# AI use cases for State of Thought (GongZhiren/State-of-Thought)

Repo: [GongZhiren/State-of-Thought](https://github.com/GongZhiren/State-of-Thought) | Use cases: 5 | Page: https://dedupedai.com/use-cases/gongzhiren-state-of-thought/

State-of-Thought is a Python release of a 582-parameter controller that reads frozen language and vision-language model states to decide when to retrieve evidence and when to stop reasoning.

This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL.

- Audit a released SoT controller checkpoint: https://dedupedai.com/use-cases/gongzhiren-state-of-thought/audit-released-controller-checkpoint/
- Check published results against bundled records: https://dedupedai.com/use-cases/gongzhiren-state-of-thought/check-bundled-results-against-records/
- Evaluate the frozen controller on a text benchmark: https://dedupedai.com/use-cases/gongzhiren-state-of-thought/evaluate-frozen-controller-text-benchmark/
- Evaluate the controller on vision-language tasks: https://dedupedai.com/use-cases/gongzhiren-state-of-thought/evaluate-controller-on-vision-language-tasks/
- Refit all 582 controller parameters from trajectories: https://dedupedai.com/use-cases/gongzhiren-state-of-thought/refit-controller-from-released-trajectories/

## How to audit a released SoT controller checkpoint with State of Thought

Repo: [GongZhiren/State-of-Thought](https://github.com/GongZhiren/State-of-Thought) | Difficulty: Intermediate | Time: 15 minutes | Basis: From the README

Confirm a released controller file is intact before you rely on it in a study.

**Who it is for:** A researcher or reviewer who received a State of Thought checkpoint and needs to confirm it matches the release before citing or reusing it.

**The problem:** A controller file copied from a shared drive or mirror may be altered or mislabeled, and the paper results depend on the exact frozen parameters.

**The outcome:** A verification result for the llama-3.1-8b controller, run on CPU in seconds without loading any backbone.

### Steps

1. Run `git clone https://github.com/GongZhiren/State-of-Thought.git`
2. Run `cd State-of-Thought`
3. Run `python -m venv .venv`
4. Run `source .venv/bin/activate`
5. Run `python -m pip install -e .`
6. Run `sot checkpoint verify checkpoints/llama-3.1-8b/controller.pt`

### Prompt to paste into your agent

```text
Using the repo GongZhiren/State-of-Thought, create a virtual environment, install it with python -m pip install -e ., then run sot checkpoint verify on <checkpoint path>. Report whether verification passes and the SHA-256 identity printed, and verify the result against the checkpoint hashes described in REPRODUCIBILITY.md.
```

### Caveat

The verify step checks the controller file only and does not run the Llama-3.1-8B backbone, so it does not confirm downstream accuracy.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/gongzhiren-state-of-thought/audit-released-controller-checkpoint/

## How to check published results against bundled records with State of Thought

Repo: [GongZhiren/State-of-Thought](https://github.com/GongZhiren/State-of-Thought) | Difficulty: Intermediate | Time: 15 minutes | Basis: From the README

Trace a reported metric back to its per-example records and manifest.

**Who it is for:** A reviewer, student, or agent writing a related-work section who needs to confirm which numbers the release actually supports.

**The problem:** Summary tables are easy to quote without knowing which per-example records and configurations produced them.

**The outcome:** A checked link from curated summaries to the bundled per-example records and the provenance manifest, plus a passing release gate.

### Steps

1. Run `git clone https://github.com/GongZhiren/State-of-Thought.git`
2. Run `cd State-of-Thought`
3. Open `results/summaries/` and pick the backbone and dataset you want to cite
4. Open `results/manifest.json` and find the matching record files
5. Run `make check test`

### Prompt to paste into your agent

```text
In the repo GongZhiren/State-of-Thought, open results/summaries/ and results/manifest.json, then find the record files that back the metric for <backbone and dataset>. Report the record file paths, the configuration named in the manifest, and whether make check test passes.
```

### Caveat

The README says the bundled records are publication-aligned, so treat them as the release's own evidence and not as an independent re-run of the paper.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/gongzhiren-state-of-thought/check-bundled-results-against-records/

## How to evaluate the frozen controller on a text benchmark with State of Thought

Repo: [GongZhiren/State-of-Thought](https://github.com/GongZhiren/State-of-Thought) | Difficulty: Advanced | Time: 2 hours | Basis: From the README

Run the released controller on a fixed text reasoning split with its backbone.

**Who it is for:** An ML researcher who needs to reproduce a text reasoning result with Llama-3.1-8B, Qwen2.5-14B, or Mixtral-8x7B before extending the method.

**The problem:** Reproducing a controller-based reasoning result requires matching the backbone, data split, and configuration exactly, which is easy to get wrong by hand.

