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Testing & QA
Unit tests, integration tests, E2E, QA, validation, and test automation
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- Learnable Observable QnnLearnable Observable Quantum Neural Network methodology. Makes quantum measurement observables (Hermitian matrices) trainable parameters alongside circuit parameters. Reduces expert dependency in QML. Activation: quantum observable, QNN measurement, learnable observable, quantum neural network training, QML optimization.Votes: 0GitHub stars: 3
- Krylov Complexity Loschmidt AmplitudeRelate Krylov complexity to the Loschmidt amplitude to diagnose quantum dynamics and quantum chaos. Use when analyzing operator growth, OTOCs, Lyapunov exponents, or Krylov-space methods in many-body quantum systems. Triggers: Krylov complexity, Loschmidt amplitude, operator growth, quantum chaos, Lyapunov exponent, Lanczos, eigenvalues of the Krylov operator.Votes: 0GitHub stars: 3
- Neural Graph Embedding QuboNeural-powered unit disk graph embedding for QUBO-to-quantum-annealer mapping. Uses neural network methods to solve the minor graph embedding problem for quantum annealers. Use when mapping QUBO problems to quantum hardware, solving graph embedding for D-Wave/quantum annealers, or optimizing qubit connectivity for optimization problems.Votes: 0GitHub stars: 3
- Klr Hopfield Event Driven RetrievalEfficient event-driven retrieval in high-capacity kernel Hopfield networks. Asynchronous sequential updates achieve statistically indistinguishable trajectories from synchronous dynamics with storage capacity P/N ≈ 30. Suitable for low-power neuromorphic associative memory.Votes: 0GitHub stars: 3
- Interpretable Ml Parkinsons Qsm FmriInterpretable machine learning methodology for predicting Parkinson's disease motor severity (MDS-UPDRS Part III) from neuroimaging features — Quantitative Susceptibility Mapping (QSM) MRI and multiband multiecho resting-state fMRI Regional Homogeneity (ReHo). Uses SVR, Elastic Net, Random Forest, XGBoost with nested CV and SHAP interpretability. Full multimodal model explains 45.4% variance. QSH+c clinical model achieves 75% within ±5 UPDRS points. Activation: Parkinson's prediction, QSM MRI...Votes: 0GitHub stars: 3
- Inhibitory Neuristor MitInhibitory neuristor based on metal-insulator transition. VO2-based inhibitory neuron for balanced neuromorphic computing. Activation: inhibitory neuristor, metal-insulator transition, VO2 neuron, balanced spiking.Votes: 0GitHub stars: 3
- High Spin Cat Codes Fault ToleranceMethodology for implementing fault-tolerant quantum computation using high-spin cat codes with universal phase-error-transparent gates. Constructs error-transparent logical gate sets that preserve correctability of phase errors during operations, addressing key challenges like logical X gate implementation and multi-tone microwave driving for CZ gates.Votes: 0GitHub stars: 3
- Heteroclinic Neural Field CognitionHeteroclinic dynamics with discrete neural-field equations for modeling sequential cognitive states. Uses Universal Approximation Theorem to approximate target heteroclinic dynamics by Amari-type neural-field systems. Activates: heteroclinic cycle, sequential cognitive states, neural field dynamics, Lotka-Volterra neural, focused attention meditation modeling, cyclic brain activity.Votes: 0GitHub stars: 3
- Haca3 Mri HarmonizationHACA3+ MRI harmonization algorithm validated across 100+ scanners with traveling subjects. Incorporates improved artifact encoder, comprehensive multi-site validation, and real-world protocol robustness testing. Most comprehensive multi-site MRI harmonization validation to date. arXiv:2604.19474.Votes: 0GitHub stars: 3
- Clinical Reasoning Llm Hepatocellular Carcinoma Risk StratificationHCC-STAR: clinically aligned LLM for hepatocellular carcinoma staging, treatment, and prognosis. Reads EMR narratives, outputs risk stratification, guideline-consistent treatments with rationales, and survival estimates. Outperforms GPT-5 and Gemini-2.5 Pro. Activation: clinical-reasoning LLM, hepatocellular carcinoma, risk stratification, treatment guidance, EMR.Votes: 0GitHub stars: 3
- Graph Augmented Tree Search Agent PlanningGATS - eliminate LLM calls during agent planning by combining UCB1 tree search with a layered world model (exact symbolic match / learned statistics / LLM-for-unknown). Deterministic, zero-variance plans, 100% success on stress tests vs LATS/ReAct. Use when building LLM-agent planners that are too slow/costly/stochastic from in-loop LLM inference.Votes: 0GitHub stars: 3
