feat: Uncertainty-qualified error localization (Conformal Agent Error Attribution)

[acailic]

Paper Reference

• Title: Conformal Agent Error Attribution
• Authors: Naihe Feng, Yi Sui, Shiyi Hou, Ga Wu, Jesse Cresswell
• Year: 2026
• URL: https://arxiv.org/abs/2605.06788
• Venue: arXiv preprint

Paper Summary

Framework for error attribution using conformal prediction with finite-sample coverage guarantees. Predicts contiguous error sequences for efficient recovery and debugging. Provides statistical guarantees on error identification accuracy.

Proposed Feature

Implement uncertainty-qualified error localization with confidence intervals:

Core Capabilities

• Error Sequence Prediction: Given a failure, predict the contiguous sequence of steps that likely caused it
• Coverage Guarantees: Provide statistical confidence intervals (e.g., "95% confident the error is in steps 12-18")
• Efficient Recovery: Narrow the search space for root cause by providing bounded error regions
• Confidence Visualization: Show error probability distribution across session steps

Technical Approach

• Implement conformal prediction-based error localization algorithm
• Add confidence interval annotations to the event stream
• Build probability distribution visualization component
• Integrate with session replay for focused review on high-confidence error regions

Impact

Combines the statistical rigor of CROP (issue #185) with the practical debugging of ErrorProbe (issue #186). Provides bounded, guaranteed error localization.

Labels

enhancement, paper-inspired, analytics