Sakana AI 防卫部门亮相 GSE 2026，谈防卫 AI 落地路径

The ontology layer plus human-approval workflows targets the gap between model performance and enterprise readiness, packaging what was previously bespoke per-project work into a reusable base. Enterprises considering agentic deployments with audit and approval requirements can compare Aquarium against platforms like Palantir Foundry's agent offerings. Sakana AI, the Tokyo-based lab behind the AI Scientist, is extending from research into enterprise tooling.

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The shift here is from optimising for one best answer to building an archive of diverse, reasoned recommendations — useful when a decision maker needs options, not rankings. Practitioners running discovery-style tasks such as M&A screening or research triage can reuse the released code with their own candidate pools. The authors note limits: results rest on LLM judges, and real deployments would need fresh data and human expert feedback.

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QD-Rec reframes recommendation as open-ended exploration, keeping diverse hypothesis angles rather than a single ranked list, which suits decisions like M&A screening or research triage. Developers experimenting with LLM-driven search can try the released GitHub code directly. It contrasts with standard top-k recommenders by optimising for coverage of reasons, not just score.

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