Real-Time Introspective Compression for Transformers

Real-Time Introspective Compression for Transformers By Jeffrey Emanuel (and various collaborators of the electronic persuasion) Written on April 1st, 2025 Introduction: Two Intertwined Problems Transformer-based large language models (LLMs) face two significant limitations that restrict their capabilities: Lack of Introspection: Unless specifically instrumented, transformer-based LLMs have no ability to explicitly access their own internal states—the activations in their feed-forward layers, attention mechanisms, and other components. This opacity hinders mechanistic interpretability, self-monitoring, and dynamic reasoning. Ephemeral Cognition: Most LLM "thinking" is fleeting—activations across billions of parameters that change during forward passes as the model processes tokens. Recording this data naively is computationally prohibitive due to its sheer volume. These limitations have profound implications for interpretability, debugging, and developing more capable AI systems. ... Read full article.