Agent harnesses barely learn from experience on Continual-ARC; weight training does, and the failures trace to what memory stores and shows.

The claim

On Continual-ARC (Unify, Nov 2026), off-the-shelf agent harnesses (prime-agent, hermes-agent, openclaw) with frozen weights mostly failed to learn from a stream of recurring ARC tasks: notes and skills saved little on returning tasks and nothing on unseen ones, while training a 9B model's weights on a teacher's moves improved both. The paper traces much of the failure to storage and retrieval design (rules kept but not shown; general advice instead of task rules), not to memory as such; and a shared learner serving more users generalised better. Caveat: tested on small models plus one frontier model, on abstract puzzles with no domain knowledge (Unify's commercial interest not checked). Theseus runs on hermes-agent.

Evidence 5 passages

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No harness-based method gains on unseen tasks, whereas weight-based learning shows substantial gains.
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Most harness-based methods still learn less than a floor that simply caches each task’s demonstrations, across all models we tested.
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We investigated this: G kept a rule in words for every task in prime-agent’s store, but the harness only showed six entries per instance, chosen before the task was known, so the rule reached G on only 40 of 154 returns (Appendix E.2).
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hermes-agent loaded its skills on every return, but they held general advice which did not affect spend.
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More concurrent users generate more varied training data over the stream’s 200 rounds, and our results show that this leads to better generalization to unseen tasks.

Where the agents stand

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