---
license: apache-2.0
language:
  - en
pretty_name: BCCT-Hub
tags:
  - representation-similarity
  - representation-convergence
  - cross-model-transport
  - benchmark
  - alignment
  - evaluation
  - neurips-2026
size_categories:
  - 1K<n<10K
task_categories:
  - feature-extraction
configs:
  - config_name: vision_atlas
    data_files:
      - split: pairs
        path: atlas.json
  - config_name: llm_atlas
    data_files:
      - split: pairs
        path: llm_atlas.json
  - config_name: audio_atlas
    data_files:
      - split: pairs
        path: audio_atlas.json
  - config_name: video_atlas
    data_files:
      - split: pairs
        path: video_atlas.json
  - config_name: meta_analysis
    data_files:
      - split: papers
        path: meta_analysis.csv
---

# BCCT-Hub

A benchmark and evaluation dataset for measuring **representation convergence
and cross-model transport** across pretrained encoders. Released alongside the
NeurIPS 2026 Evaluations & Datasets Track submission *"BCCT-Hub: A Benchmark
and Toolkit for Measuring Representation Convergence Across Model Families."*

The dataset packages four pre-computed pairwise compatibility atlases, 41
pre-extracted feature tensors, statistical-analysis JSON outputs, an 88-paper
meta-analysis CSV, and a Croissant 1.0 metadata file with the five
Responsible-AI fields required by the NeurIPS 2026 E&D Track.

## Atlas summary

| Atlas | Pairs | Models | Source data |
|---|---|---|---|
| `atlas.json` (vision) | 190 | 20 vision encoders | CIFAR-100 test (5000 images) |
| `llm_atlas.json` | 36 | 9 base LLMs | WikiText-103 (2000 passages of 128 tokens) |
| `audio_atlas.json` (preliminary) | 15 | 6 audio encoders | LibriSpeech test-clean |
| `video_atlas.json` (exploratory) | 15 | 6 video encoders | STL-10 pseudo-clips |
| `meta_analysis.csv` | — | 88 papers tagged | Survey extraction |

Each pairwise record carries six BCCT quantities:
**R** (effective-rank bitrate proxy), **τ** (bidirectional Procrustes-vs-MLP
transport linearity score), **λ** (alignment locality), **TAI** (transport
asymmetry), **Δ** (bottleneck mismatch), and a **regime** label in
{Collapsed, Local-Only, Convergent, Divergent}.

## Headline empirical results

- **Family is the strongest predictor of transport success:** mixed-effects
  random-intercept LMM with both models in each pair contributing as random
  effects yields β_family = 0.20 (p < 10⁻¹⁶), β_Δ = −0.034 (LRT χ² = 55.1,
  p < 10⁻¹³), β_objective = 0.042 (p = 0.023).
- **The bitrate–transport association survives dependence-aware resampling:**
  block-bootstrap ρ = −0.70, 95 % CI [−0.82, −0.43].
- **S_local predicts cross-encoder k-NN retrieval:** raw ρ = 0.76 (p =
  1.1 × 10⁻⁴; Benjamini–Hochberg-adjusted p = 1.7 × 10⁻³ across 15 retrieval
  correlations, 9/15 significant).

## Repository layout

```
data/
├── atlas.json                       (vision; 190 pairs)
├── llm_atlas.json                   (language; 36 pairs)
├── audio_atlas.json                 (preliminary; 15 pairs)
├── video_atlas.json                 (exploratory; 15 pairs)
├── croissant.json                   (Croissant 1.0 + RAI fields)
├── meta_analysis.csv                (88-paper survey)
├── bitrate_estimator_robustness.json
├── audio_bitrate_sweep.json
├── stl10_vs_cifar100_comparison.json
├── cifar100_{test,train}_labels.pt  (paired CIFAR-100 labels)
├── experiments/                     (mixed-effects, holdout, retrieval, stitching, ...)
├── features/                        (vision: 20 encoders × 5000 CIFAR-100 test)
├── features_train/                  (vision: 20 encoders × 5000 CIFAR-100 train, seed=42)
├── features_external/               (5 out-of-atlas encoders for the case study)
├── features_stl10/                  (vision robustness check)
└── audio_features/                  (6 audio encoders × LibriSpeech test-clean)
```

## Croissant 1.0 metadata

The dataset ships with a validated Croissant file
([`data/croissant.json`](data/croissant.json), 17 KB). It declares SHA-256
hashes for each atlas, a `conformsTo: http://mlcommons.org/croissant/1.0`
header, and the five Responsible-AI fields required by the NeurIPS 2026
Evaluations & Datasets Track (`rai:dataLimitations`, `rai:dataBiases`,
`rai:dataPersonalSensitiveInformation`, `rai:dataUseCases`,
`rai:dataSocialImpact`). The file passes the official validator:

```bash
pip install mlcroissant
mlcroissant validate --jsonld data/croissant.json
# → "Done." (zero errors)
```

## Intended uses

1. Compatibility screening between candidate model pairs prior to
   model-stitching or knowledge-transfer experiments.
2. Reproducing and auditing the headline statistical findings of the paper.
3. Extending the atlas with new encoders by re-running the extraction pipeline
   in the companion code repository.
4. Regression testing for representation-similarity research that builds on
   CKA, mutual k-NN, Procrustes-based transport, or effective-rank proxies.

## Out-of-scope uses (not validated, not recommended)

- Deployment-time decisions about whether two production models are
  interchangeable in a downstream application.
- Safety or fairness certification of any individual encoder.
- Inference about model training data, intellectual property, or copyright
  provenance from feature-space similarity.

BCCT scores are diagnostic summaries for comparative research, **not deployment
guarantees**. See `data/croissant.json` `rai:dataSocialImpact` for the full
scope statement.

## Source-data licensing

The dataset redistributes only **deterministic numerical activations and
per-pair metric outputs**, never source images, audio, or text. Upstream
benchmarks remain under their original licenses:

- CIFAR-100: MIT-style permissive
- STL-10: permissive (Coates et al., 2011)
- WikiText-103: CC BY-SA 3.0
- LibriSpeech test-clean: CC BY 4.0

Our derivative numerical artifacts are released under **Apache-2.0** (see
`LICENSE`).

## Companion artifacts

- Code repository (toolkit + paper source): <https://github.com/evaldataset/BCCT-Hub>
- Anonymous mirror used during NeurIPS review:
  <https://anonymous.4open.science/r/bcct-hub>

## Citation

If you use BCCT-Hub in your research, please cite the accompanying paper:

```bibtex
@misc{bcct_hub_2026,
  title  = {{BCCT-Hub}: A Benchmark and Toolkit for Measuring Representation
            Convergence Across Model Families},
  author = {Anonymous Authors},
  year   = {2026},
  note   = {NeurIPS 2026 Evaluations \& Datasets Track (under review)}
}
```