The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 91, in _split_generators
pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 193, in _generate_tables
examples = [ujson_loads(line) for line in batch.splitlines()]
^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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.
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