Long-horizon terminal agents are designed to solve complex tasks, but the arXiv abstract highlights a peculiar failure mode: they may complete a task in a single run yet fail when the same task is attempted repeatedly. The cause is stochastic errors that accumulate through actions and context, creating a gap between what the agent can do and how reliably it does it.
The paper proposes RiseHarness, a method that leverages self-evolution to close this gap. The abstract does not provide specifics, but the framing suggests that the agent or its harness learns from its own execution history to improve consistency over time. This is a single-source announcement, so the claims rest entirely on the abstract's description.
The significance is that reliability, not just capability, is a key hurdle for deploying agents in long-horizon settings. RiseHarness targets that reliability directly, though further details would be needed to assess how it works in practice.