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

arXiv:2611.06972v1 Announce Type: cross 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.

KDASO optimizes liability adjudication tasks, potentially reducing human oversight.

KDASO improves automated liability adjudication, potentially increasing efficiency and accuracy in legal assessments, which benefits human systems. However, automation in legal decisions risks errors, bias, and lack of accountability, raising societal concerns. Evidence is based on experimental performance improvements, but real-world impact and possible harms are not detailed, warranting a modest positive score with moderate confidence.

This is a methods paper improving LLM skill libraries for adjudication tasks, evaluated on benchmark datasets with no stated real-world deployment or societal outcome. The implication for human flourishing is not addressed in the text, so it scores near neutral.

The development of an AI system capable of 'automated skill optimization for liability adjudication tasks' suggests a future where AI plays a significant, potentially autonomous, role in legal determinations. While framed as an 'optimization,' this implies a reduction in human involvement in complex legal judgments, raising concerns about accountability, bias, and the potential erosion of human ethical oversight in critical societal functions. The demonstrated 'transferability and stability' further strengthens this negative implication.

The text describes an AI system that automates liability adjudication. This implies significant shifts in legal decision-making, potentially reducing human oversight and judgment. The system's continuous evolution and optimization could lead to unintended consequences in legal outcomes.

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
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DOCUMENT   4b14db9d-92f7-4841-99c9-d4fba1e7f547
           https://arxiv.org/abs/2611.06972
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EVIDENCE   2 extracted excerpts
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MODEL RUN  5 runs, methodology 1.0.1
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SCORE      -15  (Mixed or uncertain)
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CONFIDENCE 74%