这篇论文戳破了多智能体系统“越多越好”的迷思，做AI推理和智能体架构的开发者看完会重新思考设计方向——先试一个强模型，别急着堆智能体。
斯坦福大学最新论文指出，在相同的推理预算下，单个大语言模型（LLM）在多跳推理任务中通常优于多个协调的智能体系统。核心原因在于，单智能体将整个问题保留在内部思维链中，而多智能体系统需要将推理链分割成消息、摘要和交接，每次交接都是一次压缩步骤，容易丢失信息。实验在Qwen、DeepSeek和Gemini模型上验证了这一点，当思考令牌预算匹配时，单智能体系统在FRAMES和MuSiQue数据集上通常匹配或超越顺序、辩论、角色扮演和集成等设置。论文还发现，许多多智能体的优势并非来自架构本身，而是来自更多的测试时计算、更可见的推理或评估偏差。当单智能体的上下文被干扰时，多智能体管道才更具竞争力，因此建议将多智能体作为修复策略而非升级方案。
New Stanford paper argues that, under equal reason…
New Stanford paper argues that, under equal reasoning budgets, one LLM usually solves multi-hop problems better than many coordinated ones.
The core point is almost embarrassingly simple.
A single agent keeps the whole problem in one internal chain of thought, while a multi-agent system has to slice that chain into messages, summaries, and handoffs.
Every handoff is a compression step.
And once reasoning is compressed, some information is easier to drop than to recover, which is why the paper leans on the Data Processing Inequality as a formal explanation rather than just an empirical hunch.
The experiments back that up across Qwen, DeepSeek, and Gemini on FRAMES and MuSiQue: when thinking-token budgets are matched, single-agent systems usually match or beat sequential, debate, role-based, and ensemble setups.
Here’s the part most people miss.
Many celebrated multi-agent gains may not be architectural gains at all. They often come from spending more test-time compute, surfacing more visible reasoning, or benefiting from evaluation quirks that make the pipeline look smarter than it is.
The paper is especially sharp when it looks for the boundary case instead of pretending the rule is universal.
When the single agent’s effective context is degraded by masking, substitution, or misleading distractors, multi-agent pipelines become more competitive and sometimes win, not because message passing is magical, but because structure can partially stabilize corrupted reasoning.
That is a much narrower and more useful claim than “more agents is better.”
It suggests the real trade-off is not single versus multi so much as latent reasoning versus external coordination, with context quality and compute accounting deciding which side looks stronger.
For multi-hop reasoning, the default should now be clear: start with one strong model, and treat extra agents as a repair strategy, not an upgrade.
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Paper Link – arxiv. org/abs/2604.02460
Paper Title: "Single-Agent LLMs Outperform Multi-Agent Systems on Multi-Hop Reasoning Under Equal Thinking Token Budgets"