10 papers
R-Searcher: Calibrating Retrieval and Reasoning Boundaries for Agentic Search
Sheng Zhang, Junyi Li, Wenlin Zhang +6
Recent search agents for multi-hop reasoning often fail by either retrieving incomplete evidence or reasoning over irrelevant portions of the retrieved content, leading to a retrie…
OneReason Technical Report
OneRec Team, Biao Yang, Boyang Ding +81
Generative recommendation models in the OneRec family have been widely deployed in many real-world services, such as short-video, live-streaming, advertising, and e-commerce. Howev…
MemSearch-o1: Empowering Large Language Models with Reasoning-Aligned Memory Growth in Agentic Search
Sheng Zhang, Junyi Li, Yingyi Zhang +7
Recent advances in large language models (LLMs) have scaled the potential for reasoning and agentic search, wherein models autonomously plan, retrieve, and reason over external kno…
How to Utilize Complementary Vision-Text Information for 2D Structure Understanding
Jiancheng Dong, Pengyue Jia, Derong Xu +9
LLMs typically linearize 2D tables into 1D sequences to fit their autoregressive architecture, which weakens row-column adjacency and other layout cues. In contrast, purely visual…
MOSAIC: Composable Safety Alignment with Modular Control Tokens
Jingyu Peng, Hongyu Chen, Jiancheng Dong +5
Safety alignment in large language models (LLMs) is commonly implemented as a single static policy embedded in model parameters. However, real-world deployments often require conte…
DeepResearch-9K: A Challenging Benchmark Dataset of Deep-Research Agent
Tongzhou Wu, Yuhao Wang, Xinyu Ma +4
Deep-research agents are capable of executing multi-step web exploration, targeted retrieval, and sophisticated question answering. Despite their powerful capabilities, deep-resear…
BRIDGE: Bridging Reasoning In Distillation Gap Elimination via Structure-Aware Masking
Bowen Yu, Sheng Zhang, Binhao Wang +8
Chain-of-Thought (CoT) reasoning has significantly improved LLMs' mathematical problem-solving capabilities, but distilling such capabilities into smaller models remains challengin…
Efficient Reasoning via Reward Model
Yuhao Wang, Xiaopeng Li, Cheng Gong +4
Reinforcement learning with verifiable rewards (RLVR) has been shown to enhance the reasoning capabilities of large language models (LLMs), enabling the development of large reason…
TeaRAG: A Token-Efficient Agentic Retrieval-Augmented Generation Framework
Chao Zhang, Yuhao Wang, Derong Xu +9
Retrieval-Augmented Generation (RAG) utilizes external knowledge to augment Large Language Models' (LLMs) reliability. For flexibility, agentic RAG employs autonomous, multi-round…
AdaSwitch: Balancing Exploration and Guidance in Knowledge Distillation via Adaptive Switching
Jingyu Peng, Maolin Wang, Hengyi Cai +5
Small language models (SLMs) are crucial for applications with strict latency and computational constraints, yet achieving high performance remains challenging. Knowledge distillat…