10 papers
DecoEvo: Score-Decoupled Co-Evolution of Solver and Rubric-Generator Skills in Text Space
Jiangwang Chen, Zixin Song, Junlin Liu +10
The paper introduces DecoEvo, a method that co-evolves a solver and a rubric-generator for large language models in text space using decoupled objectives, allowing the solver to im…
RAVE: Re-Allocating Visual Attention in Large Multimodal Models
Xi Leng, Xinhong Ma, Ziqiang Dong +4
Large multimodal models (LMMs) inherit the self-attention mechanism of pretrained language backbones, yet standard attention can exhibit suboptimal allocation, including cross-moda…
From Item-Only to Query-Item: Query-Conditioned Generative Search with QGS in Quark
Yanglong Song, Zihao Yang, Shuo Meng +6
Generative sequence models have shown strong results in recommendation. Applying them to search ranking is more challenging. Search behavior is inherently query-driven. Each query…
RAG-Match: Retrieval-Augmented Knowledge Injection and Hierarchical Reasoning for Calibrated Semantic Relevance
Hengjun Jiang, Liansheng Sun, Yan Jiang +6
Semantic relevance judgment for search is particularly challenging in knowledge-intensive scenarios, where accurate ranking requires not only semantic matching but also background…
OnePred: Next-Query Prediction via Recursive Intent Memory in Multi-Turn Conversations
Jiangwang Chen, Bowen Zhang, Zixin Song +4
Although large language model (LLM) conversational systems process millions of multi-turn dialogues daily, they remain fundamentally reactive: they respond only after the user type…
Co-ReAct: Rubrics as Step-Level Collaborators for ReAct Agents
Jiazheng Kang, Bowen Zhang, Zixin Song +4
ReAct-style agents for search-intensive, multi-step reasoning tasks rely largely on their own internal judgment to decide what evidence to seek, which reasoning or action step to t…