DevOps & Cloud

Deployment, cloud, containers, CI/CD, monitoring, and infrastructure

skills in category: 20,291
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Multi Timescale Conductance SnnAMulti-Timescale Conductance Spiking Networks (MTC-SNN) methodology for energy-aware temporal processing. Introduces gradient-trainable spiking neurons using fast/slow/ultra-slow conductances to shape I-V curves, enabling direct backpropagation through time (no surrogate gradients). Rich firing regimes (tonic, phasic, bursting) within single model. Outperforms LIF and AdLIF on Mackey-Glass time-series regression with substantially sparser activity. Activation: multi-timescale conductance, MTC-...devopspythonapiVotes: 0GitHub stars: 3
Workflow Aware Serving Layer AgenticAA workflow-aware serving layer for agentic applications. Addresses the gap between model-serving engines and workflow orchestration for agentic AI workloads that form DAGs of LLM and tool calls with per-node model choices and quality operators. Activation: agentic serving, workflow-aware, LLM serving, DAG orchestration, quality operators, verifiers, agentic workload, serving infrastructure.devopsnodeperformanceVotes: 0GitHub stars: 3
Wcog Vla Dual Level World Cognitive Autonomous DrivingAWCog-VLA: dual-level World-Cognitive VLA framework bridging semantic world forecasting with generative world evolution for proactive autonomous driving. Features Game-theoretic Chain-of-Thought reasoning and Aligned Decoupled Diffusion Transformer. SOTA PDMS score of 92.9 on NAVSIM. Use when working with vision-language-action, autonomous-driving, world-cognition.devopspythongoVotes: 0GitHub stars: 3
The Blind Curator How A Biased Judge Silently Disables Skill Retirement In SelfAA self-evolving agent retires its bad skills by watching them fail, so what happens when the judge cannot see the failures? Skill retirement is the structural constraint that keeps a growing library f. Based on arXiv:2607.07436.devopsgoperformanceVotes: 0GitHub stars: 3
Scaling Self Play With Self GuidanceASelf-Guided Self-Play (SGS) prevents conjecturer collapse in LLM self-play.devopsgoawsVotes: 0GitHub stars: 3
Recursive Self Improvement In Ai From Bounded Self Refinement To AutonomousAAI systems increasingly participate in their own improvement: revising their outputs, adapting their own harnesses during deployment, training on data they generate, and, increasingly, conducting AI r. Based on arXiv:2607.07663.devopsgoVotes: 0GitHub stars: 3
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Pisas Contextual Integrity Multi User AgenticABenchmarking contextual integrity in multi-user agentic systems. As LLM agents evolve into shared organizational infrastructure, new privacy risks emerge from inter-agent messages, shared memory, and cross-user information exposure. Activation: contextual integrity, multi-user agents, privacy benchmark, agentic privacy, inter-agent communication, shared memory privacy.devopsVotes: 0GitHub stars: 3
Pats Policy Aware Training Scaffolding For Agentic ReinforcementAPATS: Policy-Aware Training Scaffolding for Agentic Reinforcement LearningdevopsgoVotes: 0GitHub stars: 3
Oracle Multi Objective Rl Circuit DesignAMulti-objective reinforcement learning framework for analog circuit design optimization using LLM-guided exploration and preference-aware conditioning.devopsperformanceVotes: 0GitHub stars: 3
Openforgerl Train Harness Native Agents In Any EnvironmentAOpenForgeRL: Train Harness-native Agents in Any EnvironmentdevopsgoexpressVotes: 0GitHub stars: 3
Nl Cps Reinforcement Learning Based KubernetesAThe placement of Kubernetes control-plane nodes is critical to ensuring cluster reliability, scalability, and performance, and therefore represents a ... Activation: control, optimal control, MPC, distributed systems, multi-agentdevopspythongoVotes: 0GitHub stars: 3
Nl Cps Reinforcement Learning Based Kubernetes ControlAThe placement of Kubernetes control-plane nodes is critical to ensuring cluster reliability, scalability, and performance, and therefore represents a significant deployment challenge in heterogeneous,... Activation: kubernetes control plane, multi-region cluster, RL-based orchestration, reconfigurable intelligent surfaces, RIS.devopspythongoVotes: 0GitHub stars: 3
