{
  "id": 3434,
  "url": "https://arxiv.org/abs/2606.02624v1",
  "title": "TadA-Bench: A Million-Variant Benchmark for Future-Round Discovery Toward Agentic Protein Engineering",
  "summary": "AI for scientific discovery is entering an agentic era, where protein-engineering systems are expected to prioritize future wet-lab experiments rather than merely fit static measurements. We introduce TadA-Bench, a million-variant wet-lab replay benchmark from 31 TadA directed-evolution rounds for future-round discovery toward agentic protein engineering. TadA-Bench preserves the campaign chronology and defines a fixed-data replay task: given earlier experimental rounds, models rank variants tha",
  "authors": "Jin Gao, Juntu Zhao, Zirui Zeng, Jiaqi Shen, Junhao Shi, Dukun Zhao et al.",
  "category": "research",
  "topics": "agents-autonomy,biotech",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-29T12:12:08.000Z",
  "fetched_at": "2026-07-14T16:30:14.371Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3434",
  "original_url": "https://arxiv.org/abs/2606.02624v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}