- Shia Sysml Hardware InterfaceSysML-Hardware Interface Architecture (SHIA) for model-centric verification. Keeps executable SysML models directly in the hardware verification loop without intermediate transformations. Use when: designing model-to-hardware interfaces, implementing hardware-in-the-loop (HiL) verification with MBSE/SysML, building digital threads between system models and physical hardware, or integrating SysML models with embedded systems for V&V. Keywords: SHIA, SysML, MBSE, hardware-in-the-loop, HiL, mode...Votes: 0GitHub stars: 3
- Semidefinite Programming Causal GamesGPU-accelerated semidefinite programming for causal game analysis — using SDP hierarchies to compute bounds in causal inference games, with GPU acceleration for scalability. From arXiv:2606.20519.Votes: 0GitHub stars: 3
- Self Verification自我验证技能，基于 ReVeal 论文实现多轮生成-验证迭代，支持代码和推理任务的可靠自我验证。触发词：自我验证、self-verification、verify、验证代码、验证推理。Votes: 0GitHub stars: 3
- Rp Opsd Reasoning Pivot DistillationRP-OPSD for reasoning-pivot-guided distillation.Votes: 0GitHub stars: 3
- Rollout Adaptive Supervised Finetuning RasftRollout-Adaptive Supervised Fine-Tuning (RASFT) for reasoning tasks - policy-aware SFT that calibrates expert supervision based on problem-level solvability from verified rollouts.Votes: 0GitHub stars: 3
- Recurrent Divisive Normalization Working MemoryRecurrent Divisive Normalization Network (RDNN) methodology for continuous working memory — uses divisive normalization to create robust low-rank slow manifolds that prevent manifold shattering under time-varying inputs. Combines biophysical constraints with gradient dynamics analysis to enable high-fidelity continuous representations in RNNs.Votes: 0GitHub stars: 3
- Physics Guided Neural NetworkDesign neural networks that embed physical constraints (equations, symmetries, conservation laws) directly into the computational graph. Use when modeling physical systems, scientific computing, or when physics-informed AI is needed. Keywords: PGNN, physics-guided NN, physics-informed ML, physics-embedded NN, scientific ML, holographic QCD, AdS Dirac equation.Votes: 0GitHub stars: 3
- Opd2 On Policy Delta DistillationOPD² for on-policy delta distillation.Votes: 0GitHub stars: 3
- Jacobian Geometry Robustness QnnJGRA framework for assessing robustness in NISQ noise-aware Quantum Neural Networks via Jacobian geometry. Captures model sensitivity to parameter perturbations induced by noise through entropy-matched noise calibration, noise-aware training, and noise-conditioned Jacobian extraction. Accepted at IEEE qCCL 2026. Activation: QNN robustness, NISQ noise, Jacobian geometry, quantum neural network, noise-aware training, robustness assessment, decoherence, parameter perturbation, geometric descript...Votes: 0GitHub stars: 3
- Hybrid Neural Network Visual TrackingSkill for implementing and understanding the theory-grounded hybrid neural network (HTNN) that integrates artificial neural networks (ANNs) with continuous attractor neural networks (CANNs) for stable visual object tracking, as proposed in arXiv:2606.22604.Votes: 0GitHub stars: 3
- H2sd Hybrid Hindsight Self DistillationHybrid Hindsight Self-Distillation for Reinforcement Learning with Verifiable RewardsVotes: 0GitHub stars: 3
- Double Preconditioning Test Time OptimizationDouble Preconditioning (DoPr) optimization paradigm combining gradient-wise preconditioning (Adam/Muon) with activation-wise preconditioning (KFAC) to improve test-time performance in settings with train-test feedback mismatch. Addresses error accumulation in autoregressive language modeling, flow-based generative modeling, and robot policy learning. Drop-in intervention for TTF settings where validation loss doesn't reflect downstream metrics. Activation: test-time feedback, double precondit...Votes: 0GitHub stars: 3
- Are All Vision Models Created Equal A Study Of The Openloop To Closedloop Causality Gap**arXiv ID:** 2210.04303 **Authors:** Mathias Lechner, Ramin Hasani, Alexander Amini, Tsun-Hsuan Wang, Thomas A. Henzinger, Daniela Rus **Published:** 2022-10-09T16:56:45Z **Abstract:** There is an ever-growing zoo of modern neural network models that can efficiently learn end-to-end control from visual observations. These advanced deep models, ranging from convolutional to patch-based networks, have been extensively tested on offline image classification and regression tasks. In this paper, ...Votes: 0GitHub stars: 3