Get the latest tech news
Real-Time Introspective Compression for Transformers
A novel approach for transformer model introspection that enables saving, compressing, and manipulating internal thought states for advanced capabilities like reasoning backtracking, latent thought...
Attention-based sidecar architectures Comprehensive compression of the full state, including KV caches Integration of RL to refine latent trajectories, treating z_t as a steerable "thought space" By learning to compress and reconstruct internal states via a structured latent manifold, we can enable fundamentally new capabilities like reasoning backtracking, thought trajectory optimization, and causal debugging. It builds a memory of challenging cognitive states It repeatedly revisits difficult thought regions It explores better continuations through trial and error Over time, it internalizes successful patterns without parameter updates
Or read this on Hacker News