**The outcome:** A validated evaluation split and an evaluation summary for the frozen controller, with the configuration, checkpoint identity, and token counts recorded.

### Steps

1. Run `git clone https://github.com/GongZhiren/State-of-Thought.git`
2. Run `cd State-of-Thought`
3. Run `python -m pip install -e .` inside an activated virtual environment
4. Prepare the evaluation data as described in `data/README.md`
5. Run `sot data validate --config experiments/paper/llama-3.1-8b.yaml`
6. Run `sot evaluate --config experiments/paper/llama-3.1-8b.yaml`

### Prompt to paste into your agent

```text
In the repo GongZhiren/State-of-Thought, install the package, prepare data as described in data/README.md, then run sot data validate and sot evaluate with experiments/paper/<backbone config>.yaml. Report the validation result and the evaluation summary, and confirm the checkpoint identity recorded in the summary matches the released one.
```

### Caveat

Backbone weights are downloaded separately and are not redistributed, so the run depends on your access to the named Hugging Face model and enough GPU memory for it.

Risk, credentials: Gated backbone weights need a Hugging Face account, license acceptance, and hf auth login on your machine, so keep those tokens out of shared logs and agent transcripts.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/gongzhiren-state-of-thought/evaluate-frozen-controller-text-benchmark/

## How to evaluate the controller on vision-language tasks with State of Thought

Repo: [GongZhiren/State-of-Thought](https://github.com/GongZhiren/State-of-Thought) | Difficulty: Advanced | Time: 2 hours | Basis: From the README

Run the Qwen2.5-VL controller on A-OKVQA, AI2D, or M3CoT.

**Who it is for:** A multimodal researcher who wants to test whether state-based evidence control transfers to image-plus-question reasoning.

**The problem:** Vision-language runs need extra dependencies and a different config than text runs, and mixing them up gives results that do not match the release.

**The outcome:** An evaluation summary for a Qwen2.5-VL backbone on one of the three released vision-language benchmarks, with records written in the same format as the text runs.

### Steps

1. Run `python -m pip install -e ".[vlm]"` inside the activated virtual environment (inferred, check the README)
2. Run `ls experiments/paper/` and pick the Qwen2.5-VL config file (inferred, check the README)
3. Prepare the visual data as described in `data/README.md`
4. Run `sot data validate --config experiments/paper/<qwen2.5-vl config>.yaml` (inferred, check the README)
5. Run `sot evaluate --config experiments/paper/<qwen2.5-vl config>.yaml` (inferred, check the README)

### Prompt to paste into your agent

```text
In the repo GongZhiren/State-of-Thought, install the package with the vlm extra, choose the Qwen2.5-VL config under experiments/paper/, then run sot data validate and sot evaluate on it. Report the benchmark, the backbone variant, and the evaluation summary path.
```

### Caveat

The README lists Qwen2.5-VL-32B as BF16, which needs substantial GPU memory, and the vision-language extras are installed with a separate command the README describes only as adding .[vlm].

Risk, credentials: Qwen2.5-VL weights may be gated on Hugging Face, so keep your hf auth login token out of shared logs and agent transcripts.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/gongzhiren-state-of-thought/evaluate-controller-on-vision-language-tasks/

## How to refit all 582 controller parameters from trajectories with State of Thought

Repo: [GongZhiren/State-of-Thought](https://github.com/GongZhiren/State-of-Thought) | Difficulty: Advanced | Time: 1 hour | Basis: From the README

Retrain the controller from released offline trajectories without a backbone pass.

**Who it is for:** A researcher testing whether the controller fit is stable, or modifying the training setup, who needs to refit it without running the backbone.

**The problem:** Re-running the backbone for every training experiment is slow, so a fit from saved trajectories is needed for controlled comparisons.

**The outcome:** A refitted controller produced from the released trajectories, with the training run recorded in the same resumable format as evaluation.

### Steps

1. Run `git clone https://github.com/GongZhiren/State-of-Thought.git`
2. Run `cd State-of-Thought`
3. Run `python -m pip install -e .` inside an activated virtual environment
4. Run `sot train --config experiments/paper/llama-3.1-8b.yaml`
5. Run `sot checkpoint verify <path to refitted controller.pt>` to check the new file (inferred, check the README)

### Prompt to paste into your agent

```text
In the repo GongZhiren/State-of-Thought, install the package and run sot train with experiments/paper/llama-3.1-8b.yaml. Report the output controller path, run sot checkpoint verify on it, and state whether its parameter count is 582.
```

### Caveat

A refitted controller will not match the frozen released checkpoint hashes, so label any refit clearly and do not present it as the paper controller.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/gongzhiren-state-of-thought/refit-controller-from-released-trajectories/