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@omarsar0

NEW paper on multi-agents from Stanford. More agents, better results, right? Not so fast. This paper challenges a core assumption in the multi-agent hype by controlling for what most studies don't: total computation. It compares single-agent and multi-agent LLM architectures on multi-hop reasoning under matched thinking-token budgets across different models. The finding is clear: Single-agent systems are more information-efficient when reasoning tokens are held constant. The authors also identify significant artifacts in API-based budget control that may artificially inflate multi-agent advantages. Why does it matter? Many reported multi-agent gains disappear once you account for unequal computation. Before building a multi-agent system, check whether a single agent with the same token budget would do the job. This paper gives you the framework to make that call. Paper: https://t.co/XJLFC83qm3 Learn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX

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  "text": "NEW paper on multi-agents from Stanford.\n\nMore agents, better results, right?\n\nNot so fast.\n\nThis paper challenges a core assumption in the multi-agent hype by controlling for what most studies don't: total computation.\n\nIt compares single-agent and multi-agent LLM architectures on multi-hop reasoning under matched thinking-token budgets across different models.\n\nThe finding is clear:\n\nSingle-agent systems are more information-efficient when reasoning tokens are held constant. The authors also identify significant artifacts in API-based budget control that may artificially inflate multi-agent advantages.\n\nWhy does it matter?\n\nMany reported multi-agent gains disappear once you account for unequal computation.\n\nBefore building a multi-agent system, check whether a single agent with the same token budget would do the job. This paper gives you the framework to make that call.\n\nPaper: https://t.co/XJLFC83qm3\n\nLearn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX",
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