activity
20242026
most citedWhere LLM Agents Fail and How They can Learn From Failures

3 citations · 8 across the 13 of their papers we have counts for

collaborators
Showing cs.AIShow all

7 papers · 1 filter

cs.AI2026

MatrAIx: Simulating the World with 8.3 Billion Persona Agents

Xiaomin Li, Yuexing Hao, Jianheng Hou +90

Human evaluation of AI systems and digital products is costly, slow, and difficult to scale. Offline evaluations are more scalable but often abstract away human diversity and inter…

cs.AI2026

Modular Cognitive Architecture Emerges in Large Language Models

Pengrui Han, Jacob Andreas, Evelina Fedorenko +1

The human brain exhibits a striking degree of functional specialization, with distinct networks supporting language, formal reasoning, reasoning about other minds, and reasoning ab…

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.AI20263 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.AI20251 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…