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Data World: Scaling Data Environments from Scratch — arXiv:2610.21656

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.21656
License (as archived): CC0-1.0
Archived: 2026-10-16T09:03:54.010Z
SHA-256 of the archived text: 3b2859f0c372f39f08ff943b1ded00e2e908e043234c29a9b99dbe6fd3f95671
Stable URL of this page: https://metraipolis.com/documento/dc_67075304cfcd

How to cite

Data World: Scaling Data Environments from Scratch — arXiv:2610.21656 — arXiv (en), https://arxiv.org/abs/2610.21656 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_67075304cfcd (Metraipolis archive dc_67075304cfcd, sha256 3b2859f0c372f39f08ff943b1ded00e2e908e043234c29a9b99dbe6fd3f95671)

BibTeX

@misc{dc67075304cfcd,
  title = {Data World: Scaling Data Environments from Scratch — arXiv:2610.21656},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.21656},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_67075304cfcd; sha256 3b2859f0c372f39f08ff943b1ded00e2e908e043234c29a9b99dbe6fd3f95671},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.21656. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Data agents use code to process heterogeneous files and derive analytical insights. Scaling their training faces costly environment curation, privacy and intellectual-property restrictions, and difficulty verifying open-ended findings without known generative truth. Our core insight is to specify the world behind the files before generating the files themselves. We introduce Data World, a multi-agent engine that synthesizes environments, tasks, and rubrics from explicit mechanisms. Independent audits check target recoverability from observed files; rubrics support evaluation and RL rewards. Human preference-based curation for question realism selects 100 tasks across 50 held-out worlds and 16 domains for DataWorld-Bench. The engine also yields 2,135 training tasks from 1,037 disjoint worlds. Evaluation of ten frontier models shows that the benchmark remains challenging, particularly for insight discovery, with a consistent computation-insight gap. RL-zero training on the synthesized tasks produces DataWorld-35B-A3B from Qwen3.6-35B-A3B without trajectory distillation. Under matched evaluation, it improves over its base by 7.06 points on DataWorld-Bench, 14.28 on DAComp, 20.90 on DABstep-Hard, and 12.13 on JobBench, a broader occupational benchmark.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.21656 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T08:44:17.782Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).