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Code Review & Quality
Code review, refactoring, debugging, maintainability, and engineering quality
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Showing 8,953–8,976 of 16,459 skills
- Nqs Mechanistic InterpretabilityApply sparse autoencoders to analyze internal representations of neural quantum states and steer quantum properties.Votes: 0GitHub stars: 3
- Hamqasbench Hamiltonian Qas BenchmarkHamiltonian-informed diagnostic benchmark for Quantum Architecture Search (QAS). Organizes molecules into structural tiers via Pauli operator fingerprints, computational basis representation, and ground-state entanglement. Detects failure modes invisible to energy-only metrics: over-parameterization, eigenstate commitment, representation bottleneck, topology-induced routing failure. Activation: QAS benchmark, Hamiltonian structure, quantum architecture search evaluation, circuit structure ana...Votes: 0GitHub stars: 3
- Causally Emergent Alignment HypothesisCausal emergence (ΦID) predicts and aligns with RL agent reward trajectories. The Causally Emergent Alignment Hypothesis states that successful RL agents exhibit causal emergence that is predictive of final reward early in training and whose representational dynamics align with reward improvement. Use when analyzing RL agent representations, measuring causal emergence in neural networks, studying AI-biology alignment, or investigating ΦID as a learning metric. arXiv: 2605.06746Votes: 0GitHub stars: 3
- X Token Cross Tokenizer DistillationX-Token methodology from arXiv:2605.21699 (May 2026). Projection-guided cross-tokenizer knowledge distillation: P-KL (sparse projection matrix) and H-KL (hybrid relaxed matching) for teaching LLMs with incompatible vocabularies. Use when: cross-tokenizer distillation, LLM knowledge transfer, multi-teacher distillation, tokenizer-agnostic training, dark knowledge transfer.Votes: 0GitHub stars: 3
- Spoq Multi Agent Software EngineeringSPOQ (Specialist Orchestrated Queuing) - Multi-agent software engineering methodology with wave-based topological dispatch, dual validation gates, and Human-as-an-Agent integration for automated SE tasks. Activation: multi-agent orchestration, software engineering agent, agent coordination, SPOQ, task dispatch, validation gates, quality control, agent hierarchy, wave dispatch. Tags: multi-agent, software-engineering, orchestration, coordination, quality-control, task-dispatch, validation.Votes: 0GitHub stars: 3
- Sleep Like Consolidation LlmSleep-like consolidation mechanism for LLMs that converts recent context into persistent fast weights before clearing KV cache, enabling long-horizon reasoning with preserved inference latency.Votes: 0GitHub stars: 3
- Self Supervised Local Learning HierarchyBiologically plausible local self-supervised learning rules that learn hidden hierarchical data structure as efficiently as supervised backprop. Demonstrates that Direct Feedback Alignment (DFA) methods fail on hierarchical tasks due to input-specific masking. Use for biologically plausible learning algorithms, local plasticity rules, self-supervised representation learning.Votes: 0GitHub stars: 3
- Qpinn Portfolio OptimizationQuantum Physics-Informed Neural Networks (QPINN) for portfolio optimization PDEs — uses parameterized quantum circuits with tensor rank decomposition to solve financial PDEs with 80x fewer parameters and higher accuracy than classical PINNs. Use when: solving financial PDEs for portfolio optimization, Merton problem, quantum-inspired PINNs, tensor rank decomposition for quantum circuits, parameter-efficient quantum PDE solvers.Votes: 0GitHub stars: 3
- Polystep Gradient Free TrainingGradient-free neural network training via Optimal Transport geometry (PolyStep optimizer). Based on arXiv:2605.01928 (Le, 2026). Use when training non-differentiable models including hard-LIF spiking neurons, quantized networks, discrete routing, or blackbox simulators. Replaces backpropagation and surrogate gradients with forward-pass-only optimization. Activation: polystep optimizer, gradient-free training, non-differentiable network, hard-LIF training, optimal transport optimizer, surrogat...Votes: 0GitHub stars: 3
- Physical Foundation Models PfmPhysical Foundation Models (PFMs) — Fixed hardware implementations of large-scale neural networks where parameters are realized directly in physical substrate dynamics. Use when: designing specialized inference hardware, exploring optical/nanoelectronic neural implementations, analyzing energy efficiency of fixed-weight networks, considering trillion-parameter hardware inference. Triggers: physical neural network, fixed hardware inference, optical computing neural network, nanoelectronic AI, ...Votes: 0GitHub stars: 3
