Papers by Xiangyan Liu

2 papers
SynthRL: Scaling Visual Reasoning with Verifiable Data Synthesis (2026.findings-acl)

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Challenge: SynthRL synthesizes over 3.3K additional verifiable, challenging questions from approximately 8K seed samples.
Approach: They propose a scalable and guaranteed pipeline for automatic data scaling in reasoning-oriented RL training.
Outcome: The proposed pipeline synthesizes over 3.3K additional verifiable, challenging questions from approximately 8K seed samples.
CodexGraph: Bridging Large Language Models and Code Repositories via Code Graph Databases (2025.naacl-long)

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Challenge: Large Language Models excel in stand-alone code tasks but struggle with handling entire code repositories.
Approach: They propose a system that integrates LLM agents with graph database interfaces extracted from code repositories.
Outcome: The proposed system integrates LLM agents with graph database interfaces extracted from code repositories.

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