I’ve released a corrected technical reference and full artifacts for a system I’ve been working on around **inference-time control and degeneration in large language models**.

The core result is that **repetition collapse corresponds to predictable internal regimes** that appear *before* emission, and can be mitigated at **decode time** using lightweight hidden-state prediction heads—**without retraining base model weights or modifying attention**.

The book documents:

* the working architecture (and several failed ones),
* a per-token labeling methodology that enabled high-separation prediction,
* decode-time intervention mechanics,
* negative results and scope limits,
* and full reproduction instructions.

This is not a new model architecture, a cognitive claim, or a statement about consciousness. It’s a narrow systems result about controllability, degeneration, and separating representation learning from control during generation.

Artifacts are public (models, adapters, code), and the document is intended as a **technical reference**, not a manifesto.

Book / technical reference (Zenodo): [https://zenodo.org/records/18367221](https://zenodo.org/records/18367221)  
Code / models: [https://huggingface.co/LoganResearch/ARC-Merged-2/tree/main](https://huggingface.co/LoganResearch/ARC-Merged-2/tree/main)

Happy to answer technical questions or discuss limitations.

Inference-time control for LLMs: a reproducible system for predicting and mitigating repetition collapse at decode time