arXiv:2606.10403v1 Announce Type: new Abstract: 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 h
The proliferation of AI reasoning models necessitates more robust and human-aligned evaluation benchmarks, making the introduction of KCSAT-ML timely.
This benchmark provides a unique and valuable tool for assessing AI model performance against actual human difficulty, which is crucial for developing truly intelligent systems.
The availability of nationwide cohort human difficulty data allows for a more nuanced and accurate evaluation of AI reasoning capabilities, moving beyond simple accuracy scores.
- · AI Researchers
- · Model Developers
- · Education Technology
- · AI Models with simplistic evaluation metrics
AI models will be evaluated more rigorously against human-level reasoning difficulty.
This could lead to the development of AI systems that are better aligned with human cognitive processes and error patterns.
Improved human-aligned AI reasoning could accelerate the deployment of AI in complex problem-solving domains like science and engineering.
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Read at arXiv cs.CL