most citedErrorLLM: Modeling SQL Errors for Text-to-SQL Refinement

1 citations · 1 across the 3 of their papers we have counts for

collaborators

21 papers

cs.CL2026

Zero-Mem: Zero-Token Memory Operations for LLM Agents

Yilin Xiao, Zhehan Zhu, Yujing Zhang +8

LLM agents need memory to act consistently over long interactions, yet many systems use additional LLM calls to operate that memory. Generating intermediate records and mediating t…

cs.IR2026

HVM-GraphRAG: Holistic-View Multimodal Graph Retrieval-Augmented Generation on Complex Document

Xin He, Yili Wang, Wenqi Fan +4

Question answering (QA) over complex documents requires models to retrieve and integrate evidence distributed across distant document regions and modalities. Multimodal GraphRAG pr…

cs.CL20261 cited

ErrorLLM: Modeling SQL Errors for Text-to-SQL Refinement

Zijin Hong, Hao Chen, Zheng Yuan +6

Despite the remarkable performance of large language models (LLMs) in text-to-SQL (SQL generation), correctly producing SQL queries remains challenging during initial generation. T…

cs.CL2026

Knapsack Optimization-based Schema Linking for LLM-based Text-to-SQL Generation

Zheng Yuan, Hao Chen, Zijin Hong +4

Generating SQLs from user queries is a long-standing challenge, where the accuracy of initial schema linking significantly impacts subsequent SQL generation performance. However, c…

cs.CL2026

LogicPoison: Logical Attacks on Graph Retrieval-Augmented Generation

Yilin Xiao, Jin Chen, Qinggang Zhang +6

Graph-based Retrieval-Augmented Generation (GraphRAG) enhances the reasoning capabilities of Large Language Models (LLMs) by grounding their responses in structured knowledge graph…

cs.AI2026

LLM-Based World Models Can Make Decisions Solely, But Rigorous Evaluations are Needed

Chang Yang, Xinrun Wang, Junzhe Jiang +2

World model emerges as a key module in decision making, where MuZero and Dreamer achieve remarkable successes in complex tasks. Recent work leverages Large Language Models (LLMs) a…