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Hardware dataset curation no longer requires manual code refactoring, thanks to an agentic pipeline that autonomously trawls academic repositories to carve out synthesizable HLS designs for LLM training.
In this work, HLS-Eval is extended with an agentic evaluation flow built on the open-source mini-swe-agent framework, which establishes a foundation for agentic HLS design and motivates harder benchmarks and new agentic tooling as model capabilities progress.
