collaborators

7 papers

cs.LG2026

Learn from Move: the Next Step for GUI Agents

Jiahao Lu, Yize Shen, Chuanyuan Tan +6

Existing benchmarks for GUI agents primarily focus on increasing task difficulty by extending task horizons. Besides, enabling agents to explore unfamiliar environments and success…

cs.LG2026

MARCO: Multi-Round Agentic Reinforcement for Conditional Molecular Optimization

Shicheng Fang, Yuxin Wang, Zhuo Yang +6

Molecular optimization is inherently iterative: a candidate is proposed, evaluated against several objectives, and revised while preserving a relationship to the source molecule. M…

cs.LG2026

ORPG: Reconciling Multiple Reward Objectives through Objective-wise Policy Gradients

Shicheng Fang, Yiwen Zhao, Wenbo Tian +4

Multi-reward policy optimization requires a joint update that reflects both the learning signals and the intended relationships among objectives. We introduce Objective-wise Reconc…

cs.CL2026

Rethinking Scientific Discovery in the Agentic Era

Yining Zheng, Yuxin Wang, Jiahao Lu +27

Artificial intelligence has advanced scientific discovery, but most AI4Science systems remain fragmented tools that rely on humans to coordinate problem formulation, literature gro…

cs.CL2026

AdaptR1: Reinforcement Learning Based Adaptive Interleaved Thinking in Multi-hop Question Answering

Yuxin Wang, Jiahao Lu, Qifeng Wu +5

Large Language Models (LLMs) have achieved remarkable performance in complex reasoning tasks through Chain-of-Thought (CoT) prompting. However, this approach often leads to ``over-…

cs.CL2026

AgentLongBench: A Controllable Long Benchmark For Long-Contexts Agents via Environment Rollouts

Shicheng Fang, Yuxin Wang, Xiaoran Liu +6

The evolution of Large Language Models (LLMs) into autonomous agents necessitates the management of extensive, dynamic contexts. Current benchmarks, however, remain largely static,…

cs.CL2026

Multi-hop Reasoning via Early Knowledge Alignment

Yuxin Wang, Shicheng Fang, Bo Wang +4

Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for Large Language Models (LLMs) to address knowledge-intensive queries requiring domain-specific or up-to-d…