arXiv:2603.23530v2 Announce Type: replace-cross Abstract: Large language models often fail to satisfy formatting instructions when they must simultaneously perform demanding tasks. We study this behaviour 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 b
The increasing complexity and deployment of large language models for critical tasks makes understanding their limitations, particularly under load, an immediate research priority.
This research highlights a fundamental cognitive-like failure mode in LLMs under complex conditions, impacting their reliability and the scope of tasks they can safely automate.
Our understanding of LLM robustness and the contexts in which they perform reliably against simple instructions is updated, indicating a need for more robust constraint handling or task decomposition.
- · AI safety researchers
- · Developers of robust AI system architectures
- · Companies offering human-in-the-loop AI solutions
- · Developers of unconstrained autonomous AI agents
- · Users relying solely on LLMs for task execution without monitoring
- · Applications requiring strict adherence to nested formatting instructions
Developers will need to implement more sophisticated error-checking and constraint enforcement mechanisms for LLM outputs, especially in agentic systems.
This could lead to a renewed focus on simpler, more modular LLM applications or enhanced human oversight for complex, multi-step AI tasks.
The identified vulnerabilities might accelerate the development of specialized small language models or multimodal foundation models that excel in following precise instructions under concurrent load.
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Read at arXiv cs.AI