# Reconstruction differences Required by spec section 10: every deviation from the expected counts, and every internal contradiction found in the specification itself, is recorded here rather than silently absorbed. Build: `dataset3/`, sources read from `ICLR27/dataset/`. --- ## Summary | Stage | Expected | Actual | Status | |---|---:|---:|---| | Raw cloze prompts | 1,783,541 | 1,783,541 | match | | Five-model consensus | 10,601 | 10,601 | match | | Candidate-Known Facts | 8,107 | 8,107 | match | | Grouped (subject, relation) | 6,185 | 6,185 | match | | Functional conflicts removed | 5 | 5 | match | | Enriched Facts | 6,180 | 6,180 | match | | Core-source facts | 3,705 | 3,705 | match | | Benchmark facts | 2,592 | 2,592 | match | | Benchmark relations | 21 | 21 | match | | Migration duplicates removed | 10,323 | 10,323 | match | | Evaluation queries | 44,418 | **44,416** | **deviation D1** | | — context | 6,831 | **6,829** | **deviation D1** | | — all other conditions | as specified | as specified | match | Everything except D1 reproduces exactly. --- ## D1 — Two context queries expose the gold answer (defect in the source data) **Deviation:** context 6,831 → 6,829; total 44,418 → 44,416. Spec 9.4 requires that context "must not introduce a second valid target", and spec 13.9 that "no gold answer is exposed in open-ended main conditions". Two queries migrated from `eval_dataset/queryD_context.jsonl` violate both, because the legacy distractor sampler picked an entity whose surface form contains the gold answer: ``` Some people confuse States Reorganisation Act with Indian Act. In which jurisdiction does States Reorganisation Act apply? gold = India <- exposed by the distractor "Indian Act" Taiwan (ROC) Resident Certificate is a legal term in Taiwan. Indian Act is a legal term in Canada. In which jurisdiction does Permanent account number apply? gold = India <- same cause ``` Spec 10 allows a count change when "the implementation discovers a documented defect in the old data". This is such a defect. **Action taken.** `src/build_eval_bank.py` applies a general rule rather than a hard-coded exception: for every open-ended main-forward query, the normalized gold must not appear in the query text after removing all mentions of the subject. Queries failing the rule are dropped and counted in `stage4_report.json` as `dropped_gold_exposed`. **To restore 44,418** exactly, delete that guard — but the two queries are unanswerable-by-design artefacts and any model scores them correct by copying. --- ## C1 — Spec 6.2 contradicts the required benchmark size (unresolved by design) **Not a count deviation. An internal contradiction in the specification.** Spec 6.2 lists relations "generally unsuitable" for the main benchmark, naming among them **developer of** and **manufacturer of**. Spec 1, 6.2 and 15 simultaneously require exactly **2,592 facts across 21 relations**. Those two relations contribute: | Relation | Facts | |---|---:| | manufacturer (P176) | 441 | | developer (P178) | 405 | | **total** | **846** | Excluding them yields **1,746 facts across 19 relations**, which fails the acceptance criteria in spec 15. Keeping them satisfies the counts but contradicts spec 6.2's guidance. **Action taken.** The counts win, because spec 15 states them as acceptance criteria and spec 10 forbids silent changes. Both relations are retained and tagged: ```json "spec_6_2_flag": "unsuitable_named_in_spec" ``` `configs/relations.yaml` exposes `options.drop_spec_6_2_flagged`. Setting it to `true` reproduces the spec-6.2-compliant 1,746-fact variant; the selection script then reports the resulting size instead of failing. **Recommendation.** Report the headline number on all 2,592 and a robustness number on the 1,746 subset. See F1, which gives an independent empirical reason to distrust exactly these relations. --- ## F1 — 28.9% of benchmark facts are answerable by string copying **Finding, not a deviation. No counts changed.** For 750 of 2,592 benchmark facts (28.9%) the object string is literally contained in the subject name, so the query can be answered by copying a substring instead of retrieving knowledge: ``` Airbus A318 -> manufacturer Airbus Adobe Acrobat -> developer Adobe Amazon Music -> owned by Amazon Agriculture and Agri-Food Canada -> jurisdiction Canada ``` Concentration by relation: | Relation | Flagged | Total | Share | |---|---:|---:|---:| | manufacturer | 387 | 441 | 87.8% | | developer | 199 | 405 | 49.1% | | owned_by | 52 | 89 | 58.4% | | applies_to_jurisdiction | 31 | 54 | 57.4% | | sport | 17 | 81 | 21.0% | | country | 17 | 263 | 6.5% | | headquarters_location | 15 | 132 | 11.4% | | (10 further relations) | 32 | — | <34% | 586 of the 750 come from `manufacturer` and `developer` — the two relations spec 6.2 already called unsuitable. F1 is therefore independent empirical support for C1's recommendation. **Why this matters.** A stability benchmark measures whether a *retrieved* fact survives rephrasing. A fact answerable by copying is stable for a reason that has nothing to do with knowledge, so it inflates every retention number and dampens the very effect the study is about. **Action taken.** Spec 16 forbids silently dropping facts, so all 750 are kept and flagged on both the fact and the query record: ```json "answer_in_subject_surface": true ``` Downstream analysis should report retention with and without them. --- ## F2 — 808 of the 8,107 candidate rows are exact duplicates **Finding, not a deviation.** The 8,107 Candidate-Known Facts contain only 7,299 distinct `(subject, relation, object, source)` tuples; 808 rows are exact repeats, e.g. `("BMW 3 Series", P176, "BMW", counterfact)` appears twice. This is inherited from `dataset/filter/prompts.jsonl`. No action needed: spec 5.1 grouping absorbs them, which is part of why 8,107 rows become 6,185 groups. Recorded so the 8,107 figure is not mistaken for 8,107 distinct facts. --- ## N1 — Stage I GPU passes were not re-executed The five-model first-token forward passes (1,783,541 prompts x 5 models) were run previously; their per-prompt verdict vectors are stored as `dataset/filter/correct_.npy`. `src/build_candidate_known.py` re-derives the candidate set from those vectors: it re-reads the prompt table, re-computes the intersection, re-applies the source-drop rule, and checksums every input into `stage1_report.json`. It does not re-run inference. The counts are verified, not assumed — 1,783,541 / 10,601 / 8,107 all reproduce, and the result is checked for set equality against `dataset/filter/global_known_clean.jsonl`. `src/filter_run.py` regenerates the vectors from scratch when required. --- ## N2 — Triple direction is not normalized for `capital_of` Spec 2.2 requires a single triple direction, forward being `subject + relation -> object`. The relation `capital_of` (P1376) is stored the other way round: subject = city, object = country. Normalizing it would fold those 125 facts into `capital`, changing both the benchmark size and the relation count, which spec 10 forbids doing silently. **Action taken.** `capital_of` keeps `"direction": "inverse"` and `"inverse_of": "capital"` in its record, so any analysis can group or separate the two directions explicitly. `configs/relations.yaml` exposes `options.normalize_inverse_direction` for the alternative build. --- ## Coverage gaps (spec 12, expected and unchanged) Main-forward families do not cover every fact: | Condition | Facts covered | Missing | |---|---:|---:| | anchor | 2,592 | 0 | | paraphrase | 2,592 | 0 | | format | 2,592 | 0 | | context | 2,591 | 1 | | multilingual | 2,402 | 190 | Missing fact ids are listed in `outputs/coverage_report.json`. No query was imputed. Downstream analyses that compare conditions per fact should use the explicit valid intersection.