arXiv:2511.05747v3 Announce Type: replace Abstract: Chain-of-Thought (CoT) reasoning enhances the problem-solving ability of large language models (LLMs) but leads to substantial inference overhead, limiting deployment in resource-constrained settings. This paper investigates efficient CoT transfer across models of different scales and architectures through an adaptive reasoning summarization framework. The proposed method compresses reasoning traces via semantic segmentation with importance scoring, budget-aware dynamic compression, and coherence reconstruction, preserving critical reasoning
The increasing computational demands of advanced AI models are pushing the boundaries of existing infrastructure, making efficiency solutions like CoT-X critically important for broader deployment.
This development addresses a key bottleneck in deploying sophisticated AI; by optimizing Chain-of-Thought reasoning, it enables more powerful AI applications in resource-constrained environments.
The ability to transfer and optimize CoT across different LLM scales means advanced AI reasoning can be applied more broadly, reducing reliance on massive proprietary models for certain tasks.
- · Edge AI providers
- · Developers of smaller LLMs
- · Industries with strict compute budgets
- · AI researchers
- · Companies relying solely on large-scale LLMs for all CoT tasks
- · Cloud computing providers if on-device AI adoption accelerates
- · Inefficient CoT methods
More efficient AI reasoning processes will become accessible to a wider range of hardware and applications.
This efficiency gain could accelerate the development and deployment of autonomous AI agents by reducing their computational overhead.
The democratization of advanced AI reasoning might lead to a proliferation of specialized, highly efficient AI models tailored for specific tasks, challenging the dominance of monolithic general-purpose LLMs.
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Read at arXiv cs.AI