6 papers
Inclusion-of-Thoughts: Mitigating Preference Instability via Purifying the Decision Space
Mohammad Reza Ghasemi Madani, Soyeon Caren Han, Shuo Yang +1
Multiple-choice questions (MCQs) are widely used to evaluate large language models (LLMs). However, LLMs remain vulnerable to the presence of plausible distractors. This often dive…
DR-Venus: Towards Frontier Edge-Scale Deep Research Agents with Only 10K Open Data
Venus Team, Sunhao Dai, Yong Deng +10
Edge-scale deep research agents based on small language models are attractive for real-world deployment due to their advantages in cost, latency, and privacy. In this work, we stud…
Careful Queries, Credible Results: Teaching RAG Models Advanced Web Search Tools with Reinforcement Learning
Yuqin Dai, Shuo Yang, Guoqing Wang +10
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating up-to-date external knowledge, yet real-world web environments present unique challenges.…
Your Code Agent Can Grow Alongside You with Structured Memory
Yi-Xuan Deng, Xiaoqin Liu, Yi Zhang +2
While "Intent-oriented programming" (or "Vibe Coding") redefines software engineering, existing code agents remain tethered to static code snapshots. Consequently, they struggle to…
EviNote-RAG: Enhancing RAG Models via Answer-Supportive Evidence Notes
Yuqin Dai, Guoqing Wang, Yuan Wang +13
Retrieval-Augmented Generation (RAG) has advanced open-domain question answering by incorporating external information into model reasoning. However, effectively leveraging externa…
Atom-Searcher: Enhancing Agentic Deep Research via Fine-Grained Atomic Thought Reward
Yong Deng, Guoqing Wang, Zhenzhe Ying +12
Large language models (LLMs) exhibit remarkable problem-solving abilities, but struggle with complex tasks due to static internal knowledge. Retrieval-Augmented Generation (RAG) en…