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MechBench is introduced, a benchmark that explicitly separates mechanism discovery from phenomenal-law recovery and reveals a substantial generalization gap in mechanistic reasoning, establishing mechanism discovery as a distinct challenge beyond recovering observable scientific laws.
Recent agentic symbolic regression approaches increasingly rely on large language models to analyze data, select scientific operations, and refine hypotheses over long search trajectories. In such systems, performance depends not only on the underlying model and search strategy, but also on the runtime infrastructure t...
Human mobility predictability concerns the best prediction performance attainable from a given target and input information, but its ground truth is not directly observable on real mobility data. We present BER-PEF, a Bayes-error-rate-based framework that converts BER estimation into mobility predictability estimation...
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