Update dataset card; add DeepMath difficulty figure

README.md

@@ -37,7 +37,7 @@ configs:
 [![Code](https://img.shields.io/badge/GitHub-Code-181717?logo=github)](https://github.com/SSubhnil/Reasoning-Failures)
 [![Dataset](https://img.shields.io/badge/Hugging%20Face-Dataset-ffd21e?logo=huggingface)](https://huggingface.co/datasets/ssubhnil/Reasoning-Failures)
 
-This dataset accompanies the paper *Effectiveness of Hidden-State Probes for Localizing Reasoning Failures* (Interpretability as a Science: NeurIPS 2026 Workshop). It contains reasoning traces from Qwen3-4B and Qwen3-8B on DeepMath-103K and Hendrycks MATH problems, Monte Carlo continuations from each step of the incorrect traces, the failure labels assigned from those continuations, probe scores and checkpoints, and the inputs of every table command in the code repository. It also contains greedy evaluation runs of Qwen3-1.7B, 4B, 8B and 14B on nine math benchmarks at token budgets from 2,048 to 32,768.
+This dataset accompanies the paper *Effectiveness of Hidden-State Probes for Localizing Reasoning Failures* (Interpretability as a Science: NeurIPS 2026 Workshop). It contains reasoning traces from Qwen3-4B and Qwen3-8B on DeepMath-103K and Hendrycks MATH problems, Monte Carlo continuations from each step of the incorrect traces, the failure labels assigned from those continuations, probe scores and checkpoints, and the inputs of every table command in the code repository. The traces are generated on DeepMath-103K examples at difficulty ≥ 7 and on levels 4 and 5 of the Hendrycks MATH training split. It also contains greedy evaluation runs of Qwen3-1.7B, 4B, 8B and 14B on nine math benchmarks at token budgets from 2,048 to 32,768.
 
 The files keep the layout that the code reads. Download the table inputs with
 
@@ -163,10 +163,14 @@ Strict accuracy against token budget for every benchmark and model. Bars are 95%
 
 Strict (solid) and answer-forced (dashed) accuracy for Qwen3-4B and Qwen3-8B.
 
+![Strict accuracy by DeepMath difficulty](assets/fig_eval_deepmath_difficulty.png)
+
+DeepMath-103K strict accuracy by difficulty at the 16k token budget, on rows whose gold answer is not yes/no, true/false, a single letter or empty (these forms are affected by the answer-checker correction; see Limitations). The lower strip gives the number of problems per difficulty; difficulties at most 2.5 and at least 9.5 are pooled. The dashed line marks difficulty 7, the lower bound of the examples used for the traces.
+
 ## Limitations
 
 - Correctness labels are stored as produced. The correction flags in `runs/paper/` mark traces whose label changes under the corrected answer checker (binary answers, letter answers and unparseable answers); the labels themselves are not rewritten.
-- The evaluation labels were produced by an answer checker that predates a correction for yes/no, true/false and letter answers. The released `correct_strict` and `correct_answer_forced` columns are these stored labels; the corrected checker is the one in the code repository, and nothing here was regraded. The stored gold answers show the affected forms: in DeepMath, 218,916 of 1,235,860 rows have a yes/no or true/false gold parsed as a product of symbols (for example `[e*s*y]` for yes), 42,406 have a single-letter gold and 18,225 have an empty gold (`[]`). The other benchmarks have few such rows.
+- The evaluation labels were produced by an answer checker that predates a correction for yes/no, true/false and letter answers. The released `correct_strict` and `correct_answer_forced` columns are these stored labels; the corrected checker is the one in the code repository, and nothing here was regraded. The stored gold answers show the affected forms: in DeepMath, 218,916 of 1,235,860 rows have a yes/no or true/false gold parsed as a product of symbols (for example `[e*s*y]` for yes), 42,406 have a single-letter gold and 18,225 have an empty gold (`[]`). The other benchmarks have few such rows. The share of affected gold forms rises with DeepMath difficulty, from 14% at difficulty 6.5 or below to above 65% at 8.5 and above.
 - The continuations were sampled with settings (temperature, top-p, seed) that were not recorded with the files and cannot be recovered. Each continuation is capped at 4,096 tokens. In the broad collection, refined steps received 16 continuations and scanned steps 8 (both models); in the gold collection, refined steps received 8 and scanned steps 4.
 - The `step_rewards` field of the traces is a constant placeholder (0.5) and carries no information. Problem ids in `probe_corpus_16k` are indices within the dataset named by their directory.
 - `data/problem_lookup.parquet` gives the DeepMath-103K level-two topic and difficulty for DeepMath problems. For Hendrycks MATH problems the difficulty is the MATH level (4 or 5) where known and is otherwise empty.