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More Agents Is All You Need
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More Agents Is All You Need
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We find that, simply via a sampling-and-voting method, the performance of large language models (LLMs) scales with the number of agents instantiated. Also, this method, termed as Agent Forest, is orthogonal to existing complicated methods to further enhance LLMs, while the degree of enhancement is correlated to the task difficulty. We conduct comprehensive experiments on a wide range of LLM benchmarks to verify the presence of our finding, and to study the properties that can facilitate its occurrence. Our code is publicly available at: https://github.com/MoreAgentsIsAllYouNeed/AgentForest
Forward citations
Cited by 29 Pith papers
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State-dependent error correlations shape voting thresholds in committees of AI agents
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Are Diversity Metrics Measuring Diversity? A Capability-Controlled Audit of Majority-Vote Gain in LLM Ensembles
Diversity metrics used to select LLM ensembles are largely capability proxies; after control, only a modest pairwise co-failure association with majority-vote gain remains.
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Budgeted Act-or-Defer Multi-Agent LLM Deliberation with Local Reliability Bounds
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Streaming Communication in Multi-Agent Reasoning
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Streaming Communication in Multi-Agent Reasoning
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Ontology-constrained multi-LLM scoring of hypothesis support in the predictive processing literature
A multi-LLM council scores predictive processing papers on an expert ontology, maps results in 3D hypothesis space, and introduces a dispersion metric showing greater spread in global versus local oddball paradigms.
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Self-Refining Topology Optimization via an LLM-Based Multi-Agent Framework
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The Reasoning Trap: An Information-Theoretic Bound on Closed-System Multi-Step LLM Reasoning
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When to Vote, When to Rewrite: Disagreement-Guided Strategy Routing for Test-Time Scaling
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Effective Strategies for Asynchronous Software Engineering Agents
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When Agents Disagree: The Selection Bottleneck in Multi-Agent LLM Pipelines
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Tacit Coordination of Large Language Models
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ALL-FEM: Agentic Large Language models Fine-tuned for Finite Element Methods
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Efficient Multi-Agent System Training with Data Influence-Oriented Tree Search
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A Technical Taxonomy of LLM Agent Communication Protocols
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Do More Agents Help? Controlled and Protocol-Aligned Evaluation of LLM Agent Workflows
Under controlled identical protocols, only one of six multi-agent LLM systems marginally exceeds a single-agent baseline on benchmark-balanced accuracy while the rest trail and cost more; a runtime workflow reaches 66...
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When to Vote, When to Rewrite: Disagreement-Guided Strategy Routing for Test-Time Scaling
A disagreement-based router that decides per math problem whether to accept, majority-vote, or rewrite improves accuracy by 3–7% with fewer samples than fixed test-time scaling methods.
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Scaling Teams or Scaling Time? Memory Enabled Lifelong Learning in LLM Multi-Agent Systems
LLMA-Mem improves long-horizon performance in LLM multi-agent systems over baselines while reducing cost and shows non-monotonic scaling where memory-enabled smaller teams can beat larger ones.
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Transition from Statistical to Hardware-Limited Scaling in Photonic Quantum State Reconstruction
Classical shadow tomography on integrated photonics shows a sharp transition from statistical O(M^{-1/2}) error scaling to a hardware-limited floor set by unitary spectral distortions.
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Scoring, Reasoning, and Selecting the Best! Ensembling Large Language Models via a Peer-Review Process
LLM-PeerReview ensembles LLMs by scoring responses with LLM-as-Judge and selecting the best via averaging or truth inference, beating Smoothie-Global by 6.9-7.3 points on four datasets.
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A Scalable Multi-LLM Collaboration System with Retrieval-based Selection and Exploration-Exploitation-Driven Enhancement
SMCS coordinates 15 open-source LLMs via retrieval-based prior selection and exploration-exploitation posterior enhancement, outperforming GPT-4.1 by 5.36% and GPT-o3-mini by 5.28% on eight benchmarks.
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Securing Multi-Agent Systems Against Corruptions via Node Contribution Backpropagation
A backward-propagation scoring scheme over a signed temporal DAG can identify malicious agents in LLM multi-agent systems and cut their communications, improving defended accuracy by 3–7 percentage points in the autho...
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Token-Operations-Oriented Inference Optimization Techniques for Large Models
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Token-Operations-Oriented Inference Optimization Techniques for Large Models
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Harnessing Multiple Large Language Models: A Survey on LLM Ensemble
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