Upload README.md

README.md

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+---
+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`.