Formalizes interface-constrained semi-Markov decision processes and proves a finite-sample bound for neural IC-Q that decomposes into neural approximation error, interface gap, and mixing-time residual, with experiments showing parity to centralized oracles.
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MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework
Canonical reference. 92% of citing Pith papers cite this work as background.
abstract
Remarkable progress has been made on automated problem solving through societies of agents based on large language models (LLMs). Existing LLM-based multi-agent systems can already solve simple dialogue tasks. Solutions to more complex tasks, however, are complicated through logic inconsistencies due to cascading hallucinations caused by naively chaining LLMs. Here we introduce MetaGPT, an innovative meta-programming framework incorporating efficient human workflows into LLM-based multi-agent collaborations. MetaGPT encodes Standardized Operating Procedures (SOPs) into prompt sequences for more streamlined workflows, thus allowing agents with human-like domain expertise to verify intermediate results and reduce errors. MetaGPT utilizes an assembly line paradigm to assign diverse roles to various agents, efficiently breaking down complex tasks into subtasks involving many agents working together. On collaborative software engineering benchmarks, MetaGPT generates more coherent solutions than previous chat-based multi-agent systems. Our project can be found at https://github.com/geekan/MetaGPT
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- abstract Remarkable progress has been made on automated problem solving through societies of agents based on large language models (LLMs). Existing LLM-based multi-agent systems can already solve simple dialogue tasks. Solutions to more complex tasks, however, are complicated through logic inconsistencies due to cascading hallucinations caused by naively chaining LLMs. Here we introduce MetaGPT, an innovative meta-programming framework incorporating efficient human workflows into LLM-based multi-agent collaborations. MetaGPT encodes Standardized Operating Procedures (SOPs) into prompt sequences for mor
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representative citing papers
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citing papers explorer
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ExCyTIn-Bench: Evaluating LLM agents on Cyber Threat Investigation
ExCyTIn-Bench is the first benchmark of 7542 questions from Microsoft Sentinel threat investigation graphs, where the best LLM agent achieves a reward of 0.606.
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Why Do Multi-Agent LLM Systems Fail?
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An Empirical Study of Testing Practices in Open Source AI Agent Frameworks and Agentic Applications
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From Standalone LLMs to Integrated Intelligence: A Survey of Compound Al Systems
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Prompt Injection Attack to Tool Selection in LLM Agents
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TokenCake: A KV-Cache-centric Serving Framework for LLM-based Multi-Agent Applications
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Dynamic Generation of Multi-LLM Agents Communication Topologies with Graph Diffusion Models
GTD generates task-adaptive, sparse communication topologies for multi-LLM agents via guided iterative graph diffusion steered by a proxy model predicting accuracy, utility, and cost.
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ARM: Discovering Agentic Reasoning Modules for Generalizable Multi-Agent Systems
ARM evolves specialized reasoning modules from basic CoT via tree search to serve as reusable components in multi-agent systems that generalize across models and domains without per-task re-optimization.
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GenoMAS: A Multi-Agent Framework for Scientific Discovery via Code-Driven Gene Expression Analysis
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DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents
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AgentSociety: Large-Scale Simulation of LLM-Driven Generative Agents Advances Understanding of Human Behaviors and Society
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RankFlow: A Multi-Role Collaborative Reranking Workflow Utilizing Large Language Models
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MARS-SQL: A multi-agent reinforcement learning framework for Text-to-SQL
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CodeWiki: Evaluating AI's Ability to Generate Holistic Documentation for Large-Scale Codebases
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Semantic-Aware Logical Reasoning via a Semiotic Framework
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GUARD: Guideline Upholding Test through Adaptive Role-play and Jailbreak Diagnostics for LLMs
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From Human Memory to AI Memory: A Survey on Memory Mechanisms in the Era of LLMs
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Foundational Design Principles and Patterns for Building Robust and Adaptive GenAI-Native Systems
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InfantAgent-Next: A Multimodal Generalist Agent for Automated Computer Interaction
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From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review
A survey consolidating benchmarks, agent frameworks, real-world applications, and protocols for LLM-based autonomous agents into a proposed taxonomy with recommendations for future research.
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A Survey of Scaling in Large Language Model Reasoning
A survey categorizing scaling in LLM reasoning across input size, steps, rounds, training, and future directions, noting that scaling can negatively affect performance.
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