arXiv:2605.28600v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting substantially improves the sample efficiency of transformers, reducing the complexity of tasks like parity learning from exponential to polynomial in the input length. However, generating explicit reasoning steps at inference is computationally expensive. Implicit Chain-of-Thought (ICoT) has emerged as a promising empirical remedy that trains models to internalize intermediate steps within their hidden states, but its theoretical foundations remain poorly understood. We give the first theoretical analysis of ICoT,
The paper provides theoretical foundations for Implicit Chain-of-Thought (ICoT), which has been a promising empirical technique for optimizing transformer efficiency, at a time when computational overhead for large models is a major constraint.
This theoretical understanding validates an approach that could significantly reduce the computational cost of AI inference while maintaining performance, impacting the scalability and accessibility of advanced AI.
The ability to formally prove how transformers internalize complex reasoning steps enables more robust development and deployment of efficient AI models, bypassing the need for explicit, costly reasoning traces.
- · AI model developers
- · Cloud AI providers
- · AI-powered applications
- · Sectors using complex AI models
- · Developers reliant solely on explicit CoT
- · Hardware manufacturers focused only on raw compute increases
More efficient and cost-effective deployment of sophisticated AI models becomes possible.
This could accelerate the adoption of AI agents and complex autonomous systems due to reduced operational costs.
Increased accessibility to advanced AI might democratize AI development, reducing the barrier to entry for smaller players.
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Read at arXiv cs.LG