Papers by Yingxuan Yang

    2 papers
    Progra: Progress-Aware Reinforcement Learning for Multi-Turn Function Calling (2026.findings-acl)

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    Challenge: Existing methods for multi-turn function calling are limited by redundancy and lack explicit integration of progress awareness into training.
    Approach: They propose a framework that explicitly integrates progress awareness into LLM training for multi-turn function calling.
    Outcome: Empirical results show that Progra outperforms existing methods on two public benchmarks.
    Attribution-Based Analysis and Optimization of Modular Agentic Workflows (2026.findings-acl)

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    Challenge: Large Language Models (LLMs) have driven the rise of agentic workflows . yet, how can we attribute performance gains to individual upgrades and their interactions?
    Approach: They propose a game-theoretic framework that models component upgrades as players and evaluates component coalitions to compute Shapley values.
    Outcome: The proposed framework provides interaction-aware attribution and recommendation for model allocation under a fixed workflow structure.

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