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Contextual forecasting seeks to improve time series forecasting by incorporating forward-looking information, such as planned events, external conditions, and expert expectations. However, reliable evaluation remains difficult: existing benchmarks often have limited domain coverage, lack standardized history-future spl...
Grounding action, verification, and memory in state, what the authors call state-grounding, shifts the main bottleneck from perception toward reasoning: failures depend more on what the agent thinks than on what it sees.
This work trains software-engineering, deep-research, and general tool-use agents on gpt-oss-20b and improves task reward in all three and presents MCP-Universe RL (MCP-U RL), an open-source framework that takes over both.
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