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| # FNE–AXIOMESH: expert review guide | |
| This guide makes the exact research artifact auditable. It supplies entry points, | |
| assumption checks, and falsification targets. Independent human peer review has | |
| not been performed; no endorsement is implied. | |
| ## The narrow claim worth examining | |
| Counterfactual Quotient Weaving couples a supplied capability extension to its | |
| future observation closure, missing ancestral information, recoverability checks, | |
| and executable consolidation. The reference code demonstrates that contract in | |
| finite linear systems. Whether joint selection gives an advantage over separately | |
| optimized learning and repair is the central unresolved question. | |
| The important boundary is between validity, distinctness, and advantage. A correct | |
| quotient compiler is not evidence that the coupled architecture is new. A novel | |
| contract is not evidence that it improves intelligence. The strong finite learner | |
| ties the principal predictive benchmark. | |
| ## Fast review path | |
| Read `README.md`, `docs/CLAIMS.json`, and `docs/IMPLEMENTATION.md`; inspect the | |
| manuscript's problem statement and exact theorems; then run: | |
| ```bash | |
| python -m pip install -r requirements.txt | |
| python validate_release.py | |
| python -m pytest -q | |
| python examples/minimal_cycle.py | |
| python agent_interface.py --request examples/agent_recover.json | |
| python run_experiments.py --seed 20261007 --output local_runs/expert-check | |
| ``` | |
| The example outputs are `[9]` after one acquisition and reunification, and `[47]` | |
| for the independently restored saved quotient. `local_runs` prevents exploratory | |
| output from replacing the archived evidence. For structural/schema checks, install | |
| `requirements-validation.txt` and run `python validate_release.py --schemas`. | |
| ## Proof-to-code map | |
| The named labels are searchable in `manuscript/FNE_AXIOMESH_Manuscript.tex`. | |
| The manuscript PDF is bundled for normal reading. Theorems are human-readable | |
| derivations; there are no Lean/Coq proof certificates. | |
| | Question / claim | Manuscript location | Implementation | Relevant tests | | |
| |---|---|---|---| | |
| | Least query space stable under declared actions | `thm:quotient`, `thm:compile` | `close_operators`, `Closure.compile` | `test_closure_exact_and_frozen`, `test_random_closure` | | |
| | New actions reveal a previously forgotten distinction | “Why present recall is not future recall” | `hole_certificate`, `Memory.recall` | `test_static_recovery_is_not_future_recovery`; adapter capability-growth test | | |
| | Exact behavior identification from memory | `thm:recall` | `Memory.recall`, `solve_affine` | `test_inconsistent_memory_rejected`, `test_partial_execution` | | |
| | Minimum missing unrestricted scalar information | `thm:debt` | `Memory.supplement`, `Memory.acquire`, `weave` | `test_acquisition_lower_bound_and_transaction` | | |
| | Minimal complement beyond combined child views | `thm:tether` | `split_with_tether`, `FractalSplit.reunify` | `test_minimal_fractal_tether`, `test_split_rejects_missing_cover_or_unidentified_parent` | | |
| | Recursive reconstruction without internal parent seeds | `thm:recursive` | `fractalize`, `regenerate_tree` | `test_recursive_fracture` | | |
| | Known-erasure coding and bounded-radius correction | “Fractal checkpoints and lower bounds” | `Capsule.recover`, `Capsule.correct` | `test_all_erasures_within_radius`, `test_one_corruption_bounded_distance` | | |
| | Mixed-action complementarity | “Why individual branch novelty can fail” | `emergence_rank`; scheduler experiment | `test_emergence_requires_mixed_words` | | |
| | Unrestricted future actions defeat universal compression | `thm:full` | Interpretation limit, not an all-worlds solver | Inspect the theorem assumptions directly | | |
| ## Assumptions requiring explicit review | |
| 1. State is a finite-dimensional column vector over a prime field. Queries and | |
| action matrices are supplied; their semantics are not autonomously discovered. | |
| 2. Every execution word uses the admitted operations; witnesses are chronological. | |
| 3. Measurement values are trusted and consistent. Identifiability is relative to | |
| this declared family, rather than a certificate that the family fits reality. | |
| 4. The tether rank subtraction assumes combined child information lies within | |
| the parent relevant row space. Without inclusion, use the appropriate rank | |
| deficit of the stacked spaces rather than blindly subtracting dimensions. | |
| 5. Minimum acquisition debt assumes unrestricted scalar linear observations. | |
| A physical sensor family, noise, cost weights, or unavailable observables changes | |
| the attainable minimum. | |
| 6. Recursive recovery requires the needed leaf information, tether values, and | |
| transformation metadata to survive or be recoverable. The construction does | |
| not retrieve independent information after all identifying traces are lost. | |
| 7. Coding guarantees concern known erasures or the explicitly declared small | |
| bounded-distance regime. Consistent corruption of unchecked shares or program | |
| metadata can invalidate the recovered semantics. | |
| 8. State symbols, serialized bytes, and total memory have different meanings. | |
| Charge operators, queries, maps, certificates, code, and search history. | |
| ## Disciplinary review routes | |
| | Expertise | Focus | What would change the conclusion | | |
| |---|---|---| | |
| | Observability / abstract interpretation | Reduction to standard minimal sufficient state constructions | A direct established equivalence for the full coupled admission transaction | | |
| | Coding / information theory | Conditional information, erasure assumptions, repair cost | An invalid minimality bound or uncounted state channel | | |
| | Program synthesis / library learning | Whether representation growth is supplied or learned | Strong shared-language learning baselines that explain any gain | | |
| | Continual learning / neural memory | Relation to representation-dependent forgetting | A useful implementation on trained models with controlled ablations | | |
| | RSI / empirical evaluation | Next-discovery productivity under fixed resources | Repeated improvement in discovery efficiency, or persistent controlled null results | | |
| ## Highest-value falsification targets | |
| - Construct a supplied-family case where `Memory.recall` reports exact but two | |
| consistent states give different declared future outputs. | |
| - Find a parent/child split with an inclusion or overlap assumption missing from | |
| the stated tether bound; report the exact spaces and dimensions. | |
| - Show that an alleged advantage disappears when both systems receive the same | |
| operators, compiler, coding tools, proposal language, oracle calls and total cost. | |
| - Determine whether apparent neural regeneration uses hidden replay samples, | |
| a retained full state, privileged sensor information, or an uncharged teacher. | |
| - Test whether next-generation search productivity stays flat after deployment | |
| gains; that would support retention or synthesis without recursive acceleration. | |
| Use `docs/EVALUATION_PROTOCOL.md` for a proposed controlled experiment and | |
| `docs/REVIEW_REPORT_TEMPLATE.md` to report findings. The prior-art material in | |
| `docs/PRIOR_ART.md` is inherited from the original release and is not exhaustive. | |