KCSAT-ML: Probing Reasoning Models with Nationwide-Cohort Human Difficulty
Math reasoning benchmarks have proliferated, yet most lack a per-item difficulty signal grounded in actual human performance. We introduce KCSAT-ML, a decade (2014-2025) of Korean College Scholastic Ability Test (KCSAT; Suneung) mathematics: 664 problems with a 339-item core set carrying official per-item error rates from nationwide cohorts of hundreds of thousands of examinees. We pair the benchmark with Difficulty-aligned Reasoning Gain (DRG): a score-orthogonal metric that asks whether a model's mistakes concentrate on the items humans found hard, or on items humans found easy. Together they expose, across a wide range of VLMs (and LLMs with OCR), three patterns: (i) low-budget accurac...
| Authors | Sanghee Park, Geewook Kim, Kee-Eung Kim |
|---|---|
| Primary category | cs.CL |
| Published | 9 June 2026 |
| Last updated at source | 4 September 2026 |
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