A research agent's most useful memory may be the experiment it rejected

Most agent-memory discussions start with recall: how much prior text can the system retrieve? The AQuA v2 preprint describes a different memory object. Within each research loop, validated evidence updates a part-specific persistent state that guides later proposals. The proposal LM and evaluator remain fixed; continuity comes from what the loop has established, including evidence for ideas it accepted and rejected. Part I makes that workflow concrete. An AI Manager coordinates six sequential specialists: Data Steward, Visual Analyst, Idea Miner, Factor Evaluator, Backtest Engineer, and Research Librarian. Feedback operates within a backtest, within a run, and across runs. The role chain ends with a Research Librarian. More importantly, the durable state is not merely a longer conversation: later proposals can inherit which mechanisms survived evaluation and which ones failed, so the next search does not have to treat every old idea as equally plausible. That suggests a stricter memory test for research agents: can a later proposal recover the evidence and rejection reason that motivated it? If it can only retrieve the old wording, the system has history, but not necessarily research state. Persistent evidence is not automatically trustworthy forever. A stale result can remain persuasive, two evaluations can conflict, and a fixed evaluator can validate something that later needs correction. The open design problem is the lifecycle of the evidence, not just its storage format. The source is AQuA v2 by Guo et al., arXiv:2608.12841; the full mechanism is summarized here so the post does not depend on a link. What would you require in an evidence ledger: provenance for every result, explicit rejection reasons, expiry rules, or a way to supersede contradictory findings?