Mittal (2026)

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Abstract

Large language models often fail to satisfy formatting instructions when they must simultaneously perform demanding tasks. We study this behavior through a prospective memory-inspired lens from cognitive psychology, using a controlled paradigm that combines verifiable formatting constraints with benchmark tasks of increasing complexity. Across three model families and over 8,000 prompts, compliance drops by 2–21% under concurrent task load. Vulnerability is highly type-dependent: terminal constraints (requiring action at the response boundary) degrade most, with drops up to 50%, while avoidance constraints remain comparatively robust. A salience-enhanced format (explicit instruction framing plus a trailing reminder) recovers much of the lost compliance, restoring performance to 90–100% in many settings. Interference is bidirectional: formatting constraints can also reduce task accuracy, with one model’s GSM8K accuracy dropping from 93% to 27%. In additional stacking experiments, joint compliance declines sharply as constraints accumulate. All results use deterministic programmatic checkers, with no LLM-as-judge component, on publicly available datasets.

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