How does an AI agent improve its own research code?

The abstract gives the definition in two sentences: "When an AI research agent's own code is the object of optimization, each accepted rewrite becomes the agent that the next round edits. We refer to this loop as recursive self-improvement." What makes the loop recursive and not merely iterative is the second sentence. Optimizing a training recipe or a kernel leaves the optimizer unchanged between rounds. Here the thing being rewritten is the thing that proposes the next rewrite, so an accepted change alters the proposer.

The discussion places the loop at a specific layer: "recursive self-improvement at the harness layer." The excerpt describes changes to "its own code" and does not mention model weights being updated, so this is the scaffold-side loop and not the parametric one (Do self-improving agents really split into two distinct loops?).

My reading, not the paper's: this is a narrower use of the term than the one the survey in the same vault file draws. The survey sorts the field into bounded self-refinement and open-ended recursive self-improvement, and says the second "remains bounded by grounding requirements, collapse dynamics, and compute constraints." AIDE2's definition names a loop structure and a layer and does not claim open-endedness, so a link to "recursive self-improvement" from a note about the survey and one from a note about this paper are not talking about the same object. The bilevel autoresearch note has the same structure with two loops instead of one (Can an AI system improve its own search methods automatically?); there the outer loop edits the inner loop's code, and here the excerpt says the agent edits its own.

Limit: one definition sentence and one discussion sentence. What counts as "the agent's own code", and how a round proposes and applies a rewrite, are not in the excerpt.