# SFT Benchmarks (Inference + Artifacts)

These benchmarks demonstrate the packaging and the failure discipline:

- Target inference returns CST on coupled data and torus on uncoupled data.
- Failure modes return structured obstruction outputs rather than overclaims.
- Outputs are artifacts (WSC/WOB/CC) consistent with schemas.
- Optional empirical checks provide conservative evidence signals for RNF and NDB-style diffusion models.

Run:
  python3 run_benchmarks.py

This will:
- infer target type for 2-cycle and T^3 datasets
- build a WSC template from loop phases
- write outputs to `benchmarks/output/`

Additional inference obstruction cases included:
- `infer_t3_wob_insufficient_excitation.json` (WOB-14-insufficient-excitation)
- `infer_t3_wob_ambiguity.json` (WOB-13-ambiguity)
- `infer_t3_wob_resonance_masking.json` (WOB-15-resonance-masking)

Empirical evidence outputs (bench-only, not proofs):
- `rnf_empirical_checks_torus2.json`
- `rnf_empirical_checks_circle_mean_motion.json`
- `ndb_mxc_empirical_checks_random_walk.json`
