Intern-Decision
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- Intern-Decision paper details 0.8B–4B multimodal decision models
A new arXiv paper by Tong Zhu and co-authors presents Intern-Decision, a family of 0.8B, 2B and 4B multimodal decision models that return structured choices and candidate probabilities from text and optional images. The method places one decision placeholder per field in a complete assistant JSON skeleton and evaluates all fields in a single causal forward pass, with ground-truth symbols used only in supervision. The authors report that the 4B model reaches 90.02% unweighted average accuracy across seven evaluation suites versus 88.74% for Jev v1.13.0, and a mean latency of 44.16 ms for a fixed 289-token, three-field request on one RTX 4090; they also describe a confidence-based handoff study in which routing 42 of 111 Jevbench-Hard items to Atria-Dawn-Preview raised accuracy from 82/111 to 97/111.
Hugging Face · Papers · 🔥 0
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