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Gemini 4 Argon is now available for everyone

Speeding up robotics development By bringing together advanced visual-semantic reasoning, deep physical understanding, and autonomous code generation, Argon unlocks new workflows across the robotics stack and accelerates progress toward physical AGI. This leap in capability is reflected in Argon’s performance on the Embodied Reasoning (ERQA) benchmark, which tests a model's ability to understand and solve…

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A Developer’s Guide to OmniParseBench: Leaderboard Benchmarks and OCR Testing

In this tutorial, we implement OmniParseBench, Datalab’s open benchmark for document parsers, which grades a parser’s output with 16,288 small pass-or-fail tests across 2,937 pages, from scanned forms and handwriting to arXiv math and dense tables. Rather than trust the leaderboard, we rebuild it from the verdicts Datalab released for 27 parsers, after feeding the…

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Gemini 4 Argon for Robotics

Gemini 4 Argon marks a step change in physical understanding, code generation, and visual-semantic reasoning—unlocking new workflows across the robotics stack and accelerating progress toward physical AGI. You can see this leap in capability on the Embodied Reasoning (ERQA) benchmark, which tests a model's ability to understand and solve physical, spatial problems in 3D environments.…

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Agentic RAG vs. Naive RAG: When Your Retrieval System Should Think for Itself

Large language models are remarkable text generators, but they have a well-documented weakness: they hallucinate. Without access to verified, up-to-date information, they confidently produce plausible-sounding answers that are factually wrong. Retrieval-Augmented Generation (RAG) emerged as the standard solution, grounding model outputs in external knowledge sources and dramatically reducing hallucinations across enterprise applications. But as organizations…

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