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GitHub - Dicklesworthstone/llm_introspective_compression_and_metacognition: A novel approach for transformer model introspection that enables saving, compressing, and manipulating internal thought states for advanced capabilities like reasoning backtracking, latent thought optimization, and metacognitive control.

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, at...

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