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Testing & QA
Unit tests, integration tests, E2E, QA, validation, and test automation
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- Cross Seed Explainability Procrustes Sparse AutoencodersProcrustes-conditioned Joint End-to-end Top-K SAE for extracting cross-seed universal features from independently trained BERT models. Combines Top-K sparsity, end-to-end optimization, and dead-feature revival. Pearson r ≥ 0.70 across seeds. Activation: sparse autoencoder, cross-seed universality, Procrustes alignment, mechanistic interpretability, feature universality.Votes: 0GitHub stars: 3
- Zuna1 1 Flexible Eeg Foundation ModelZUNA1.1: A 380M-parameter diffusion autoencoder for flexible EEG signal reconstruction, denoising, and super-resolution with arbitrary channel configurations and temporal intervals.Votes: 0GitHub stars: 3
- Winner Take All Bottleneck DisentangledWinner-Take-All (WTA) bottlenecks enforce disentangled symbolic representations in multi-task learning. Shows WTA circuits (a core cortical motif) within deep networks extract categorical latent factors where single neurons encode single abstract features (object, color, position). Theoretical proof + empirical validation. Activation: WTA, winner-take-all, disentangled representations, symbolic AI, latent factors, cortical circuits, multi-task learning, neural bottleneck, softmax attention, o...Votes: 0GitHub stars: 3
- Weighted Partitions Interval RestrictionsExact formulas and bivariate master identity methodology for weighted partitions with interval restrictions. Use when: deriving generating functions for restricted partition functions, proving partition coefficient bounds via Rogers-Fine evaluation, establishing master identities with auxiliary variables, or analyzing quantum modular forms from interval-restricted partitions. Activation: weighted partitions, interval restrictions, bivariate master identity, Rogers-Fine evaluation, false theta...Votes: 0GitHub stars: 3
- Visual Semantic Decoding Ecog VideoEnd-to-end deep learning framework for visual semantic decoding from ECoG, demonstrating promising performance without handcrafted features while maintaining interpretability.Votes: 0GitHub stars: 3
- Triple Config Brain Network RnnTriple Configuration Brain Networks framework using RNNs to model EEG source-localized dynamics. Separates exogenous stimuli, task demands, and spontaneous activity contributions to brain network configurations. Identifies parietal network as critical hub. Activation: triple configuration brain, RNN brain network, EEG source localization, parietal hub, brain network configuration, exogenous endogenous brain dynamicsVotes: 0GitHub stars: 3
- Tribe Fmri Encoding ValidationValidation framework for brain-encoding models like TRIBE — testing whether predicted fMRI signals correlate with behavioral engagement metrics. Use when evaluating brain-encoding models, testing fMRI predictions against behavioral data, or validating neural prediction models.Votes: 0GitHub stars: 3
- Topology Dependent Png Recurrence PlotTopology-Dependent Emergence of Polychronous Neuronal Groups via Recurrence Plot characterization. Analyzes how small-world network topology drives PNG formation in spiking networks with STDP and heterogeneous delays.Votes: 0GitHub stars: 3
- Topo Omni Deep Topographic MultimodalTopo-Omni deep topographic multimodal model for discovering functionally selective brain regions across visual, auditory, and language processing streams. Activation: topographic model, multimodal brain model, cortical organization, brain regions discovery.Votes: 0GitHub stars: 3
- Spiking Reservoir RobustnessRobust spiking reservoir computing framework bridging theory and practice. Introduces robustness interval for tuning reservoirs at edge-of-chaos under experimental uncertainty. Use when working with: (1) Spiking neural networks, (2) Reservoir computing, (3) Neuromorphic computing, (4) Temporal pattern processing. Activation: spiking reservoirs, robustness interval, edge-of-chaos, LIF reservoirs, temporal processing.Votes: 0GitHub stars: 3
- Spiking Dynamic Neural Manifolds ImplementationConcrete implementation recipe for dynamic neural manifold control on neuromorphic hardware (SpiNNaker 2), derived from arXiv 2607.07373. Covers rate→spike conversion, sparse circulant weight memory, three control knobs (gain/speed, additive current/shape, subspace inhibition/selection), PCA-based manifold validation, and closed-loop maze navigation with linear readout training. Use when porting ring-attractor sequential SNNs to spike-based neuromorphic chips or building explainable low-dimen...Votes: 0GitHub stars: 3
- Sbtg Neural Circuit InferenceScore-Block Time Graphs (SBTG) methodology for inferring lag-specific directed neural circuit interactions from population activity data. Uses denoising score models and cross-block score products to recover the Jacobian of transition maps under nonlinear dynamics. Based on arXiv:2605.02852 (Kinger et al., 2026).Votes: 0GitHub stars: 3
