Sign inGet started
Paper · arXiv

Recursive Multi-Agent Systems

Recursive or looped language models have recently emerged as a new scaling axis by iteratively refining the same model computation over latent states to deepen reasoning. We extend such scaling principle from a single model to multi-agent systems, and ask: Can agent collaboration itself be scaled through recursion? To this end, we introduce RecursiveMAS, a recursive multi-agent framework that casts the entire system as a unified latent-space recursive computation. RecursiveMAS connects heterogeneous agents as a collaboration loop through the lightweight RecursiveLink module, enabling in-distribution latent thoughts generation and cross-agent latent state transfer. To optimize our framewor...

AuthorsJiaru Zou, Rui Pan, Ruizhong Qiu et al.
Primary categorycs.AI
Published28 April 2026
Last updated at source13 July 2026
cs.AIcs.CLcs.LG
Embed this entry
<iframe src="https://silytics.si/embed/item/db5ebfaf-e442-4a2e-b64f-71c84c79d7d8" width="420" height="190" style="border:0" loading="lazy" title="Silytics entry"></iframe>

Free, no tracking. More about feeds and embeds.

Connected in the knowledge graph

Explore this entry in the graph

Models and datasets referencing this paper

Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.