GCPR–RWL–FELRA · PYTHON-FIRST VERIFICATION WORKBENCH
FELRA
形式演化邏輯—規則架構 Formal Evolving Logic-Rule Architecture
FELRA turns theory and data into reproducible computational evidence first, then promotes stable results into formal specifications and proof obligations. Every run leaves behind hashes, seeds, and environment versions — results can be re-verified by anyone, on any machine, not just trusted.
Core Capabilities
From hypothesis to evidence bundle
A declarative project.yaml describes the parameter space, claims, and analyses; FELRA produces a complete Evidence Bundle — CSV, JSON, Markdown, figures, and hashes, all traceable.
01 · SPEC
Declarative specification
YAML describes claims and analyses; safe AST numerical expression evaluation — no extra glue code needed.
02 · SEARCH
Counterexample search
Finite declared grids, boundary checks, and random sampling — systematic counterexample search, not a single-point check.
03 · DATA
Data contracts & quality
Typed CSV/JSON/JSONL/gzip datasets; missing, invalid, and duplicate rows are all recorded as evidence, with source hashes.
04 · STATS
Statistical inference
Descriptive statistics, hypothesis tests, bootstrap confidence intervals, power analysis, and robustness analysis (bootstrap/subsample).
05 · SENS
Sensitivity & Pareto
Local finite-difference sensitivity, residual diagnostics, one- to high-dimensional parameter sweeps, two-objective non-dominated fronts.
06 · MODEL
Cross-validation & effect sizes
Leakage-aware numeric regression cross-validation, shared-fold model comparison, standardized effect sizes, multiple-comparison correction.
07 · REGISTRY
Experiment registry
Append-only JSONL experiment registry — felra registry inspects run history by project or status.
08 · LOCK
Preregistration
Immutable scientific-plan locking, with warning and strict execution modes; the plan itself carries a stable SHA-256 fingerprint.
09 · REPLAY
Provenance & replay
JSON/DOT/SVG evidence-provenance graphs, a stable result_sha256, felra replay for independent re-verification, and felra export for a paper-ready Methods/Results/Limitations bundle.
10 · SYMBOLIC
Symbolic verification
Exact algebraic equivalence and derivative checks via SymPy, with per-variable assumptions (positive, real, integer, …) — a proof over the whole declared domain, complementing the sampling-based channels above.
11 · SOUNDNESS
Numerical soundness
Overflow/NaN detection, exact-derivative condition numbers, and float64-vs-arbitrary-precision catastrophic-cancellation detection — driven from the same symbolic parser as V2, applied to real sampled points.
12 · CROSS-METHOD
Cross-method consistency
Declare two or more independent formulations or evaluators (numeric/symbolic/high_precision) for the same quantity and check pairwise agreement — arbitrary precision alone can't rescue a formula's own singularity; a different method can.
LIVE REPRODUCIBILITY CHECK · Evidence Ledger
Not a marketing number — evidence re-run on this machine today
The record below is from the examples/reproducibility example project, run end to end with FELRA v0.7.0 on 2026-07-20: executed, independently replayed, and exported as a paper bundle.
x ** 2 >= 0) — no counterexample was found within the declared sampling budget. This is computational evidence, not a mathematical proof.9e4817f6 af425cb0 77a9e3c7 0f12608b
felra replay artifacts/reproducibility — the recomputed result hash matches byte-for-byte.felra export produced 18 indexed files: Methods, Results, Limitations, CITATION, figures, report, and file hashes.
Quick Start
Five commands, from install to your first evidence bundle
01 · Install
$ python -m venv .venv $ .venv\Scripts\activate # Windows $ source .venv/bin/activate # Linux / macOS $ pip install -e ".[dev]"
02 · Create and run your first project
$ felra init my-theory $ felra run my-theory/project.yaml \ --output my-theory/artifacts/run
03 · Run the built-in examples
$ felra run examples/data/project.yaml \ --output artifacts/data $ felra batch examples/repeated_batch/batch.yaml \ --output artifacts/repeated --workers 2
04 · Preregister, replay & export (v0.7)
$ felra verify-preregistration project.yaml \ --record preregistration.json $ felra replay artifacts/run --output artifacts/replay $ felra export artifacts/run --output artifacts/paper
SCIENTIFIC BOUNDARIES
FELRA won't inflate your conclusions for you
This is an academic verification workbench, not a proof generator. The boundaries below are written into AGENTS.md, and the same rules bind anyone — human or agent — working on this project.
FELRA's Python results are computational evidence under finite data, finite precision, a finite budget, and specific statistical assumptions — not a general mathematical proof.
A p-value is not "the probability the hypothesis is true"; rejecting the null does not automatically establish causation.
Failing to reject the null does not mean the two are equivalent — "no counterexample found" is never rewritten as "theorem proved."
Demonstration datasets must stay labeled synthetic — they are not empirical evidence.
A matching replay proves computational reproduction in the same environment — not external replication, scientific validity, or universal proof.
Version Arc
From MVP to full verification-channel coverage in one week
v0.1–v0.7 had their first implementation on 2026-07-14 (v0.5–v0.7 were synced into the main branch and fully re-validated on this machine on 2026-07-20); v0.8.0 through v1.0.0 were each implemented and validated directly, also on 2026-07-20 — the three defined steps that complete the whitepaper's stage-2 verification-channel roadmap (V0-V8).
v1.0.0 Cross-Method Consistency (V8) · Stage 2 Complete
Independent-method agreement checks (numeric/symbolic/high-precision backends) complete the full V0-V8 verification-channel set. 6 new regression tests — 51/51 total.
v0.9.0 Numerical Soundness (V3)
Overflow/NaN detection, exact-derivative condition numbers, and float64-vs-arbitrary-precision catastrophic-cancellation detection. 5 new regression tests.
v0.8.0 Symbolic Verification (V2)
Exact algebraic-equivalence and derivative checks via SymPy, with per-variable assumptions — complements the sampling-based channels with a proof over the whole declared domain. 7 new regression tests.
v0.7.0 Preregistration, Provenance, Replay & Paper Export
Immutable scientific-plan preregistration, JSON/DOT/SVG evidence-provenance graphs, a stable result_sha256, felra replay and felra export. 33 regression tests.
v0.6.0 Effect Sizes, Cross-Validation & Registry
Bonferroni/Holm/Benjamini–Hochberg multiple-comparison correction, standardized effect sizes, leakage-aware cross-validation, an append-only experiment registry.
v0.5.0 Power, Robustness & Cache
JSON/JSONL/gzip datasets, noncentral-t and Fisher-z power analysis, bootstrap/subsample robustness analysis, content-addressed caching.
v0.4.0 Data, Statistics & Replication Pipeline
Typed external CSV datasets, dataset-row claims, grouped descriptive statistics, the hypothesis-test family, percentile bootstrap confidence intervals, repeated experiments, and parallel batching.
v0.3.0 Sensitivity, Residual & Pareto
Local sensitivity, residual analysis, parameter sweeps, exact two-objective Pareto fronts, felra batch.
v0.2.0 Executable Project Specs
Executable project.yaml, safe AST expression evaluation, felra run and project-level Evidence Bundles, multiple figure types.
v0.1.0 Initial MVP
The first version of the Python-first research workbench.