2 citations · 3 across the 17 of their papers we have counts for
6 papers · 1 filter
Exploring Reasoning Reward Model for Agents
Kaixuan Fan, Kaituo Feng, Manyuan Zhang +7
Agentic Reinforcement Learning (Agentic RL) has achieved notable success in enabling agents to perform complex reasoning and tool use. However, most methods still relies on sparse…
Thinking by Doing: Building Efficient World Model Reasoning in LLMs via Multi-turn Interaction
Bao Shu, Yan Cai, Jianjian Sun +11
Developing robust world model reasoning is crucial for large language model (LLM) agents to plan and interact in complex environments. While multi-turn interaction offers a superio…
Simulating Environments with Reasoning Models for Agent Training
Yuetai Li, Huseyin A Inan, Xiang Yue +6
LLM agents excel in compact environments requiring deep reasoning but remain brittle when operating in broader, more complex contexts that demand robustness across diverse tools an…
MME-Reasoning: A Comprehensive Benchmark for Logical Reasoning in MLLMs
Jiakang Yuan, Tianshuo Peng, Yilei Jiang +8
Logical reasoning is a fundamental aspect of human intelligence and an essential capability for multimodal large language models (MLLMs). Despite the significant advancement in mul…
InternAgent: When Agent Becomes the Scientist -- Building Closed-Loop System from Hypothesis to Verification
InternAgent Team, Bo Zhang, Shiyang Feng +23
Artificial Intelligence (AI) is accelerating the transformation of scientific research paradigms, not only enhancing research efficiency but also driving innovation. We introduce I…
Worse than Random? An Embarrassingly Simple Probing Evaluation of Large Multimodal Models in Medical VQA
Qianqi Yan, Xuehai He, Xiang Yue +1
Large Multimodal Models (LMMs) have shown remarkable progress in medical Visual Question Answering (Med-VQA), achieving high accuracy on existing benchmarks. However, their reliabi…