activity
20242026
most citedRAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning

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

collaborators

9 papers

cs.LG2026

RAGEN-2: Reasoning Collapse in Agentic RL

Zihan Wang, Chi Gui, Xing Jin +13

RL training of multi-turn LLM agents is inherently unstable, and reasoning quality directly determines task performance. Entropy is widely used to track reasoning stability. Howeve…

cs.AI2026

ODESteer: A Unified ODE-Based Steering Framework for LLM Alignment

Hongjue Zhao, Haosen Sun, Jiangtao Kong +8

Activation steering, or representation engineering, offers a lightweight approach to align large language models (LLMs) by manipulating their internal activations at inference time…

cs.AI2026

Theory of Space: Can Foundation Models Construct Spatial Beliefs through Active Exploration?

Pingyue Zhang, Zihan Huang, Yue Wang +11

Spatial embodied intelligence requires agents to act to acquire information under partial observability. While multimodal foundation models excel at passive perception, their capac…

cs.AI2025

ENACT: Evaluating Embodied Cognition with World Modeling of Egocentric Interaction

Qineng Wang, Wenlong Huang, Yu Zhou +8

Embodied cognition argues that intelligence arises from sensorimotor interaction rather than passive observation. It raises an intriguing question: do modern vision-language models…

cs.AI2025

VAGEN: Reinforcing World Model Reasoning for Multi-Turn VLM Agents

Kangrui Wang, Pingyue Zhang, Zihan Wang +13

A key challenge in training Vision-Language Model (VLM) agents, compared to Language Model (LLM) agents, lies in the shift from textual states to complex visual observations. This…

cs.AI2025

MindCube: Spatial Mental Modeling from Limited Views

Qineng Wang, Baiqiao Yin, Pingyue Zhang +11

Can Vision-Language Models (VLMs) imagine the full scene from just a few views, like humans do? Humans form spatial mental models naturally, internal representations of unseen spac…