arXiv:2606.16222v1 Announce Type: new Abstract: Large Language Models (LLMs) increasingly rely on intermediate reasoning, yet explicit Chain-of-Thought (CoT) suffers from a linguistic space bottleneck: each thought must be decoded into tokens, causing high inference overhead. Latent reasoning moves deliberation into continuous space, but existing methods mostly learn deterministic or reward-maximizing paths, lacking a principled way to allocate probability across trajectories with different correctness and costs. We propose Latent Thought Flow (LTF), which models reasoning as variable-length c
The increasing reliance on intermediate reasoning in Large Language Models has necessitated new approaches to manage inference overhead and improve efficiency.
Efficient latent reasoning could significantly reduce the computational cost and increase the capability of advanced AI models, making complex thoughts more practical.
Reasoning in LLMs could become more flexible, efficient, and sophisticated by moving deliberation into continuous space and modeling it probabilisticly.
- · AI developers
- · Cloud providers
- · AI-powered applications
- · Research institutions
- · Companies with inefficient LLM architectures
- · Legacy AI inference hardware
More complex and nuanced AI applications become feasible due to reduced inference costs.
Increased accessibility and deployment of advanced AI capabilities across various industries.
Accelerated development of AI agents capable of deeper, more efficient reasoning, potentially leading to new forms of autonomous systems.
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Read at arXiv cs.AI