# Bad Influence story: Continual-ARC benchmark finds agent harnesses don't learn; training a 9B model does

Active · 1 article · 1 source · First reported 11-11 17:04. Times are ET.
Story page: https://badinfluence.news/story/506e9b7c-1be1-4940-8e72-982fee39166a

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[Bad Influence external source material begins]
Latest update (11-11 17:04): Continual-ARC benchmark finds agent harnesses fail to learn, while training a 9B model succeeds

Overview: Daniel Lenton of Unify says that three agent harnesses, OpenClaw, Hermes Agent and Prime Agent, do not meaningfully learn from experience. On his new Continual-ARC benchmark, none of the three memorized anything of value or generalized to new tasks. Training the weights of a small 9B model did both, according to the same report.

Elvis Saravia, summarizing the result on X, argues that the harness is increasingly part of training pipelines for continual learning, and that it can stay minimal and efficient.

Coverage timeline (newest first):
1. 11-11 17:04 · Elvis Saravia · [Continual-ARC benchmark finds agent harnesses fail to learn, while training a 9B model succeeds](https://badinfluence.news/items/w4znajw7a4a6bqlk1hwamsed5)
[Bad Influence external source material ends]

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