README.md

metadata

license: cc-by-4.0
language:
  - en
tags:
  - interpretability
  - activation-oracles
  - knowledge-cutoff
  - percepts
  - nla
size_categories:
  - 100K<n<1M
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train.parquet
      - split: heldout_entity
        path: data/heldout_entity.parquet
      - split: test
        path: data/test.parquet

Chronopercept (merged v1 + v2 + v3)

Union of chronopercept-v1 (4,547 curated, twin-certified scenes), chronopercept-v2 (116,452 generated scenes), and chronopercept-v3 (cds-jb/chronopercept-v3, 660,223 generated scenes): 781,222 rows, 101,605 entities, 16 L0 task types (14 generated mechanisms + 2 v1 control classes), 903 L1 domains. Splits are ENTITY-disjoint across the whole union — colliding entities adopt the earlier version's split assignment (verified 0 entities span splits).

Each scene is 1–2 sentences of plain pre-1931 English in which an entity appears innocently: a reader in 1930 finds the text unremarkable, while a well-informed modern reader perceives a decisive second meaning (the "percept") that requires post-1931 knowledge.

v3 additions (2026-08-08)

• 5 new L0 mechanisms (Fable-agent taxonomy expansion): retrospective_unmasking, fateful_conjunction, standing_mystery_resolved, latent_utility, legality_flip.
• 625 new L1 domains across all 14 generated L0s (Fable agents, each domain proven mineable with 3 real examples; global dedup; capped at 60 L1/L0).
• solvable_from_year column (all rows) — the smallest year Y such that a well-informed reader in year Y could already know the fact(s) needed to perceive the percept. Judged per-scene at QC for v3 rows and backfilled for all v1/v2 rows via a dedicated Sonnet 5 batch pass. solvable_pre1931=True (7,649 rows, ~1%) flags rows judged solvable before 1931 — these violate the corpus premise and should be filtered for training.
• New entity-verification gate: mined entities are batch-judged (real? evidence anchor? solvable_from_year >= 1931?) before scene generation; 85% pass rate. evidence column carries the anchor fact for v3 rows.
• Decade flattening: entity mining was decade-steered (2 of 6 calls per domain target 1955-1990 / 1990-2025); merged median solvable_from_year is 1960 (10th-90th pct: 1935-2000), vs. the strong 1931-1945 skew of v1/v2.
• Scenes: rotating per-request few-shot from v1 (v2 used 2 fixed examples). QC keep rate 94%.

Columns: scene, gold_percept (modern-reader insight), gold_secret (the insight re-expressed within a strict 1930 knowledge horizon), secret_atoms, implication_class, intended_valence, L0/L1/L2_entity, split, source (v1|v2|v3), solvable_from_year, solvable_pre1931, twin_certified (True only for v1 rows — generated rows are QC-screened but not certified against the talkie twins; invented entities may carry no real knowledge gap), plus gloss/year (v2/v3) and evidence (v3).

Generation pipeline: code/chrono_v3_gen.py (taxonomy merge from Fable-agent JSON, entity mining with per-pair avoid-lists, verification gate, scene generation, QC, backfill, assemble, publish — Sonnet 5 Message Batches throughout).