Decathlon selected Chronos-2 as the core model in its demand forecasting stack after benchmarking multiple time series foundation models on its retail data and testing it in production on AWS. Chronos-2 fine-tuned runs with inference under 2 minutes per cutoff for 25,000 products. Decathlon now forecasts weekly on 12-week replenishment and 52-week horizons with less retraining and faster region rollout, reducing WAPE versus its prior models and improving inventory savings and availability.
The Open ASR Leaderboard added two new evaluation sets, Monsoon en-IN and Monsoon hi-IN, to expand ASR testing to Hindi and Indian English with speaker-disjoint public and private splits. Monsoon contains 4,888 speakers across the four splits. Developers can now score models on these datasets (and do private-split evaluation without tuning to the leaderboard), making it easier to measure and reduce performance gaps that are not visible in other leaderboard tests.
Agent Seer was introduced to synthesize realistic multi-turn tool-use evaluation scenarios directly from a single MCP tool specification instead of hand-built benchmarks. It was evaluated on seven MCP specifications and achieved complete tool coverage on small and medium specifications. As a result, it produces graded scenarios with synthetic outputs and improves measurements of tool-calling correctness and conversational coherence, highlighting argument value accuracy as the main failure mode and parameter schema complexity as the strongest quality correlate.
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