A novel method called DKA is presented, which introduces a limited set of concept-supervised data to enhance the knowledge base, effectively solving the reasoning shortcut problem and improving the applicability of the NeSy system.
Yu-Feng Li, Xiaowen Yang, Wenda Wei et al.· Proceedings of the 32nd ACM...· 0 citations
Long-horizon web agents frequently fail without knowing where or why execution broke down. This issue is particularly pronounced in skill-based agentic web systems, where failures arise within composite skills whose internal decision processes are not directly traceable, making precise diagnosis and repair especially difficult over long horizons. We introduce SkillTracer, a framework that represents skills as attributed plan graphs structured by hierarchical nodes and verifiable edge transitions, enabling programmatic verification of execution progress. By decomposing skills into inspectable hierarchies, SkillTracer converts raw interaction traces into structural evidence, making execution breakdowns localizable to specific node-level decision points and attributable to failing components. This attribution signal facilitates targeted structural repair, allowing the agent to selectively revise failing components while preserving the integrity of valid substructures for partial reuse and adaptive recovery. Furthermore, SkillTracer synthesizes short-term traces with long-term historical evidence to construct a persistent skill graph, enabling failure patterns to drive continual refinement across episodes. Evaluated on challenging long-horizon benchmarks, SkillTracer achieves a 17.7% average improvement in success rate over strong baselines, with gains of up to 56.3% in cross-domain settings, demonstrating that structural attribution and skill repair are critical for reliable long-horizon web interaction. A project page is available at: https://liyuuuuy.github.io/SkillTracer/.
Yuyang Li, Yiran Dou, Jiejing Shao et al.· Proceedings of the 32nd ACM...· 5 citations
Recent advancements in neuro-symbolic learning (NeSy) have shown significant promise in integrating deep learning with symbolic reasoning, offering both interpretability and generalization. However, the prevalence of reasoning shortcuts, where the NeSy system predicts incorrect intermediate concepts while maintaining high final accuracy, poses a substantial challenge. This is especially problematic in domains requiring reliable and transparent decision-making. Inspired by recent theories, we find that existing methods fail to address the reasoning shortcut issue when the knowledge base lacks sufficient complexity, highlighting their vulnerability in real-world applications. In this work, we present a novel method called DKA to address this issue. It introduces a limited set of concept-supervised data to enhance the knowledge base, effectively solving the reasoning shortcut problem and improving the applicability of the NeSy system. Theoretical analysis reveals that DKA can reduce shortcut risks with improved data efficiency. Empirical studies across multiple tasks within various neuro-symbolic frameworks also verify the effectiveness of the DKA method.
Yu-Feng Li, Xiaowen Yang, Wenda Wei et al.· Proceedings of the 32nd ACM...· 0 citations
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