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FADE: Detection Is Not Enough

Code and results for the paper

Detection Is Not Enough: Reasoning Traces as Testable Program Hypotheses Vaidehi Hegde, Indian Institute of Technology (Indian School of Mines) Dhanbad arXiv: link to be added

The paper asks how far a single chain-of-thought trace can be verified with no reference solution, no judge model and no weight update, using a frozen LLaMA-2-7B-chat on GSM8K. It reports three findings:

  1. A static detector that checks a trace against itself reaches AUROC 0.741 on the test set, and most failures (67.1%) contain no false equation.
  2. A metamorphic probe that extracts a program from the trace, executes it on a perturbed question and compares the prediction with the same model's answer exposes many of those failures.
  3. No tested retry policy improves accuracy significantly, and an accounting identity shows why: a flag pays only when detector precision exceeds D / (R + D).

Every measurement in the pipeline is made with regular expressions and sympy; no language model grades anything.

Check the paper's numbers (CPU only, about two minutes)

pip install -r requirements.txt
cd src
python check_paper_numbers.py

This recomputes, from the stored test outputs in results/paper_v1/, the probe verdict table, the refutation rate by failure type, the accuracy of every policy with McNemar tests, the repair and damage rates and break-even precision, the detection AUROCs (including the blend over a grid of weights) and the training-split calibration table. REPRODUCE.md maps each number in the paper to the script and file it comes from.

Repository layout

Detection-is-not-enough/
├── README.md               this file
├── REPRODUCE.md            paper number -> script -> stored output
├── requirements.txt
├── CITATION.cff
├── src/                    all pipeline code (flat; run every command from src/)
│   ├── artifacts/          fixed seed selection and evaluation order
│   └── check_paper_numbers.py
├── notebooks/              the Kaggle notebooks that ran each stage
└── results/
    ├── paper_v1/           the stored outputs behind the paper's test results
    ├── legacy_exports/     flat exports of earlier runs, kept for history
    └── README.md           index of the full run archive (see below)

The modules in src/ import each other by name, so they are kept in one directory and every script is run from inside src/.

What the main modules do

The code uses project-internal names; the paper uses descriptive ones.

In the code In the paper Main files
kaggle_run.py the generation pipeline: first attempt, diagnosis, retry kaggle_run.py, generation.py, exemplar_selector.py, similarity.py, loopguard.py, preprocessing.py
extraction and scoring equation chain and the four trace signals extraction.py, components.py, categorizer.py, classification.py
diagnosis failure taxonomy (WP, SM, CE, ST, AL, NR, TR, UNCLASSIFIED) diagnosis.py, negatives.py, cure_bank.py, symbolic_repair.py
CTC (v2) static detector p_wrong and its type head ctc.py, ctc_data.py, ctc_features.py, ctc_train2.py, run_ctc_validate.py
MPV metamorphic probe mpv.py, run_mpv.py, score_mpv.py, blend_check.py, mpv_offline_check.py
selector gold-free choice between first attempt and retry selector.py, score_selector.py
statistics McNemar and Fisher tests, intervals stats.py

Other groups of scripts belong to work that is not part of the paper's first version: fst_*, features.py, run_predictive.py and run_stability.py (predicting the failure type before the first attempt); run_multicure.py and score_multicure.py; and nm_* (re-running the pipeline on Qwen3.5-4B and GSM-Symbolic, in progress).

Running the full pipeline

The experiments ran on Kaggle (one NVIDIA T4) with meta-llama/Llama-2-7b-chat-hf, frozen, max_new_tokens = 320, an exemplar budget of 8 and three-stage retrieval from a pool of the model's own correct traces. Each notebook in notebooks/ clones this repository and runs one stage:

Stage Notebook Entry point
training run (first attempts, diagnosis, retries) fade-typed.ipynb, fade-generic.ipynb kaggle_run.py
static detector training ctc-train.ipynb ctc_data.py, ctc_train2.py
held-out development check ctc-validate.ipynb run_ctc_validate.py
test run fade_ctc_test.ipynb run_ctc_validate.py
metamorphic probe and scoring — run_mpv.py, score_mpv.py, blend_check.py
selection policies fade_selector.ipynb selector.py, score_selector.py

The notebooks expect two Kaggle secrets: HF_TOKEN (to download the model) and GH_TOKEN (to push run outputs to the run archive described below). No token is stored in this repository.

Run archive

Every run pushed its raw outputs, checkpoint by checkpoint, to the results branch of S2V3/fade (62 runs, about 550 MB). This repository holds the code and the outputs behind the paper; results/README.md indexes the archive. The trained detector (ctc2.joblib, 85 MB) is in the archive at results/ctc2_audit2_latest/.

Citation

See CITATION.cff.