Datasets:

CinderD
/

wildtrace

Everything needed to reproduce the leaderboard end-to-end. The runnable harness is in eval/ (run_eval.py → model answers, run_judge.py → rubric scores); this document is the exact specification those scripts implement, so a clean-room reimplementation matches.

1. Evaluation is evidence-withheld

The model receives ONLY the document and the question. The supporting clues, the gold answer, and the rubric are never shown to the model under test. The exact prompt is:

Answer using ONLY the document below. Include every specific detail from the text.

Question: {question}

Document:
{document}

{question} = the task's question_text. {document} = the full corpus file (corpus/<corpus_file>), truncated to the model's context cap (§3). No system prompt, no few-shot examples, no chain-of-thought instruction.

2. Generation settings

temperature = 0.1 for models that accept it. Omit temperature for models that reject it (Anthropic Opus 4.6/4.8, GPT-5.4) — set send_temperature: false in config.
max_tokens (completion budget): 32768 for reasoning models, 8192 otherwise. Reasoning models truncated below this lose ~7pp — do not under-budget.
One user turn, no retries on content (transient HTTP/rate-limit errors retry up to 5× with backoff; only a real refusal or empty output is a failure).

3. Context caps and out-of-context scoring

Each model is evaluated at its native context window. The document is measured in characters; if len(document) > cap the task is out_of_context_scope and scored 0 — the document is NOT sent (a system that cannot ingest the evidence fails the task). This is the single most important convention: bottom-of-leaderboard models score low mainly because 40–50% of documents exceed their window.

CJK detection: if the first 4000 chars contain >20 CJK ideographs (一–鿿), use the cjk cap, else the en cap (CJK packs more tokens per character).

Per-model caps used in the paper (characters; en / cjk). ~3.3 chars/token EN, ~1.5 CJK:

model	en cap	cjk cap
gpt-4.1, gpt-5.4, gpt-5.5, qwen3.5-plus, qwen3.6-plus, qwen3.7-max, qwen3.7-plus, gemini-2.5-pro, gemini-3.1, deepseek-v4, glm-5.2, claude-opus-4.6, claude-opus-4.8	2,850,000	850,000
deepseek-v3.2	1,200,000	400,000
gpt-5.1	1,050,000	320,000
kimi-k2.6	1,000,000	320,000
doubao-seed-2.1	1,000,000	320,000
qwen3-max	690,000	210,000
minimax-m2.7	570,000	220,000
default (new/unlisted model)	2,850,000	850,000

For a NEW model, probe its real window and add an entry — do not inherit an older version's cap (an earlier release of this benchmark mis-ranked Qwen3.7 by giving it Qwen3-Max's smaller cap).

4. Judging — 3-judge non-contestant panel, averaged

Each answer is graded by three judges that are NOT on the leaderboard, and the three 0–1 scores are averaged (simple mean, no same-family exclusion — we verified family judges show no measurable bias on this set). Panel used in the paper:

role	family	route used in the paper
judge 1	Claude-Sonnet-4.6	`mr.claude-sonnet-4-6-20260217`
judge 2	Qwen3.5	`qwen3.5-plus`
judge 3	Gemini-2.5-Flash	`gemini-2.5-flash`

(Qwen3.5 is a judge here, hence it is not a graded contestant.) out_of_context_scope answers stay 0 and are not sent to judges.

Exact judge prompt (per answer):

STRICT grader. Only award points for SPECIFIC details present.
QUESTION: {question[:600]}
RUBRIC:
P1 ({points}pts): {correct_criterion[:260]}
P2 (...): ...
RESPONSE:
{response[:5000]}
Reply JSON: {"points_awarded":[<pts>],"total":<sum>}

The rubric is the task's ground_truth.scoring_rubric (a list of {points, correct_criterion}). Judge settings: temperature = 0.1, max_tokens = 16384. Parse the total field (a 0–100 sum), score = min(total / 100, 1.0). A task's final score = mean of the available judges' scores.

5. Aggregation

Per task, per model: mean of the 3 judges for valid scored responses; out-of-context rows are assigned 0 and are not sent to judges.
The paper reports two denominator views. Scored is valid-response quality: the mean over valid scored responses for that route, with n reporting the number of valid scored rows. All481 is coverage-sensitive quality: all 481 attempted tasks are the denominator, and missing, failed, and out-of-context rows are zero-filled.
A matched-panel variant (tasks scored by all models) is reported only as a robustness check. It is not the headline denominator because it removes many long-context access failures.

6. Reproducing with the provided scripts

export API_KEY=...                      # bearer token for your endpoint(s)
cd eval
# 1) edit config.json: base_url + model (+ caps entry if your model is new)
python run_eval.py  --config config.json --data ../data/wildtrace_strict481.with_answers.json \
                    --corpus ../corpus --out ../results/mymodel.responses.json
# 2) edit config.json "judges" to your three judge endpoints
python run_judge.py --config config.json --data ../data/wildtrace_strict481.with_answers.json \
                    --responses ../results/mymodel.responses.json --out ../results/mymodel.scores.json
# overall % is printed and stored at results/mymodel.scores.json -> "overall"

LLM judges are non-deterministic, so a fresh run should be reported with the model route, endpoint date, context policy, decoding settings, and judge panel used for that run.