RECAST trains a small router to compute evidence before answering
A learned router combines retrieval, SQL and generated Python to construct task-specific evidence from mixed sources.
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A learned router combines retrieval, SQL and generated Python to construct task-specific evidence from mixed sources.
The open-source toolkit evaluates whether removing target knowledge also damages useful capabilities, and whether evaluation metrics survive attempts to recover that knowledge.
Recent agent history weakly predicts extra errors and repeated calls after a summary replaces its working context.
Adaptive teaching improves benchmark results and teacher preferences, but the study does not measure real students' long-term learning.
The benchmark measures quality and cost when agents create programs or small models for large repetitive workloads.
Two full-duplex models respond to each other, but their turn-taking timing differs from the human telephone-speech reference.
The report explains local runtimes, cross-device coordination and approval controls, while distinguishing implemented features from roadmap goals.
The benchmark combines execution, rendered behavior and numerical physical checks across 168 simulation tasks.
Two linked improvement loops adapt policy training and human-calibrated verification as new failure patterns appear.
Paired repository tasks measure unnecessary precautions separately from successful task completion.
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