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Code review, refactoring, debugging, maintainability, and engineering quality
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- Engineering Grounded Ai Power SystemsEngineering-Grounded AI (EGAI) framework for power systems education using hands-on executable modules that follow domain rules rather than acting as black boxes.Votes: 0GitHub stars: 3
- Eeg Sae InterpretabilityMechanistic interpretability of EEG foundation models via sparse autoencoders. Extracting interpretable features from EEG foundation model internal representations using sparse autoencoder decomposition. Use when: interpreting EEG foundation models, extracting clinically meaningful features from EEG, understanding neural network representations of EEG signals, mechanistic analysis of neural signal processing.Votes: 0GitHub stars: 3
- Distributed Iqft CommunicationCommunication-efficient distributed Inverse Quantum Fourier Transform (iQFT) using communication horizon pruning to reduce inter-node quantum communication from O(P^2) to O(P). Use when: distributed quantum computing, iQFT across quantum networks, quantum communication optimization, distributed Shor algorithm, scalable QFT implementation, or quantum network resource allocation.Votes: 0GitHub stars: 3
- Discounted Mpc RobustnessRobustness analysis for MPC and infinite-horizon optimal control under plant-model mismatch with quadratic costs. Covers discounted and undiscounted scenarios, stability guarantees, and suboptimality bounds. Use when: (1) MPC robustness analysis, (2) plant-model mismatch effects, (3) discounted infinite-horizon control, (4) model uncertainty in optimal control, (5) stability under model errors, (6) data-driven surrogate models.Votes: 0GitHub stars: 3
- Dfs Quantum Reservoir NetworksQuantum reservoir computing using decoherence-free subspaces (DFS) for room-temperature quantum AI. Classifies entangled vs product states without cooling. Activation: quantum reservoir, decoherence-free subspace, DFS, room temperature quantum, quantum classifier.Votes: 0GitHub stars: 3
- Piper Programmable Distributed TrainingPiper framework for user-controllable distributed training that decouples parallelism strategy from runtime implementation using unified global training DAG intermediate representation. Use for distributed ML training, parallelism strategy design, and flexible training system architecture.Votes: 0GitHub stars: 3
- Curriculum Multiple Shooting Neural OdesGeneral-purpose training strategy for fitting ordinary differential equation models to time-series data by integrating curriculum learning with multiple shooting. Accelerates and stabilizes training convergence for Neural ODEs, Universal Differential Equations, and mechanistic ODEs.Votes: 0GitHub stars: 3
- Tide Ei DynamicsTIDE (Temporal Inhibitory-Excitatory Dynamic Engine) methodology — neuro-inspired architecture using asymmetric Excitatory-Inhibitory (E-I) networks with Wilson-Cowan dynamics and lateral inhibition for stabilized neural dynamics. Integrates Dale's principle (80:20 E-I ratio), hierarchical receptive fields, and game-theoretic energy-based optimization. Use when: designing neuro-inspired architectures with stability guarantees, building continuous thought/reasoning systems with internal dynami...Votes: 0GitHub stars: 3
- Qshift Adaptive SamplingqSHIFT adaptive sampling protocol for higher-order quantum simulation. Implements L-independent gate complexity with O(t^(1+r)) error scaling through adaptive distribution updates. Use when: performing quantum Hamiltonian simulation, designing quantum algorithms requiring high-precision time evolution, implementing qDRIFT-style randomized product formulas, or optimizing circuit depth for NISQ devices. Triggers on: qshift, adaptive sampling quantum, quantum simulation protocol, higher-order Tr...Votes: 0GitHub stars: 3
- Neurosymbolic Robustness AnalysisNeurosymbolic robustness analysis framework for discrete systems. Uses LLM neural reasoning layer + symbolic verification layer to analyze robustness of discrete-event systems against transition deviations. Based on arXiv:2606.03872 (Jun 2026).Votes: 0GitHub stars: 3
- Feedforward Dynamics Stimulus EncodingThe illusory simplicity of the feedforward pass — evidence for dynamical nature of stimulus encoding in neural networks. Demonstrates that feedforward computation involves complex temporal dynamics rather than static transformations. Applicable to neural network analysis, computational neuroscience, dynamical systems analysis. 触发词: feedforward dynamics, stimulus encoding, temporal computation, dynamical neural networks, illusory simplicityVotes: 0GitHub stars: 3
- Scene Adaptive MoeScene-Adaptive Mixture of Experts (SAMoE-C) for continual learning in CSI-based human activity recognition. Uses MoE architecture with domain-specific expert routing and scene-adaptive gating for domain-incremental HAR. Use when: CSI sensing, HAR, domain-incremental learning, MoE continual learning, wireless sensing CL.Votes: 0GitHub stars: 3
