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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. TadA-Bench: A Million-Variant Benchmark for Future-Round Discovery Toward Agentic Protein Engineering

    Researchers have introduced TadA-Bench, a new benchmark designed to evaluate AI systems in agentic protein engineering. This benchmark consists of one million protein variants from 31 directed evolution rounds, preserving the chronological order of experiments. TadA-Bench aims to test AI's ability to predict successful variants for future experimental rounds, moving beyond static data fitting. AI

    IMPACT This benchmark could accelerate the development of AI agents capable of guiding complex scientific experiments like protein engineering.