arXiv:2602.01472v2 Announce Type: replace Abstract: Large reasoning models (LRMs) typically solve reasoning-intensive tasks by generating long chain-of-thought (CoT) traces, leading to substantial inference overhead. We identify a reproducible inference-time phenomenon, termed Self-Compression: when multiple independent and answerable questions are presented within a single prompt, the model spontaneously produces shorter reasoning traces for each question. This phenomenon arises from multi-question contextual pressure during generation and consistently manifests across models and benchmarks.
The continuous drive for more efficient and cost-effective AI inference, coupled with advancements in understanding model behavior, makes this discovery timely for immediate application in large reasoning models.
This discovery offers a path to significantly reduce inference overhead for large reasoning models, directly impacting the operational costs and scalability of advanced AI applications.
By leveraging multi-question contextual pressure, AI models can inherently produce shorter, more efficient reasoning traces, potentially democratizing access to powerful AI by lowering computational barriers.
- · AI compute providers
- · Developers of large reasoning models
- · Companies deploying AI agents
- · End-users of AI services
- · Companies with inefficient AI inference architectures
- · Cloud providers solely reliant on raw compute sales
- · Developers of less optimized reasoning frameworks
Reduced operational costs for AI inference will allow for wider deployment and more complex AI applications.
Increased accessibility due to lower costs could accelerate the development and adoption of AI agents across various industries.
The freed-up compute capacity might be redirected towards training even larger, more capable models or into entirely new AI research areas.
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Read at arXiv cs.CL