How RecursiveMAS speeds up multi-agent inference by 2.4x and reduces token usage by 75%

One of the key challenges of current multi-agent AI systems is that they communicate by generating and sharing text sequences, which introduces latency, drives up token costs, and makes it difficult to train the entire system as a cohesive unit. 

To overcome this challenge, researchers at University of Illinois Urbana-Champaign and Stanford University developed can help tackle complex tasks that single-agent systems struggle to handle. When scaling multi-agent systems for real-world applications, a big challenge is enabling the system to evolve, improve, and adapt to different scenarios over time. 

Prompt-based adaptation improves agent interactions by iteratively refining the shared context provided to the agents. By updating the prompts, the system acts as a director, guiding the agents to generate responses that are more aligned with the overarching goal. The fundamental limitation is that the capabilities of the models underlying each agent remain static. 

A more sophisticated approach is to train the agents by updating the weights of the underlying models. Training an entire system of agents is difficult because updating all the parameters across multiple models is computationally non-trivial.

Even if an engineering team commits to training their models, the standard method of agents communicating via text-based interactions creates major bottlenecks. Because agents rely on sequential text generation, it causes latency as each model must wait for the previous one to finish generating its text before it can begin its own processing. 

Forcing models to spell out their intermediate reasoning token-by-token just so the next model can read it is highly inefficient. It severely inflates token usage, drives up compute costs, and makes iterative learning across the whole system painfully slow to scale. 

How RecursiveMAS works

Instead of trying to improve each agent as an isolated, standalone component, RecursiveMAS is designed to co-evolve and scale the entire multi-agent system as a single integrated whole. 

The framework is inspired by (LoRA). Another advantage of this system comes into effect when you have multiple agents on top of the same backbone model. 

If you have a multi-agent system where two agents are built on the exact same foundation model acting in different roles, you do not need to load two copies of the model into your GPU memory, nor do you train them separately. The agents will share the same backbone as the brain and use the RecursiveLink as the connective tissue.

RecursiveMAS in action

The researchers evaluated RecursiveMAS across nine benchmarks spanning mathematics, science and medicine, code generation, and search-based question answering. They created a multi-agent system using open-weights models including Qwen, Llama-3, Gemma3, and Mistral. These models were assigned roles to form different agent collaboration patterns such as sequential reasoning and mixture-of-experts collaboration. 

RecursiveMAS was compared to baselines under identical training budgets, including standalone models enhanced with LoRA or full supervised fine-tuning, alternative multi-agent frameworks like Mixture-of-Agents and TextGrad, and recursive baselines like LoopLM. It was also compared to Recursive-TextMAS, which uses the same recursive loop structure as RecursiveMAS but forces the agents to explicitly communicate via text.

RecursiveMAS achieved an average accuracy improvement of 8.3% compared to the strongest baselines across the benchmarks. It excelled particularly on reasoning-heavy tasks, outperforming text-based optimization methods like TextGrad by 18.1% on AIME2025 and 13% on AIME2026. 

Because it avoids generating text at every step, RecursiveMAS achieved 1.2x to 2.4x end-to-end inference speedup. RecursiveMAS is also much more token efficient than the alternative. Compared to the text-based Recursive-TextMAS, it reduces token usage by 34.6% in the first round of the recursion, and by round three, it achieves 75.6% token reduction. RecursiveMAS also proved remarkably cheap to train. Because it only updates the lightweight RecursiveLink modules, which consist of roughly 13 million parameters or about 0.31% of the trainable parameters of the frozen models, it requires the lowest peak GPU memory and cuts training costs by more than half compared to full fine-tuning.

Enterprise adoption

The efficiency gains — lower token consumption, reduced GPU memory requirements, and faster inference — are intended to make complex multi-step agent workflows viable in production environments without the compute overhead that limits enterprise agentic deployments. The researchers have released the under the Apache 2.0 license.