most citedLLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation

1 citations · 1 across the 12 of their papers we have counts for

collaborators

13 papers

cs.AI2026

QREF: Improving DeepSearch Agents by Aligning Search with Reasoning via Query Refinement and Evidence Filtering

Yujia Chen, Yuhui Wang, Junzhe Wang +15

DeepSearch agents achieve strong performance on complex information-seeking tasks through iterative search and reasoning. However, they often issue intent-misaligned, under-constra…

cs.AI2026

Sci-MMR: Benchmarking Multi-Step Evidence-Grounded Scientific Reasoning in Multimodal Agents

Jiaqiang Li, Yajie Yang, Zhiheng Xi +15

Autonomous research agents are increasingly expected to search the literature, analyze experimental evidence, and generate scientific hypotheses. These capabilities require multi-s…

cs.AI2026

CAFE: Self-Improving Search Agents Need Co-Evolving Feedback

Boyang Liu, Senjie Jin, Peixin Wang +17

Reliable search requires more than acquiring external evidence. An agent must also recognize and recover from errors as its trajectory unfolds. In-trajectory feedback provides a me…

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.MM2026

Multimodal Emotion Recognition with Large Language Models

Hongrui Zhang, Daiqing Wu, Yangyang Li +4

Multimodal Emotion Recognition (MER) focuses on identifying and interpreting emotions from modality-compound inputs. Closely mirroring human cognitive processes in real-world envir…

cs.CL2026

HyperLogic: A Hard, Forward-Authored Chinese Logical Reasoning Benchmark with Execution-Derived Answers

Ming Zhang, Qiyuan Peng, Yinxi Wei +16

Existing logic benchmarks primarily measure models' ability to answer reasoning questions directly. Scalable benchmarks often generate text from formal structures, which makes answ…

cs.AI2026

JFTA-Bench: Evaluate LLM's Ability of Tracking and Analyzing Malfunctions Using Fault Trees

Yuhui Wang, Zhixiong Yang, Ming Zhang +10

In the maintenance of complex systems, fault trees are used to locate problems and provide targeted solutions. To enable fault trees stored as images to be directly processed by la…

cs.LG2026

DFPO: Scaling Value Modeling via Distributional Flow towards Robust and Generalizable LLM Post-Training

Dingwei Zhu, Zhiheng Xi, Shihan Dou +17

Training reinforcement learning (RL) systems in real-world environments remains challenging due to noisy supervision and poor out-of-domain (OOD) generalization, especially in LLM…

cs.LG2025

DVPO: Distributional Value Modeling-based Policy Optimization for LLM Post-Training

Dingwei Zhu, Zhiheng Xi, Shihan Dou +15

Reinforcement learning (RL) has shown strong performance in LLM post-training, but real-world deployment often involves noisy or incomplete supervision. In such settings, complex a…

cs.CL2025

Parrot: A Training Pipeline Enhances Both Program CoT and Natural Language CoT for Reasoning

Senjie Jin, Lu Chen, Zhiheng Xi +9

Natural language chain-of-thought (N-CoT) and Program chain-of-thought (P-CoT) have emerged as two primary paradigms for large language models (LLMs) to solve mathematical reasonin…

cs.CL2025

LLMEval-Fair: A Large-Scale Longitudinal Study on Robust and Fair Evaluation of Large Language Models

Ming Zhang, Yujiong Shen, Jingyi Deng +19

Existing evaluation of Large Language Models (LLMs) on static benchmarks is vulnerable to data contamination and leaderboard overfitting, critical issues that obscure true model ca…

cs.CL2025★ 1 cited

LLMEval-Med: A Real-world Clinical Benchmark for Medical LLMs with Physician Validation

Ming Zhang, Yujiong Shen, Zelin Li +13

Evaluating large language models (LLMs) in medicine is crucial because medical applications require high accuracy with little room for error. Current medical benchmarks have three…

cs.CL2025

PFDial: A Structured Dialogue Instruction Fine-tuning Method Based on UML Flowcharts

Ming Zhang, Yuhui Wang, Yujiong Shen +16

Process-driven dialogue systems, which operate under strict predefined process constraints, are essential in customer service and equipment maintenance scenarios. Although Large La…