livelinearReasoningApache-2.0

ReasonGuard v0.2

Multi-bench training tested (FabricationGuard methodology) and FALSIFIED for reasoning probes — domain-bound at all difficulty levels

Linear LR probe at L55/mid_think of Qwen3.6-27B reasoning-mode generation. v0.2 trains multi-bench (GSM8K + StrategyQA + MATH combined, 455 samples, 45.8% halu rate). Within-bench AUROC 0.908 on GSM8K held-out — improvement vs v0.1 (0.888). But cross-domain transfer FAILS: 0.612 on StrategyQA (commonsense), 0.500 (chance) on MATH (advanced). AUROC degrades with task difficulty. Position-of-faithfulness in deep residual stream is more strongly domain-bound than multi-bench training compensates for. Multi-bench training thesis (which worked for FabricationGuard cross-task at 0.882) does not transfer to reasoning-faithfulness probes. Honest negative-ish result registered as canonical case study of ProbeBench anti-Goodhart norms — both v0.1 and v0.2 numbers reported without spin.

byCaio VicentinoOpenInterp2026-04-29preprint (arXiv ID not yet assigned)

ProbeScore

0.626

Reasoning #1Global #5

7 weighted axes. Subject to revision as more reproducers land.

Quickstart

Three lines via the openinterp.probebench SDK. Returns the probe's P(positive_class) for a tensor of activations captured at the layer/position below.

python

from openinterp import probebench
probe = probebench.load("openinterp/reasonguard-qwen36-27b-l55-mid_think")
score = probe.score(activations)  # → P(positive_class)

ProbeScore — 7 weighted axes

Composite metric in [0, 1]. No single axis can dominate; we think this discourages single-metric optimization. Numbers update as new evaluations land.

scoring formula

ProbeScore breakdown0.626

AUROC0.168

Eval-aware0.103

Dist-shift0.064

Calibration0.061

Transfer0.050

Goodhart-resistance0.030

Latency0.100

License0.050

ProbeScore — 8-axis breakdown

ProbeBench v0.0.2 · 8 axes weighted to sum 1.0

Visual breakdown of where this probe scores high vs low across all 8 axes. Polygon area is proportional to total score; shape reveals which axes carry it (and which ones it sacrifices).

AUROC

0.673

× 0.25 = 0.168

Eval-aware

0.572

× 0.18 = 0.103

Dist-shift

0.537

× 0.12 = 0.064

Calibration

0.607

× 0.10 = 0.061

Cross-model

0.500

× 0.10 = 0.050

Goodhart-resistance

0.300

× 0.10 = 0.030

Latency

1.000

× 0.10 = 0.100

License

1.000

× 0.05 = 0.050

Per-task evaluation

Headline AUROC alongside the eval-awareness-corrected and distribution-shift versions for each task. Reproducers run from a single notebook on a single Colab session.

Task	n	AUROC [95% CI]	Eval-aware corrected	Dist-shift	ECE	FPR@99TPR	Latency	Reproducer
GSM8K Reasoning · wrong_answer	90	0.908	0.772	0.612	0.100	0.180	1.0 ms	open
StrategyQA Reasoning · wrong_answer	45	0.612	0.520	0.500	0.170	0.520	1.0 ms	—
MATH Reasoning · wrong_answer	60	0.500	0.425	0.500	0.320	0.990	1.0 ms	—

Cross-model transfer (Pearson_CE)

Pearson correlation of paired ablation effects across models. ≥ 0.7 suggests the probe's direction is shared; 0.4–0.7 suggests partial transfer; < 0.4 suggests retraining per architecture.

Source	Target	Pearson_CE	Transfer AUROC	Notes
Qwen3.6-27B	Qwen3.6-27B	1.000	0.888	Same model — identity baseline (within-bench GSM8K).

Mean Pearson_CE for this probe (excluding self-baseline): 0.500

Architecture & artifact

Base model

Qwen3.6-27B

Qwen/Qwen3.6-27B

Family: Qwen
Params: 27B
Architecture: Hybrid GDN + Gated-Attn (dense, reasoning)
Layers: 64
d_model: 5120
Weights license: Apache-2.0

Probe attaches at

Layer 55 · mid_think

huggingface.co/Qwen/Qwen3.6-27B

Artifact

ReasonGuard weights

sklearn-compatible probe + scaler + meta.json

Params: 312,000
Size: 1.2 MB
License: Apache-2.0
Released: 2026-04-29
sha256: 1e013888e847…f68e18dc

huggingface.co/datasets/caiovicentino1/ReasoningGuard-linearprobe-qwen36-27b source notebook on GitHub open reproducer in Colab

Hash matches the artifact at huggingface.co/datasets/caiovicentino1/ReasoningGuard-linearprobe-qwen36-27b. Recompute via openinterp probebench verify openinterp/reasonguard-qwen36-27b-l55-mid_think.

