Pre-Neural Multi-Agent Abstractions Are Sleeping Giants in LLM Training Data
max_paperclips · x · 2026-08-07
The post explores the deep connection between LLMs and traditional multi-agent systems. The author notes that before the neural network boom, the academic world spent decades refining coordination abstractions such as blackboards, BDI (Belief-Desire-Intention) models, tuple spaces, actor models, and concurrent programming.
These classic computer science concepts are now part of the high-quality training corpus for LLMs. They are waiting for models to figure out accurate credit assignment via reinforcement learning, potentially unlocking decades of complex coordination capabilities.
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