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[2602.19239] Attention Deficits in Language Models: Causal Explanations for Procedural Hallucinations
Summary
This article investigates procedural hallucinations in language models, identifying specific attention deficits that lead to errors in executing tasks, even when correct information is present.
Why It Matters
Understanding procedural hallucinations is crucial for improving the reliability of language models in practical applications. By pinpointing the errors related to attention deficits, researchers can develop better models that minimize these failures, enhancing their usability in critical tasks.
Key Takeaways
- Procedural hallucinations occur when language models fail to utilize computed values correctly.
- Errors can be categorized into gating and binding failures during the readout stage.
- Interventions, such as oracle checkpointing, can significantly reduce these errors.
- Understanding the mutual information between available and used data can help diagnose issues.
- Improving attention mechanisms is essential for enhancing model performance in long-context tasks.
Statistics > Machine Learning arXiv:2602.19239 (stat) [Submitted on 22 Feb 2026] Title:Attention Deficits in Language Models: Causal Explanations for Procedural Hallucinations Authors:Ahmed Karim, Fatima Sheaib, Zein Khamis, Maggie Chlon, Jad Awada, Leon Chlon View a PDF of the paper titled Attention Deficits in Language Models: Causal Explanations for Procedural Hallucinations, by Ahmed Karim and 5 other authors View PDF HTML (experimental) Abstract:Large language models can follow complex procedures yet fail at a seemingly trivial final step: reporting a value they themselves computed moments earlier. We study this phenomenon as \emph{procedural hallucination}: failure to execute a verifiable, prompt-grounded specification even when the correct value is present in context. In long-context binding tasks with a known single-token candidate set, we find that many errors are readout-stage routing failures. Specifically, failures decompose into Stage~2A (gating) errors, where the model does not enter answer mode, and Stage~2B (binding) errors, where it enters answer mode but selects the wrong candidate (often due to recency bias). In the hard regime, Stage~2B accounts for most errors across model families in our tasks (Table~1). On Stage~2B error trials, a linear probe on the final-layer residual stream recovers the correct value far above chance (e.g., 74\% vs.\ 2\% on Qwen2.5-3B; Table~2), indicating that the answer is encoded but not used. We formalize ``present but not us...