| --- |
| license: cc-by-4.0 |
| language: |
| - en |
| tags: |
| - interpretability |
| - activation-patching |
| - feature-steering |
| - mechanistic |
| pretty_name: ConsisitencyBench-Interpretability Extension |
| --- |
| |
| # ConsistencyBench-Interpretability Extension |
|
|
| White-box mechanistic analysis of logical inconsistency using Qwen/Qwen2.5-1.5B-Instruct (local, |
| full activation access) as a dedicated interpretability testbed, distinct from the |
| 17-model black-box leaderboard. |
|
|
| ## Contents |
|
|
| - `layer_probe_results.csv` - per-layer logistic-regression probe accuracy for decoding |
| "will this response be inconsistent?" directly from residual-stream activations |
| - `activation_patching.csv` - literal patching results: copying a consistent run's |
| activation into an inconsistent run and checking whether the output flips |
| - `steering_dose_response.csv` - IR at increasing diff-in-means steering strength (a |
| causal dose-response curve, not just a correlational probe) |
| - `hint_sensitivity.csv` - flip rate and hint-induced inconsistency under misleading |
| epistemic pressure (across the 6-model API subset used for this analysis) |
| - `metadata.json` - best probe layer, probe accuracy, model config |
|
|
| ## Method Summary |
|
|
| 1. Train a linear probe at every layer to decode inconsistency from the residual stream |
| at the final prompt token (Alain & Bengio, 2017 methodology) |
| 2. Take the best layer's probe direction (and the diff-in-means direction) as the |
| "shortcut direction", in place of a pretrained SAE, which does not exist for this |
| checkpoint |
| 3. Run two causal tests: literal activation patching between matched consistent/ |
| inconsistent pairs, and a steering dose-response sweep with a specificity check on |
| unrelated control questions |
