10 citations · 26 across the 11 of their papers we have counts for
13 papers
Same Probabilities, Different Advice: Gain-Loss Framing Effects in High-Stakes LLM Decision Support
Yuyao Zhang, Yakun Cui, Han Zhu +3
Large language models (LLMs) are increasingly used to support consequential decisions, yet their reliability under equivalent representations of uncertainty remains unclear. Existi…
SkillProx: Self-Evolving Agent Skills via Proximal Textual Gradient Descent
Mingxuan Zheng, Yujin Zhou, Chuxue Cao +6
LLM agents increasingly adapt to recurring tasks by accumulating procedural knowledge in skills. These skills are lightweight, reusable textual artifacts that are loaded into the a…
Delegation Intelligence in Deep Search: A Controllable Framework for Disentangled Capability Diagnosis
Xinhao Yao, Yuanzhuo Liu, Changhao Wang +6
Deep search is becoming a core capability of modern agent systems, yet it is typically evaluated solely based on end-to-end answer accuracy. This coupled evaluation paradigm entang…
SearchOS-V1: Towards Robust Open-Domain Information-Seeking Agent Collaboration
Yuyao Zhang, Junjie Gao, Zhengxian Wu +11
Recent advances in Tool-Integrated Large Language Models have made web search a core capability of information-seeking agents. However, as interaction histories grow, agents increa…
When Text Hijacks Vision: Benchmarking and Mitigating Text Overlay-Induced Hallucination in Vision Language Models
Cui Yakun, Xingqun Qi, TianTian Geng +3
Recent advances in Vision-Language Models (VLMs) have substantially enhanced their ability across multimodal video understanding benchmarks spanning temporal, action, object, and s…
FinSight: Towards Real-World Financial Deep Research
Jiajie Jin, Yuyao Zhang, Yimeng Xu +3
Generating professional financial reports is a labor-intensive and intellectually demanding process that current AI systems struggle to fully automate. To address this challenge, w…
HiRA: A Hierarchical Reasoning Framework for Decoupled Planning and Execution in Deep Search
Jiajie Jin, Xiaoxi Li, Guanting Dong +5
Complex information needs in real-world search scenarios demand deep reasoning and knowledge synthesis across diverse sources, which traditional retrieval-augmented generation (RAG…
Leveraging LLM-Assisted Query Understanding for Live Retrieval-Augmented Generation
Guanting Dong, Xiaoxi Li, Yuyao Zhang +1
Real-world live retrieval-augmented generation (RAG) systems face significant challenges when processing user queries that are often noisy, ambiguous, and contain multiple intents.…
Neuro-Symbolic Query Compiler
Yuyao Zhang, Zhicheng Dou, Xiaoxi Li +5
Precise recognition of search intent in Retrieval-Augmented Generation (RAG) systems remains a challenging goal, especially under resource constraints and for complex queries with…
Hierarchical Document Refinement for Long-context Retrieval-augmented Generation
Jiajie Jin, Xiaoxi Li, Guanting Dong +6
Real-world RAG applications often encounter long-context input scenarios, where redundant information and noise results in higher inference costs and reduced performance. To addres…
Search-o1: Agentic Search-Enhanced Large Reasoning Models
Xiaoxi Li, Guanting Dong, Jiajie Jin +5
Large reasoning models (LRMs) like OpenAI-o1 have demonstrated impressive long stepwise reasoning capabilities through large-scale reinforcement learning. However, their extended r…
Enabling Discriminative Reasoning in LLMs for Legal Judgment Prediction
Chenlong Deng, Kelong Mao, Yuyao Zhang +1
Legal judgment prediction is essential for enhancing judicial efficiency. In this work, we identify that existing large language models (LLMs) underperform in this domain due to ch…
From Matching to Generation: A Survey on Generative Information Retrieval
Xiaoxi Li, Jiajie Jin, Yujia Zhou +4
Information Retrieval (IR) systems are crucial tools for users to access information, which have long been dominated by traditional methods relying on similarity matching. With the…