Papers by Xueqiao Sun

2 papers
KARL: Reinforcement Learning for LLM Agents on Multi-Turn Knowledge-Intensive Agentic Tasks (2026.acl-long)

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Challenge: Large Language Models have shown remarkable potential as autonomous agents, but their effectiveness in knowledge-intensive tasks remains limited by passive knowledge utilization.
Approach: They propose a framework that enables LLM agents to dynamically explore structured knowledge sources through multi-turn interactions.
Outcome: The proposed framework outperforms existing retrieval-augmented approaches on knowledge graph and database tasks while maximizing tool-use behaviors end-to-end.
AndroidLab: Training and Systematic Benchmarking of Android Autonomous Agents (2025.acl-long)

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Challenge: Existing studies on Android agents lack systematic research on open-source and closed-source models.
Approach: They propose a framework for Android agents that includes an operation environment and a reproducible benchmark.
Outcome: The proposed framework lifts the success rate of open-source LLMs and LMMs from 4.59% to 21.50% for LLM and 1.93% to 13.28% for LMM.

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