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From the 1 of 38 linked papers with an AI index.

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20242026
most citedPredict the Retrieval! Test time adaptation for Retrieval Augmented Generation

1 citations · 1 across the 6 of their papers we have counts for

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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.CL20261 cited

Predict the Retrieval! Test time adaptation for Retrieval Augmented Generation

Xin Sun, Zhongqi Chen, Qiang Liu +5

The paper introduces TTARAG, a test-time adaptation technique that updates a language model's parameters during inference to better integrate retrieved knowledge for specialized do…

cs.CL2026

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.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.CL2025

Attention-guided Self-reflection for Zero-shot Hallucination Detection in Large Language Models

Qiang Liu, Xinlong Chen, Yue Ding +4

Hallucination has emerged as a significant barrier to the effective application of Large Language Models (LLMs). In this work, we introduce a novel Attention-Guided SElf-Reflection…