collaborators

13 papers

cs.CL2026

Harness-Search: Guiding Long-Horizon Search through Multi-Agent Coordination

Shanyong Wang, Zhenwen Ji, Lei Jin +7

Long-horizon search requires agents to gather evidence across multiple steps and synthesize it into well-supported answers. The recent agent harnesses provide a natural and promisi…

cs.CL2026

Momentum-Coupled Rubric Adaptation for Detailed Image Captioning

Zhenwen Ji, Lei Jin, Shanyong Wang +6

Detailed image captioning requires accurate and comprehensive descriptions of fine-grained visual content, yet caption quality spans factual accuracy, information coverage, and cla…

cs.CV2026

From Glance to Scrutiny: Progressive Distortion Reasoning for Fine-Grained Image Quality Assessment

Aoting Zhang, Mingze Gao, Dongbao Yang +5

Multi-modal large language models (MLLMs) have demonstrated significant potential in image quality assessment (IQA) by bridging visual perception with descriptive evaluations. Howe…

cs.CV2026

Multi-Image Visual Token Pruning in Large Visual Language Models

Rongyang Zhang, Chengqiang Lu, Cong Li +9

With the growing demand for processing multiple image sequences in real-world applications, various visual token pruning methods have emerged to mitigate the computational and cont…

cs.CL2026

RING: Retrieval-Internalized Generation for Continual Large-Scale Knowledge Injection

Shicheng Xu, Liang Pang, Liyi Chen +7

Retrieval-augmented generation (RAG) improves factuality but adds latency and engineering overhead at serving time. We propose RING (Retrieval-Internalized Generation), a holistic…

cs.CL2026

Aligning Large Language Models with Searcher Preferences

Wei Wu, Peilun Zhou, Liyi Chen +6

The paradigm shift from item-centric ranking to answer-centric synthesis is redefining the role of search engines. While recent industrial progress has applied generative technique…

cs.AI2026

Self-Compression of Chain-of-Thought via Multi-Agent Reinforcement Learning

Yiqun Chen, Jinyuan Feng, Wei Yang +9

The inference overhead induced by redundant reasoning undermines the interactive experience and severely bottlenecks the deployment of Large Reasoning Models. Existing reinforcemen…

cs.AI2026

JADE: Bridging the Strategic-Operational Gap in Dynamic Agentic RAG

Yiqun Chen, Erhan Zhang, Tianyi Hu +8

The evolution of Retrieval-Augmented Generation (RAG) has shifted from static retrieval pipelines to dynamic, agentic workflows where a central planner orchestrates multi-turn reas…

cs.AI2026

Anti-Length Shift: Dynamic Outlier Truncation for Training Efficient Reasoning Models

Wei Wu, Liyi Chen, Congxi Xiao +7

Large reasoning models enhanced by reinforcement learning with verifiable rewards have achieved significant performance gains by extending their chain-of-thought. However, this par…

cs.IR2025

RAG-IGBench: Innovative Evaluation for RAG-based Interleaved Generation in Open-domain Question Answering

Rongyang Zhang, Yuqing Huang, Chengqiang Lu +8

In real-world scenarios, providing user queries with visually enhanced responses can considerably benefit understanding and memory, underscoring the great value of interleaved imag…

cs.CL2025

DecEx-RAG: Boosting Agentic Retrieval-Augmented Generation with Decision and Execution Optimization via Process Supervision

Yongqi Leng, Yikun Lei, Xikai Liu +7

Agentic Retrieval-Augmented Generation (Agentic RAG) enhances the processing capability for complex tasks through dynamic retrieval and adaptive workflows. Recent advances (e.g., S…

cs.CL2025

RealBench: A Chinese Multi-image Understanding Benchmark Close to Real-world Scenarios

Fei Zhao, Chengqiang Lu, Yufan Shen +9

While various multimodal multi-image evaluation datasets have been emerged, but these datasets are primarily based on English, and there has yet to be a Chinese multi-image dataset…

cs.CL2025

SelfAug: Mitigating Catastrophic Forgetting in Retrieval-Augmented Generation via Distribution Self-Alignment

Yuqing Huang, Rongyang Zhang, Qimeng Wang +9

Recent advancements in large language models (LLMs) have revolutionized natural language processing through their remarkable capabilities in understanding and executing diverse tas…