Papers by Shuyue Xing

2 papers
LoopCoder: Scaling Code Intelligence via Looped Language Models (2026.findings-acl)

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Challenge: Large language models have mastered syntax-level code generation, but complex algorithmic reasoning remains a challenge.
Approach: They propose a recurrent inductive bias that aligns with the recursive nature of programming logic.
Outcome: The proposed model achieves comparable performance to standard dense models with more parameters.
EvoMemKG: An Evolvable Memory Agent for Multi-hop Knowledge Graph Reasoning (2026.findings-acl)

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Challenge: Existing methods for integrating knowledge graphs with large language models lack continuous learning capabilities.
Approach: They propose an agent framework with a dynamic, evolvable memory mechanism specifically designed for KG reasoning.
Outcome: EvoMemKG achieves state-of-the-art performance without training or tools . it achieves improvements of up to 20% over baseline on multi-hop queries .

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