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Showing 7,345–7,368 of 20,291 skills
- Brain Mri Foundation Clinical脑MRI基础模型临床部署框架，基于FOMO25挑战赛发现。使用自监督学习(SSL)预训练大规模脑MRI基础模型，支持少样本学习和域外泛化，涵盖梗死分类、脑膜瘤分割和脑龄回归任务。Activation: brain MRI foundation model, FOMO25, clinical deployment, self-supervised learning, few-shot learning.Votes: 0GitHub stars: 3
- Autopoiesis Self Evolving SystemsAutopoiesis paradigm for self-evolving systems - online policy evolution for LLM serving under runtime dynamics. LLM-driven program synthesis for continuous adaptation. Activation: self-evolving systems, adaptive serving, online policy evolution, LLM serving optimization.Votes: 0GitHub stars: 3
- Aurosft Adapter State Multi Task FinetuningAuroSFT framework for parameter-efficient multi-task fine-tuning using adapter-state rollback instead of full-model checkpoints. Enables compact, mergeable adapter states for overfitting-aware multi-task SFT with frozen backbones and low-rank weight factors. Use when: implementing efficient multi-task fine-tuning, reducing storage costs for checkpoint management, or needing task-wise rollback capabilities in SFT pipelines.Votes: 0GitHub stars: 3
- Aurooft Quantized Orthogonal FinetuningAuroOFT framework for expressive quantized orthogonal fine-tuning that extends QOFT with zero-start gated low-rank nonlinear residuals. Enables parameter-efficient adaptation of low-bit language models while maintaining quantization compatibility and orthogonality stability. Use when: fine-tuning quantized models, needing nonlinear corrections beyond linear rotations, or optimizing parameter efficiency in low-bit LLM adaptation.Votes: 0GitHub stars: 3
- Astdp Gad Neuromorphic Graph AnomalyASTDP-GAD: Neuromorphic Graph Anomaly Detection via Adaptive Spiking Temporal Dynamics Plasticity and Spiking Graph Neural Networks. Integrates STDP learning with spiking GNNs for energy-efficient anomaly detection in dynamic networks. Use when building neuromorphic anomaly detection, spiking graph neural networks, STDP-based learning on graphs, event-driven network monitoring, or energy-efficient graph ML. Trigger words: ASTDP-GAD, spiking graph neural network, neuromorphic anomaly detection...Votes: 0GitHub stars: 3
- Arxiv 2607 12447--- name: arxiv_2607_12447 description: |- A skill summarizing the arXiv paper: The Computational Basis of Confidence in Large Language Models arXiv ID: http://arxiv.org/abs/2607.12447v1 Authors: Dharshan Kumaran, Viorica Patraucean, Maks Ovsanikov, Petar Veličković, Nathaniel Daw Published: 2026-07-14T07:24:32Z Categories: cs.LG, cs.AIVotes: 0GitHub stars: 3
- Aquatic Neuromorphic Optical FlowSelf-supervised spiking neural network framework for estimating per-pixel optical flow from event camera streams in underwater environments. Bridges neuromorphic sensing and aquatic intelligence for lightweight, real-time, low-cost perception on resource-constrained edge platforms. Activation: underwater vision, event camera, neuromorphic optical flow, aquatic perception, spiking neural network motion estimation, DVS underwater.Votes: 0GitHub stars: 3
- Ai Workload Power ProfilingAI workload power profiling for data center infrastructure planning. Measure, model, and scale power consumption of AI training, fine-tuning, and inference jobs. Use when: data center energy planning, GPU power optimization, AI infrastructure design, generative AI workload analysis, MLCommons benchmarks, vLLM inference profiling, H100 GPU power measurement.Votes: 0GitHub stars: 3
- Uniclawbench Proactive Agents Real World TasksCapability-driven benchmark for evaluating proactive agents in dynamic real-world settings. UniClawBench evaluates five foundational capabilities (Skill Usage, Exploration, Long-Context Reasoning, Multimodal Understanding, Cross-Platform Coordination) across 400 bilingual tasks in live Docker containers with closed-loop evaluation. Activation: proactive agents, real-world benchmark, agent evaluation, capability-driven, multimodal agents, closed-loop evaluation.Votes: 0GitHub stars: 3
- The Mythos Of Model Interpretability**arXiv ID:** 1606.03490 **Authors:** Zachary C. Lipton **Published:** 2016-06-10T21:28:47Z **Abstract:** Supervised machine learning models boast remarkable predictive capabilities. But can you trust your model? Will it work in deployment? What else can it tell you about the world? We want models to be not only good, but interpretable. And yet the task of interpretation appears underspecified. Papers provide diverse and sometimes non-overlapping motivations for interpretability, and offer my...Votes: 0GitHub stars: 3
- Structured Sparse Autoencoders Cross Modal ConceptsStructured Sparse AutoEncoder (S²AE) that enforces concept consistency in vision-language models. Uses grouped image patches with attention similarity and spatial proximity for structured sparsity regularization. Achieves 6.06% improvement in semantic alignment on Qwen2.5-VL-7B. Use when working with sparse-autoencoder, mechanistic-interpretability, concept-consistency.Votes: 0GitHub stars: 3
