Datasets:

PureOne
/

FNE-AXIOMESH

# Instructions for AI coding and research agents

This repository is FNE–AXIOMESH v0.2.0, with the Agent and Expert Support edition
of its distribution. The scientific kernel remains a small exact finite-field
reference implementation. Read `README.md`, `STATUS.json`, `docs/CLAIMS.json`,
and `docs/IMPLEMENTATION.md` before interpreting results or changing semantics.

## Start here

1. Use Python 3.10+; install `requirements.txt` in a virtual environment.
2. Run `python -m pytest -q` and `python validate_release.py`.
3. Run `python agent_interface.py --request examples/agent_recover.json`.
4. Read `docs/AGENT_SUPPORT.md` for JSON requests and result interpretation.
5. Read `docs/EXPERT_REVIEW.md` for a proof-to-code review map.

## Research contracts

- Action words are chronological: `[a, b]` applies `a`, then `b`.
  Thus the ambient matrix is `T_b @ T_a`.
- The declared query, finite prime field, operator family, and trusted surviving
  measurements determine exactness. Do not generalize certificates to arbitrary
  neural representations, noisy sensors, unknown model families, or corrupt code.
- Preserve `ambiguous` and `inconsistent` outcomes. A missing distinction is not
  identified by choosing a plausible completion or sampling an LLM response.
- Exact acquisition debt assumes unrestricted scalar linear measurements.
  Restricted sensors require a separate attainability analysis.
- Tether length counts additional field symbols. Count matrices, maps, metadata,
  training history, compilation, sensing, search, and decoding costs separately.
- A conventional pooled learner ties the principal prediction benchmark.
  A rank ratio is not an intelligence multiplier. The five integer seed labels
  are pseudorandom seeds, not five calendar dates.
- The prior-art report is a bounded inherited search. Do not turn it or old
  subjective scores into a world-first, 100%-novel, or peer-reviewed claim.

## Working protocol

Use the data-only `agent_interface.py` for external proposals inside its declared
limits. It validates small matrices and does not execute submitted code. A larger
or nonlinear proposal needs an explicit new backend and validity contract.
The interface supplies no oracle: suggested measurement rows still need real
evidence before an agent may append their values to memory.

Run exploratory experiments with `--output local_runs/<run-id>` so the bundled
scientific evidence is preserved. Agent guidance is documentation; the repo does
not launch background agents, allocate GPU jobs, or submit messages to experts.

For edits to the adapter, test malformed inputs, budget exhaustion, and honest
abstention as well as successful execution. For scientific changes, document
assumptions, add a meaningful counterexample/regression check, update the claim
ledger and evaluation protocol, and run the relevant suite. Distinguish a rerun
of published evidence from a new claim.

After intended edits, refresh checksums with `python validate_release.py
--refresh-manifest`, then verify them. Do not refresh the manifest to conceal
unexpected changes. Public upload is an explicit action via the supplied uploader;
local review commands do not publish. Preserve the owner's existing license status.
## Reporting
Return concrete file/function references, exact commands, environment, assumptions,
results, negative results, and unresolved issues. A review by an AI is an automated
review, not independent human peer review. Use the status vocabulary in the claim
ledger and the report template in `docs/REVIEW_REPORT_TEMPLATE.md`.

	# Instructions for AI coding and research agents

	This repository is FNE–AXIOMESH v0.2.0, with the Agent and Expert Support edition
	of its distribution. The scientific kernel remains a small exact finite-field
	reference implementation. Read `README.md`, `STATUS.json`, `docs/CLAIMS.json`,
	and `docs/IMPLEMENTATION.md` before interpreting results or changing semantics.

	## Start here

	1. Use Python 3.10+; install `requirements.txt` in a virtual environment.
	2. Run `python -m pytest -q` and `python validate_release.py`.
	3. Run `python agent_interface.py --request examples/agent_recover.json`.
	4. Read `docs/AGENT_SUPPORT.md` for JSON requests and result interpretation.
	5. Read `docs/EXPERT_REVIEW.md` for a proof-to-code review map.

	## Research contracts

	- Action words are chronological: `[a, b]` applies `a`, then `b`.
	Thus the ambient matrix is `T_b @ T_a`.
	- The declared query, finite prime field, operator family, and trusted surviving
	measurements determine exactness. Do not generalize certificates to arbitrary
	neural representations, noisy sensors, unknown model families, or corrupt code.
	- Preserve `ambiguous` and `inconsistent` outcomes. A missing distinction is not
	identified by choosing a plausible completion or sampling an LLM response.
	- Exact acquisition debt assumes unrestricted scalar linear measurements.
	Restricted sensors require a separate attainability analysis.
	- Tether length counts additional field symbols. Count matrices, maps, metadata,
	training history, compilation, sensing, search, and decoding costs separately.
	- A conventional pooled learner ties the principal prediction benchmark.
	A rank ratio is not an intelligence multiplier. The five integer seed labels
	are pseudorandom seeds, not five calendar dates.
	- The prior-art report is a bounded inherited search. Do not turn it or old
	subjective scores into a world-first, 100%-novel, or peer-reviewed claim.

	## Working protocol

	Use the data-only `agent_interface.py` for external proposals inside its declared
	limits. It validates small matrices and does not execute submitted code. A larger
	or nonlinear proposal needs an explicit new backend and validity contract.
	The interface supplies no oracle: suggested measurement rows still need real
	evidence before an agent may append their values to memory.

	Run exploratory experiments with `--output local_runs/<run-id>` so the bundled
	scientific evidence is preserved. Agent guidance is documentation; the repo does
	not launch background agents, allocate GPU jobs, or submit messages to experts.

	For edits to the adapter, test malformed inputs, budget exhaustion, and honest
	abstention as well as successful execution. For scientific changes, document
	assumptions, add a meaningful counterexample/regression check, update the claim
	ledger and evaluation protocol, and run the relevant suite. Distinguish a rerun
	of published evidence from a new claim.

	After intended edits, refresh checksums with `python validate_release.py
	--refresh-manifest`, then verify them. Do not refresh the manifest to conceal
	unexpected changes. Public upload is an explicit action via the supplied uploader;
	local review commands do not publish. Preserve the owner's existing license status.

	## Reporting

	Return concrete file/function references, exact commands, environment, assumptions,
	results, negative results, and unresolved issues. A review by an AI is an automated
	review, not independent human peer review. Use the status vocabulary in the claim
	ledger and the report template in `docs/REVIEW_REPORT_TEMPLATE.md`.