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RAMS: Resource-Adaptive Model Switching for Embedded Edge Perception Under Load Open-SWE-Traces: 207K Multilingual Trajectories Set New Standard for Autonomous Software Engineering Agents Infant-Inspired Noise Boosts Deep RL Exploration, Research from arXiv Shows Mutual Distillation of Dual Foundation Models Achieves State-of-the-Art PET/CT Segmentation with Only 5 Labeled Cases SPARK Method Activates Latent Security Knowledge in LLMs for Secure Code Generation Apple explains why Siri AI took so long: first version ready last year but rebuilt from ground up New LLM Framework Detects Phishing Emails with Over 90% Accuracy Dual-Granularity Orthogonal Disentanglement: New Framework Boosts Generalizable Audio Deepfake Detection Medical Image Segmentation Survey: U-Net, Transformers, SAM and Clinical Translation Challenges Bayesian Inference and Decision Audits Reveal Unreliability in Frontier AI Evaluation Archives RAMS: Resource-Adaptive Model Switching for Embedded Edge Perception Under Load Open-SWE-Traces: 207K Multilingual Trajectories Set New Standard for Autonomous Software Engineering Agents Infant-Inspired Noise Boosts Deep RL Exploration, Research from arXiv Shows Mutual Distillation of Dual Foundation Models Achieves State-of-the-Art PET/CT Segmentation with Only 5 Labeled Cases SPARK Method Activates Latent Security Knowledge in LLMs for Secure Code Generation Apple explains why Siri AI took so long: first version ready last year but rebuilt from ground up New LLM Framework Detects Phishing Emails with Over 90% Accuracy Dual-Granularity Orthogonal Disentanglement: New Framework Boosts Generalizable Audio Deepfake Detection Medical Image Segmentation Survey: U-Net, Transformers, SAM and Clinical Translation Challenges Bayesian Inference and Decision Audits Reveal Unreliability in Frontier AI Evaluation Archives

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MA-SBI: Misspecification-Aware Simulation-Based Inference via Side-Channel Guidance

Software #simulation-based inference#misspecification

MA-SBI: Misspecification-Aware Simulation-Based Inference via Side-Channel Guidance

Researchers propose MA-SBI, a misspecification-aware simulation-based inference framework that leverages unstructured side-channel information—such as regime labels or policy bulletins—to correct posterior estimates without requiring ground-truth parameter pairs. The method matches oracle performance on hide-the-calibration benchmarks and improves log-likelihood on real COVID epidemiological data.

Jun 16, 2026 1 source

Bayesian 3D Steerable CNNs Combine Equivariance and Uncertainty Quantification

Artificial Intelligence #bayesian#3d

Bayesian 3D Steerable CNNs Combine Equivariance and Uncertainty Quantification

A research paper proposes a Bayesian Steerable-CNN that simultaneously preserves SE(3)-equivariance and enables uncertainty quantification. The model achieves an expected calibration error of 0.0263 and outperforms its deterministic counterpart by up to 6.17% under distributional shift. The framework decomposes uncertainty into epistemic and aleatoric components, with a statistically significant negative correlation between epistemic uncertainty and prediction error.

Jun 16, 2026 1 source