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.
This project helps AI researchers and engineers manage the internal "thought processes" of large language models (LLMs). It allows you to save, inspect, and even rewind the complex intermediate states within an LLM as it processes information, much like saving a game's progress. This enables deeper understanding, debugging, and advanced control over how LLMs reason, without overwhelming storage resources.
Use this if you need to understand, debug, or manipulate the step-by-step reasoning and internal states of large language models without prohibitive computational cost.
Not ideal if you are only interested in the final output of an LLM and do not require detailed access to its internal mechanisms or if you are working with models other than transformers.
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Mar 03, 2026
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