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.
Visual detector compression via location-aware discrimi- nant analysis
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
FlowTime introduces continuous generative regression using a one-step VAE and normalizing flows for personalized priors to predict watch time while addressing mean-collapse, quantization, and latency issues in prior paradigms.
Introduces DCD, a wavelet-based stage-wise distillation technique that preserves structural details in efficient 3D multi-modal MRI segmentation models.
A two-stage framework for cross-domain cervical abnormality detection that uses Spatially-Continuous Unpaired Neural Schrödinger Bridge for image synthesis and dual-level knowledge distillation for feature alignment.
citing papers explorer
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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.
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FlowTime: Towards Continuous Generative Watch Time Prediction via Flow-based Personalized Priors
FlowTime introduces continuous generative regression using a one-step VAE and normalizing flows for personalized priors to predict watch time while addressing mean-collapse, quantization, and latency issues in prior paradigms.