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Showing 11,353–11,376 of 21,409 skills
- Retain Consolidate Budget Dependent Operator Selection Language Agent MemorySkill derived from arXiv:2607.17545 - Retain or Consolidate? Budget-Dependent Operator Selection for Language Agent MemoryVotes: 0GitHub stars: 3
- Ohp Rl Human Preference GuidanceOHP-RL methodology — using online human preference interventions to guide reinforcement learning policy for robot manipulation. Addresses unsafe exploration in real-world RL by encoding human interventions as relative preference signals. Activation: OHP-RL, online human preference, human-in-the-loop RL, robot manipulation RL, human-guided RL, human intervention RL.Votes: 0GitHub stars: 3
- Reinforcement Learning With Prediction Based RewarSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Reinforcement Learning Algorithms Foundation ModelsSkill derived from arXiv:2607.17560 - Reinforcement Learning: From Algorithms To Foundation ModelsVotes: 0GitHub stars: 3
- Regulating Autonomous And Agentic AiRegulating autonomous and agentic AIVotes: 0GitHub stars: 3
- Realworld Validation Of Safe Reinforcement Learning Model Predictive Control And Decision Treebased Home Energy Management Systems**arXiv ID:** 2408.07435 **Authors:** Julian Ruddick, Glenn Ceusters, Gilles Van Kriekinge, Evgenii Genov, Cedric De Cauwer, Thierry Coosemans, Maarten Messagie **Published:** 2024-08-14T10:12:15Z **Abstract:** Recent advancements in machine learning based energy management approaches, specifically reinforcement learning with a safety layer (OptLayerPolicy) and a metaheuristic algorithm generating a decision tree control policy (TreeC), have shown promise. However, their effectiveness has onl...Votes: 0GitHub stars: 3
- Real World Evaluation Of An Ai Agent Drafting TranDerived from arXiv:2607.16989 - Real-World Evaluation of an AI Agent Drafting Translational Impact SummariesVotes: 0GitHub stars: 3
- Real World Evaluation Ai Agent Drafting Translational Impact SummariesSkill derived from arXiv:2607.16989 - Real-World Evaluation of an AI Agent Drafting Translational Impact SummariesVotes: 0GitHub stars: 3
- Racing Control Variable Genetic Programming For Symbolic Regression**arXiv ID:** 2309.07934 **Authors:** Nan Jiang, Yexiang Xue **Published:** 2023-09-13T21:38:06Z **Abstract:** Symbolic regression, as one of the most crucial tasks in AI for science, discovers governing equations from experimental data. Popular approaches based on genetic programming, Monte Carlo tree search, or deep reinforcement learning learn symbolic regression from a fixed dataset. They require massive datasets and long training time especially when learning complex equations involving ...Votes: 0GitHub stars: 3
- Quantifying Generalization In Reinforcement LearniSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Qmarl Entanglement CoordinationQuantum Multi-Agent Reinforcement Learning (QMARL) with entanglement-based coordination. Demonstrates provable quantum advantage via CHSH game (Tsirelson limit 0.854 vs classical ceiling 0.75). Hybrid quantum actor + classical critic outperforms both fully classical and fully quantum.Votes: 0GitHub stars: 3
- Proof Carrying Agent ActionsRuntime governance framework for heterogeneous agent systems using action certificates. Model-agnostic governance centered on action proofs rather than vendor-native session records. From Anthropic research (arXiv:2606.04104).Votes: 0GitHub stars: 3
- Position Leverage Foundational Models For Blackbox Optimization**arXiv ID:** 2405.03547 **Authors:** Xingyou Song, Yingtao Tian, Robert Tjarko Lange, Chansoo Lee, Yujin Tang, Yutian Chen **Published:** 2024-05-06T15:10:46Z **Abstract:** Undeniably, Large Language Models (LLMs) have stirred an extraordinary wave of innovation in the machine learning research domain, resulting in substantial impact across diverse fields such as reinforcement learning, robotics, and computer vision. Their incorporation has been rapid and transformative, marking a significan...Votes: 0GitHub stars: 3
- Physics Aware Quadcopter Drl ControlPhysics-aware end-to-end deep reinforcement learning methodology for quadcopter control with actuator dynamics modeling.Votes: 0GitHub stars: 3
- Pearl Auditable Repair Scientific Reasoning Graph ExtractionSkill derived from arXiv:2607.17917 - PEARL: Auditable Repair for Scientific Reasoning Graph ExtractionVotes: 0GitHub stars: 3
- Pearl Auditable Repair For Scientific Reasoning GrDerived from arXiv:2607.17917 - PEARL: Auditable Repair for Scientific Reasoning Graph ExtractionVotes: 0GitHub stars: 3
- Paving Way Agents BiologyMethodology from Anthropic research (Jun 2026) on making biological data infrastructure agent-friendly. Case study shows that adding deterministic retrieval layers (like gget virus) to scientific research agents improves accuracy from inconsistent results to nearly 100% for dataset construction tasks.Votes: 0GitHub stars: 3
- Partition Logic Social ComplementarityPartition-logic framework for modeling complementarity in social measurement. Applies non-Boolean event structures (partition logics) to social-science settings where mutually incompatible observation modes reveal different aspects of a definite latent state. Use when: social complementarity, measurement incompatibility in social science, partition logic applications, quantum-inspired social measurement, personnel assessment modeling, survey design, organizational auditing.Votes: 0GitHub stars: 3
- Otap Structure Aware Optimal Transport Evaluating Planning Execution Agent TrajectoriesSkill derived from arXiv:2607.17082 - Otap:Structure-Aware Optimal Transport for Evaluating Planning and Execution in Agent TrajectoriesVotes: 0GitHub stars: 3
- One Shot Imitation LearningSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- On The Power Of Gradual Network Alignment Using Dualperception Similarities**arXiv ID:** 2201.10945 **Authors:** Jin-Duk Park, Cong Tran, Won-Yong Shin, Xin Cao **Published:** 2022-01-26T14:01:32Z **Abstract:** Network alignment (NA) is the task of finding the correspondence of nodes between two networks based on the network structure and node attributes. Our study is motivated by the fact that, since most of existing NA methods have attempted to discover all node pairs at once, they do not harness information enriched through interim discovery of node correspondenc...Votes: 0GitHub stars: 3
- Multi Goal Reinforcement Learning Challenging RoboSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Multi Agent System 5g Throughput Prediction Multi Operator Urban EnvironmentsSkill derived from arXiv:2607.16930 - A Multi-Agent System for 5G Throughput Prediction in Multi-Operator Urban EnvironmentsVotes: 0GitHub stars: 3
- MeldaprotocolformergingknowledgeacrossdistributedaResearch paper: MELD: A Protocol for Merging Knowledge Across Distributed Agentic Memories.Votes: 0GitHub stars: 3