---
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`.