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Notes on multi-agent systems
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TreeAgent: A Generalizable Multi-Agent Framework for Automated Bias Labeling in Forestry via Compiled Expert Rules and Vision-Language Models
Researchers have proposed a multi-agent system that uses expert decision trees and Vision-Language Models (VLMs) to automate the bias classification in tree height in forestry remote sensing. The system mitigates VLM…
AgentsNet: Coordination and Collaborative Reasoning in Multi-Agent LLMs
The paper proposes AgentsNet, a new benchmark for multi-agent reasoning. This benchmark is designed to measure the ability of multi-agent systems to collaborate effectively and leverage their network topology. The…
Latent Actions from Factorized Transition Effects under Agent Ambiguity
The research introduces Observed Transition Factorization (OTF), a method to decompose transitions in multi-object or distracting scenes into reusable transition primitives. These primitives are used to form…
Linguistic Firewall: Geometry as Defense in Multi-Agent Systems Routing
A new routing architecture called ANTAP (Automatic Non-Textual Agent Picker) was introduced in this research. It aims to address security vulnerabilities in multi-agent systems by discarding indirect proxies and…
MESA: Prioritizing Vulnerable Communication Channels for Securing Multi-Agent Systems
The paper introduces a framework called Mesa to prioritize the security of vulnerable communication channels in multi-agent systems (MAS). The framework uses graph-theoretic metrics and dynamic probes to rank which MAS…
MESA: Prioritizing Vulnerable Communication Channels for Securing Multi-Agent Systems
The paper presents Mesa, a framework for prioritizing vulnerable communication channels in multi-agent systems (MAS). Mesa uses graph-theoretic metrics and dynamic probes to identify the edges most likely to affect the…
MESA: Prioritizing Vulnerable Communication Channels for Securing Multi-Agent Systems
In this research paper titled 'MESA: Prioritizing Vulnerable Communication Channels for Securing Multi-Agent Systems', the authors propose a framework called Mesa to prioritize and secure communication channels in…
Translating Natural Language to Strategic Temporal Specifications via LLMs
Researchers have developed a framework for translating Natural Language descriptions of strategic requirements into well-formed ATL/ATL* formulas using Large Language Models (LLMs). They created and validated a novel…
Multi-Agent Goal Recognition with Team- and Goal-Conditioned Reinforcement Learning and Factorized Branch-and-Bound
Researchers propose a method called Multi-Agent Goal Recognition with Branch-and-Bound (MAGR-BB) to infer the goals of multiple agents in real applications like drone surveillance and collaborative robotics, where only…
Multi-Agent Goal Recognition with Team- and Goal-Conditioned Reinforcement Learning and Factorized Branch-and-Bound
The paper introduces Multi-Agent Goal Recognition with Team- and Goal-Conditioned Reinforcement Learning and Factorized Branch-and-Bound (MAGR-BB), a method for inferring which agents are working together and what each…
Multi-Agent Goal Recognition with Team- and Goal-Conditioned Reinforcement Learning and Factorized Branch-and-Bound
The paper presents Multi-Agent Goal Recognition with Team- and Goal-Conditioned Reinforcement Learning and Factorized Branch-and-Bound (MAGR-BB), a method to infer which agents in a multi-agent system are working…
Probabilistic Agents in Deterministic Audits: Evaluating Multi-Agent Systems for Automated Audits Based on the German IT-Grundschutz
This research paper presents a Multi-Agent System (MAS) architecture with Hybrid Retrieval Augmented Generation (HybridRAG) for partially automating IT-Grundschutz certification, a German standard for IT security. The…
Probabilistic Agents in Deterministic Audits: Evaluating Multi-Agent Systems for Automated Audits Based on the German IT-Grundschutz
This research presents the implementation and evaluation of a Multi-Agent System (MAS) architecture combined with Hybrid Retrieval Augmented Generation (HybridRAG) for partially automating IT-GS certification, which is…
Governed Shared Memory for Multi-Agent LLM Systems
The paper proposes a solution for managing shared knowledge in multi-agent language model (LLM) environments. The authors identify four failure modes and introduce four primitives: scoped retrieval, temporal…
Governed Shared Memory for Multi-Agent LLM Systems
The paper discusses the challenges in managing shared knowledge in multi-agent language models (LLMs) environments and proposes solutions to prevent four failure modes: unauthorized leakage, stale propagation…
Privacy-Preserving RAG via Multi-Agent Semantic Rewriting: Achieving Confidentiality Without Compromising Contextual Fidelity
Researchers propose a new method to enhance large language models while maintaining privacy in sensitive scenarios. The method uses a multi-agent framework that sanitizes retrieved content through semantic rewriting…
Dynamic multi-agent deep reinforcement learning-based pricing and incentivization approach in multimodal transportation networks
A study proposes a multi-agent deep reinforcement learning framework to coordinate shared mobility services (SMSs) and public transport in multimodal transportation systems. The framework aims to improve efficiency…
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