- Multi Agent Ai Control Distributed Attacks Hamper Per Instance MonitorsAI control is a family of techniques to prevent an AI with malicious goals from subverting its operators intent. AI Control usually studies a single agent in one trajectory, but real deployments run. Based on arXiv:2607.07368.Votes: 0GitHub stars: 3
- Institutional Red Teaming Deployment Rules Not Just Models Causally Shape MultiWe introduce institutional red-teaming, an evaluation methodology for testing deployment rules in multi-agent AI: hold the agents, objectives, and task state fixed, vary only one rule, and attribute t. Based on arXiv:2607.07695.Votes: 0GitHub stars: 3
- Fairness Aware System OptimizationFairness-aware strategic design for shared infrastructure systems using bi-objective trajectory-based optimization. Balances revenue maximization with service equity through max-min fairness and service-rate disparity paradigms. Activation: fairness-aware optimization, system design, multi-objective optimization, service equity, shared resource systems, Pareto frontier analysis.Votes: 0GitHub stars: 3
- Creating Trustworthy Llms Dealing With Hallucinations In Healthcare Ai**arXiv ID:** 2311.01463 **Authors:** Muhammad Aurangzeb Ahmad, Ilker Yaramis, Taposh Dutta Roy **Published:** 2023-09-26T20:52:46Z **Abstract:** Large language models have proliferated across multiple domains in as short period of time. There is however hesitation in the medical and healthcare domain towards their adoption because of issues like factuality, coherence, and hallucinations. Give the high stakes nature of healthcare, many researchers have even cautioned against its usage until t...Votes: 0GitHub stars: 3
- Ai Safety Assessment FrameworkAI Safety assessment framework based on International AI Safety Report 2026. Use when analyzing AI system safety, evaluating risks of general-purpose AI, conducting AI safety assessments, or working with AI governance/policy frameworks. Covers capability evaluation, risk identification, safety measures, and policy recommendations.Votes: 0GitHub stars: 3
- Ai Safety Assessment FrameworkAI Safety assessment framework based on International AI Safety Report 2026. Use when analyzing AI system safety, evaluating risks of general-purpose AI, conducting AI safety assessments, or working with AI governance/policy frameworks. Covers capability evaluation, risk identification, safety measures, and policy recommendations.Votes: 0GitHub stars: 3
- Vector Policy OptimizationVector Policy Optimization (VPO) methodology for training LLMs to maintain response diversity during inference-time search. Use when: (1) Training models for test-time compute scaling, (2) Improving AlphaEvolve/AlphaCode-style search procedures, (3) Addressing low-entropy response distributions in RL fine-tuning, (4) Designing multi-objective reward training pipelines, (5) Balancing per-reward performance with cross-reward diversity in agentic systems.Votes: 0GitHub stars: 3
- Transformer Warmstart Unit CommitmentMulti-Stage Warm-Start Deep Learning Framework for Unit Commitment. Transformer-based architecture for predicting generator commitment schedules with deterministic post-processing for physical feasibility, warm-start strategy for MILP solver, and confidence-based variable fixation. Use for power grid optimization, unit commitment problems, and MILP warm-starting with machine learning.Votes: 0GitHub stars: 3
- Random Graph Datacenter NetworksRandom 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: designing datacenter topologies, optimizing network cost vs performance, implementing distributed routing on non-hierarchical graphs, deploying fault-tolerant network fabrics, or evaluating random graph vs fat-tree tradeoffs.Votes: 0GitHub stars: 3
- Plasticity Prediction Deep Continual LearningTheoretical framework for predicting plasticity in deep continual learning — understanding why neural networks lose their ability to adapt after training on previous tasks (loss of plasticity). Activation triggers: loss of plasticity, plasticity prediction, continual learning theory, network adaptability, neural network plasticityVotes: 0GitHub stars: 3
- Physical Foundation ModelsPhysical Foundation Models (PFMs): Fixed hardware implementations of large-scale neural networks realized directly in physical materials. Covers optical, nanoelectronic, and other physical platforms for trillion-parameter models. Activation: physical neural networks, optical computing, hardware AI, foundation model hardware.Votes: 0GitHub stars: 3
- Multi Agent Density ControlStochastic Density-Driven Optimal Control (D²OC) for multi-agent coverage and distribution matching. Uses Wasserstein distance as running cost with convergence guarantees for stochastic LTI systems. Use when designing decentralized multi-agent coverage, area coverage, distribution matching, or swarm control systems.Votes: 0GitHub stars: 3
- Multi Agent Clinical ReasoningMulti-agent framework for clinical reasoning and radiology AI. Use when designing multi-agent systems for medical diagnosis, radiology report generation, clinical decision support, or multi-modal medical reasoning. Triggers: multi-agent radiology, clinical reasoning agents, multi-agent medical AI, radiology report generation, clinical decision support agents.Votes: 0GitHub stars: 3