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The FedDRLPD system architecture.

FedDRLPD: Deep Reinforcement Learning Defense Against Poisoning Attacks in Federated Learning

Leave a Comment / Machine Learning, Cybersecurity /

FedDRLPD: Deep Reinforcement Learning Defense Against Poisoning Attacks in Federated Learning | AI Security Research AISecurity Research Machine Learning About Federated Learning Security · Knowledge-Based Systems 2026 · 16 min read FedDRLPD: Teaching AI to Defend Itself Against Poisoning Attacks Through Deep Reinforcement Learning A novel defense framework that integrates Deep Q-Network algorithms with Mahalanobis […]

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K2-Agent: The Cognitive Architecture That Taught AI to Think Like Humans About Mobile Tasks.

K2-Agent: Co-Evolving Know-What and Know-How for Hierarchical Mobile Device Control

Leave a Comment / Machine Learning, Agent Systems /

K2-Agent: Co-Evolving Know-What and Know-How for Hierarchical Mobile Device Control | AI Security Research AISecurity Research Machine Learning About Agent Systems · ICLR 2026 · 18 min read K2-Agent: The Cognitive Architecture That Taught AI to Think Like Humans About Mobile Tasks A hierarchical framework separates “knowing what” from “knowing how” — enabling co-evolution of

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PDF: PUF-based DNN Fingerprinting for Knowledge Distillation Traceability.

PDF: PUF-based DNN Fingerprinting for Knowledge Distillation Traceability

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PDF: PUF-based DNN Fingerprinting for Knowledge Distillation Traceability | AI Security Research AISecurity Research Machine Learning About Model Security · DAC 2026, Long Beach, CA · 15 min read The Hardware Fingerprint That Traces Stolen AI Models Back to Their Source A novel PUF-based framework embeds unclonable hardware signatures into teacher models during knowledge distillation,

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Preference Score Distillation: Leveraging 2D Rewards to Align Text-to-3D Generation with Human Preference.

Preference Score Distillation: Leveraging 2D Rewards to Align Text-to-3D Generation with Human Preference

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Preference Score Distillation: Leveraging 2D Rewards to Align Text-to-3D Generation with Human Preference | MedAI Research nn.Module: # Actual implementation would load from HuggingFace or checkpoint return nn.Identity() # Placeholder @torch.no_grad() def forward(self, images: torch.Tensor, prompts: List[str]) -> torch.Tensor: “”” Compute reward scores for images. Args: images: [B, 3, H, W] RGB images in [-1,

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TPMRI Framework Architecture.

TPMRI: How Three-Stage Progressive Fusion Is Solving RGB-T Tracking’s Temporal Blindness

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TPMRI: How Three-Stage Progressive Fusion Is Solving RGB-T Tracking’s Temporal Blindness | MedAI Research MedAI Research Machine Learning About Computer Vision · Knowledge-Based Systems, 2026 · 14 min read When RGB-T Trackers Lose Track: How TPMRI Learned to Remember Through Time TPMRI introduces a three-stage progressive fusion framework that fixes RGB-T tracking’s most frustrating failures

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RCD framework addresses three critical bottlenecks in text-to-image generation.

RCD: How Three Simple Fixes Are Solving Stable Diffusion’s Biggest Problem

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RCD: How Three Simple Fixes Are Solving Stable Diffusion’s Biggest Problem | MedAI Research MedAI Research Machine Learning About Deep Learning · TPAMI, 2026 · 16 min read When Stable Diffusion Forgets: How RCD Learned to Remember Every Detail RCD introduces a training-free framework that fixes text-to-image diffusion models’ most frustrating failures — missing objects

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The WEMoE framework transforms critical MLP modules into dynamic mixture-of-experts structures while statically merging non-critical components. Input-dependent routing weights allow the model to adaptively blend task-specific knowledge, achieving superior multi-task performance over static merging methods.

WEMoE: How a Mixture-of-Experts Approach Is Solving the Multi-Task Model Merging Problem

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WEMoE: How a Mixture-of-Experts Approach Is Solving the Multi-Task Model Merging Problem | MedAI Research MedAI Research Machine Learning About Deep Learning · TPAMI, 2026 · 18 min read The Static Model Merging Problem — and How WEMoE Learned to Adapt WEMoE introduces a dynamic mixture-of-experts approach to multi-task model merging, transforming how we combine

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the proposed ESM-AnatTractNet model

ESM-AnatTractNet: Deep Learning for Eloquent White Matter Tractography in Pediatric Epilepsy Surgery

Leave a Comment / Machine Learning, Medical AI /

ESM-AnatTractNet: Deep Learning for Eloquent White Matter Tractography in Pediatric Epilepsy Surgery | MedAI Research MedAI Research Machine Learning About Neurosurgical AI · Medical Image Analysis, 2026 · 22 min read The Deep Learning System That Learned to Map Eloquent Brain Circuits from Electrical Stimulation and Anatomy ESM-AnatTractNet integrates electrophysiological validation with anatomical context to

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TAM: Plug-and-Play Temporal Attention Module for Motion-Guided Cardiac Segmentation

TAM: Plug-and-Play Temporal Attention Module for Motion-Guided Cardiac Segmentation

Leave a Comment / Machine Learning, Image Segmentation, Medical AI /

TAM: Plug-and-Play Temporal Attention Module for Motion-Guided Cardiac Segmentation | MedAI Research MedAI Research machine Learning About Cardiac AI · Medical Image Analysis, 2026 · 17 min read The Plug-and-Play Module That Taught Neural Networks to Watch the Heart Move A compact temporal attention module called TAM quietly outperforms much heavier architectures on cardiac segmentation

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The MT-Net encoder-decoder architecture with dimension transformation. D-DOWN operations compress depth while preserving lateral structure; D-UP operations restore volumetric resolution during decoding

MT-Net: 3D Retinal Microvascular Segmentation via Multi-Scale Topology Regulation

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MT-Net: 3D Retinal Microvascular Segmentation via Multi-Scale Topology Regulation Medical Image Analysis · 2026 Vol. 110 · doi:10.1016/j.media.2026.103988 When the Vessels Disappear in Three Dimensions:MT-Net and the Geometry of Retinal Blood Flow Ophthalmic AI ~2,600 words · 12 min read Luo, Zhang et al. — Ningbo University & Chinese Academy of Sciences Slug: /mt-net-3d-retinal-microvascular-segmentation Every

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