arXiv:2508.02178v3 Announce Type: replace Abstract: Large reasoning models (LRMs) often exhibit overthinking, producing verbose Chain-of-Thought (CoT) traces that increase inference cost and obscure the underlying reasoning process. Existing CoT compression methods mainly rely on global length rewards, which conflate necessary intermediate reasoning with redundant text and may therefore compromise reasoning fidelity. This paper revisits overthinking from a semantic-efficiency perspective and decomposes CoT redundancy into two distinct forms: internal redundancy, defined as informational stagna
The proliferation of verbose Chain-of-Thought (CoT) reasoning in large language models has created a need for more efficient and robust methods of managing AI inference, leading to focused research on redundancy reduction.
Improving the efficiency and interpretability of AI models, particularly in complex reasoning tasks, directly impacts the scalability, cost, and trustworthiness of advanced AI applications.
Approaches to optimizing AI models will shift from broad length-based compression to more semantically aware methods that differentiate between necessary reasoning steps and superfluous output, potentially leading to more reliable AI agents.
- · AI developers
- · Cloud providers
- · AI-powered SaaS companies
- · Researchers in AI efficiency
- · Inefficient AI models
- · Users with high inference costs
- · Companies reliant on verbose AI outputs
Reduced computational cost and increased speed for CoT-based AI applications.
More reliable and transparent AI systems due to clearer reasoning processes, enhancing public trust and adoption.
Acceleration of complex AI agent development as reasoning becomes more manageable and less resource-intensive, enabling new classes of autonomous systems.
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Read at arXiv cs.AI