StepProbe 🔬

Step-Level Diagnosis of Reasoning under Weight Quantization, with Diagnosis-Driven Interventions

Where exactly does reasoning break when you quantize a thinking model?

This repository hosts the code, paper, and released experiment artefacts for the manuscript:

StepProbe: Step-Level Diagnosis of Reasoning under Weight Quantization, with Diagnosis-Driven Interventions Tran Huy Hoang Son. Submitted to Neurocomputing, 2026.

The full PDF lives at paper/main.pdf; the LaTeX source, the highlights file, and the two appendices (LLM-judge prompts and qualitative error-type examples) are under paper/.

What StepProbe does

StepProbe is a diagnostic-plus-intervention framework that answers three questions about quantized reasoning LLMs:

Where in the chain-of-thought does reasoning first fail?
What type of error dominates at each bit-width?
Can we fix it with minimal, diagnosis-driven intervention?

The framework introduces three step-level metrics:

Metric	Meaning
FFS — First Failure Step	Step index where the quantized chain first diverges
ECR — Error Cascade Rate	Fraction of post-FFS steps that are also incorrect
SSR — Step Survival Rate	Probability that the chain is still correct at depth d

…plus a 4-way error-type taxonomy (conceptual / methodological / executional / logical), and two downstream interventions (targeted QLoRA fine-tuning; training-free FP16 prompt-prefix injection).

Headline findings (paper §5)

>85% of failed traces fail in the first three reasoning steps — damage is concentrated at the chain's opening, not distributed evenly.
Conditional cascade rate >0.90 on the harder benchmarks (MATH-500, GPQA-Diamond): once a quantized chain breaks, it almost never recovers.
Methodological errors dominate, not conceptual: under GPT-4o-mini re-classification, methodological accounts for 54–60% of failed steps; conceptual is only ~9%.
Targeted QLoRA recovers up to +7.6 pp on Qwen-family cells; the diagnosed-vs-random selection gap is +1.8 pp (significant; honestly reported as small relative to the overall recovery).
Training-free prompt-prefix injection adds +7.6 pp at k=4 under a leak-controlled ablation, matching the QLoRA comparator at k=2.

Repository layout

StepProbe/
├── paper/              # LaTeX manuscript + highlights + form
│   ├── main.tex        # Source (elsarticle, Neurocomputing target)
│   ├── main.pdf        # Compiled paper (44 pp)
│   ├── highlights.txt  # Editorial-Manager highlights file (5 bullets)
│   └── references.bib  # 38 references
├── stepprobe/          # Core package
│   ├── segment.py      # CoT step segmentation (rule-based)
│   ├── align.py        # DTW step alignment
│   ├── diagnose.py     # Per-step scoring + LLM-judge prompts
│   ├── metrics.py      # FFS / ECR / SSR computation
│   └── restore.py      # QLoRA targeted fine-tuning
├── scripts/            # Top-level runners and figure generators
│   ├── run_inference.py
│   ├── run_eval.py
│   ├── compute_ci.py           # Bootstrap CIs + paired sig tests
│   ├── eval_accuracy.py
│   ├── make_paper_figures.py   # Figs 1, 2, 3, 4, 5, 11–13 + Table 4
│   ├── make_ablation_figure.py # Fig 9
│   ├── make_baselines_figure.py# Fig 10
│   ├── make_lr_sweep_figure.py # Fig 12
│   ├── make_multi_seed_figure.py# Fig 13
│   ├── make_prefix_injection_figure.py # Fig 14
│   ├── rediagnose_error_types.py
│   └── validate_classifier.py
├── run_*.sh            # Experiment launchers (one per ablation)
├── configs/default.yaml
├── requirements.txt
└── figures/paper/      # Final figure PDFs referenced by main.tex

Reproducing the paper

The released results/ and logs/ directories are not in this repository (too large for git). To reproduce from scratch you need a single 24-GB GPU and roughly 24 hours of compute.

# 0. Install dependencies
pip install -r requirements.txt

# 1. Main matrix (Table 4): 4 models × 3 benchmarks × 6 conditions
bash run_all.sh

# 2. Ablation: silver-bullet dataset size N (Table 5)
bash run_ablation.sh

# 3. Sampling-strategy baselines (Table 6)
bash run_baselines.sh

# 4. Llama LR sweep (Fig 12)
bash run_llama_lr_sweep.sh

# 5. Multi-seed robustness (Fig 13)
bash run_multi_seed.sh

# 6. Prompt-prefix injection (Fig 14, Table 7)
bash run_prompt_prefix.sh

# 7. Render all paper figures + tables from results/metrics/
python scripts/make_paper_figures.py --metrics results/metrics \
    --output figures/paper --primary-model r1-qwen-7b \
    --primary-benchmark math500

The full paper PDF rebuilds with:

cd paper && tectonic main.tex      # or: latexmk -pdf main.tex

Quick demo (no GPU required)

For a 20-problem dry run on a small model:

python scripts/run_eval.py \
    --model deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B \
    --benchmark gsm8k --quant-methods bnb_nf4 --quick

Models, benchmarks, quantization

Models tested in the paper (24-GB-VRAM-friendly in 4-bit):

DeepSeek-R1-Distill-Qwen-{1.5B, 7B, 14B}
DeepSeek-R1-Distill-Llama-8B
Qwen2.5-7B-Instruct (non-reasoning control / primary intervention cell)

Quantization methods: AWQ, GPTQ, BitsAndBytes NF4 (SmoothQuant supported but not part of the main matrix.)

Benchmarks: GSM8K, MATH-500, GPQA-Diamond.

Hardware

All experiments ran on a single NVIDIA RTX 3090 Ti (24 GB).

Citing

If you use StepProbe, please cite the paper:

@article{son2026stepprobe,
  title  = {StepProbe: Step-Level Diagnosis of Reasoning under Weight
            Quantization, with Diagnosis-Driven Interventions},
  author = {Tran Huy Hoang Son},
  journal= {Neurocomputing},
  year   = {2026},
  note   = {Under review}
}

License

MIT — see LICENSE.