Multi-Agent Architectures

Related topics:

• A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems
  To address this limitation, recent research has explored agent evolution techniques that aim to automatically enhance agent systems based on interaction data and environmental feedback. This emerging …
• A Practical Guide for Designing, Developing, and Deploying Production-Grade Agentic AI Workflows
  However, building production-grade agentic AI workflows remains challenging. While prototypes are easy to build with simple scripts or notebooks, scaling them into reliable, governed, and observable s…
• AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges
  Abstract—This review critically distinguishes between AI Agents and Agentic AI, offering a structured conceptual taxonomy, application mapping, and challenge analysis to clarify their divergent design…
• Adaptation of Agentic AI
  Cutting-edge agentic AI systems are built on foundation models that can be adapted to plan, reason, and interact with external tools to perform increasingly complex and specialized tasks. As these sys…
• Agent Development Kit
  LLM Agent [https://google.github.io/adk-docs/agents/llm-agents/](https://google.github.io/adk-docs/agents/llm-agents/) Workflow Agent [https://google.github.io/adk-docs/agents/workflow-agents/](htt…
• Agent-as-a-Judge: Evaluate Agents with Agents
  we introduce the Agent-as-a-Judge framework, wherein agentic systems are used to evaluate agentic systems. This is an organic extension of the LLM-as-a-Judge framework, incorporating agentic features …
• AgentRxiv: Towards Collaborative Autonomous Research
  To address these challenges, we introduce AgentRxiv—a framework that lets LLM agent laboratories upload and retrieve reports from a shared preprint server in order to collaborate, share insights, and …
• AgentsNet: Coordination and Collaborative Reasoning in Multi-Agent LLMs
  Large-language models (LLMs) have demonstrated powerful problem-solving capabilities, in particular when organized in multi-agent systems. However, the advent of such systems also raises several quest…
• Beyond Brainstorming: What Drives High-Quality Scientific Ideas? Lessons from Multi-Agent Collaboration
  While AI agents show potential in scientific ideation, most existing frameworks rely on single-agent refinement, limiting creativity due to bounded knowledge and perspective. Inspired by real-world re…
• Drop the Hierarchy and Roles: How Self-Organizing LLM Agents Outperform Designed Structures
  Abstract—We present a 25,000-task computational experiment comparing coordination architectures in multi-agent LLM systems across 8 models, 4–256 agents, and 8 protocols. Our key finding is the endoge…
• Equipping agents for the real world with Agent Skills
  As model capabilities improve, we can now build general-purpose agents that interact with full-fledged computing environments. Claude Code , for example, can accomplish complex tasks across domains us…
• Evolving Deeper LLM Thinking
  We explore an evolutionary search strategy for scaling inference time compute in Large Language Models. The proposed approach, Mind Evolution, uses a language model to generate, recombine and refine c…
• Exploring Autonomous Agents: A Closer Look at Why They Fail When Completing Tasks
  Abstract—Autonomous agent systems powered by Large Language Models (LLMs) have demonstrated promising capabilities in automating complex tasks. However, current evaluations largely rely on success rat…
• Federation of Agents: A Semantics-Aware Communication Fabric for Large-Scale Agentic AI
  We present Federation of Agents (FoA), a distributed orchestration framework that transforms static multi-agent coordination into dynamic, capability-driven collaboration. FoA introduces Versioned Cap…
• How we built our multi-agent research system
  Research work involves open-ended problems where it’s very difficult to predict the required steps in advance. You can’t hardcode a fixed path for exploring complex topics, as the process is inherentl…
• Intelligent AI Delegation
  AI agents are able to tackle increasingly complex tasks. To achieve more ambitious goals, AI agents need to be able to meaningfully decompose problems into manageable sub-components, and safely delega…
• Latent Collaboration in Multi-Agent Systems
  Multi-agent systems (MAS) extend large language models (LLMs) from independent single-model reasoning to coordinative system-level intelligence. While existing LLM agents depend on text-based mediatio…
• Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence
  We propose MODEL SWARMS, a collaborative search algorithm to adapt LLMs via swarm intelligence, the collective behavior guiding individual systems. Specifically, MODEL SWARMS starts with a pool of LLM…
• Multi-agent cooperation through in-context co-player inference
  Achieving cooperation among self-interested agents remains a fundamental challenge in multi-agent reinforcement learning. Recent work showed that mutual cooperation can be induced between “learning-aw…
• Nex-N1: Agentic Models Trained via a Unified Ecosystem for Large-Scale Environment Construction
  The evolution of Large Language Models (LLMs) from passive responders to autonomous agents necessitates a fundamental shift in learning paradigms—from static imitation to incentive-driven decision mak…
• OpenClaw-RL: Train Any Agent Simply by Talking
  Every agent interaction generates a next-state signal, namely the user reply, tool output, terminal or GUI state change that follows each action, yet no existing agentic RL system recovers it as a liv…
• Revisiting RAG Ensemble: A Theoretical and Mechanistic Analysis of Multi-RAG System Collaboration
  theoretical analysis, we provide the first explanation of the RAG ensemble framework from the perspective of information entropy. In terms of mechanism analysis, we have explored the RAG ensemble fram…
• Self-Adaptive Large Language Model (LLM)-Based Multiagent Systems
  Abstract—In autonomic computing, self-adaptation has been proposed as a fundamental paradigm to manage the complexity of multiagent systems (MASs). This achieved by extending a system with support to …
• SkillClaw: Let Skills Evolve Collectively with Agentic Evolver
  Large language model (LLM) agents such as OpenClaw rely on reusable skills to perform complex tasks, yet these skills remain largely static after deployment. As a result, similar workflows, tool usage…
• Small Language Models are the Future of Agentic AI
  Large language models (LLMs) are often praised for exhibiting near-human performance on a wide range of tasks and valued for their ability to hold a general conversation. The rise of agentic AI system…
• The Fellowship of the LLMs: Multi-Agent Workflows for Synthetic Preference Optimization Dataset Generation
  This paper presents synthetic Preference Optimization (PO) datasets generated using multi-agent workflows and evaluates the effectiveness and potential of these workflows in the dataset generation pro…
• Thought Virus: Viral Misalignment via Subliminal Prompting in Multi-Agent Systems
  Subliminal prompting is a phenomenon in which language models are biased towards certain concepts or traits through prompting with semantically unrelated tokens. While prior work has examined sublimin…
• Towards a Science of Scaling Agent Systems
  Agents, language model (LM)-based systems that are capable of reasoning, planning, and acting are becoming the dominant paradigm for real-world AI applications. Despite this widespread adoption, the p…
• When AIs Judge AIs: The Rise of Agent-as-a-Judge Evaluation for LLMs
  As large language models (LLMs) grow in capability and autonomy, evaluating their outputs— especially in open-ended and complex tasks—has become a critical bottleneck. A new paradigm is emerging: usin…
• Why Do Multi-agent LLM Systems Fail?
  [[Routers]] Despite growing enthusiasm for Multi-Agent LLM Systems (MAS), their performance gains across popular benchmarks often remain minimal compared to single-agent frameworks. This gap highlig…