| --- |
| license: mit |
| language: |
| - en |
| pretty_name: TabMI-Bench |
| tags: |
| - mechanistic-interpretability |
| - tabular-foundation-models |
| - tabpfn |
| - tabicl |
| - tabdpt |
| - iltm |
| - benchmark |
| - protocol-benchmark |
| - neurips-2026 |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: rd5_fullscale_aggregated |
| data_files: rd5_fullscale_aggregated.json |
| - config_name: tabdpt_probing_3seed |
| data_files: tabdpt_probing_3seed.json |
| - config_name: tabdpt_causal_3seed |
| data_files: tabdpt_causal_3seed.json |
| - config_name: nam_holdout |
| data_files: nam_holdout.json |
| - config_name: lofo_primary_endpoint |
| data_files: lofo_primary_endpoint.json |
| - config_name: c1c2_baselines |
| data_files: c1c2_baselines.json |
| - config_name: scale_10k_multiseed |
| data_files: scale_10k_multiseed.json |
| - config_name: tabpfn25_fullscale_aggregated |
| data_files: tabpfn25_fullscale_aggregated.json |
| --- |
| |
| # TabMI-Bench |
|
|
| A protocol benchmark for **mechanistic interpretability (MI) of tabular foundation models (TFMs)**. NeurIPS 2026 Evaluations & Datasets Track submission. |
|
|
| ## What's in this dataset |
|
|
| This Hugging Face repository hosts the **frozen aggregated artifacts** that drive every numbered table and figure in the paper. Bundling these allows reviewers to verify the paper's key numerics without re-running 40 GPU-hours of experiments. |
|
|
| | File | Source experiment | Used by | |
| |---|---|---| |
| | `rd5_fullscale_aggregated.json` | Phase 5 multi-seed core comparison (5 seeds × 3 models × 4 functions) | Table 1 (3 strategies), Figure 1 | |
| | `tabdpt_probing_3seed.json` | TabDPT in-family holdout probing (3 seeds) | Table 1 TabDPT row, §4.3 | |
| | `tabdpt_causal_3seed.json` | TabDPT noising-based causal tracing (3 seeds) | §4.4 TabDPT causal claim | |
| | `nam_holdout.json` | NAM out-of-family holdout (5 seeds) | §4.3 NAM holdout boundary case | |
| | `lofo_primary_endpoint.json` | Leave-one-function-out robustness on primary endpoint | LOFO appendix | |
| | `c1c2_baselines.json` | Shuffled-label and random-target negative controls | §4.6, Tables 21–22 | |
| | `scale_10k_multiseed.json` | N=10K probing scale validation (3 seeds) | Appendix G | |
| | `tabpfn25_fullscale_aggregated.json` | TabPFN v2 vs v2.5 comparison (3 seeds) | Table 7, §G.1 | |
|
|
| ## Code & full benchmark suite |
|
|
| The hooks, synthetic probe generators, evaluation scripts, statistical analysis, figure generation, and tests are hosted at: **<https://github.com/evaldataset/TabMI-Bench>** |
|
|
| To regenerate paper-facing tables and figures from this dataset *without GPU access*: |
|
|
| ```bash |
| git clone https://github.com/evaldataset/TabMI-Bench |
| cd TabMI-Bench |
| pip install -r requirements.txt |
| make reproduce-paper-frozen |
| ``` |
|
|
| ## What is TabMI-Bench? |
|
|
| TabMI-Bench provides: |
| 1. **Hook-based activation extraction** for 5 TFMs spanning 3 architectural families (TabPFN v2/v2.5, TabICL v2, TabDPT, iLTM) plus NAM out-of-family holdout |
| 2. **4 controlled synthetic probe families** (bilinear, sinusoidal, polynomial, mixed) with known ground-truth intermediary variables |
| 3. **4-step evaluation protocol** (synthetic profile → causal validation → negative controls → real-world transfer) |
| 4. **Evidence-coded MI applicability matrix** (8 techniques × 4 architectures with seed-count superscripts) |
| 5. **A primary diagnostic finding**: whole-layer clean activation patching is *uninformative* on ICL-style TFMs due to deterministic cascading; corruption-based (noising) tracing is the informative alternative. |
|
|
| Three descriptive reference computation profiles emerge as calibration baselines: |
| - **Staged** (TabPFN): U-shaped intermediary recoverability with mid-layer concentration |
| - **Distributed** (TabICL, TabDPT): uniformly high recoverability across layers |
| - **Preprocessing-dominant** (iLTM): tree+PCA preprocessing performs the heavy lifting |
|
|
| ## Croissant 1.0 metadata |
|
|
| The repository includes `croissant.json` with **12 RAI fields** (data limitations, biases, sensitive information, use cases, social impact, synthetic data flag, source datasets, provenance, collection, maintenance plan, etc.). See `croissant.json` in the repository. |
|
|
| ## Source datasets |
|
|
| Real-world evaluation uses **public datasets only** (no new collection): |
|
|
| | Dataset | Source | License | Use | |
| |---|---|---|---| |
| | California Housing | OpenML 8092 | CC0 | causal tracing + steering | |
| | Diabetes | scikit-learn | BSD-3-Clause | causal tracing + steering | |
| | Wine Quality | OpenML 287 | CC0 | steering | |
| | Bike Sharing | OpenML 44063 | CC0 | steering | |
| | Abalone, Boston, Energy, Breast Cancer, Iris, Adult, Credit-G | OpenML / scikit-learn | CC0 / BSD-3-Clause | causal tracing | |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{anonymous2026tabmibench, |
| title={TabMI-Bench: Evaluating Mechanistic Interpretability Methods Across Tabular Foundation Model Architectures}, |
| author={Anonymous}, |
| booktitle={Advances in Neural Information Processing Systems (NeurIPS) Evaluations \& Datasets Track}, |
| year={2026} |
| } |
| ``` |
|
|
| ## License |
|
|
| MIT. See `LICENSE`. |
|
|
