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SkillsMP は hiyenwong/ai_collection から 4,114 件の skill を収集しています。skill を開くとソースと詳細を確認できます。
hiyenwong/ai_collection収集済み skill 4,114 件中 40 件を表示しています。
Preisach Attention Layer (PAL) — a novel sequence modeling architecture that replaces softmax attention with the classical Preisach hysteresis operator from mathematical physics. Uses binary relay operators with learned thresholds and a stack of local extrema…
原文の言語: 英語
Universal prompt optimization skill that applies systematic techniques to improve prompt quality, clarity, and effectiveness for any AI agent.
原文の言語: 英語
Parallel multi-circuit quantum feature fusion methodology for medical image classification. Use when: (1) building hybrid quantum-classical CNN architectures for biomedical image classification, (2) comparing quantum vs classical models with statistical rigor…
原文の言語: 英語
Quantum 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…
原文の言語: 英語
Quasi-equivariant metanetworks for weight-space learning. Use for designing neural architectures that operate on pretrained model parameters, implementing equivariant and quasi-equivariant transformations that respect architectural symmetries while…
原文の言語: 英語
QUBO-based Byzantine-resilient federated learning methodology. Reformulates client selection in federated learning as Quadratic Unconstrained Binary Optimization solved by quantum annealers. Jointly optimizes over all client subsets to find mutually closest…
原文の言語: 英語
Research methodology from paper 'RAD-2: Scaling Reinforcement Learning in a Generator-Discriminator Framework'. arXiv:2604.15308v1. Covers key techniques and approaches for neuroscience research. Activation: rad, 2, scaling, cs.CV
原文の言語: 英語
Random graph-based datacenter network design (RNG) for cost-optimized, fault-tolerant distributed systems. Covers distributed routing protocols exploiting random graph properties, passive optical cabling shuffles, and production deployment patterns. Use when:…
原文の言語: 英語
RecursiveMAS framework for scaling multi-agent collaboration through recursive latent-space computation. Enables heterogeneous agents to form collaboration loops via RecursiveLink module with inner-outer loop learning. Activation triggers: recursive…
原文の言語: 英語
Relaxation-Informed Training (RIT) for neural network surrogate models enabling exact MILP embedding with reduced binary variables. Activation triggers: surrogate optimization, MILP embedding, neural network relaxation, ReLU pruning, optimization surrogate
原文の言語: 英語
Resolvent-RNN (R-RNN) methodology for constraining multi-hop temporal pathways in recurrent neural networks to achieve temporal sparsity alignment. Use when: (1) analyzing or designing RNN architectures with multi-hop temporal dependencies, (2) studying…
原文の言語: 英語
Using Reinforcement Fine-Tuning (RFT/GRPO) to overcome catastrophic forgetting in visual continual learning. Activation triggers: reinforcement fine-tuning continual learning, GRPO visual CL, RL visual continual learning, catastrophic forgetting visual,…
原文の言語: 英語
Methodology for analyzing how recurrent neural networks with neuron-specific adaptive time constants switch between multiple frequency band rhythms. Covers rhythm-switching mechanisms, time constant-frequency relationships, and degeneracy of learned…
原文の言語: 英語
RiM (Reasoning in Memory) methodology for unlocking working memory capacity in LLMs via fixed memory blocks, enabling compute-efficient latent reasoning without autoregressive thought generation.
