KDASO: Knowledge-Data Dual-Driven Automated Skill Optimization for Liability Adjudication Tasks

Authors: Zi-Jian Cheng , Weiming Wu , Jie Meng , Zhen Peng , Shan Huang , Qun Li , Guobing Wu , Lan-Zhe Guo

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Abstract: Liability adjudication tasks (e.g., legal charge determination) require analyzing event descriptions against domain-specific skills to determine whether a party should bear liability. While Large Language Models (LLMs) have shown promise in this area by leveraging executable skills for interpretable reasoning, existing approaches face three critical limitations: skill libraries become stale as real-world scenarios dynamically evolve, skill descriptions often suffer from a semantic execution gap where LLMs deviate from intended expert logic, and growing skill collections exceed context budgets causing critical skill omission. To address these challenges, we propose Knowledge-Data Dual-Driven Automated Skill Optimization (KDASO), which continuously evolves a skill library through neighborhood-conditioned induction from recent adjudication data, execution-aligned refinement based on LLM trajectory analysis, and context-budgeted routing for scalable skill selection. Experiments across ride-hailing adjudication, legal liability determination, and logical reasoning judgment demonstrate that KDASO significantly outperforms current general-purpose and domain-specific baselines. KDASO further achieves 8.96% improvement when transferring skills to concept-similar scenarios and maintains consistent performance gains through continuous optimization, underscoring the high transferability and stability of generated skills.

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Artificial Intelligence (cs.AI) ; Computers and Society (cs.CY)

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arXiv:2611.06972 [cs.AI]

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arXiv:2611.06972v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2611.06972

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arXiv-issued DOI via DataCite (pending registration)

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From: Zijian Cheng [ view email ]

[v1]

Wed, 4 Nov 2026 13:05:02 UTC (1,889 KB)

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