Update README.md

README.md

@@ -1,3 +1,89 @@
----
-license: cc-by-4.0
----
+---
+license: cc-by-4.0
+language:
+- en
+tags:
+- llm
+- interpretability
+- inference-control
+- hidden-states
+- repair-loop
+- code-generation
+- mechanistic-interpretability
+- alignment
+pretty_name: "The Missing Value Function — Interim Research Report"
+---
+
+# Between Hidden States and Control: Hidden-State Signals in Iterative LLM Repair
+
+**The Missing Value Function — Interim Research Report**
+
+> *Can minimal, non-learned signals derived from hidden states during inference serve as an internal value function to distinguish productive from unproductive revisitation in large language models?*
+
+## Overview
+
+This repository contains the interim research report and supplementary materials for the project **"The Missing Value Function"**, an independent empirical investigation into whether biological valence signal principles (Damasio's Somatic Marker Hypothesis, Sutskever's emotion-as-value-function framing) can be operationalized as lightweight inference-time control signals in transformer-based LLMs.
+
+**Author:** Benjamin Weise (Independent Research / Prooftrail)
+**Date:** March 10, 2026
+**Version:** 1.0
+**License:** CC BY 4.0
+
+---
+
+## Key Findings
+
+- **Signal Discovery:** Hidden-state cosine similarity at Layer 27, Stride 50 detects semantic stagnation that text-based loop detectors (n-gram, codeblock) miss entirely — two reproducible dissociation cases
+- **Negative Boundary:** Simple prompt-based and sampling-based actuators showed no robust improvement over baseline (Phase 10.3, 10.4)
+- **Ambiguity of Coherence:** High coherence values mark both productive convergence and unproductive stagnation — coherence alone is insufficient as a standalone actuator
+- **Multi-Signal Direction:** entropy + margin combination shows modest improvement for regression detection (AUC 0.59)
+- **Monotonic Controller:** Boundary result — preservation alone does not solve the bottleneck; productive diversity is the missing ingredient
+- **Repair Loop Testbed:** frontier_02_hard (LRU Cache, 7 test blocks) achieves 37.5% baseline success — the right difficulty corridor for hypothesis testing
+
+**Current Status:** The evidence supports that hidden-state signals are diagnostically valuable but not yet sufficient as standalone actuators. The research has identified real signal dissociation, established negative boundaries for simple interventions, and motivated a shift toward multi-signal policy design.
+
+---
+
+## Repository Contents
+
+| File | Description |
+|------|-------------|
+| `Between_Hidden_States_and_Control_Interim_Report.pdf` | Full interim research report (10 phases, all findings) |
+| `MVF_Supplementary_Materials.zip` | Experiment protocol, result files, core scripts |
+
+---
+
+## Experimental Setup
+
+| Component | Value |
+|-----------|-------|
+| Model | Qwen/Qwen2.5-7B-Instruct (4-bit quantized) |
+| GPU | NVIDIA GeForce RTX 5070 (11.9 GB VRAM) |
+| Monitor Layer | 27 (96% depth) |
+| Checkpoint Stride | 50 tokens |
+| Primary Metric | `max_prev_similarity` (cosine similarity) |
+| Primary Task | `frontier_02_hard` — LRU Cache, 7 test blocks |
+
+---
+
+## Citation
+
+If you use this work, please cite:
+
+```
+Weise, B. (2026). Between Hidden States and Control: Hidden-State Signals in Iterative LLM Repair.
+Interim Research Report, Prooftrail / Independent Research.
+```
+
+---
+
+## Related Work
+
+- Damasio, A. (1996). Somatic Marker Hypothesis
+- Sutskever, I. (2025). Emotions as evolutionarily hardcoded value functions (Dwarkesh Patel interview)
+- Pathak et al. (2017). Curiosity-driven Exploration (ICM)
+- Bengio et al. (2021). Inductive Biases for Deep Learning
+
+---
+
+*This is an interim report. Negative results are documented as completed steps. The project is ongoing.*