New benchmark reveals LLM instruction-following degrades with complexity

A new benchmark called Instruction Stacking Collapse has been developed to study how large language models' ability to follow instructions degrades as the number of constraints increases. The benchmark reveals that instruction-following rates can drop significantly, with certain combinations of instructions becoming unsatisfiable. A training-free instruction compiler was found to mitigate this issue, recovering up to 11 percentage points of follow rate for weaker models, while having minimal impact on stronger models. AI

IMPACT Highlights a key limitation in current LLMs and proposes a practical solution for improving instruction following in complex prompts.

RANK_REASON Academic paper introducing a new benchmark and methodology. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• Claude Sonnet 4.6
• Gemini 2.5 Flash
• GPT-5 mini
• Hugging Face
• Instruction Stacking Collapse

• paper
• model release

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