arXiv:2605.25745v1 Announce Type: new Abstract: Explicit chain-of-thought (CoT) reasoning substantially improves the reasoning ability of large language models (LLMs), but incurs high inference cost due to lengthy autoregressive traces. Existing latent reasoning methods offer a promising alternative, yet they often treat reasoning as uniformly compressible, causing precision-critical intermediate steps to be overly compressed and thereby degrading reasoning accuracy. In this work, we propose Selective Latent Thinking (SLT), a framework that selectively compresses redundant reasoning spans into
The rapid development and widespread adoption of Large Language Models (LLMs) have highlighted their computational inefficiencies, prompting urgent research into optimization techniques to scale their capabilities and reduce operational costs.
This development is crucial for optimizing the cost-effectiveness and scalability of advanced AI, directly impacting the economic viability and deployment speed of LLM-powered applications across industries.
LLMs can now perform complex reasoning with significantly reduced inference costs by intelligently compressing repetitive or less critical steps in their thought processes, making sophisticated AI more accessible and efficient.
- · AI developers
- · Cloud computing providers
- · Industries adopting LLMs
- · LLM-as-a-service companies
- · Inefficient AI inference architectures
Reduced computational overhead for complex LLM tasks leads to lower operational costs for AI services.
The cost savings accelerate the deployment and integration of advanced AI into more products and workflows, potentially broadening market access.
Increased accessibility might democratize high-level AI capabilities, fostering innovation in smaller firms or leading to new forms of autonomous agents and services.
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Read at arXiv cs.CL