TRACE is a RANO 2.0-aligned concept bottleneck model for 4-class glioblastoma response classification on longitudinal 3D MRI that reports 0.4769 macro F1 on the LUMIERE dataset via 5-fold patient-wise cross-validation.
2025.11357220
2 Pith papers cite this work. Polarity classification is still indexing.
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RSEA adds a strict held-out keep-better gate to recursive self-evolution of agent artifacts, yielding monotone-safe gains or parity with the base ReAct agent on ALFWorld, GAIA, τ-bench, and WebShop.
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TRACE: A Concept Bottleneck Model for Longitudinal 3D Glioblastoma Response Assessment
TRACE is a RANO 2.0-aligned concept bottleneck model for 4-class glioblastoma response classification on longitudinal 3D MRI that reports 0.4769 macro F1 on the LUMIERE dataset via 5-fold patient-wise cross-validation.
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Recursive Self-Evolving Agents via Held-Out Selection
RSEA adds a strict held-out keep-better gate to recursive self-evolution of agent artifacts, yielding monotone-safe gains or parity with the base ReAct agent on ALFWorld, GAIA, τ-bench, and WebShop.