Workflow-Localized Mechanism Learning: Attribution-Guided Repair and Knowledge Reuse for Structured Agent Skills

2026-07-23

AI Summary

Existing workflow debugging approaches struggle to jointly localize faulty nodes, identify error-inducing mechanisms, and effectively reuse third-party skill knowledge. This work proposes a Workflow Local Mechanism Learning framework that pinpoints minimal repair targets through node–mechanism attribution and introduces a six-module optimization loop integrating source- and scope-aware knowledge selection, bounded patching, and compilation-based execution. Innovatively incorporating L2/L3 hierarchical repair strategies and an optimizer-side memory mechanism, the framework achieves, for the first time, joint optimization of fault localization, mechanism attribution, and localized knowledge reuse. Evaluated on SpreadsheetBench, it attains Hard Accuracy scores of 90.33% and 74.67%; when transferred to WikiTableQuestions, it achieves 84.00% and 83.00%. Furthermore, it obtains the highest hard-PASS rate and lowest per-task cost on Compiler-Supported50.

Abstract

Agent Skills package reusable procedural knowledge as external artifacts for frozen language-model agents, yet existing optimizers do not jointly resolve where a failure occurs in a workflow, which mechanism caused it, and how relevant knowledge from third-party Skills should be reused locally. We introduce Workflow-Localized Mechanism Learning (WML). Its Node--Mechanism Attribution identifies the failed workflow node, implicated mechanisms, and smallest valid edit target, routing single-mechanism defects to L3 resources and relational defects across mechanisms to L2 composition protocols. A six-module Workflow-Guided Skill Optimization (WGSO) loop then selects provenance- and scope-aware third-party knowledge, applies bounded patches, evaluates candidates, and stores verified outcomes in optimizer-side memory. On SpreadsheetBench, WML reaches 90.33 +/- 1.53 and 74.67 +/- 3.51 Hard Accuracy with DeepSeek and Qwen3.6-Flash, respectively; without additional optimization, the learned Skills transfer to WikiTableQuestions with 84.00 +/- 2.00 and 83.00 +/- 2.00 Denotation Accuracy. On Compiler-Supported50, WML attains both the highest hard-PASS rate and the lowest cost per successful task; compiled execution sharply reduces tokens and calls relative to a direct SkillAgent while retaining most of its successful tasks. Code and artifacts are available at https://github.com/xiaolin9595/workflow-localized-mechanism-learning.

Problem

• workflow failure localization
• mechanism attribution
• knowledge reuse
• structured agent skills
• skill optimization

Innovation

• Workflow-Localized Mechanism Learning
• Node–Mechanism Attribution
• Skill Optimization
• Knowledge Reuse
• Structured Agent Skills