Real-Time Introspective Compression: Giving Transformers a Conscience
Large Language Models (LLMs) suffer from two key limitations: lack of introspection and ephemeral cognition. This article proposes a novel real-time introspective compression method that addresses both. A lightweight "sidecar" model is trained to compress the internal states of a transformer, allowing for efficient access and replay of the model's internal workings. The method compresses transformer states into a low-dimensional latent space, similar to saving a game state, thus overcoming the computational hurdle of storing the full state. This enables new capabilities such as reasoning backtracking, reinforcement learning over thought trajectories, and memory-efficient checkpointing, ultimately leading to more powerful and interpretable AI systems.
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