most citedLarge Language Model Reasoning Failures

3 citations · 4 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CL2026

Representational Control over Self-Report & Behavior Coherence in LLM Risk-Taking

Rafal Kocielnik, Peiyang Song, Pengrui Han +4

Self-report is an appealing low-cost probe of an LLM's dispositions, but recent work finds only selective agreement between what models report and how they behave. Prior accounts e…

cs.AI2026

Rethinking Psychometric Evaluation of LLMs: When and Why Self-Reports Predict Behavior

Rafal Kocielnik, Pengrui Han, Peiyang Song +5

Anticipating LLM behavioral tendencies from low-cost psychometric probes is critical for safe deployment, but only if self-reports (SR) reliably predict behavior. Recent work docum…

cs.AI2026

Human agency in initial human-AI proof formalization workflows

Katherine M. Collins, Simon Frieder, Jonas Bayer +14

For centuries, human mathematicians have written proofs to substantiate their mathematical arguments; yet, the ability to automatically verify the validity of proofs has long been…

cs.AI2026

Interactive Evaluation Requires a Design Science

Keyang Xuan, Peiyang Song, Pan Lu +10

AI evaluation is undergoing a structural change. Large language models (LLMs) are increasingly deployed as systems that act over time through tools, environments, users, and other…

cs.AI2026

Steer2Adapt: Dynamically Composing Steering Vectors Elicits Efficient Adaptation of LLMs

Pengrui Han, Xueqiang Xu, Keyang Xuan +12

Activation steering has emerged as a promising approach for efficiently adapting large language models (LLMs) to downstream behaviors. However, most existing steering methods rely…

cs.AI2026★ 3 cited

Large Language Model Reasoning Failures

Peiyang Song, Pengrui Han, Noah Goodman

Large Language Models (LLMs) have exhibited remarkable reasoning capabilities, achieving impressive results across a wide range of tasks. Despite these advances, significant reason…

cs.LG2025

How and Why LLMs Generalize: A Fine-Grained Analysis of LLM Reasoning from Cognitive Behaviors to Low-Level Patterns

Haoyue Bai, Yiyou Sun, Wenjie Hu +5

Large Language Models (LLMs) display strikingly different generalization behaviors: supervised fine-tuning (SFT) often narrows capability, whereas reinforcement-learning (RL) tunin…

cs.AI2025★ 1 cited

Adaptation of Agentic AI: A Survey of Post-Training, Memory, and Skills

Pengcheng Jiang, Jiacheng Lin, Zhiyi Shi +31

Large language model (LLM) agents are moving beyond prompting alone. ChatGPT marked the rise of general-purpose LLM assistants, DeepSeek showed that on-policy reinforcement learnin…