Nl Cps Reinforcement Learning Based Kubernetes ConAThe placement of Kubernetes control-plane nodes is critical to ensuring cluster reliability, scalability, and performance, and therefore represents a significant deployment challen... Activation: reinforcement, learning, based, kubernetes, controldevopsgonodeVotes: 0GitHub stars: 3
Multi Agent Robotic Control With Onboard Vision LanguageAMulti-Agent Robotic Control with Onboard Vision-Language Models. Vision Language Models (VLMs) and Vision Language Action (VLA) models have shown promise in robotic control. Yet, they face significant challenges regarding explainability, generalization, and compute... Activation: agent, multi-agent, safety, control, planningdevopsgoperformanceVotes: 0GitHub stars: 3
Llmoxie Agentic Scientific SoftwareAInstitutional AI platform (LLMoxie) with three-tiered architecture supporting multi-cloud and on-premise inference, LiteLLM/MLflow control plane for authentication, budgeting, PII masking, and observability, and an application augmentation layer for AI coding agents in scientific software development. Activation: LLMoxie, scientific software, AI coding agent, LiteLLM, MLflow, multi-cloud, PII masking, institutional AI.devopsgorailsVotes: 0GitHub stars: 3
Flashrt Agent Harness For Guiding Agents To DeployADerived from arXiv:2607.18171 - FlashRT: Agent Harness for Guiding Agents to Deploy Real-Time Multimodal ApplicationsdevopsperformanceVotes: 0GitHub stars: 3
Evolutionary Bilevel Reward Shaping For GeneralizaADerived from arXiv:2606.16236 - Evolutionary Bilevel Reward Shaping for Generalization in Reinforcement LearningdevopsapiperformanceVotes: 0GitHub stars: 3
Evidence Grounded Verified Agentic Reasoning A PatAEvidence-Grounded Verified Agentic Reasoning: A Path Toward Eliminating LLM Hallucination in Empirical Inference via Tool-Attested Kernel Proofs - Tool access alone does not make LLM empirical reasoning governable: accepted outputs need not descend from attested evidence, and accepted deductions ...devopsgoapiVotes: 0GitHub stars: 3
Empirical Study Multi Agent Collaboration AutomatedAAs AI agents evolve, the community is rapidly shifting from single Large Language Models (LLMs) to Multi-Agent Systems (MAS) to overcome cognitive bottlenecks in automated research. However, the optim... Activation: multi-agent systems, agent collaboration, MAS, saddle-point dynamics, constrained optimization.devopspythongoVotes: 0GitHub stars: 3
Cognitive Structured Multimodal Agent Understanding GenerationACognitive-structured Multimodal Agent (CMA) with Episodic Visual Memory for long-horizon multimodal dialogue. Perceptual Abstraction Engine, Cognitive Retrieval Engine, and Multimodal Executive Controller. 8B agent achieves 91.4% retrieval accuracy over 20-turn sessions, surpassing 32B baselines. Use when working with multimodal-agent, episodic-visual-memory, cognitive-retrieval.devopspythongoVotes: 0GitHub stars: 3
Autonomous Racing Using A Hybrid Imitationreinforcement Learning ArchitectureA**arXiv ID:** 2110.05437 **Authors:** Chinmay Vilas Samak, Tanmay Vilas Samak, Sivanathan Kandhasamy **Published:** 2021-10-11T17:26:55Z **Abstract:** In this work, we present a rigorous end-to-end control strategy for autonomous vehicles aimed at minimizing lap times in a time attack racing event. We also introduce AutoRACE Simulator developed as a part of this research project, which was employed to simulate accurate vehicular and environmental dynamics along with realistic audio-visual eff...devopsgoreactVotes: 0GitHub stars: 3
Arxiv 2608 20318 Ai4ai Bench Benchmarking Llm Agents In AlgorithmicAAI4AI-Bench: Benchmarking LLM Agents in Algorithmic Design for Recursive Self-Improvement (arXiv: 2608.20318)devopsgoVotes: 0GitHub stars: 3
Arxiv 2608 20011 Manifold Drift In Flow Preference Optimization A RAManifold Drift in Flow Preference Optimization: A Root Cause of Reward Hacking (arXiv: 2608.20011)devopsgoVotes: 0GitHub stars: 3
Arxiv 2608 19899 The Impact Of Feature Engineering And An OptimisatAThe impact of feature engineering and an optimisation framework for ocean colour machine learning (arXiv: 2608.19899)devopsgoVotes: 0GitHub stars: 3