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What you can do with 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.

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GongZhiren/State-of-Thought on GitHub

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What do you want to do?

5 use cases

  • Audit a released SoT controller checkpoint

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

    View prompt
    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.

  • Check published results against bundled records

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

    View prompt
    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.

  • Evaluate the frozen controller on a text benchmark

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

    View prompt
    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.

  • Evaluate the controller on vision-language tasks

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

    View prompt
    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.

  • Refit all 582 controller parameters from trajectories

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

    View prompt
    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.

How to use State of Thought

  1. Check you have what it needs: Python with pip and git installed; A virtual environment for the install; Hugging Face access for backbone weights, with license acceptance and hf auth login for gated models.
  2. Open GongZhiren/State-of-Thought on GitHub and read the README and license.
  3. Run git clone https://github.com/GongZhiren/State-of-Thought.git
  4. Run cd State-of-Thought
  5. Run python -m venv .venv
  6. Run source .venv/bin/activate

These steps come from our top use case, Audit a released SoT controller checkpoint, which also has a copy-paste agent prompt.

State of Thought alternatives · Best AI repos for Research

About this repo

Requirements

  • Python with pip and git installed
  • A virtual environment for the install
  • Hugging Face access for backbone weights, with license acceptance and hf auth login for gated models
  • Enough disk space and GPU memory for the chosen backbone when running evaluation
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Questions about State of Thought

What is 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. It is an open-source project on GitHub: https://github.com/GongZhiren/State-of-Thought.

Who is State of Thought for?

Researchers and engineers who want to verify, reproduce, or refit the NeurIPS 2026 State-of-Thought controller on text and vision-language benchmarks.

What can I do with State of Thought?

5 use cases so far, for example: Audit a released SoT controller checkpoint; Check published results against bundled records; Evaluate the frozen controller on a text benchmark. Each has steps and a copy-paste agent prompt.

What do I need to run State of Thought?

Python with pip and git installed; A virtual environment for the install; Hugging Face access for backbone weights, with license acceptance and hf auth login for gated models; Enough disk space and GPU memory for the chosen backbone when running evaluation.

What are alternatives to State of Thought?

Other hot AI repos in Research: Scientific Agent Skills, Speaker Mem R1, DSH Council, Evo Bot, AI Engineering Course.

Is State of Thought safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.