- ComemnetCoMemNet: Contrastive sampling with Memory Replay Network for continual traffic prediction. Dual-branch architecture with contrastive positive/negative sample generation for efficient memory replay in spatio-temporal forecasting. Use when: traffic prediction, spatio-temporal forecasting, continual learning with memory replay, contrastive CL.Votes: 0GitHub stars: 3
- Computational Affordance Landscape Brain NetworksComputational Affordance Landscape framework for quantifying neural network computation costs using control theory. Use when analyzing brain network structure-function relationships, neural circuit computations, or applying control theory to quantify computational costs in biological and artificial neural networks.Votes: 0GitHub stars: 3
- Composite Quantum Gates Error CancellationComposite quantum pulse gates that simultaneously compensate multiple systematic errors via derivative-based error cancellation. Covers derivative removal by adiabatic gate (DRAG)-extended pulse shaping, multi-error compensating composite sequences, Wimperis/Trotter-Suzuki derived composite pulses, and numerical optimization of gate fidelity under simultaneous amplitude, frequency, and timing errors. Use when: designing robust quantum gates, compensating pulse errors, building composite seque...Votes: 0GitHub stars: 3
- Cmos Invertible Logic Stochastic ComputingCMOS invertible logic using spiking stochastic computing methodology. Implements bidirectional (forward/reverse) logic gates via Boltzmann machines with simple spiking neural networks. Activation: invertible logic, stochastic computing, CMOS neuromorphic, Boltzmann machine, factorization hardware, spiking logic gate.Votes: 0GitHub stars: 3
- Circuit Level Noise EstimationCircuit-Level Noise Estimation via Shuttling in Plaquette Circuits. Use when analyzing quantum algorithms, complexity bounds, quantum ML architectures, or quantum error correction involving mathematical analysis and statistical methods.Votes: 0GitHub stars: 3
- Bus Brain Inspired Self Reflection VlmBUS (Brain-Inspired Unsupervised Self-Reflection) methodology for training Vision-Language Models to self-correct reasoning without labeled supervision. Implements unsupervised reflection via brain-inspired feedback loops, enabling VLMs to review and improve generated reasoning traces without requiring large annotated datasets. Activation: brain-inspired self-reflection, VLM reasoning improvement, unsupervised reasoning correction, self-correcting vision-language models, BUS methodologyVotes: 0GitHub stars: 3
- Brain Dnn Transformation AlignmentNaturality Violation Score (NVS) for brain-DNN alignment beyond object-level comparison. Uses category theory to test whether brains and DNNs preserve the same transformations among stimuli. Covers approximate naturality, axis-resolved alignment analysis, and hierarchy crossover detection. Use when: (1) evaluating brain-DNN alignment at the transformation level, (2) comparing neural representations across model architectures, (3) analyzing semantic vs low-level visual alignment, or (4) design...Votes: 0GitHub stars: 3
- Brain Dit Fmri Foundation Model V4Brain-DiT universal multi-state fMRI foundation model methodology. Metadata-conditioned diffusion pretraining with DiT on 349,898 sessions across 24 datasets spanning resting, task, naturalistic, disease, and sleep states. Activation: brain-dit, fmri foundation model, diffusion transformer brain, metadata-conditioned pretraining, multi-state fmri, brain diffusionVotes: 0GitHub stars: 3
- Bakron Efficient Quantization Kron HessianEfficient quantization using Kronecker-factored Hessians.Votes: 0GitHub stars: 3
- Alternative Adiabatic Quantum DynamicsAlternative adiabatic quantum dynamics methodology — gate-based implementations of adiabatic computing without time-dependent Hamiltonian simulation overhead. For quantum algorithms, optimization, and adiabatic quantum computing.Votes: 0GitHub stars: 3
- Tour A Trajectory Level Unlearning Benchmark For OfflineTOUR: A Trajectory-Level Unlearning Benchmark for Offline Reinforcement LearningVotes: 0GitHub stars: 3
- The Standard Interpretable Model A General Theory Of Interpretable Machine Learning To Deductively Design Interpretable Methods Using Lagrangian Mechanics**arXiv ID:** 2606.12289 **Authors:** Pietro Barbiero, Giovanni De Felice, Mateo Espinosa Zarlenga, Francesco Giannini, Filippo Bonchi, Mateja Jamnik, Giuseppe Marra, Ruggero Noris **Published:** 2026-06-10T16:26:22Z **Abstract:** As Artificial Intelligence models grow in complexity, interpretability has become an indispensable tool for understanding, debugging, and controlling their computations. However, interpretability lacks general theories to deductively design interpretable methods. Th...Votes: 0GitHub stars: 3