Sentence-level episode-labeling corpus and RoBERTa classifier trained on ThinkARM annotations

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Evidence · 2 claims (2 for) · 1 study (1 design), q2 · 0 of 1 report an effect size · 2 claims rest on one study

Description

A sentence-level corpus of ThinkARM-annotated Omni-MATH reasoning traces with a RoBERTa classifier for automatically labeling problem-solving episodes, developed by Chenguang Wang et al.

Components

Claims

• Episode-composition and transition features contribute predictive process evidence beyond semantics, with Epi.+Tran. reaching 0.136 permutation importance versus 0.084 for Sem. in the Both (Role) setting [+W]
• Harder SAT Math items are associated with more implementation-centered and iterative episode dynamics — stronger Implement counts and Analyze–Implement transitions — rather than a proportional increase in all reasoning activity [+W]

Related Products and Programmes

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Key Sources

• Chenguang Wang, Ming Li, Xinyue Zeng, Zhuochun Li, Hong Jiao, Tianyi Zhou, Dawei Zhou. (2026). Cognitive Episodes in LLM Reasoning Traces Enable Interpretable Human Item Difficulty Prediction. https://arxiv.org/abs/2606.28186

Page metadata

Field	Value
type	product
id	sentence-level-episode-labeling-corpus-and-roberta-classifier-trained-on-thinkarm-annotati
title	Sentence-level episode-labeling corpus and RoBERTa classifier trained on ThinkARM annotations
description	A sentence-level corpus of ThinkARM-annotated Omni-MATH reasoning traces with a RoBERTa classifier for automatically labeling problem-solving episodes, developed by Chenguang Wang et al.
status	review
product_kind	dataset
generated	by process:settle-candidates on 2026-10-11
trust	unverified — no one has confirmed this page's content

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