EVEMISSLAB

TheoryTHY-2026-0007v0.1

Capability memory and substrate-neutral compute: Reuse ≻ Adapt ≻ Create

Beyond facts, the system keeps the algorithms, tools, execution contracts, costs, versions, applicability conditions and success/failure history it has used, and prefers reusing a known solution path over adapting one over creating one. Algorithms and compute containers are different layers: any environment that accepts representable input, performs a valid state transition and returns readable output is a container with its own cost, latency, error and availability model, so algorithm/container pairs are selected jointly.

Research status
EXPERIMENTAL being tested experimentally
Evidence level
E2 Controlled experiment
Data basis
THEORY Theoretical reasoning only; no measurement.
Version
0.1
Updated
2026-09-08
Created
2026-09-08
Domain
Computation, AI Architecture, AI Infrastructure
Program
PRG-2026-0001 Adaptive Epistemic Systems
Authors
Neo.K (EveMissLab)
AI collaborators
Sol (GPT-5.6, OpenAI ChatGPT)

Claims

claims
  • Reuse ≻ Adapt ≻ Create.
  • Algorithm ≠ container; a capability/container pair is the unit of selection and of execution trace.

Predictions

predictions
  • Planning cost falls with repeated related tasks; ReuseGain grows with task similarity within the applicability range, and constraint mismatch produces measurable false reuse (Paper 11 §83–84).

Falsification / failure conditions

falsification_conditions
  • No planning-cost reduction with experience; negative transfer dominates.

Known limitations

known_limitations
  • R1/R2: persistent workflow memory outside the model is not unique to AER; the capability/container separation was 'AER default distinct' only because no core LangGraph primitive was found, not because it cannot be built there.

Evidence

SourceRelationTargetStatusID
EXP-2026-0001 AER-0 MVP v0.1 closure: are the invariants executable?testsTHY-2026-0007 Capability memory and substrate-neutral compute: Reuse ≻ Adapt ≻ CreateACTIVEREL-2026-0103
EXP-2026-0003 R2 — source-grounded structural comparison with LangGraph 1.2.11testsTHY-2026-0007 Capability memory and substrate-neutral compute: Reuse ≻ Adapt ≻ CreateACTIVEREL-2026-0120

Relations

SourceRelationTargetStatusID
RES-2026-0001 Blind derivation of an adaptive epistemic architecturedevelopsTHY-2026-0007 Capability memory and substrate-neutral compute: Reuse ≻ Adapt ≻ CreateACTIVEREL-2026-0017
PAP-2026-0005 Paper 05 — Memory, algorithm libraries and reusable solution pathsformalizesTHY-2026-0007 Capability memory and substrate-neutral compute: Reuse ≻ Adapt ≻ CreateACTIVEREL-2026-0030
PAP-2026-0006 Paper 06 — Substrate-neutral computational containersformalizesTHY-2026-0007 Capability memory and substrate-neutral compute: Reuse ≻ Adapt ≻ CreateACTIVEREL-2026-0033
PAP-2026-0013 Adaptive Epistemic AI Runtime — technical whitepaper v0.1formalizesTHY-2026-0007 Capability memory and substrate-neutral compute: Reuse ≻ Adapt ≻ CreateACTIVEREL-2026-0056
SYS-2026-0001 AER-0 — Adaptive Epistemic Runtime MVPimplementsTHY-2026-0007 Capability memory and substrate-neutral compute: Reuse ≻ Adapt ≻ CreateACTIVEREL-2026-0068
EXP-2026-0001 AER-0 MVP v0.1 closure: are the invariants executable?testsTHY-2026-0007 Capability memory and substrate-neutral compute: Reuse ≻ Adapt ≻ CreateACTIVEREL-2026-0103
EXP-2026-0003 R2 — source-grounded structural comparison with LangGraph 1.2.11testsTHY-2026-0007 Capability memory and substrate-neutral compute: Reuse ≻ Adapt ≻ CreateACTIVEREL-2026-0120
RST-2026-0002 R2 classification matrixqualifiesTHY-2026-0007 Capability memory and substrate-neutral compute: Reuse ≻ Adapt ≻ CreateACTIVEREL-2026-0126

History and provenance

Canonical URL
https://evemisslab.com/ai/theory/THY-2026-0007/
Machine-readable
/ai/theory/THY-2026-0007/index.json
Snapshot
AI-SNAPSHOT-v0.1-fe85b9694a45
Provenance
source
EveMissLab research collection: Adaptive Epistemic Systems (真本體論13)
extracted_by
Splice (Claude Code), reading the canonical UTF-8 sources and each lab's own result reports
extracted_at
2026-09-11
generator
tools/extract_aes/extract.py
claim_boundary
status, evidence level and result type follow the source artifact's own stated claim boundary; nothing is upgraded beyond what the report supports