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I built plant-bench, an open, reproducible benchmark for plant genomics foundation models. The problem it solves: there are now several strong DNA sequence models for plants (Evo2, PlantCAD2, AgroNT), but no consistent way to compare them. Every paper uses its own fine-tuning scripts, unpinned datasets, and unshared weights, so a promoter-strength R² from one paper can't really be trusted against another. plant-bench fixes that by making every benchmark number a verifiable artifact, pinned to the exact model revision, dataset hash, and code commit that produced it, and rerunnable to the same value. I built it around a simple idea: a benchmark is just models × datasets × tasks, evaluated into content-addressed results. Adding a model, dataset, or task is literally dropping one file into the repo, with no central registration. All the heavy inference and fine-tuning runs on Modal, and every result is stored with full provenance. The other thing I cared about is making it agent-first. It's designed to be driven by Claude Code, with skills that handle the common loops, so two kinds of people can contribute in a single prompt: plant biologists who have a labeled dataset and want a benchmark that other people's models get scored on, and ML folks who have a model and want to see how it stacks up on real plant-biology tasks. I've got five models wired, four datasets, and four tasks (promoter strength, gene expression, lncRNA classification, and a zero-shot regulatory test), plus a live leaderboard UI, and the first real numbers are landing now

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sarhaan

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