A Survey on Latent Reasoning
Free while signed in. Answers cite the passages they came from.
Provides a comprehensive overview of latent reasoning, an emerging field that shifts AI reasoning from explicit, token-based "chain-of-thought" to implicit computations within a model's continuous hidden states. Key ideas:
Beyond Explicit Reasoning – While traditional Chain-of-Thought (CoT) improves transparency, it is limited by the constraints of natural language. Latent reasoning overcomes this by performing multi-step inference directly in the model's hidden state, unlocking more expressive and efficient reasoning pathways.
Two Paths to Deeper Thinking – The survey identifies two main approaches to latent reasoning: vertical recurrence, where models loop through the same layers to refine their understanding, and horizontal recurrence, where models evolve a compressed hidden state over long sequences of information. Both methods aim to increase computational depth without altering the model's core architecture.
The Rise of Infinite-Depth Models – The paper explores advanced paradigms like text diffusion models, which enable infinite-depth reasoning. These models can iteratively refine an entire sequence of thought in parallel, allowing for global planning and self-correction, a significant leap beyond the fixed, sequential nature of traditional autoregressive models.
Get next week’s papers.
The same picks and the same summaries, in your inbox. Free, and 176 issues deep.
Subscribe on Substack