8 papers
SceneActBench: Can Agents Act on the 3D Scenes They See?
Yifei Zhao, Xiangxin Zhou, Wenhao Yang +11
Vision-language model (VLM) agents increasingly use tools to act on 3D scenes rather than only describe them. Existing 3D benchmarks score textual responses or single-object operat…
AMR-SD: Asymmetric Meta-Reflective Self-Distillation for Token-Level Credit Assignment
Zhenlin Wei, Pu Jian, Yingzhuo Deng +6
The alignment of Large Language Models (LLMs) for complex reasoning heavily relies on Reinforcement Learning with Verifiable Rewards (RLVR). However, standard algorithms like GRPO…
Towards Scientific Intelligence: A Survey of LLM-based Scientific Agents
Shuo Ren, Can Xie, Pu Jian +3
As scientific research becomes increasingly complex, innovative tools are needed to manage vast data, facilitate interdisciplinary collaboration, and accelerate discovery. Large la…
KTAE: A Model-Free Algorithm to Key-Tokens Advantage Estimation in Mathematical Reasoning
Wei Sun, Wen Yang, Pu Jian +4
Recent advances have demonstrated that integrating reinforcement learning with rule-based rewards can significantly enhance the reasoning capabilities of large language models, eve…
TableRAG: A Retrieval Augmented Generation Framework for Heterogeneous Document Reasoning
Xiaohan Yu, Pu Jian, Chong Chen
Retrieval-Augmented Generation (RAG) has demonstrated considerable effectiveness in open-domain question answering. However, when applied to heterogeneous documents, comprising bot…
CROP: Contextual Region-Oriented Visual Token Pruning
Jiawei Guo, Feifei Zhai, Pu Jian +2
Current VLM-based VQA methods often process entire images, leading to excessive visual tokens that include redundant information irrelevant to the posed question. This abundance of…