---
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?*

## Links

| Resource | URL |
|----------|-----|
| 📄 **Zenodo (DOI, citable)** | https://doi.org/10.5281/zenodo.18941566 |
| 📦 **This repository** | https://huggingface.co/datasets/airVen/missing-value-function-interim-report |

**Cite as:** Weise, B. (2026). *The Missing Value Function: A Preliminary Report on Hidden-State Signals in Iterative LLM Repair.* Zenodo. https://doi.org/10.5281/zenodo.18941566

---

## 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

```
Weise, B. (2026). The Missing Value Function: A Preliminary Report on Hidden-State Signals
in Iterative LLM Repair. Zenodo. https://doi.org/10.5281/zenodo.18941566
```

---

## 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.*