Datasets:
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, 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:
pip install mlcroissant
mlcroissant validate --jsonld data/croissant.json
# → "Done." (zero errors)
Intended uses
- Compatibility screening between candidate model pairs prior to model-stitching or knowledge-transfer experiments.
- Reproducing and auditing the headline statistical findings of the paper.
- Extending the atlas with new encoders by re-running the extraction pipeline in the companion code repository.
- 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:
@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)}
}