DataClaw0: Agentic Tailoring Multimodal Data from Raw Streams
Raw multimodal streams are abundant but noisy, redundant, and unaligned with any particular training objective. Turning them into supervision today means either brittle heuristics or repeatedly querying a proprietary vision-language model, a cost that recurs with every new sample. We ask whether this conversion can instead be learned once and reused, and formalise intent-conditioned Data Tailoring: given a raw stream and a high-level intent, a model must return schema-aligned, evidence-grounded training instances. Training DataClaw0 at 4B, 9B and 27B, we find that whether five heterogeneous domains should share one model depends on capacity. A jointly trained model is worse than per-domai...
| Authors | Cong Wan, Zeyu Guo, Zijian Cai et al. |
|---|---|
| Primary category | cs.LG |
| Published | 19 June 2026 |
| Last updated at source | 31 July 2026 |
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