Fix the Mind, Not the Move: Interpretable AI Assistance via Knowledge-Gap Localization

2026-06-06 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

The SENSEI framework introduces a novel approach to AI assistance, moving beyond immediate behavioral corrections to address underlying human misconceptions in long-horizon decision-making tasks. Unlike systems that provide alerts or steering, SENSEI infers user knowledge gaps from interaction behavior using a structured knowledge representation, then offers targeted, minimal suggestions to correct these sources of error. The framework demonstrates zero-shot compositional generalization across three complex planning domains, disentangling multiple overlapping misconceptions despite training only on single-misconception cases. A user study involving 20 participants further validated SENSEI's utility, showing it successfully identified and corrected 90% of student misconceptions, significantly improving long-horizon task performance.

Key takeaway

For AI Scientists and Machine Learning Engineers developing assistive systems, consider shifting from action-level interventions to knowledge-aware assistance. SENSEI's approach of diagnosing and correcting underlying misconceptions, rather than just steering behavior, offers a path to more robust and transferable human learning. You should explore structured knowledge representations like PDDL and two-stage inference pipelines to provide interpretable guidance that improves long-term task performance and user understanding.

Key insights

SENSEI infers and corrects human misconceptions via structured knowledge representation for long-term behavioral alignment.

Principles

• Robust mastery requires correcting underlying misconceptions, not just individual mistakes.
• Knowledge should factorize into interpretable components for targeted correction.
• Decouple model complexity from knowledge complexity for scalability.

Method

SENSEI uses a two-stage inference pipeline: Knowledge Gap Localization (predicts inconsistent components) and Latent Knowledge Editing/Decoding (generates interpretable corrections) using PDDL and CodeT5+.

In practice

• Tune SENSEI's detection threshold and temporal consistency for desired precision/recall.
• Represent task knowledge using PDDL for structured, human-readable components.
• Train on incremental trajectory prefixes for causal localization and robust error identification.

Topics

• Knowledge-Aware AI Assistance
• Misconception Diagnosis
• Human-AI Collaboration
• Planning Domain Definition Language
• CodeT5+
• Long-Horizon Decision Making

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.