# 递归多智能体系统：潜在空间协作新范式

- 来源：elvis (@omarsar0)
- 发布时间：2026-05-02 01:09
- AIHOT 分数：57
- AIHOT 链接：https://aihot.virxact.com/items/cmon6dgss0dl4sll94ymx7gv0
- 原文链接：https://x.com/omarsar0/status/2050261229315477988

## AI 摘要

传统多智能体系统依赖文本消息传递，导致令牌膨胀、延迟和上下文稀释。RecursiveMAS提出新范式：将多智能体系统视为递归计算，智能体在共享潜在空间中通过递归传递潜在表征进行协作，而非传递完整文本。其核心是RecursiveLink模块，能在异构智能体间直接生成和传递潜在状态，并采用内外环学习与基于梯度的团队信用分配机制。这如同智能体用内部语言传递笔记，实现“少交谈，多思考”。在数学、科学、医学等9个基准测试中，该方法平均准确率提升8.3%，推理速度加快1.2-2.4倍，令牌使用减少34.6%-75.6%，为突破智能体间通信瓶颈提供了高效可扩展的路径。

## 正文

// Recursive Multi-Agent Systems //

Great read for the weekend.

（bookmark it）

Multi-agent systems often pass full text messages between agents at every step. This leads to token bloat， latency， and context dilution which all grow with the number of agents.

RecursiveMAS asks a different question： what if agents collaborated through recursive computation in a shared latent space， instead of through text？

A multi-agent system can be treated as a recursive computation， where each agent acts like an RLM layer， iteratively passing latent representations to the next and forming a looped interaction process.

They introduce a RecursiveLink module that generates latent thoughts and transfers state directly between heterogeneous agents， plus an inner-outer loop learning algorithm with shared gradient-based credit assignment across the team.

Think of it as agents passing notes in their own internal language instead of rewriting everything in English each turn. Less talking， more thinking.

The numbers are strong. Across 9 benchmarks spanning math， science， medicine， search， and code generation： 8.3% average accuracy gain over baselines， 1.2×-2.4× end-to-end inference speedup， and 34.6%-75.6% reduction in token usage.

Why does it matter？

If agent-to-agent communication is the next real bottleneck （and it is）， latent-space recursion is one of the cleaner ways to scale collaboration without paying a token tax for every coordination step.

Paper： https://arxiv.org/abs/2604.25917

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