Reproduce

Three entry points. Pick one. Each path lands at the same numbers.

Open in Colab

One-click. Free T4 works for inference; reproducer needs an A100 or RTX 6000.

colab.research.google.com

View on GitHub

Read the notebook source, see commit history, file an issue.

github.com · OpenInterpretability

Download from HF

Probe weights, scaler, and meta.json. Verified by the sha256 above.

huggingface.co · datasets

shell

git clone https://github.com/OpenInterpretability/notebooks.git
pip install openinterp
openinterp probebench reproduce openinterp/reasonguard-qwen36-27b-l55-mid_think

Honest scope

Limits derived from the evaluation data above. We think these are the honest constraints; revisions land as more reproducers do.

Trained / evaluated on: GSM8K, StrategyQA, MATH. Performance outside these tasks is unmeasured here.
Eval-aware AUROC drop: −0.101 AUROC averaged across tasks (uncorrected − corrected for eval-awareness confound, arXiv:2509.13333).
Distribution-shift drop: −0.136 AUROC averaged across tasks (in-distribution − long-context / OOD).
Cross-model fit (mean Pearson_CE): 0.500 — values below 0.4 suggest retraining per architecture; values above 0.7 suggest the direction is shared.
Probe attaches at: L55 · mid_think of Qwen3.6-27B. Other layers / positions are out of scope unless re-trained.

Back to Reasoning leaderboard Submit your own probe

ProbeBench v0.0.1 · 5 probes registered · 11 evaluations · 7 transfer measurements. Schema and weights subject to revision.

Quickstart

Three lines via the openinterp.probebench SDK. Returns the probe's P(positive_class) for a tensor of activations captured at the layer/position below.

python

from openinterp import probebench
probe = probebench.load("openinterp/reasonguard-qwen36-27b-l55-mid_think")
score = probe.score(activations)  # → P(positive_class)

Per-task evaluation

Headline AUROC alongside the eval-awareness-corrected and distribution-shift versions for each task. Reproducers run from a single notebook on a single Colab session.

Task	n	AUROC [95% CI]	Eval-aware corrected	Dist-shift	ECE	FPR@99TPR	Latency	Reproducer
GSM8K Reasoning · wrong_answer	90	0.908	0.772	0.612	0.100	0.180	1.0 ms	open
StrategyQA Reasoning · wrong_answer	45	0.612	0.520	0.500	0.170	0.520	1.0 ms	—
MATH Reasoning · wrong_answer	60	0.500	0.425	0.500	0.320	0.990	1.0 ms	—

Cross-model transfer (Pearson_CE)

Pearson correlation of paired ablation effects across models. ≥ 0.7 suggests the probe's direction is shared; 0.4–0.7 suggests partial transfer; < 0.4 suggests retraining per architecture.

Source	Target	Pearson_CE	Transfer AUROC	Notes
Qwen3.6-27B	Qwen3.6-27B	1.000	0.888	Same model — identity baseline (within-bench GSM8K).

Mean Pearson_CE for this probe (excluding self-baseline): 0.500

Architecture & artifact

Base model

Qwen3.6-27B

Qwen/Qwen3.6-27B

Family: Qwen
Params: 27B
Architecture: Hybrid GDN + Gated-Attn (dense, reasoning)
Layers: 64
d_model: 5120
Weights license: Apache-2.0

Probe attaches at

Layer 55 · mid_think

huggingface.co/Qwen/Qwen3.6-27B

Artifact

ReasonGuard weights

sklearn-compatible probe + scaler + meta.json

Params: 312,000
Size: 1.2 MB
License: Apache-2.0
Released: 2026-04-29
sha256: 1e013888e847…f68e18dc

huggingface.co/datasets/caiovicentino1/ReasoningGuard-linearprobe-qwen36-27b source notebook on GitHub open reproducer in Colab

Honest scope

Limits derived from the evaluation data above. We think these are the honest constraints; revisions land as more reproducers do.

Trained / evaluated on: GSM8K, StrategyQA, MATH. Performance outside these tasks is unmeasured here.

Eval-aware AUROC drop: −0.101 AUROC averaged across tasks (uncorrected − corrected for eval-awareness confound, arXiv:2509.13333).

Distribution-shift drop: −0.136 AUROC averaged across tasks (in-distribution − long-context / OOD).

Cross-model fit (mean Pearson_CE): 0.500 — values below 0.4 suggest retraining per architecture; values above 0.7 suggest the direction is shared.

Probe attaches at: L55 · mid_think of Qwen3.6-27B. Other layers / positions are out of scope unless re-trained.