Princeton paper finds teams of specialized AI agents beat single models

Researchers report around 70% accuracy on SWE-bench by combining narrow agents built by different developers The AI industry has spent years assuming that a bigger, smarter single model is the answer. A new paper from Princeton University suggests a well-organized committee may beat the lone genius. The researchers found that combining specialized agents, each built independently by different developers, improves overall performance beyond what a single general-purpose agent can manage. Their multi-agent setup reached around 70% accuracy on demanding benchmarks covering software engineering and reasoning tasks. That matters now because the race to build better AI agents is crowded. OpenAI , Anthropic , and Google DeepMind are all competing hard on agent performance. A finding that favors coordination over sheer scale could shift how that race gets run.

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