Year: 2026
Type: conference-paper
Abstract: Large language models often fail to satisfy formatting instructions when they must simultaneously perform demanding tasks.We study this behavior through a prospective memoryinspired 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.
Author: Avni Mittal
Institution: Microsoft (United States)
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166862870