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Evidence record 1579 · automatically gathered

Cognitive Episodes in LLM Reasoning Traces Enable Interpretable Human Item Difficulty Prediction

arXiv:2606.28186v2 Announce Type: replace-cross Abstract: Predicting human item difficulty is central to educational assessment, where reliable estimates support fairness and effective test construction. Existing methods often depend on costly human calibration or item-level textual representations, providing limited evidence about the cognitive processes that make items difficult. We argue that difficulty should be viewed not only as a property of item text, but also as an observable consequenc

Record details

Published: 14 July 2026
Source: arXiv cs.CY
Category: Research
Topics: Bias & fairness
Retrieved: 14 July 2026

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Shared topic: Bias & fairness

The Benchmark Ceiling: Human Judgment, Evaluation Scarcity, and the Political Economy of AI Capability Measurement

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Shared topic: Bias & fairness

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Shared topic: Bias & fairness

DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs

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Shared topic: Bias & fairness

Can Induced Emotion Bias LLM Behaviors in Sequential Decision Making?

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How to cite this record

ethics.ai (14 July 2026), “Cognitive Episodes in LLM Reasoning Traces Enable Interpretable Human Item Difficulty Prediction,” evidence record 1579, https://ethics.ai/record/1579 (originally published by arXiv cs.CY).

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