On the Fragility of Tasks under Composition: Implicit Budgeting in Language Models

Key points

• Doing so requires solving each task correctly while integrating the resulting answers into a coherent response.1
• In this paper, we identify a fundamental limitation in this process, where models make substantially more errors when tasks are composed, even when they can solve every constituent task reliably in isolation.1
• Across both open-weight models and the frontier model Claude Opus 5, our findings instead point to an implicit thinking budget underlying this effect.1
• However, by grouping tasks according to their fragility, the implicit budgeting can be manipulated to reduce token usage by 43\% across a range of math and linguistic tasks while also preserving accuracy.1

Sources (2)

• [1]On the Fragility of Tasks under Composition: Implicit Budgeting in Language Models
  Hugging Face Daily Papers · Oct 19, 12:00 AM

  Modern language models are increasingly asked to handle several tasks within a single prompt.

  Doing so requires solving each task correctly while integrating the resulting answers into a coherent response.

• [2]On the Fragility of Tasks under Composition: Implicit Budgeting in Language Models
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 07:23 PM · same content

Extractive summary: sentences quoted from the sources.