works on

From the 1 of 58 linked papers with an AI index.

activity
20242026
collaborators

58 papers

cs.CL2026

IACM-RL: Intent-Aware Context Management and Reinforcement Learning for Complex Tool Invocation under Dynamic Intent Fluctuations

Dingwei Zhu, Jiahan Li, Chengjun Pan +22

Executing long-horizon tool invocations in real-world environments is severely challenged by dynamic user intent noise. Existing methods attempt robustness via implicit history sca…

cs.CL2026

AI Can Learn Scientific Taste

Jingqi Tong, Mingzhe Li, Hangcheng Li +20

The paper introduces a reinforcement‑learning framework that uses citation‑based community feedback to train models that can judge the impact of scientific papers and generate high…

cs.AI2026

AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments

Zhiheng Xi, Dingwen Yang, Jiaqi Liu +21

Language agents, i.e., LLM agents, progress rapidly and are increasingly deployed in production environments. This trend underscores the urgent need for rigorous and realistic eval…

cs.CL2026

AGORA: An Archive-Grounded Benchmark for Agentic Workplace Document Reasoning

Honglin Guo, Qi Zhang, Yu Zhang +6

Large language models are increasingly deployed as agents that reason over documents rather than answer from parametric knowledge. We study archive-grounded reasoning: locating spa…

cs.LG2026

VRPO: Rethinking Value Modeling for Robust RL under Noisy Supervision in LLM Post-Training

Dingwei Zhu, Shihan Dou, Zhiheng Xi +16

Reinforcement Learning (RL) in real-world environments often suffers from ambiguous or incomplete reward supervision, which undermines policy stability and generalization. Such noi…

cs.AI2026

Entropy Is Not Enough: Unlocking Effective Reinforcement Learning for Visual Reasoning via Vision-Anchored Token Selection

Senjie Jin, Peixin Wang, Boyang Liu +8

While token-level entropy is commonly recognized as effective for credit assignment in text-only reinforcement learning with verifiable rewards (RLVR), it remains unclear whether t…