19 citations · 19 across the 15 of their papers we have counts for
19 papers
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…
PEARL: Process-Experience Aware Reinforcement Learning for Multi-Turn Agents
Jie Yang, Mianqiu Huang, Yiwei Zhao +3
Terminal rewards make multi-turn agents trainable without step-level annotations, but group-relative advantages provide little guidance about which intermediate decisions to adjust…
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…
VehicleArena: A Realistic Urban Environment for Multi-Agent Driving
Jie Yang, Jiajun Chen, Jiazheng Zhou +4
Real-world embodied agents often pursue independent objectives within a shared physical environment, where their actions can alter the conditions faced by others. Existing benchmar…
SWE-bench Science: Can Coding Agents Resolve Engineering Tasks in Science?
Zhipeng Xu, Jiahao Lu, Yining Zheng +2
Software increasingly functions as part of the scientific instrument itself, making failures in scientific code capable of compromising not only program behavior but also the evide…
AgentHPOBench: A Benchmark For Evaluating LLM Agents as Sequential Hyperparameter Optimizers
Tianyu Huai, Tingshuo Fan, Xinchi Chen +5
As LLMs evolve from code completion systems into autonomous scientific agents, evaluating their ability to conduct experiments has become increasingly important. Existing benchmark…
ABOPD: Antibody CDR Design via On-Policy Distillation
Zhuo Yang, Jiaying He, Jiaqing Xie +5
Antibodies are essential therapeutic molecules, and their complementarity-determining regions (CDRs) form the primary antigen-recognition interface. Recent protein generative model…
Eigenbasis-Independent Learnable Spectral Positional Encodings for Directed Graphs via Hermitian Block Krylov Subspaces
Jiaqing Xie, Yuxin Wang
Spectral positional encodings (PEs) for \emph{directed} graphs face two obstacles: magnetic Laplacians require an Hermitian eigendecomposition per potential, and their com…
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…
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-…
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,…
ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development
Jie Yang, Honglin Guo, Li Ji +11
The evolution of Large Language Models (LLMs) into autonomous agents has expanded the scope of AI coding from localized code generation to complex, repository-level, and execution-…
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…
MARAG-R1: Beyond Single Retriever via Reinforcement-Learned Multi-Tool Agentic Retrieval
Qi Luo, Xiaonan Li, Yuxin Wang +4
Large Language Models (LLMs) excel at reasoning and generation but are inherently limited by static pretraining data, resulting in factual inaccuracies and weak adaptability to new…
VehicleWorld: A Highly Integrated Multi-Device Environment for Intelligent Vehicle Interaction
Jie Yang, Jiajun Chen, Zhangyue Yin +7
Intelligent vehicle cockpits present unique challenges for API Agents, requiring coordination across tightly-coupled subsystems that exceed typical task environments' complexity. T…
FamilyTool: A Multi-hop Personalized Tool Use Benchmark
Yuxin Wang, Yiran Guo, Yining Zheng +7
The integration of tool learning with Large Language Models (LLMs) has expanded their capabilities in handling complex tasks by leveraging external tools. However, existing benchma…
R3-RAG: Learning Step-by-Step Reasoning and Retrieval for LLMs via Reinforcement Learning
Yuan Li, Qi Luo, Xiaonan Li +7
Retrieval-Augmented Generation (RAG) integrates external knowledge with Large Language Models (LLMs) to enhance factual correctness and mitigate hallucination. However, dense retri…
Prior-Fitted Networks Scale to Larger Datasets When Treated as Weak Learners
Yuxin Wang, Botian Jiang, Yiran Guo +4
Prior-Fitted Networks (PFNs) have recently been proposed to efficiently perform tabular classification tasks. Although they achieve good performance on small datasets, they encount…
Efficient Link Prediction via GNN Layers Induced by Negative Sampling
Yuxin Wang, Xiannian Hu, Quan Gan +3
Graph neural networks (GNNs) for link prediction can loosely be divided into two broad categories. First, \emph{node-wise} architectures pre-compute individual embeddings for each…