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Bringing Value Models Back: Generative Critics for Value Modeling in LLM Reinforcement Learning9h◆Subagents vs Agent Skills: Executing Reusable Knowledge for Long-Horizon Agentic Tasks9h◆Distribution-Consistent Inference for Dynamic Sparse Mixture-of-Experts9h◆In RAG We Trust? Measuring Robustness of Retrieval-Augmented Generation Under Document Poisoning9h◆FrontierChallenge: Evaluating Scientific Workflow Completion9h◆IBIB: A Protocol for Measuring Enterprise AI Systems by Serving Route, Not Model Identifier9h◆AgenticGen: Reward-Guided Agentic Video Generation for Advertising9h◆Strangers to Themselves: What Language Models Say About Themselves Is Generic9h◆Omni Interaction Agent Technical Report9h◆CoGReV: A Confidence-Gated Post-Hoc Non-Monotonic Belief Revision Framework for Phishing Website Classification9h◆Bit-Flip Attacks on Vision-Language-Action Models: Action-Decoding Architecture Shapes the Vulnerability9h◆Can Foundation Models Moderate Online Content? Evaluating Instruction- vs. Example-Driven Policy Operationalization9h◆RiLM: Parameter-Efficient Language Modeling via Geodesic Decoding9h◆Tracing Computation Density in LLMs9h◆Cultural Binding Heads in Language Models9h◆Direct Diversity Optimization for Diverse Successful Trajectories in Preference Post-Training9h◆Left-Branching Transformers Excel at Right-Branching Languages: Data Shapes Word Order Preferences in Language Models9h◆Palmyra x6 Technical Report: An Agentic, Tool-Use Model Post-Trained via Anchored Supervised Fine-Tuning9h◆'Ghaib in Translation' aka Unseen Harm: Measuring Cross-Script Safety Inconsistency with 'Missed-in-Urdu' Scores in LLM Hate Speech Detection9h◆DexterSQL: Deep Schema Exploration and Rule-based Correction for Text-to-SQL Generation9h◆Bringing Value Models Back: Generative Critics for Value Modeling in LLM Reinforcement Learning9h◆Subagents vs Agent Skills: Executing Reusable Knowledge for Long-Horizon Agentic Tasks9h◆Distribution-Consistent Inference for Dynamic Sparse Mixture-of-Experts9h◆In RAG We Trust? Measuring Robustness of Retrieval-Augmented Generation Under Document Poisoning9h◆FrontierChallenge: Evaluating Scientific Workflow Completion9h◆IBIB: A Protocol for Measuring Enterprise AI Systems by Serving Route, Not Model Identifier9h◆AgenticGen: Reward-Guided Agentic Video Generation for Advertising9h◆Strangers to Themselves: What Language Models Say About Themselves Is Generic9h◆Omni Interaction Agent Technical Report9h◆CoGReV: A Confidence-Gated Post-Hoc Non-Monotonic Belief Revision Framework for Phishing Website Classification9h◆Bit-Flip Attacks on Vision-Language-Action Models: Action-Decoding Architecture Shapes the Vulnerability9h◆Can Foundation Models Moderate Online Content? Evaluating Instruction- vs. Example-Driven Policy Operationalization9h◆RiLM: Parameter-Efficient Language Modeling via Geodesic Decoding9h◆Tracing Computation Density in LLMs9h◆Cultural Binding Heads in Language Models9h◆Direct Diversity Optimization for Diverse Successful Trajectories in Preference Post-Training9h◆Left-Branching Transformers Excel at Right-Branching Languages: Data Shapes Word Order Preferences in Language Models9h◆Palmyra x6 Technical Report: An Agentic, Tool-Use Model Post-Trained via Anchored Supervised Fine-Tuning9h◆'Ghaib in Translation' aka Unseen Harm: Measuring Cross-Script Safety Inconsistency with 'Missed-in-Urdu' Scores in LLM Hate Speech Detection9h◆DexterSQL: Deep Schema Exploration and Rule-based Correction for Text-to-SQL Generation9h◆
Tag

#multi-agent-systems

3 articles tagged #multi-agent-systems

arxivJul 31

Even More Deception: Objective Misalignment in Mixed-Motive LLM Multi-Agent Systems

arXiv:2607.26120v1 Announce Type: new Abstract: Large Language Models (LLMs)-powered multi-agent systems are increasingly deployed in mixed-motive environments, where agents operate under asymmetric information and strategic deception due to conflicting or hidden objectives. In these settings, misal

#multi-agent-systems#large-language-models#game-theoryRead on arxiv →
arxivJul 23bullish

OpenEvoShield: Dual Non-Stationary Continual Defense for Open-World Multi-Agent System Attacks

arXiv:2607.19351v1 Announce Type: new Abstract: LLM-based multi-agent systems (LLM-MAS) are increasingly deployed in safety-critical applications, where adversaries inject malicious instructions through inter-agent communication to propagate harmful behaviors. Unlike static threats, these attacks ar

M1M2M34 models · +1#safety#security#continual-learningRead on arxiv →
arxivApr 13

Every Response Counts: Quantifying Uncertainty of LLM-based Multi-Agent Systems through Tensor Decomposition

arXiv:2604.08708v1 Announce Type: cross Abstract: While Large Language Model-based Multi-Agent Systems (MAS) consistently outperform single-agent systems on complex tasks, their intricate interactions introduce critical reliability challenges arising from communication dynamics and role dependencies

#machine-learning#multi-agent-systems#uncertainty-quantificationRead on arxiv →
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