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Researchers propose 'Catch Your Breath' to adapt AI computation dynamically

Researchers have developed a new supervised loss function called Catch Your Breath (CYB) designed to enable foundation models to adaptively scale their computation during sequence production. Unlike standard methods that treat processing delays as static, CYB trains models to dynamically signal when they need additional compute steps by emitting a special '<don't know>' output, effectively delaying their response. This approach allows models to autonomously adjust their processing time per token, leading to improved perplexity and downstream accuracy without increasing computational or memory costs. AI

Summary written by gemini-2.5-flash-lite from 1 source. How we write summaries →

IMPACT Introduces a method for more efficient and adaptive computation in sequence generation models, potentially improving performance without increased resource usage.

RANK_REASON This is a research paper detailing a novel training method for sequence production models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

COVERAGE [1]

  1. arXiv cs.CL TIER_1 · Alexandre Galashov, Matt Jones, Rosemary Ke, Yuan Cao, Vaishnavh Nagarajan, Michael C. Mozer ·

    Catch Your Breath: Adaptive Computation for Self-Paced Sequence Production

    arXiv:2510.13879v2 Announce Type: replace Abstract: Within the landscape of inference-time scaling methods for foundation models, a width-based approach to scaling -- which involves the insertion of tokens in the input stream to delay model responses -- offers a unique advantage …