原文の言語: 英語
RNN权重初始化、解的多样性与性能退化分析框架。研究不同初始化如何收敛到不同动力学解，分析网络规模、时间间隔、连接损伤对性能的优雅退化影响。适用于计算神经科学、RNN模型分析、脑皮层建模。触发词：RNN初始化、解多样性、性能退化、网络鲁棒性、优雅退化、weight initialization、degradation analysis、RNN dynamics、graceful degradation。
原文の言語: 複数言語
Routing analysis and intervention for Multimodal Mixture-of-Experts models. Use when: (1) Debugging vision-language reasoning failures, (2) Analyzing expert routing in MoE architectures, (3) Improving multimodal MoE performance, (4) Understanding cross-modal…
原文の言語: 英語
Sample complexity bounds and algorithms for learning bosonic Gaussian quantum states. Use when: (1) analyzing sample requirements for quantum state tomography, (2) designing efficient measurement strategies for Gaussian states, (3) computing sample complexity…
原文の言語: 英語
Search-E1 methodology from arXiv:2605.22511 (May 2026). Self-evolution for search-augmented reasoning agents via vanilla GRPO + Offline Self-Distillation (OFSD) with token-level forward KL objective. No external supervision needed. Use when: training…
原文の言語: 英語
Biologically-inspired selective forgetting framework for LLM agent memory management. Combines hippocampal indexing theory and Ebbinghaus forgetting curve for efficient, secure, and quality-preserving memory pruning. Use for: agent memory optimization,…
原文の言語: 英語
Self-organising transformer architectures that determine their own structure during training. DDCL-INCRT pattern with hierarchical prototype structure. Use when designing adaptive neural architectures, self-organising networks, or architectures that evolve…
原文の言語: 英語
Formalizes how attractor networks emerge from the free energy principle applied to universal partitioning of random dynamical systems. Results in self-orthogonalizing attractor representations, biologically plausible multi-level Bayesian active inference. Use…
原文の言語: 英語
Self-Policy Distillation (SPD) methodology from arXiv:2605.22675 (May 2026). Capability-selective self-distillation by extracting low-rank subspace from gradients on correctness-defining tokens, projecting KV activations during self-generation, and…
原文の言語: 英語
Biologically 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…
原文の言語: 英語
Layerwise self-supervised local learning rules for deep networks on the Random Hierarchy Model (RHM). Direct feedback approximations fail due to input-specific masking nonlinearity; layerwise contrastive/non-contrastive self-supervised rules succeed and match…
原文の言語: 英語
Sensation Modulating Network (SMN) architecture for embodied cognition - haltability-based framework reconciling cognitivism and 4E approaches through opponent dynamics. Use when studying embodied cognitive architecture, intentional directedness without…
原文の言語: 英語
Sequential chaotic oscillations (SCOs) in excitatory-inhibitory threshold-linear networks - dynamical mechanism for sequential metastability in brain dynamics. Activation: sequential metastability, chaotic itinerancy, E-I oscillation, SCO, threshold-linear…
原文の言語: 英語
Sleep-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.
原文の言語: 英語
梯度突触可塑性稀疏实现方法论。实现真正稀疏且通用的梯度突触可塑性规则，保持在线学习能力。适用于脉冲神经网络、在线学习、突触可塑性研究。触发词：突触可塑性、梯度下降、稀疏实现、在线学习、sparse plasticity、gradient-based、online learning。
原文の言語: 中国語
Workflow and architecture patterns for building robust AI agents using Specs, Contracts, and Repository patterns.
原文の言語: 英語
特异性感知联邦图学习框架(SFGL)。在保护数据隐私的前提下进行多站点fMRI协作训练，通过共享分支和个性化分支平衡知识共享与站点特异性保持。适用于多中心脑疾病识别、联邦学习、rs-fMRI分析。触发词：联邦学习、图神经网络、fMRI分析、多站点、数据隐私、federated learning、GNN、multi-site、brain disorder identification。
原文の言語: 中国語
Optimize LLM inference using speculative decoding with KV cache compression. Covers adaptive gamma selection, compression-aware token verification, and KV cache management for reduced latency. Use when: (1) Optimizing LLM serving throughput, (2) Implementing…
原文の言語: 英語
Spike-driven Large Language Model (SDLLM) methodology. Eliminates dense matrix multiplications in LLMs through sparse addition operations using gamma-SQP two-step spike encoding. Reduces energy consumption by 7x while improving accuracy by 4.2% over previous…
原文の言語: 英語
Spike-driven Large Language Model - Spike-based computation for large language models. Activation triggers: spike, driven, large, neuroscience, SNN.
原文の言語: 英語
Spike-NVPT: Noise-robust visual prompt tuning using bio-inspired temporal filtering and spike-based discretization. Parameter-efficient adaptation for pre-trained vision models with enhanced robustness to input perturbations. Keywords: Spike-NVPT, visual…
原文の言語: 英語
SPOQ (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…
原文の言語: 英語
Staged VLM post-training methodology - decomposing VLM capabilities into visual perception, visual reasoning, and textual reasoning stages with specialized data and RL-based perception learning
原文の言語: 英語
Stateful streaming transformer inference methodology with persistent KV cache, Flash Queries prefetching, and multi-tenant continuous-batching for efficient streaming workloads.
原文の言語: 英語
Methodology for improving LLM reasoning by making search tree structures explicit in reasoning traces. LinTree (arXiv:2605.31492) shows that adding parent pointers to linearized search traces significantly outperforms implicit reasoning and LLM-heuristic…
原文の言語: 英語
Structured-Sparse Attention methodology from arXiv:2605.22476 (May 2026). Blockwise resolvent-style attention operator achieving subquadratic sequence complexity O(n^(4/3)) for entity tracking by exploiting localized attention structure. Use when: efficient…
原文の言語: 英語
Superintelligent Retrieval Agent methodology for building retrieval-augmented systems that actively reason about information needs beyond black-box query issuance.
原文の言語: 英語