activity
20172026
most citedDeep Graph Contrastive Representation Learning

413 citations · 1k across the 61 of their papers we have counts for

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20 papers · 1 filter

cs.CL2026

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…

cs.CL2026

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…

cs.CL2026

NAG: A Unified Native Architecture for Encoder-free Text-Graph Modeling in Language Models

Haisong Gong, Zhibo Liu, Qiang Liu +2

Prevailing methods for integrating graphs into Language Models (LMs) typically rely on a segregated architecture: external Graph Neural Networks (GNNs) encode structural topology,…

cs.CL20261 cited

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…

cs.CL2025

KBQA-R1: Reinforcing Large Language Models for Knowledge Base Question Answering

Xin Sun, Zhongqi Chen, Xing Zheng +6

Knowledge Base Question Answering (KBQA) challenges models to bridge the gap between natural language and strict knowledge graph schemas by generating executable logical forms. Whi…

cs.CL2025

Divide-Then-Align: Honest Alignment based on the Knowledge Boundary of RAG

Xin Sun, Jianan Xie, Zhongqi Chen +7

Large language models (LLMs) augmented with retrieval systems have significantly advanced natural language processing tasks by integrating external knowledge sources, enabling more…