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- Vor Spiking Cerebellar RobotSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Tecos Lvm Spiking ModelSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Snn Internal Noise Analysis V2Internal noise analysis in Spiking Neural Networks examining how intrinsic noise affects network dynamics, computation, and learning. Covers noise sources, propagation effects, and computational benefits. Trigger words: internal noise, SNN noise, spiking noise, intrinsic noise, stochastic dynamics.Votes: 0GitHub stars: 3
- Snn Complexity Classification脉冲神经网络复杂度分类方法论。使用Lempel-Ziv复杂度（LZC）评估SNN分类性能，比较不同神经元模型（LIF、元神经元、Levy-Baxter）和学习规则（STDP、tempotron）。适用于生物信号处理、SNN架构选择、脉冲序列分析。触发词：脉冲神经网络、SNN、Lempel-Ziv复杂度、神经元模型、STDP、LIF、spiking neural network、complexity classification。Votes: 0GitHub stars: 3
- Fractional Order SnnSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Eas Snn Event DetectionSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Github IssuesCreate, manage, triage, and close GitHub issues. Search existing issues, add labels, assign people, and link to PRs. Works with gh CLI or falls back to git + GitHub REST API via curl.Votes: 0GitHub stars: 3
- Spatiotemporal Neural Coherence时空神经一致性解码方法论。使用 EEG 和光流特征构建预测性神经动力学的时空表征。适用于视觉语言处理的神经解码、预测推理分析。触发词：神经解码、预测推理、视觉语言、EEG 分析、光流特征、neural decoding、predictive inference、visual language。Votes: 0GitHub stars: 3
- Safety Liveness Control ContractsDesign layered control architectures (LCAs) using safety-liveness decomposition via heterogeneous assume-guarantee contracts. Use when: (1) designing hierarchical control systems with discrete planning + continuous execution, (2) enforcing safety constraints while achieving long-horizon objectives, (3) co-designing multi-layer controllers with formal guarantees, (4) building reference governor bridges between MPC planners and low-level controllers. Based on arXiv:2605.04222.Votes: 0GitHub stars: 3
- Reflection Driven ControlSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Mpc Plant Model MismatchModel Predictive Control stability analysis under plant-model mismatch. Covers discounted/infinite-horizon optimal control, suboptimality bounds, and robustness guarantees. Use for: MPC design, control system robustness analysis, plant-model mismatch tolerance, stability proofs, optimal control with surrogate models.Votes: 0GitHub stars: 3
- Core Periphery State Space核心-边缘状态空间模型方法论。使用Mamba选择性状态空间模型线性复杂度捕获脑网络长程依赖，CP-MoE专家混合学习连接模式。适用于功能连接组分类、fMRI分析。触发词：核心-边缘、状态空间模型、Mamba、功能连接、core-periphery、state space model、fMRI classification。Votes: 0GitHub stars: 3
- Arxiv 2609 04009v1 The Blind Spot In 2d Infants Pose Estimation Robus**arXiv ID:** 2609.04009v1 **Authors:** Emanuele Cardinale, Marco Proietti, Alessandro Cacciatore, Maria Francesca Spadea, Lucia Migliorelli, Sara Moccia **URL:** http://arxiv.org/abs/2609.04009v1 **Utility Score:** 1.00Votes: 0GitHub stars: 3
- Active2 Learning Actively Reducing Redundancies In Active Learning Methods For Sequence Tagging And Machine Translation**arXiv ID:** 2103.06490 **Authors:** Rishi Hazra, Parag Dutta, Shubham Gupta, Mohammed Abdul Qaathir, Ambedkar Dukkipati **Published:** 2021-03-11T06:27:31Z **Abstract:** While deep learning is a powerful tool for natural language processing (NLP) problems, successful solutions to these problems rely heavily on large amounts of annotated samples. However, manually annotating data is expensive and time-consuming. Active Learning (AL) strategies reduce the need for huge volumes of labeled data...Votes: 0GitHub stars: 3
- Two Timescale Markovian Sa ConvergenceConvergence theory for two-timescale stochastic approximations under Markovian noise, applicable to TDC and actor-critic methods. First almost sure convergence proof for TDC with eligibility traces under off-policy learning with linear function approximation. ICML 2026.Votes: 0GitHub stars: 3
- Reasoning Decision TimingUnderstanding when LLM reasoning models make decisions - before or during chain-of-thought. Use when discussing reasoning model interpretability, AI safety, chain-of-thought reliability, or the philosophical implications of LLM decision-making processes. Triggers on questions about "reasoning models decide first", "chain-of-thought rationalization", "LLM interpretability", or "reasoning timing".Votes: 0GitHub stars: 3
- Reasoning As A Double Edged Sword Architecture AndDerived from arXiv:2607.17786 - Reasoning as a Double-Edged Sword: Architecture and Cross-Stage Robustness in Vision-Language-Action ModelsVotes: 0GitHub stars: 3
- Multilingual ReasoningUnderstanding cross-language reasoning patterns in Large Reasoning Models. Use when designing multilingual reasoning systems, building non-English LLM applications, or addressing language-specific reasoning optimization. Triggers on "multilingual reasoning", "cross-language reasoning", "non-English reasoning models", or "language-specific reasoning patterns".Votes: 0GitHub stars: 3
- Graph Laplacian J Divergence**来源论文：** arXiv:2012.11240 - Improving J-divergence of brain connectivity states by graph Laplacian denoisingVotes: 0GitHub stars: 3
- Chart Visual ReasoningSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- A Quantum Like Benchmark For Context Sensitive AssDerived from arXiv:2606.12449 - A quantum-like benchmark for context-sensitive associative memory with adaptive plasticityVotes: 0GitHub stars: 3
- Debate On Graph Reliable And Adaptive Reasoning OfDerived from arXiv:2607.17266 - Debate-on-Graph: Reliable and Adaptive Reasoning of Large Language Model on Uncertain Knowledge GraphVotes: 0GitHub stars: 3
- A Monadbased Clause Architecture For Artificial Age Score Aas In Large Language Models**arXiv ID:** 2512.11835 **Authors:** Seyma Yaman Kayadibi **Published:** 2025-12-03T12:48:40Z **Abstract:** Large language models (LLMs) are often deployed as powerful yet opaque systems, leaving open how their internal memory and "self-like" behavior should be governed in a principled and auditable way. The Artificial Age Score (AAS) was previously introduced and mathematically justified through three theorems that characterise it as a metric of artificial memory aging. Building on this fou...Votes: 0GitHub stars: 3
- Tutorial On Answering Questions About Images With Deep Learning**arXiv ID:** 1610.01076 **Authors:** Mateusz Malinowski, Mario Fritz **Published:** 2016-10-04T16:29:28Z **Abstract:** Together with the development of more accurate methods in Computer Vision and Natural Language Understanding, holistic architectures that answer on questions about the content of real-world images have emerged. In this tutorial, we build a neural-based approach to answer questions about images. We base our tutorial on two datasets: (mostly on) DAQUAR, and (a bit on) VQA. Wit...Votes: 0GitHub stars: 3