4 citations · 8 across the 13 of their papers we have counts for
7 papers · 1 filter
EviSD: Evidence-Conditioned Self-Distillation for Search-Augmented Agents
Jianan Xie, Xin Sun, Zhongqi Chen +4
Outcome-based reinforcement learning enables search-augmented language agents to learn from verifiable final answers, but its trajectory-level credit cannot distinguish the contrib…
HopRefusalBench: Diagnosing Refusal Failures in Search-Augmented Agents for Multi-Hop Reasoning
Jianan Xie, Xin Sun, Zhongqi Chen +3
Search-augmented large language model agents are increasingly capable of solving knowledge-intensive tasks, but their behavior when a multi-hop question is fundamentally unanswerab…
GAPD: Gold-Action Policy Distillation for Agentic Reinforcement Learning in Knowledge Base Question Answering
Xin Sun, Jianan Xie, Zhongqi Chen +6
Reinforcement learning (RL) is a natural fit for agentic knowledge base question answering (KBQA), where a model must issue executable actions, observe knowledge-base feedback, and…
Revealing Combinatorial Reasoning of GNNs via Graph Concept Bottleneck Layer
Yue Niu, Zhaokai Sun, Jiayi Yang +5
Despite their success in various domains, the growing dependence on GNNs raises a critical concern about the nature of the combinatorial reasoning underlying their predictions, whi…
Multimodal Adaptive Retrieval Augmented Generation through Internal Representation Learning
Ruoshuang Du, Xin Sun, Qiang Liu +4
Visual Question Answering systems face reliability issues due to hallucinations, where models generate answers misaligned with visual input or factual knowledge. While Retrieval Au…
ToolWeaver: Weaving Collaborative Semantics for Scalable Tool Use in Large Language Models
Bowen Fang, Wen Ye, Yunyue Su +8
Prevalent retrieval-based tool-use pipelines struggle with a dual semantic challenge: their retrievers often employ encoders that fail to capture complex semantics, while the Large…
Predict the Retrieval! Test time adaptation for Retrieval Augmented Generation
Xin Sun, Zhongqi Chen, Qiang Liu +5
Retrieval-Augmented Generation (RAG) has emerged as a powerful approach for enhancing large language models' question-answering capabilities through the integration of external kno…