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Showing 11,377–11,400 of 21,409 skills
- Mechanistic Attention Guidance Agent Memory RefinementSkill derived from arXiv:2607.17621 - Mechanistic Attention Guidance for Agent Memory RefinementVotes: 0GitHub stars: 3
- Logact Enabling Agentic ReliabilityAgents are LLM-driven components that can mutate environments in powerful, arbitrary ways. Extracting guarantees for the execution of agents in produc... Activation: systems engineering, control systemsVotes: 0GitHub stars: 3
- Llms Agentic Ai Systems Smart Grids Tutorial Architectures ApplicationsSkill derived from arXiv:2607.18147 - LLMs and Agentic AI Systems for Smart Grids: A Tutorial on Architectures and ApplicationsVotes: 0GitHub stars: 3
- Llm Agent Economies Information LimitsPre-registered experiment on small economies of frontier LLM agents (Claude Opus 4.8), testing information-theoretic capacity regions for wealth growth under market coupling and mean-field residual attractor dynamics. Activation: LLM agent economies, information-theoretic capacity, mean-field dynamics, attractor dynamics, market coupling, multi-agent simulation, wealth growth.Votes: 0GitHub stars: 3
- Limbomorphs Emergent Agent DynamicsMethodology for studying emergent lifelike patterns (Limbomorphs) in Gifbreeder systems that encode spatiotemporal fields through aesthetic selection, analyzing their species-specific reactions to perturbations and assessing whether they exhibit genuine goal-directed behavior or merely its appearance.Votes: 0GitHub stars: 3
- Large Scale Study Of Curiosity Driven LearningSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Introducing Symmetries To Black Box Meta Reinforcement Learning**arXiv ID:** 2109.10781 **Authors:** Louis Kirsch, Sebastian Flennerhag, Hado van Hasselt, Abram Friesen, Junhyuk Oh, Yutian Chen **Published:** 2021-09-22T15:09:58Z **Abstract:** Meta reinforcement learning (RL) attempts to discover new RL algorithms automatically from environment interaction. In so-called black-box approaches, the policy and the learning algorithm are jointly represented by a single neural network. These methods are very flexible, but they tend to underperform in terms of ...Votes: 0GitHub stars: 3
- IntersagethesecureandverifiableinteroperabilityproResearch paper: InterSAGE: The Secure and Verifiable Interoperability Protocol for An Internet of Agents.Votes: 0GitHub stars: 3
- Hierarchical Agent SearchSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Generalize Guide Decomposing Rewards Shot Inverse Reinforcement LearningSkill derived from arXiv:2607.17760 - Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement LearningVotes: 0GitHub stars: 3
- From Triggers To Emotions A Cpm Grounded AppraisalFrom Triggers to Emotions: A CPM-Grounded Appraisal Multi-Agent for Dynamic Emotional Evolution in Persona-Based Dialogue. Large Language Models (LLMs) have substantially advanced persona-based dialogue agents for emotion-sensitive role simulation in healthcare, education, counseling, customer service, and interactive sto... Activation: agent, multi-agent, llm, simulation, frameworkVotes: 0GitHub stars: 3
- From Datafitting To Discovery Interpreting The Neural Dynamics Of Motor Control Through Reinforcement Learning**arXiv ID:** 2305.11107 **Authors:** Eugene R. Rush, Kaushik Jayaram, J. Sean Humbert **Published:** 2023-05-18T16:52:27Z **Abstract:** In motor neuroscience, artificial recurrent neural networks models often complement animal studies. However, most modeling efforts are limited to data-fitting, and the few that examine virtual embodied agents in a reinforcement learning context, do not draw direct comparisons to their biological counterparts. Our study addressing this gap, by uncovering stru...Votes: 0GitHub stars: 3
- Evopinn Agentic Discovery Of Executable AlgorithmsEvoPINN: Agentic Discovery of Executable Algorithms for Physics-Informed Neural NetworksVotes: 0GitHub stars: 3
- Evolved Policy GradientsSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Etas Effect Typed Language Agent SystemsSkill derived from arXiv:2607.17780 - ETAS: An Effect-Typed Language for Agent SystemsVotes: 0GitHub stars: 3
- Equivalence Between Policy Gradients And Soft Q LeSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Edge Cloud Multi Agent DecentralizationCollaborative edge-cloud frameworks have emerged as the main- stream paradigm for mobile automation, mitigating the latency and privacy risks inherent to monolithic cloud agents. However, existing app... Activation: reinforcement learning, multi-agent systems, edge computingVotes: 0GitHub stars: 3
- Early To Share Late To Save Synchronisation DrivenEarly to Share, Late to Save: Synchronisation-Driven Communication Gating in Bandwidth-Constrained Cooperative VLN. Most cooperative Vision-Language Navigation (VLN) methods assume unlimited communication, not considering real-world applications where bandwidth is restricted and information efficiency is critical. ... Activation: agent, alignment, communication, cooperative, vision-languageVotes: 0GitHub stars: 3
- Drills Deep Reinforcement Learning For Logic Synthesis**arXiv ID:** 1911.04021 **Authors:** Abdelrahman Hosny, Soheil Hashemi, Mohamed Shalan, Sherief Reda **Published:** 2019-11-11T00:38:39Z **Abstract:** Logic synthesis requires extensive tuning of the synthesis optimization flow where the quality of results (QoR) depends on the sequence of optimizations used. Efficient design space exploration is challenging due to the exponential number of possible optimization permutations. Therefore, automating the optimization process is necessary. In thi...Votes: 0GitHub stars: 3
- Dota 2 With Large Scale Deep Reinforcement LearninSkill for AI agent capabilitiesVotes: 0GitHub stars: 3
- Distinguishing Learning Rules With Brain Machine Interfaces**arXiv ID:** 2206.13448 **Authors:** Jacob P. Portes, Christian Schmid, James M. Murray **Published:** 2022-06-27T16:58:30Z **Abstract:** Despite extensive theoretical work on biologically plausible learning rules, clear evidence about whether and how such rules are implemented in the brain has been difficult to obtain. We consider biologically plausible supervised- and reinforcement-learning rules and ask whether changes in network activity during learning can be used to determine which lea...Votes: 0GitHub stars: 3
- Diagnostic Framework Ai Agent BehaviorSkill derived from arXiv:2607.17149 - A Diagnostic Framework for AI Agent BehaviorVotes: 0GitHub stars: 3
- Delay Aware Active Triangulation With Uncertainty Driven MultiDelay-Aware Active Triangulation with Uncertainty-Driven Multi-Agent Reinforcement Learning for Counter-UAS. Multi-agent active visual triangulation enables precise 3D localization of aerial targets by coordinating mobile observers with controllable cameras. However, existing methods assume instantaneous sta... Activation: agent, multi-agent, reinforcement, control, memoryVotes: 0GitHub stars: 3
- Deep Qnetwork Using Reservoir Computing With Multilayered Readout**arXiv ID:** 2203.01465 **Authors:** Toshitaka Matsuki **Published:** 2022-03-03T00:32:55Z **Abstract:** Recurrent neural network (RNN) based reinforcement learning (RL) is used for learning context-dependent tasks and has also attracted attention as a method with remarkable learning performance in recent research. However, RNN-based RL has some issues that the learning procedures tend to be more computationally expensive, and training with backpropagation through time (BPTT) is unstable bec...Votes: 0GitHub stars: 3