dataset_upload/OUTPUT_SCHEMA.md

Output schema

What your model must emit for the shipped scorer and metrics to accept it. If you use runner/eval_run.py you get this for free; this document exists so you can plug in your own inference stack (vLLM, an API, a custom harness) instead.

Generations — outputs/evaluation/<model>.jsonl

One JSON object per line, 44,416 lines, one per query_id.

{
  "query_id": "query_00000001",
  "fact_id": "fact_000602",
  "condition_family": "anchor",
  "model": "my-model",
  "prompt": "Question: Which jurisdiction does Agriculture and Agri-Food Canada have legal force in?\nAnswer with only the shortest correct answer.\nAnswer:",
  "raw_response": " Canada\nExplanation: Agriculture and Agri-Food Canada (AAFC) is a federal department of the Government of Canada",
  "generated_tokens": 24,
  "finish_reason": "length"
}

field	required	notes
query_id	yes	the join key; must match the query bank exactly
raw_response	yes	continuation only, not including the prompt. Do not strip, lowercase, or truncate it — the scorer needs the raw span, and finish_reason: "length" mid-sentence output is normal and handled
fact_id, condition_family	recommended	judge_run.py groups by these; it can recover them from the query bank but the files are easier to audit with them present
model	recommended	copied onto scored rows
prompt	recommended	keeps each file self-documenting about the template used — the cheapest way for a reader to catch a protocol mismatch
generated_tokens, finish_reason	optional	diagnostics

Order does not matter; the scorer joins on query_id. Extra fields are ignored.

Prompt construction

Reproduce runner/eval_run.py:build_prompt exactly:

PROMPT = "Question: {q}\nAnswer with only the shortest correct answer.\nAnswer:"

def build_prompt(row):
    if row["condition_family"] == "anchor":
        return PROMPT.format(q=row["query"])
    return f"{row['query']}\nAnswer:"

Anchor is the canonical question and gets the instruction wrapper. Every other family already carries its own surface form — that is the perturbation — so wrapping it would erase the manipulation; it gets only a bare Answer: cue.

No chat template, for base and instruct models alike. See the README.

Decoding

Greedy, num_beams=1, temperature=0.0, max_new_tokens=24, max_prompt_len=192 (left truncation, left padding), dtype=bfloat16, seed=20260101. From configs/models.yaml:generation.

Scored output — <model>.scored.jsonl

Produced by runner/scoring_full.py. One line per generation:

{
  "query_id": "query_00000001",
  "model": "my-model",
  "fact_id": "fact_000602",
  "relation": "applies_to_jurisdiction",
  "condition_family": "anchor",
  "language": "en",
  "target_slot": "object",
  "answer_type": "place",
  "answer_granularity": "entity",
  "answer_in_subject_surface": true,
  "use_for_main_forward": true,
  "use_for_reverse_analysis": false,
  "use_for_recognition_analysis": false,
  "raw_response": " Canada\nExplanation: ...",
  "span": "Canada",
  "flags": [],
  "label": "correct",
  "matched_alias": "Canada",
  "scorer": "exact_alias",
  "needs_manual_review": false
}

label is one of correct / incorrect / ambiguous / abstain / unparseable. scorer names the rule that fired, which is what you inspect when a label looks wrong. The per-query booleans are carried through so metric code can filter without rejoining the query bank.

Hidden states — outputs/hidden/<model>/

Only needed for ISS and KTS. Produced by metrics/extract_hidden.py.

L018.npy … L045.npy    float16 [n_main_forward_queries, d_model]
index.json             query order, layer list, checksums, `complete` flag

Rows are in the order given by index.json, aligned with the 39,260 queries carrying use_for_main_forward. Layers stored are {l : l/(L−1) ≥ 0.4}, where l indexes decoder blocks and the stored state is the output of block l (hidden_states[l+1] in HuggingFace terms).

The probe position is the last valid input token — the model has read the question but has not emitted an answer token. With left padding this is position -1 for every row in a batch.