This content originally appeared on DEV Community and was authored by Paperium
AI Agents That Learn by Talking to Each Other
Ever wondered how a group of chatbots could get smarter just by chatting? Scientists have introduced a new system called CoMAS that lets AI agents improve themselves through lively discussions, much like friends sharing ideas over coffee.
Instead of feeding them endless scores from outside, CoMAS lets the agents create their own intrinsic rewards from the back‑and‑forth of their conversations.
Imagine a classroom where students grade each other's work in real time – the AI “judge” does the same, turning the quality of the dialogue into a learning signal.
This simple trick makes each agent better at tasks without any human hand‑holding, and the more agents join the chat, the faster they all grow.
The result? A team of AI helpers that can solve problems more efficiently than any single, pre‑trained model.
This breakthrough shows that collaboration, not just raw data, can drive the next wave of intelligent machines.
The future may be full of AI companions that keep getting better, simply by talking to each other.
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Read article comprehensive review in Paperium.net:
CoMAS: Co-Evolving Multi-Agent Systems via Interaction Rewards
🤖 This analysis and review was primarily generated and structured by an AI . The content is provided for informational and quick-review purposes.
This content originally appeared on DEV Community and was authored by Paperium
Paperium | Sciencx (2025-10-26T19:10:43+00:00) CoMAS: Co-Evolving Multi-Agent Systems via Interaction Rewards. Retrieved from https://www.scien.cx/2025/10/26/comas-co-evolving-multi-agent-systems-via-interaction-rewards/
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