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researcher

Xiaodong Gu

35 papers hereh-index 7217 citations37 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author22
  • last author11

Across the 33 of 35 papers where every author was matched, so the position is known.

fields
  • cs.SE15
  • cs.CL6
  • cs.CV4
  • cs.GR4
  • cs.LG3
  • cs.AI2
same name
  • Xiaodong Gu — 18 papers, h 9
  • Xiaodong Gu — 15 papers, h 12
  • Xiaodong Gu — 14 papers, h 26
  • Xiaodong Gu — 14 papers, h 5
  • Xiaodong Gu — 11 papers, h 15
  • Xiaodong Gu — 2 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedSWE-Debate: Competitive Multi-Agent Debate for Software Issue Resolution

2 citations · 5 across the 32 of their papers we have counts for

collaborators
Showing cs.GRShow all

4 papers · 1 filter

cs.GR2025

Empowering LLMs with Structural Role Inference for Zero-Shot Graph Learning

Heng Zhang, Jing Liu, Jiajun Wu +8

Large Language Models have emerged as a promising approach for graph learning due to their powerful reasoning capabilities. However, existing methods exhibit systematic performance…

cs.GR2025

Can Representation Gaps Be the Key to Enhancing Robustness in Graph-Text Alignment?

Heng Zhang, Tianyi Zhang, Yuling Shi +6

Representation learning on text-attributed graphs (TAGs) integrates structural connectivity with rich textual semantics, enabling applications in diverse domains. Current methods l…

cs.GR2025

D3MAS: Decompose, Deduce, and Distribute for Enhanced Knowledge Sharing in Multi-Agent Systems

Heng Zhang, Yuling Shi, Xiaodong Gu +5

Multi-agent systems powered by large language models exhibit strong capabilities in collaborative problem-solving. However, these systems suffer from substantial knowledge redundan…

cs.GR2025

GraphTracer: Graph-Guided Failure Tracing in LLM Agents for Robust Multi-Turn Deep Search

Heng Zhang, Yuling Shi, Xiaodong Gu +5

Multi-agent systems powered by Large Language Models excel at complex tasks through coordinated collaboration, yet they face high failure rates in multi-turn deep search scenarios.…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.