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On October 20, 2026, Shao Meng recommended Lectures 4-5 of the MIT course MAS.S62 "How to AI an AI," themed "Harness evolution." According to his introduction, these two lectures respectively survey the evolution of harness components, memory, tools, and skills, as well as the evolution of planning, task decomposition, and multi-agent systems. The memory section uses a three-axis taxonomy to connect MemGPT, mem0, MEM1, and Reflexion/ExpeL, and uses CLM to unify context management as file operations; the tools section moves from Toolformer, ToolLLM, and CodeAct to Meta-Harness, which represents the entire harness as a file system, with feedback information roughly 400 times that of previous methods. The multi-agent section presents five empirical findings, including that MAS wins 81% on decomposable tasks and loses 70% on sequentially dependent tasks, and that CORAL achieves new SOTA on 8 tasks, with multi-agent evolution improving 4%–20% over single-agent. Currently, this recommendation has only one source published by Shao Meng himself, with no subsequent independent reports.
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Oct 20
Shao Meng
MIT Course MAS.S62: Two Lectures on the Evolution of Harnesses and Multi-Agent Systems
Lectures 4-5 of the MIT course MAS.S62 "How to AI an AI" focus on Harness evolution, covering the evolution of harness components, memory, tools, and skills, as well as the evolution of planning, task decomposition, and multi-agent systems. The memory section uses a three-axis taxonomy to connect MemGPT, mem0, MEM1, and Reflexion/ExpeL, and uses CLM to unify context management as file operations; the tools section moves from Toolformer, ToolLLM, and CodeAct to Meta-Harness, which represents the entire harness as a file system and provides about 400 times the amount of feedback information of previous methods. The multi-agent section presents five empirical findings, including that MAS wins 81% on decomposable tasks and loses 70% on tasks with sequential dependencies, and that CORAL achieves new SOTA on 8 tasks, with multi-agent evolution improving over single-agent by 4%-20%.