Eight peer-reviewed technical specifications authored for ML engineers and pre-training researchers—covering open-versus-commercial corpus economics, cryptographic SPDX and Croissant lineage, synchronized video/audio curation, synthetic model collapse bounds, Confident Learning outlier pruning, Parquet vs. JSONL vs. WebDataset throughput, RLDS robotics teleoperation schemas, and MinHash holdout decontamination.
Engineering audit of open-source web corpora versus proprietary human AI datasets across token signal density, contamination risk, and SPDX licensing.
Read SpecificationTechnical architecture for cryptographic AI dataset lineage using SPDX 3.0 AI profiles, MLCommons Croissant metadata, C2PA manifests, and TDM compliance.
Read SpecificationProduction specification for curating synchronized video, spatial audio, and speech corpora with SMPTE frame alignment, diarization QA, and temporal IoU.
Read SpecificationEmpirical analysis of recursive model collapse, tail-distribution truncation, and hybrid human-seed verification pipelines in LLM and multimodal training.
Read SpecificationTechnical guide to Confident Learning outlier matrices, Cleanlab noise estimation, Dawid-Skene EM aggregation, and embedding k-NN anomaly triage for ML.
Read SpecificationSystems benchmark comparing JSONL, Apache Parquet, WebDataset TAR shards, Lance/MDS, and Croissant JSON-LD for high-throughput GPU AI training pipelines.
Read SpecificationEngineering specification for embodied AI and VLA robotics datasets: VR/ALOHA teleoperation, <5ms PTP sensor sync, SE(3) frames, RLDS, and LeRobot schemas.
Read SpecificationEngineering protocol for AI benchmark decontamination: 13-gram MinHash LSH, suffix arrays, dense paraphrase detection, canary GUIDs, and dynamic holdouts.
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