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- Arxiv 2608 19880 Envharness Awakening Static Worlds For Agent LearnEnvHarness: Awakening Static Worlds for Agent Learning (arXiv: 2608.19880)Votes: 0GitHub stars: 3
- Aligned Agents Biased Swarm MeasuringWhile Multi-Agent Systems (MAS) are increasingly deployed for complex workflows, their emergent properties-particularly the accumulation of bias-remain poorly understood. Because real-world MAS are to... Activation: multi-agent systems, agent collaboration, MAS, bias amplification, multi-agent bias.Votes: 0GitHub stars: 3
- Agentic Iot Internet Of AgentsSurvey of agentic IoT architectures, applications, and challenges toward the Internet of Agents. Examines how AI integration transforms IoT from passive data collection to intelligent systems with anomaly detection, predictive maintenance, and optimization. Activation: agentic IoT, internet of agents, AIoT, IoT architecture, anomaly detection, predictive maintenance, smart systems.Votes: 0GitHub stars: 3
- Agent Delivery Engineering Predictive Reliability FrameworkLong-horizon LLM multi-agent systems face reliability risks invisible to infrastructure monitoring. We propose the ADE Predictive Reliability Framework (ADE-PRF), enabling proactive health trajectory. Based on arXiv:2607.07689.Votes: 0GitHub stars: 3
- Working Memory Heterogeneous DelaysWorking memory implementation in recurrent spiking neural networks using heterogeneous synaptic delays. Models memory as sequential chains of overlapping Spiking Motifs for precise temporal pattern storage and recall. Activation: working memory SNN, heterogeneous delays, spiking motifs, recurrent SNN memory, temporal pattern storage, neuromorphic working memory.Votes: 0GitHub stars: 3
- Working Memory Heterogeneous Delays 2026Working memory in recurrent spiking neural networks using heterogeneous synaptic delays (2026-04 update). Enables energy-efficient temporal pattern storage and recall in SNNs.Votes: 0GitHub stars: 3
- Adaptive Hybrid Feature Fusion MedicalAdaptive Hybrid Quantum-Classical Feature Fusion methodology for medical image classification. Addresses optimization asymmetries between quantum and classical paradigms using Temperature-Scaled Hybrid Fusion (TSHF), Dynamic Hybrid Fusion (DHF), and Static Hybrid Fusion (SHF) strategies. Use when designing hybrid quantum-classical ML pipelines for healthcare/medical imaging, especially when combining ResNet backbones with variational quantum circuits for diagnostic tasks.Votes: 0GitHub stars: 3
- Medical Ai DiagnosisPatterns for building AI-based medical diagnosis systems with clinical explainability. Covers foundation models for medical imaging, clinical reasoning trace generation, multi-modal patient data integration, and explainable AI for healthcare. Use when building medical AI diagnosis tools, clinical decision support systems, explainable medical ML models, or medical foundation models. Trigger: medical AI, clinical diagnosis AI, explainable healthcare, medical foundation model, DeepMedix, clinica...Votes: 0GitHub stars: 3
- Mamba Spike Behavioral DecodingMamba forecaster methodology for implicit behavioral decoding from next-step spike forecasts at population scale. A single sequence model trained only on next-step Poisson rate prediction produces predicted firing rates that decode animal behavior better than raw spike counts under matched temporal context. Enables closed-loop BCI without separate behavioral decoding networks.Votes: 0GitHub stars: 3
- Lonic Algorithm Hardware Codesign SnnLonic: INT4 algorithm-hardware co-design for SNNs. Energy-efficient fully local online SNN training methodology with algorithm-hardware co-optimization.Votes: 0GitHub stars: 3
- Llm Emotion Dynamics DecodingLLM-enhanced multi-target regression framework for decoding continuous naturalistic emotion dynamics from brain fMRI signals using dynamic functional connectivity and graph-theoretical explainable AI, supporting psychological constructionist frameworks over locationist accountsVotes: 0GitHub stars: 3
- Liquidtad Efficient Method Temporal ActionLiquidTAD: Efficient temporal action detection via liquid neural dynamics. Replaces self-attention with parallelized ActionLiquid blocks for parameter-efficient video understanding. Use when building temporal action detection systems, deploying on edge devices, or replacing attention with liquid neural networks.Votes: 0GitHub stars: 3
