arXiv:2605.23926v1 Announce Type: cross Abstract: Reasoning-capable large language models solve hard problems by emitting long chains of thought, paying heavily in latency, GPU time, and energy. Casual inspection of their traces reveals extensive reformulation, verification, and circular self-reflection, yet how much of this deliberation is actually necessary has never been measured at scale or explained from first principles. This paper closes both gaps. We formalise reasoning redundancy directly in terms of the reasoning model itself: the redundancy of a correct trace is the largest fraction
The accelerating deployment and economic impact of large language models are making their operational efficiency a critical bottleneck and research frontier.
Understanding and quantifying redundancy in LLM reasoning directly addresses the significant resource consumption (latency, GPU, energy) of AI, impacting scalability and deployment costs.
The ability to systematically measure and potentially reduce 'thinking' redundancy fundamentally alters the cost-benefit analysis of deploying advanced LLMs for complex tasks.
- · AI compute infrastructure providers
- · LLM developers focused on efficiency
- · Enterprises deploying AI at scale
- · Energy producers
- · Inefficient LLM architectures
- · Hardware manufacturers relying solely on 'more compute' for growth
Reduced operational costs and latency for large language model applications.
Accelerated adoption of LLMs in cost-sensitive and real-time environments, expanding market reach.
Increased accessibility and democratization of advanced AI capabilities due to lower resource requirements, potentially fostering new AI research paradigms.
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Read at arXiv cs.LG