TFRBench is a new benchmark and multi-agent synthesis method that generates reasoning traces for time-series forecasting and shows these traces raise average accuracy from ~40% to ~57% when used to prompt LLMs.
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Multi-field oscillons in the Friedberg-Lee-Sirlin model form bound states of two co-located oscillons that oscillate at their respective masses due to attractive interactions.
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TFRBench: A Reasoning Benchmark for Evaluating Forecasting Systems
TFRBench is a new benchmark and multi-agent synthesis method that generates reasoning traces for time-series forecasting and shows these traces raise average accuracy from ~40% to ~57% when used to prompt LLMs.
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Multi-field oscillons/I-balls in the Friedberg-Lee-Sirlin model
Multi-field oscillons in the Friedberg-Lee-Sirlin model form bound states of two co-located oscillons that oscillate at their respective masses due to attractive interactions.