Evaluating human-AI co-design requires testing whether coding agents can translate real users' multimodal requests into working application revisions. We introduce Co-Design Bench, a reference-free be
Evaluating human-AI co-design requires testing whether coding agents can translate real users' multimodal requests into working application revisions. We introduce Co-Design Bench, a reference-free benchmark of 273 tasks curated from 37,463 deduplicated queries across 26,314 real user coding sessions. Its two tracks, Multimodal Editing (ME) and Repository Editing (RE), cover localized edits with optional image, audio, video, or document inputs and coordinated repository revisions. Tasks span three capability dimensions: Grounded Understanding, UI Transformation, and Functional Implementation. The tasks pair normalized requests and runnable pre-edit applications with 2,331 hidden…
Summary and abstract excerpt via Hugging Face Papers ↗ · arXiv.
Co-Design Bench: Multimodal Collaborative Coding Evaluation for Agentic Models was first seen on Hugging Face · Daily papers on Oct 30, 2026. Since then it has appeared in 38 checks on 4 different days. Its best position was #10 of 30, reached on Oct 30, 17:28 UTC. It dropped off the list after Nov 2, 07:28 UTC.
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Not at the moment. It was last on the list on Nov 2, 07:28 UTC, after 4 days of trending.
#10 on Hugging Face · Daily papers, on Oct 30, 17:28 UTC. Trendoday checks this list every 120 minutes.
It was first seen on Oct 30, 2026 and has been on the list on 4 days (38 checks) since.