Local output inspection and evaluation

All bundled operations in this reference are deterministic and model-free. They do not establish scientific truth.

Hypothesis-bank inspection

The pinned upstream serializer writes a JSON object whose keys are hypothesis text and whose values contain:

• hypothesis: same text as the object key;
• acc: finite number in [0, 1];
• reward: finite ranking value;
• num_visits: non-negative integer;
• correct_examples: [row_index, label] pairs;
• optionally num_select in some literature/union outputs.

Inspect without echoing text:

python3 scripts/inspect_outputs.py hypotheses \
  --input outputs/hypotheses.json \
  --root .

The report includes file SHA-256, bank size, normalized duplicate counts, length/statistic ranges, and a bounded sample of hypothesis SHA-256 values. Candidate strings are never printed or interpreted.

Stored acc and reward are algorithm state from the generation/update workflow. They are not an independently reproduced evaluation and should not be described as p-values, confidence intervals, causal effects, or scientific validation.

Strict saved-result schema

The upstream inference CLI logs metrics but does not define a durable prediction artifact. This skill therefore uses a small local interchange schema:

{
  "schema_version": "1.0",
  "dataset_manifest_sha256": "<64 lowercase hex>",
  "hypothesis_bank_sha256": "<64 lowercase hex>",
  "split": "test",
  "records": [
    {
      "id": "stable-nonsecret-id",
      "label": "class-a",
      "prediction": "class-a"
    }
  ]
}

prediction may be null for an abstention or extraction failure. IDs must be unique. Do not store prompts, chain-of-thought, provider responses, credentials, or raw sensitive features in this file. Replace the placeholder hashes in assets/result.example.json with hashes of the exact reviewed artifacts.

Inspect structure:

python3 scripts/inspect_outputs.py results \
  --input results/test_predictions.json \
  --root .

The report redacts IDs, labels, and predictions; category values are represented by short SHA-256 fingerprints.

Evaluation plan

Freeze a plan before looking at test metrics:

python3 scripts/evaluate_local.py plan \
  --config reviewed_run_config.json \
  --manifest dataset_manifest.json \
  --root .

The plan records:

• immutable data source revision and manifest hash;
• provider/model/destination;
• train, validation, and test roles;
• row and hypothesis caps;
• planned metrics;
• required provenance and interpretation limits.

It does not read dataset rows or invoke a model.

Metrics report

python3 scripts/evaluate_local.py report \
  --results results/test_predictions.json \
  --root . \
  --expected-split test

Implemented metrics:

• coverage: non-null predictions divided by all records;
• accuracy_all_records: exact matches divided by all records; null predictions count as incorrect;
• accuracy_covered_records: exact matches among non-null predictions;
• macro_f1_all_records: unweighted mean of per-label F1 over the union of observed true and non-null predicted labels;
• redacted confusion matrix, with a separate <missing> prediction column.

All arithmetic uses the supplied saved strings exactly. There is no label normalization beyond schema validation. A custom label extractor must be frozen before test evaluation and its behavior documented.

Reporting checklist

Report at minimum:

1. hypogenic version, source commit, and artifact hash;
2. dataset repository, immutable revision, manifest SHA-256, and file hashes;
3. task config hash, provider wrapper, exact model, and data destination;
4. train/validation/test/OOD roles and sample counts;
5. generation/inference settings, seeds, hypothesis count, and selection rule;
6. token/request/cost caps and actual provider usage when available;
7. result and hypothesis-bank hashes;
8. coverage, accuracy, macro-F1, class support, and uncertainty across seeds;
9. duplicate/leakage audit results;
10. failures, exclusions, abstentions, retries, and deviations;
11. provider retention/privacy terms reviewed for the run;
12. a statement that generated hypotheses are candidates, not evidence.

The local tool does not calculate confidence intervals or significance tests. Choose those methods from a prespecified design that respects dependence, repeated seeds, multiple comparisons, class imbalance, and the data-generating process.

Scientific interpretation limits

Predictive benchmark performance can show that a textual heuristic was useful for a declared classification setup. It does not by itself establish:

• that the proposed mechanism is true or causal;
• that the pattern generalizes beyond the sampled population/time/domain;
• novelty relative to all scientific literature;
• robustness to paraphrases, near duplicates, annotation artifacts, or distribution shift;
• clinical, policy, or safety validity;
• absence of data leakage or provider/model memorization.

Validate promising candidates using independent data, domain-expert review, appropriate controls, alternative explanations, sensitivity analyses, and, where warranted, prospective or experimental tests.