Really nice work! This supports some of my ad-hoc observations and intuitions from working on evolutionary systems and self-verification over the past year. - While experimenting with verifier-free evolution methods combining parallel and sequential reasoning (RSA/PDR: arxiv.org/abs/2509.26626, arxiv.org/abs/2510.01123), I found that truncating the reasoning traces and keeping only the final answers (or a summary + final answers) before aggregation (roughly analogous to the communication step in multi-agent systems) often worked better. IIRC, @sudomonish also noticed something similar with SqueezeEvolve (arxiv.org/abs/2604.07725). We also observed that removing the reasoning traces made models reason for longer during aggregation, whereas retaining them made models more conservative and more likely to terminate early. This seems closely related to the papers finding that forgetting increased median reasoning tokens from 2.9k to 9.4k per proposal and leads to larger code edits as well. - While working on self-verification (our V1 paper: arxiv.org/abs/2603.04304v1), I also noticed that self-verification performance improved when we removed the original reasoning trace and gave the model only the final answer to verify. My intuition was that models become more biased toward their own answers when they see the reasoning that led to those answers. This is consistent with @Jacklu_me's paper on verification (arxiv.org/abs/2512.02304), which shows that models are more likely to accept solutions resembling their own, while cross-family verification tends to work better. Truncating the original reasoning trace might make the answer feel more "alien" to the model during self-verification, potentially reducing this bias. The model might then spend more tokens independently verifying the answer rather than relying on the earlier reasoning. Interestingly, autoresearch agents also might be implicitly or explicitly self-verifying their own ideas and proposed changes, even when external evaluators score the resulting candidates. So I wonder if better self-verification after forgetting might also be contributing to the improvements observed in the autoresearch loops of the paper.