. At least one proposed organism-specific mechanism is experimentally indistinguishable from an existing primitive at matched interface and cost and is removed rather than renamed.
4. Experiments built around shared principles reuse telemetry, null models, and failure regimes across more than one source domain.
5. Per-principle ablations attribute lifecycle savings to distinct avoided operations; overlapping savings disappear when all variants use one common baseline and boundary.
6. A principle promoted from recurrent evidence survives at least one regime derived from a domain outside the one that originally motivated its AI translation.
7. A typed propose–challenge–prove–check lifecycle reduces bad acceptance, localizes repair, and improves dependency invalidation beyond a generator plus checker alone after search, certificates, libraries, human effort, and joules are charged; otherwise Fixture F-004 retires the composition.