- Sae Brain Llm TopographySparse Autoencoders (SAEs) bridge mechanistic interpretability with neural encoding models to map LLM features onto cortical semantic topography — decomposing GPT-2 XL and Llama-3.1-8B into 16K-32K interpretable features and showing semantic features dominate brain alignment (94% of peak encoding performance, r=0.285), validated across English/Chinese/French (arXiv: 2605.23035, CoNLL 2026).Votes: 0GitHub stars: 3
- Reward Valuation Vlm Anhedonia CausalMechanistic framework identifying reward-anticipatory units in Vision-Language Models (VLMs) that parallel Nucleus Accumbens (NAc) function. Causal perturbation of NAc-selective units induces anhedonia-like behavioral shifts toward low-effort, low-reward options. Validates alignment between AI reward circuits and human dopaminergic reward system.Votes: 0GitHub stars: 3
- Quantum Synchronization Dynamics FrameworkUnified quantum synchronization framework combining Fock state synchronization (phase-locking non-classical states with negative Wigner function, Arnold tongue regime, phase slip rate extraction) and limit cycle desynchronization (quantum phase slip proliferation degrading phase locking, Keldysh path integral, non-Markovian effects). Applies to quantum control, quantum optics, bosonic systems, quantum information processing. Activation: quantum synchronization, Fock state, phase locking, Arno...Votes: 0GitHub stars: 3
- Predictable Mean Field Chaos RnnPredictable Mean-Field Chaos in Random Recurrent Networks methodology — Krylov state space analysis revealing latent determinism in mean-field dynamics for analytic nonlinearities with fast Fourier decay. Demonstrates that microscopic sensitivity and predictive complexity are distinct aspects of chaos. Use when: analyzing RNN chaos, mean-field theory, Krylov complexity, Lyapunov exponents, Hamiltonian chaotic dynamics, classical dissipative systems, or prediction theory in recurrent networks.Votes: 0GitHub stars: 3
- Path Integral Default Intensity PricingPath-integral formalism for semi-analytical pricing of default intensity models in quantitative finance. Enables accurate derivatives pricing (CDS, XVA) under stochastic default intensity without full numerical simulation.Votes: 0GitHub stars: 3
- Mixed Potential Memristor Circuit ConvergenceMixed Potential approach for analyzing convergence of nonlinear RLC circuits with memristors using flux-charge analysis method (FCAM). Provides Lyapunov-like stability proofs for circuits with all four basic elements (resistors, inductors, capacitors, memristors). Applications: content addressable memories (CAMs), neuromorphic computing, nonlinear circuit stability analysis. Activation: memristor, circuit convergence, mixed potential, nonlinear RLC, flux-charge analysis, Lyapunov stability, c...Votes: 0GitHub stars: 3
- Learning Reasoning Strategies In Endtoend Differentiable Proving**arXiv ID:** 2007.06477 **Authors:** Pasquale Minervini, Sebastian Riedel, Pontus Stenetorp, Edward Grefenstette, Tim Rocktäschel **Published:** 2020-07-13T16:22:14Z **Abstract:** Attempts to render deep learning models interpretable, data-efficient, and robust have seen some success through hybridisation with rule-based systems, for example, in Neural Theorem Provers (NTPs). These neuro-symbolic models can induce interpretable rules and learn representations from data via back-propagation, ...Votes: 0GitHub stars: 3
- Krylov Mean Field Chaos RnnKrylov Mean-Field Chaos in Random Recurrent Networks - Deterministic prediction theory for individual trajectories in mean-field dynamics. Analytic nonlinearities with fast Fourier decay expose latent determinism via Krylov state space hierarchy. Krylov growth rate sets prediction complexity and bounds largest Lyapunov exponent. Extends Hamiltonian chaotic dynamics ideas to classical dissipative systems. Activation: mean-field theory, Krylov chaos, RNN prediction, Lyapunov exponent, temporal ...Votes: 0GitHub stars: 3
- Ia Qcn Ring GlioblastomaImportance-Aware Quantum Convolutional Neural Network (IA-QCNN) with ring-topology for MGMT promoter methylation prediction in glioblastoma. Specialized quantum CNN architecture for medical biomarker prediction.Votes: 0GitHub stars: 3
- Flow Matching In Context Brain DynamicsFlow Matching with In-Context Priors for Out-of-Distribution Brain Dynamics methodology. First generative model of whole-cortex fMRI dynamics for unseen cognitive tasks. Per-timestep conditioned diffusion transformer with compositional language priors and spatial priors for counterfactual neuroscience.Votes: 0GitHub stars: 3
- Exploring Brain Networks Noninvasive Electrophysiological Measurements---\nname: exploring-brain-networks-noninvasive-electrophysiological-measurements\ndescription: \"Methodology for exploring brain networks using noninvasive electrophysiological measurements (EEG/MEG) based on arXiv:2607.17602\"\n---\n\n## Context\n\nThis skill provides a comprehensive framework for EEG/MEG-based brain network analysis, covering the methodological foundations and practical workflows for investigating functional and effective interactions within large-scale brain networks usin...Votes: 0GitHub stars: 3
- Entanglement Hyperlink RepresentationExact multipartite entanglement characterization using entanglement hyperlinks (EHLs) defined through the inclusion-exclusion principle.Votes: 0GitHub stars: 3