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Ctenophores: Wonders of Biology

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  1. HypoEvolve: Genetic Algorithms Enable Multi-Agent LLMs to Discover Scientific Hypotheses

    The paper presents HypoEvolve, a generational genetic algorithm that coordinates specialized LLM agents to iteratively propose, critique, and refine scientific hypotheses. On a drug‑repurposing benchmark across 34 cancer types, it outperforms six baselines, achieving a DepMap selectivity of 0.171 versus 0.115.

    Hugging Face Daily Papersarxiv.org1 minpaper
  2. Advancing AI for biology: Teaching models to design and characterize antibodies

    Amazon Bio Discovery developed three AI models: MochiBind for fast, sequence-based antibody binding ranking, CA-MAP for context-aware developability prediction robust to batch effects, and an agent-guided system for de novo antibody design. These advancements aim to accelerate and improve the accuracy of antibody drug discovery, with experimental validation for a novel cancer target.

    Amazon Scienceamazon.science10 min
  3. ScienceBuddy: Recursive-in-Recursive Self-Improvement for Interactive Scientific Agents

    ScienceBuddy is an interactive workspace that converts researcher prompts, feedback, and execution traces into continual‑learning tasks for AI agents. It introduces a "recursive‑in‑recursive" self‑improvement loop that alternates harness refinement and model training, and showcases case studies across four scientific task families.

    Hugging Face Daily Papersarxiv.org1 minpaperHN2