most citedAtom-Searcher: Enhancing Agentic Deep Research via Fine-Grained Atomic Thought Reward

1 citations · 1 across the 11 of their papers we have counts for

collaborators

13 papers

cs.CV2026

CrossProjection: Geometric Grounding Beyond Viewpoint Change in Architectural Drawings

Kaho Li, Pengyu Zeng, Yuqin Dai +3

Architectural drawings violate the usual assumption behind multi-view reasoning: plans and sections are cuts, while elevations are facade projections, so corresponding components c…

cs.CV2026

PlanCraft: Sketch, Refine, and Furnish for Architect-Inspired Progressive 3D Residential Scene Generation

Pengyu Zeng, Yuqin Dai, Jun Yin +7

Two structural insights have been overlooked in automated residential floor plan generation. First, design is inherently progressive. Architects begin with rough strokes and refine…

cs.IR2026

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…

cs.AI2026

Unlocking Proactivity in Task-Oriented Dialogue

Azure Zhang, Ning Gao, Yuqin Dai +7

Proactive task-oriented dialogue (TOD), such as outbound sales, demands a persuasive agent that actively probes the user's concerns and steers the conversation toward acceptance wi…

cs.AI2026

SAGE: A Service Agent Graph-guided Evaluation Benchmark

Ling Shi, Yuqin Dai, Ziyin Wang +7

The development of Large Language Models (LLMs) has catalyzed automation in customer service, yet benchmarking their performance remains challenging. Existing benchmarks predominan…

cs.CL2026

SEAD: Self-Evolving Agent for Multi-Turn Service Dialogue

Yuqin Dai, Ning Gao, Wei Zhang +6

Large Language Models have demonstrated remarkable capabilities in open-domain dialogues. However, current methods exhibit suboptimal performance in service dialogues, as they rely…

cs.AI2025

GreenPlanner: Practical Floorplan Layout Generation via an Energy-Aware and Function-Feasible Generative Framework

Pengyu Zeng, Yuqin Dai, Jun Yin +7

Building design directly affects human well-being and carbon emissions, yet generating spatial-functional and energy-compliant floorplans remains manual, costly, and non-scalable.…

cs.CL2025

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…

cs.CL2025★ 1 cited

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…

cs.CV2025

Logic Unseen: Revealing the Logical Blindspots of Vision-Language Models

Yuchen Zhou, Jiayu Tang, Shuo Yang +6

Vision-Language Models (VLMs), exemplified by CLIP, have emerged as foundational for multimodal intelligence. However, their capacity for logical understanding remains significantl…

cs.IR2025

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.…

cs.CL2025

RAMA: Retrieval-Augmented Multi-Agent Framework for Misinformation Detection in Multimodal Fact-Checking

Shuo Yang, Zijian Yu, Zhenzhe Ying +6

The rapid proliferation of multimodal misinformation presents significant challenges for automated fact-checking systems, especially when claims are ambiguous or lack sufficient co…

cs.CL2025

RealFactBench: A Benchmark for Evaluating Large Language Models in Real-World Fact-Checking

Shuo Yang, Yuqin Dai, Guoqing Wang +6

Large Language Models (LLMs) hold significant potential for advancing fact-checking by leveraging their capabilities in reasoning, evidence retrieval, and explanation generation. H…