3 citations · 8 across the 17 of their papers we have counts for
18 papers
Mitigating Context Interference for Reliable and Efficient Search Agents
Boyang Xue, Bin Wu, Shuofei Qiao +8
Recent research empowers Large Language Models (LLMs) as multi-turn search agents to iteratively retrieve and generate outputs until complex tasks are solved. However, the contexts…
Demystifying On-Policy Distillation: Roles, Pathologies, and Regulations
Rui Wang, Hongru Wang, Yi Chen +4
On-policy distillation (OPD) has become a key paradigm in LLM post-training, yet its training dynamics remain poorly understood. We present a systematic study examining the role, p…
InnoEval: On Research Idea Evaluation as a Knowledge-Grounded, Multi-Perspective Reasoning Problem
Shuofei Qiao, Yunxiang Wei, Xuehai Wang +10
The rapid evolution of Large Language Models has catalyzed a surge in scientific idea production, yet this leap has not been accompanied by a matching advance in idea evaluation. T…
Memory-T1: Reinforcement Learning for Temporal Reasoning in Multi-session Agents
Yiming Du, Baojun Wang, Yifan Xiang +11
Temporal reasoning over long, multi-session dialogues is a critical capability for conversational agents. However, existing works and our pilot study have shown that as dialogue hi…
WebAggregator: Enhancing Compositional Reasoning Capabilities of Deep Research Agent Foundation Models
Rui Wang, Ce Zhang, Jun-Yu Ma +10
The hallmark of Deep Research agents lies in compositional reasoning, the capacity to aggregate distributed, heterogeneous information into coherent logical insights. However, curr…
ReliableMath: Benchmark of Reliable Mathematical Reasoning on Large Language Models
Boyang Xue, Qi Zhu, Rui Wang +8
Although demonstrating remarkable performance on reasoning tasks, Large Language Models (LLMs) still tend to fabricate unreliable responses when confronted with problems that are u…
Position: Agent Should Invoke External Tools ONLY When Epistemically Necessary
Hongru Wang, Cheng Qian, Manling Li +6
As large language models evolve into tool-augmented agents, a central question remains unresolved: when is external tool use actually justified? Existing agent frameworks typically…
DAST: Difficulty-Aware Self-Training on Large Language Models
Boyang Xue, Qi Zhu, Hongru Wang +8
Present Large Language Models (LLM) self-training methods always under-sample on challenging queries, leading to inadequate learning on difficult problems which limits LLMs' abilit…
Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models
Rui Wang, Hongru Wang, Boyang Xue +9
Recent advancements in Large Language Models (LLMs) have significantly enhanced their ability to perform complex reasoning tasks, transitioning from fast and intuitive thinking (Sy…
UAlign: Leveraging Uncertainty Estimations for Factuality Alignment on Large Language Models
Boyang Xue, Fei Mi, Qi Zhu +6
Despite demonstrating impressive capabilities, Large Language Models (LLMs) still often struggle to accurately express the factual knowledge they possess, especially in cases where…
MlingConf: A Comprehensive Study of Multilingual Confidence Estimation on Large Language Models
Boyang Xue, Hongru Wang, Rui Wang +5
The tendency of Large Language Models (LLMs) to generate hallucinations raises concerns regarding their reliability. Therefore, confidence estimations indicating the extent of trus…
AppBench: Planning of Multiple APIs from Various APPs for Complex User Instruction
Hongru Wang, Rui Wang, Boyang Xue +5
Large Language Models (LLMs) can interact with the real world by connecting with versatile external APIs, resulting in better problem-solving and task automation capabilities. Prev…
Role Prompting Guided Domain Adaptation with General Capability Preserve for Large Language Models
Rui Wang, Fei Mi, Yi Chen +5
The growing interest in Large Language Models (LLMs) for specialized applications has revealed a significant challenge: when tailored to specific domains, LLMs tend to experience c…
UniRetriever: Multi-task Candidates Selection for Various Context-Adaptive Conversational Retrieval
Hongru Wang, Boyang Xue, Baohang Zhou +5
Conversational retrieval refers to an information retrieval system that operates in an iterative and interactive manner, requiring the retrieval of various external resources, such…
MlingConf: A Comprehensive Study of Multilingual Confidence Estimation on Large Language Models
Boyang Xue, Hongru Wang, Rui Wang +6
The tendency of Large Language Models (LLMs) to generate hallucinations raises concerns regarding their reliability. Therefore, confidence estimations indicating the extent of trus…
Self-DC: When to Reason and When to Act? Self Divide-and-Conquer for Compositional Unknown Questions
Hongru Wang, Boyang Xue, Baohang Zhou +5
Previous research has typically concentrated on leveraging the internal knowledge of Large Language Models (LLMs) to answer known questions (i.e., \textit{internal reasoning such a…
Improving Factual Consistency for Knowledge-Grounded Dialogue Systems via Knowledge Enhancement and Alignment
Boyang Xue, Weichao Wang, Hongru Wang +7
Pretrained language models (PLMs) based knowledge-grounded dialogue systems are prone to generate responses that are factually inconsistent with the provided knowledge source. In s…
TPE: Towards Better Compositional Reasoning over Conceptual Tools with Multi-persona Collaboration
Hongru Wang, Huimin Wang, Lingzhi Wang +6
Large language models (LLMs) have demonstrated exceptional performance in planning the use of various functional tools, such as calculators and retrievers, particularly in question…