- Learning Neuron Dynamics Deep SnnLearning rich neuron dynamics within deep Spiking Neural Networks (SNNs). Moves beyond simple LIF neuron models to capture complex temporal dynamics in deep SNN architectures. Enables more biologically realistic and computationally powerful spiking networks. Applicable to deep SNN training, advanced neuron modeling, temporal feature learning. Trigger: deep SNN neuron dynamics, LIF limitations, complex neuron models SNN, temporal dynamics spiking networks, neuron model learningVotes: 0GitHub stars: 3
- Input Constrained Spatiotemporal TubesInput-constrained spatiotemporal tube (STT) control framework for safe navigation of unknown Euler-Lagrange systems in dynamic environments. Provides finite-time reach-avoid-stay guarantees with explicit actuator constraint handling. Approximation-free and computationally efficient.Votes: 0GitHub stars: 3
- Quantum Network RoutingQuantum network routing and entanglement distribution using surface code error correction — reliable quantum communication over noisy channels.Votes: 0GitHub stars: 3
- Implicit Behavioral Decoding Spike ForecastsImplicit behavioral decoding from next-step spike forecasts at population scale. A single Mamba forecaster trained only on next-step spike counts at Neuropixels scale can deliver both neural population forecasts and behavioral state readouts in one forward pass. Activation: behavioral decoding, spike forecasting, Mamba neural population, Neuropixels, closed-loop BCI, implicit readout, spike prediction, population neural modelsVotes: 0GitHub stars: 3
- Holonic Digital Twins Physical Ai NetworksHDT-Nets framework for Physical AI over Networks.Votes: 0GitHub stars: 3
- Triple Phase Multimodal Medical DiagnosisTriple-phase multimodal framework for medical image classification — combines cross-modality contrastive learning, modality-specific fine-tuning, and feature-level multimodal ensemble learning for patient-level prediction. Validated on microbial keratitis subtype diagnosis with 85.84% accuracy across 1645 patients.Votes: 0GitHub stars: 3
- Robots That Can Adapt Like Animals**arXiv ID:** 1407.3501 **Authors:** Antoine Cully, Jeff Clune, Danesh Tarapore, Jean-Baptiste Mouret **Published:** 2014-07-13T19:06:08Z **Abstract:** As robots leave the controlled environments of factories to autonomously function in more complex, natural environments, they will have to respond to the inevitable fact that they will become damaged. However, while animals can quickly adapt to a wide variety of injuries, current robots cannot "think outside the box" to find a compensatory beh...Votes: 0GitHub stars: 3
- Medical Ai DiagnosisPatterns for building AI-based medical diagnosis systems with clinical explainability. Covers foundation models for medical imaging, clinical reasoning trace generation, multi-modal patient data integration, and explainable AI for healthcare. Use when building medical AI diagnosis tools, clinical decision support systems, explainable medical ML models, or medical foundation models. Trigger: medical AI, clinical diagnosis AI, explainable healthcare, medical foundation model, DeepMedix, clinica...Votes: 0GitHub stars: 3
- Federated Ecg WearableFederated Learning methodology for privacy-preserving ECG monitoring on ultra-resource-constrained wearable devices. Family-grouped hierarchical FL for sub-5KB cardiovascular models. Activation: federated learning ECG, wearable cardiac monitoring, privacy-preserving ML, sub-5KB model, arrhythmia detection.Votes: 0GitHub stars: 3
- Hardware Faithful Plc VerificationHardware-faithful formal verification for IEC 61131-3 PLC programs on resource-constrained microcontrollers. Closes the deployment gap between abstract verification models and real MCU hardware by using declarative HAL descriptors (word width, ADC/PWM resolution, I/O binding) and sound lowering that constrains inputs to hardware-realizable ranges. Use when: (1) verifying PLC programs for embedded MCUs, (2) formal verification of industrial control systems, (3) eliminating false alarms in boun...Votes: 0GitHub stars: 3
- Hardware Aware Snn Design Space ExplorationHardware-aware open-source simulation framework for mixed-signal spiking neural networks. Enables cross-layer design space exploration across neuron models (LIF, HH, AH), synaptic devices (floating-gate, ReRAM), and architectures. Reports accuracy alongside hardware metrics (area, power, quantization sensitivity) for neuromorphic computing at the edge.Votes: 0GitHub stars: 3
- Hardware Aware Qaoa CybersecurityHardware-aware QAOA methodology for cybersecurity applications. Combines hardware noise modeling, error-mitigated expectation estimation, and application-specific problem formulation for 100+ qubit NISQ processors.Votes: 0GitHub stars: 3