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NVIDIA AVO Completes All 183 Levels in the ARC-AGI-3 Public Set With About 12% Fewer Actions Than VISTA

NVIDIA adapted the AVO agent architecture, originally used for evolutionary search over GPU kernels, to interactive reasoning and achieved a 100.00 RHAE score with Claude Opus 5. The results show that agent memory and verification loops can substantially change the performance of the same model, but they cover only public environments, and the comparison with VISTA is not a controlled ablation.

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Pandora’s Router Factors Model-Evaluation Costs Into Routing, Cutting EmbedLLM’s Combined Loss to 0.386

Google DeepMind formulates the decision of whether to run costly model evaluations as a “Pandora’s box” search problem, deciding sequentially whether an additional assessment is worthwhile based on its value of information. It achieved the lowest average routing regret plus evaluation cost across all three tests, although its effectiveness depends on calibrated signal distributions and cost settings.

最新模型

DECOWAM Separates Robot Dog Base and Arm Representations, Cutting Action MSE by 21.7% While Updating Just 25.95 Million Parameters

DECOWAM adds base, arm, and camera egomotion interfaces to the frozen 6.02-billion-parameter FastWAM, using only the current observation at deployment to predict 48 action steps. Its overall completion rate in physical robot tests is only slightly higher than FastWAM’s, but its robustness success rate under base disturbances rises from 12.7% to 30.4%.