Datasets:
Tasks:
Feature Extraction
Languages:
English
Size:
1K<n<10K
Tags:
representation-similarity
representation-convergence
cross-model-transport
benchmark
alignment
evaluation
License:
File size: 16,028 Bytes
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"name": "BCCT-Hub",
"description": "BCCT-Hub: a benchmark and toolkit for measuring representation convergence across model families. The release contains four pre-computed pairwise compatibility atlases (vision: 190 pairs across 20 encoders; language: 36 pairs across 9 LLMs; audio: 15 pairs; video: 15 pairs), 41 pre-extracted feature tensors, statistical-analysis JSON outputs, an 88-paper meta-analysis CSV, and a per-claim artifact manifest mapping every paper claim to its producing script. The release accompanies a NeurIPS 2026 Evaluations & Datasets Track submission and is intended for representation-similarity benchmarking research.",
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"citeAs": "Anonymous Authors. BCCT-Hub: A Benchmark and Toolkit for Measuring Representation Convergence Across Model Families. NeurIPS 2026 Evaluations & Datasets Track (under review).",
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"rai:dataCollection": "Pairwise BCCT metrics (effective-rank bitrate, null-calibrated mutual k-NN, CKA, Procrustes-based transport linearity, transport asymmetry, bottleneck mismatch) were computed on features extracted from publicly available pretrained encoders (CLIP, DINOv2, ResNet, ViT, ConvNeXt, MAE, BEiT, MLP-Mixer, Swin, EfficientNet, Pythia, OPT, Phi-2, GPT-2, LLaMA-style models, and others). Source data are CIFAR-100 (vision test, 5000 images), STL-10 (vision robustness check; pseudo-clip rendering for video atlas), WikiText-103 (language, 2000 passages of 128 tokens), and LibriSpeech test-clean (audio). All extraction is deterministic with seed 42.",
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"rai:dataAnnotationProtocol": "No human annotation. All numerical values are deterministic outputs of standardized scripts (scripts/extract_features*.py, scripts/compute_alignment_atlas.py, scripts/compute_audio_atlas.py, scripts/compute_video_atlas.py, scripts/llm_experiment.py).",
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"rai:dataPreprocessingProtocol": "Vision images: resize 256, center-crop 224, ImageNet mean/std normalization. STL-10: 96 to 224 upscale. LibriSpeech: 16 kHz mono, model-specific feature extractors. WikiText-103: 128-token windows, attention-mask-aware mean pooling. All pipelines fixed in scripts/ with seed=42.",
"rai:dataLimitations": "Audio uses a single clean English corpus (LibriSpeech test-clean) and is preliminary at n=15 pairs. Video uses STL-10 pseudo-clips (no genuine temporal structure) and is preliminary at n=15 pairs. The largest LLMs are 7B parameters (Mistral-7B, Falcon-7B, Pythia-6.9B); whether the regime distribution shifts at frontier scale (70B+) is untested. All LLMs in the atlas are base/pretrained-only, no instruction-tuned or RLHF models. The vision atlas is monolingual-equivalent (image-only, no text). Bitrate is reported as an effective-rank covariance proxy, not a calibrated mutual-information estimate. Transport linearity is defined relative to a standardized 2-layer MLP probe and is not an oracle nonlinear comparison. Pre-computed compatibility predictions assume the atlas's feature-extraction protocol; cross-protocol pairs are systematically flagged Divergent (see Appendix G of the accompanying paper).",
"rai:dataBiases": "The 88-paper meta-analysis (data/meta_analysis.csv) is biased toward vision (51/88) over language (16) and audio (3), reflecting publication frequency in representational similarity research up to 2026. The vision atlas is biased toward English-language web/image-corpus pretraining (CLIP, DINOv2 trained on LVD-142M; ImageNet-trained supervised baselines). The LLM atlas is English-only. Family selection is biased toward openly available checkpoints; closed-source models (GPT-4 class) are excluded by necessity, and recent permissively licensed code-generation or multilingual models may be under-represented.",
"rai:personalSensitiveInformation": "The release contains no personal or sensitive information. All four atlases are statistical summaries (similarity scores, regime labels) over public benchmark stimuli (CIFAR-100, STL-10, WikiText-103, LibriSpeech test-clean), and the released feature tensors are deterministic activations of public pretrained models on these public benchmarks. CIFAR-100 and STL-10 contain no personal data. LibriSpeech test-clean contains read public-domain audiobook recordings with speaker IDs released by the LibriVox/LibriSpeech project; we use only model activations, not raw audio. WikiText-103 is derived from Wikipedia featured/good articles. No demographic, health, financial, political, or religious information is collected, inferred, or released.",
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"rai:syntheticDataDescription": "No synthetic data is used or released. All numerical artifacts are deterministic outputs of public pretrained models evaluated on public real-world benchmarks (CIFAR-100, STL-10, WikiText-103, LibriSpeech test-clean).",
"rai:dataUseCases": "Intended uses (validated in the accompanying paper): (a) compatibility screening between candidate model pairs prior to model-stitching or knowledge-transfer experiments; (b) reproducing and auditing the headline statistical findings of the paper (mixed-effects family beta=0.20, block-bootstrap rho=-0.70, retrieval rho=0.76); (c) extending the atlas with new encoders by re-running the extraction pipeline; (d) 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 and explicitly not recommended): (i) deployment-time decisions about whether two production models are interchangeable in a downstream application; (ii) safety or fairness certification of any individual encoder; (iii) inference about model training data, intellectual property, or copyright provenance from feature-space similarity.",
"rai:dataSocialImpact": "Positive impact: standardized evaluation protocols reduce wasted compute on incompatible model pairs, support reproducible representational-similarity research, and surface honest negative findings (e.g., the 35/190 disagreement cases in which CKA looks high but linear transport fails). Negative impact / misuse risk: BCCT scores are diagnostic summaries for comparative research, not deployment guarantees; we explicitly state that BCCT scores must not be used as certifications of safe model interchangeability. Misreading the scorecard as a safety stamp could be harmful when applied to production-critical interchangeability decisions. We mitigate this risk by making the scope statement prominent in the abstract, the conclusion, and the Reviewer Checklist, and by releasing the full per-claim manifest so any user can trace any reported number back to its producing script.",
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