![[2511.17621] From Competition to Coordination: Market Making as a Scalable Framework for Safe and Aligned Multi-Agent LLM Systems](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2511.17621] From Competition to Coordination: Market Making as a Scalable Framework for Safe and Aligned Multi-Agent LLM Systems
Summary
This article presents a market-making framework for coordinating multi-agent large language models (LLMs), enhancing trustworthiness and accountability in AI systems.
Why It Matters
As AI systems become more prevalent, ensuring their safe and aligned operation is crucial. This framework addresses challenges in multi-agent interactions, promoting transparency and self-correction, which are vital for responsible AI deployment.
Key Takeaways
- Introduces a market-making framework for multi-agent LLM coordination.
- Enhances accuracy and transparency in AI decision-making processes.
- Aligns local incentives with collective goals for better accountability.
- Demonstrates empirical gains in reasoning tasks over traditional methods.
- Offers a scalable approach to socially responsible AI deployment.
Computer Science > Multiagent Systems arXiv:2511.17621 (cs) [Submitted on 18 Nov 2025 (v1), last revised 23 Feb 2026 (this version, v2)] Title:From Competition to Coordination: Market Making as a Scalable Framework for Safe and Aligned Multi-Agent LLM Systems Authors:Brendan Gho, Suman Muppavarapu, Afnan Shaik, Tyson Tsay, Atharva Mohan, James Begin, Kevin Zhu, Archana Vaidheeswaran, Vasu Sharma View a PDF of the paper titled From Competition to Coordination: Market Making as a Scalable Framework for Safe and Aligned Multi-Agent LLM Systems, by Brendan Gho and 8 other authors View PDF HTML (experimental) Abstract:As foundation models are increasingly deployed as interacting agents in multi-agent systems, their collective behavior raises new challenges for trustworthiness, transparency, and accountability. Traditional coordination mechanisms, such as centralized oversight or adversarial adjudication, struggle to scale and often obscure how decisions emerge. We introduce a market-making framework for multi-agent large language model (LLM) coordination that organizes agent interactions as structured economic exchanges. In this setup, each agent acts as a market participant, updating and trading probabilistic beliefs, to converge toward shared, truthful outcomes. By aligning local incentives with collective epistemic goals, the framework promotes self-organizing, verifiable reasoning without requiring external enforcement. Empirically, we evaluate this approach across factual ...
