Why Limit the Residual Stream to Layers and Not Tokens? Persistent Memory for Continuous Latent Reasoning
arXiv:2606.07720v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated remarkable reasoning abilities on mathematical and multi-hop planning tasks. The CoCoNuT (Chain of Continuous Thought) paradigm~\cite{hao2024coconut} extends this by enabling models to reason in latent space, exploring multiple reasoning paths simultaneously rather than committing to a single chain early on. However, we identify a limitation we term the \textbf{concept bottleneck}. At each reasoning pass, intermediate hidden states are overwritten, causing the model to lose critical facts computed
The paper identifies a critical limitation in current LLM reasoning — the 'concept bottleneck' — suggesting that current architectural approaches hinder persistent memory essential for complex, multi-step problem solving, pushing for a re-evaluation of residual stream design.
This research could significantly advance the reasoning capabilities of AI, pushing LLMs beyond current limitations in complex tasks by enabling more robust and persistent latent memory, which is crucial for advanced AI applications.
The proposed 'Persistent Memory for Continuous Latent Reasoning' could fundamentally alter how LLMs process and retain information during reasoning, moving away from ephemeral layer-based states to more persistent token-level memory, thereby enabling more efficient and less error-prone complex thought processes.
- · AI research labs developing new LLM architectures
- · Developers of complex AI agents and reasoning systems
- · Sectors requiring advanced AI planning and problem-solving
- · Companies heavily invested in current LLM architectures without adaptability
- · AI applications constrained by limited reasoning capabilities
Improved long-context reasoning and reduced hallucinations in LLMs become achievable through architectural enhancements.
More reliable and autonomous AI agents capable of planning and executing multi-step tasks over extended periods could emerge.
This could accelerate the deployment of sophisticated AI in high-stakes domains like scientific discovery or adaptive robotics, leading to unforeseen breakthroughs.
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Read at arXiv cs.LG