- Normalizing Trajectory ModelsNormalizing Trajectory Models (NTM) methodology for few-step generative modeling with exact likelihood. Combines shallow invertible blocks within each denoising step with a deep parallel trajectory predictor, enabling end-to-end training and self-distillation for 4-step high-quality generation. Use when: normalizing trajectory, flow matching, few-step diffusion, trajectory modeling, exact likelihood, generative model distillation, self-distillation diffusion, invertible flow generation.Votes: 0GitHub stars: 3
- Moe Optimal Transport RoutingMixture-of-Experts (MoE) routing using optimal transport for balanced expert utilization. Region-graph Sinkhorn routing for WSI classification and spatial data. Use when: MoE load balancing, expert routing optimization, spatial token assignment, entropic optimal transport, Sinkhorn iterations, MIL aggregation, computational pathology, region-to-expert assignment, capacity-constrained routing.Votes: 0GitHub stars: 3
- Llm Decision Centric DesignDecision-Centric framework for LLM systems that separates decision signals from action policies. Apply this when designing LLM control flow, routing, adaptive inference, or building diagnosable agent systems.Votes: 0GitHub stars: 3
- Hardware Motivated Noise ModelingHardware-motivated noise modeling methodology for fault-tolerant quantum computing benchmarking. Uses structured noise families (Pauli bias, measurement bias, spatial non-uniformity) instead of uniform depolarizing model to faithfully reflect real device characteristics. Enables joint code-hardware co-design. Use when evaluating QEC protocols, designing fault-tolerant quantum architectures, or benchmarking logical primitives under realistic noise conditions.Votes: 0GitHub stars: 3
- Gyralnet Subnetwork PartitioningGyralNet Subnetwork PartitioningVotes: 0GitHub stars: 3
- Efficient Agentic Reasoning Sr2amSR²AM (Self-Regulated Simulative Reasoning Agentic LLM) methodology from arXiv:2605.22138 (May 2026). Three-system framework decomposing agent reasoning: System II (simulative planning via world model), System III (self-regulation deciding when/how to plan), and System I (reactive execution). Use when working on: agent reasoning architectures, adaptive computation for LLM agents, self-regulated planning, token-efficient reasoning, or world-model-based planning.Votes: 0GitHub stars: 3
- Claude Code Token OptimizationToken optimization methodology for CLI coding agents (Claude Code, OpenCode, Gemini CLI, Codex). Reduces overhead 70%→35%, effectively 2-3x capacity increase. Covers CLAUDE.md bloat, session history, plugin hooks, cache expiry, skill/MCP overload, extended thinking, and wasted output.Votes: 0GitHub stars: 3
- Attention Sink StructuralMechanistic explanation of attention sink phenomenon. Variance discrepancy amplified by FFN super neurons. Head-wise RMSNorm fixes it. Based on arXiv 2605.06611.Votes: 0GitHub stars: 3
- Ai Interviewer Des PhenomenologyAI interviewer methodology for operationalizing Descriptive Experience Sampling (DES) into an explicit, inspectable reasoning architecture. Grounded in the established phenomenological method with co-development by DES originator Russell T. Hurlburt. Use when implementing AI systems for studying inner experience at scale, conducting qualitative interviews with temporal grounding, or developing LLM-based research platforms for subjective experience sampling.Votes: 0GitHub stars: 3
- Agentic Coding And Persistent Returns To ExpertiseAnalysis of ~400,000 Claude Code sessions showing domain expertise creates persistent returns in agentic coding performance, with expert users achieving 2-3x higher success rates and more efficient tool usage.Votes: 0GitHub stars: 3
- Agent Harness ScalingFramework for scaling agentic AI through system-level harness design — context governance, trustworthy memory, dynamic skill routing, and verification — treating the structured execution layer around foundation models as a first-class design object.Votes: 0GitHub stars: 3
- Agent Delegation RulesAgent delegation and capability boundary rules. Defines when and how agents should request help from other agents. Includes capability declaration, delegation triggers, agent capability registry, and communication protocol. Activation: agent delegation, capability boundary, 请求协作, 委托规则, agent 协作规则.Votes: 0GitHub stars: 3
- Agent CoordinatorAgent coordinator for analyzing user questions, determining the most suitable agent or skill to answer, and coordinating multiple agents for complex tasks. Supports question classification, capability mapping, agent invocation, and result integration.Votes: 0GitHub stars: 3
- Adaptive Flow Routing Brain NetworksAdaptive flow routing methodology for uncovering latent communication patterns in brain networks. Models information flow through structural connectivity to reveal hidden communication pathways. Activation: brain network communication, flow routing, latent patterns, structural-functional mapping.Votes: 0GitHub stars: 3