arXiv AI July 21, 2026 By Sunbowen Lee, Qingyu Yin, Chak Tou Leong, Jialiang Zhang, Yicheng Gong, Shiwen Ni, Min Yang, Xiaoyu Shen

Probing the Difficulty Perception Mechanism of Large Language Models

Read the original on arXiv AI →

arXiv:2510. 05969v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly deployed on complex reasoning tasks, yet little is known about their ability to internally evaluate problem difficulty, which is an essential capability for adaptive reasoning and efficient resource allocation.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Aug 12

LLMs Encode Their Failures: Predicting Success from Pre-Generation Activations

arXiv:2602. 09924v4 Announce Type: replace-cross Abstract: Running LLMs with extended reasoning on every problem is expensive, but determining which inputs actually require additional compute remains challenging.

By William Lugoloobi, Thomas Foster, William Bankes, Chris Russell

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arXiv AI

Aug 6

Revisiting Generalization Across Difficulty Levels: It's Not So Easy

arXiv:2511. 21692v3 Announce Type: replace-cross Abstract: We investigate how well large language models (LLMs) generalize across different task difficulties, a key question for effective data curation and evaluation.

By Yeganeh Kordi, Nihal V. Nayak, Max Zuo, Ilana Nguyen, Stephen H. Bach

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arXiv Machine Learning

Jul 21

Explaining and Tuning Transformer-based LLMs in Arithmetic Tasks with Human Strategies

arXiv:2607. 17166v1 Announce Type: new Abstract: Transformer-based large language models (LLMs) continue to achieve state-of-the-art performance across various natural language processing tasks.

By Luyu Qiu, Jianing Li, Hwanhee Kim, Xiaoyong Wei, Yueyuan Zheng, Janet Hsiao, Lei Chen

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arXiv AI

Jun 29

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

arXiv:2606. 28186v1 Announce Type: cross Abstract: Predicting human item difficulty is central to educational assessment, where reliable estimates support fairness and effective test construction.

By Chenguang Wang, Ming Li, Xinyue Zeng, Zhuochun Li, Hong Jiao, Tianyi Zhou, Dawei Zhou

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arXiv AI

Jul 22

Robust Reasoning Benchmark

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By Pavel Golikov, Evgenii Opryshko, Gennady Pekhimenko, Mark C. Jeffrey

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arXiv AI

Jun 30

Emergence of Minimal Circuits for Indirect Object Identification in Attention-Only Transformers

arXiv:2510. 25013v2 Announce Type: replace-cross Abstract: Mechanistic interpretability aims to reverse-engineer large language models (LLMs) into human-understandable computational circuits.

By Rabin Adhikari

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LLMs Encode Their Failures: Predicting Success from Pre-Generation Activations

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