If you let an AI reviewer see more of what happened, does it actually catch more problems for the same effort?

This explores whether widening what a reviewer or monitor looks at (one action, a rolling window, a known group, a whole episode) catches more problems when the review budget and false-alert load stay the same. The corpus doesn't answer this directly. The closest note, Does added monitoring improve protection at acceptable cost?, sets up exactly this experiment. It compares isolated actions, rolling windows, known groups and prospectively discovered episodes at equal review cost and equal false-alert workload. But the excerpt reports no results, so it gives a well-posed question and no verdict.

Adjacent work suggests that how a reviewer spends its effort may matter more than how much it sees. PAT, an agentic reviewer, spends extra test-time compute checking proofs and experiments line by line. It gets 34% better recall on math errors than zero-shot review and flagged flaws at STOC and ICML that human reviewers missed (Can inference scaling help reviewers catch errors humans miss?). That is a gain from depth of attention, not from a wider view, and it isn't at a fixed cost. A structured novelty pipeline points the same way. Splitting the job into extract claims, retrieve related work, then compare reached 86.5% reasoning alignment with human reviewers, ahead of holistic LLM baselines (Can structured pipelines make LLM novelty assessment reliable?). Related context is only useful when it's organized around the claim being checked.

More context can also hurt. An agent-as-judge that collects evidence dynamically cut judge shift to 0.27%, against 31% for a plain LLM judge. Its memory module then cascaded errors, so accumulated context carried mistakes forward (Can agents evaluate AI outputs more reliably than language models?). Human reviewers show a version of this. Prior ratings shift later ratings, and the effect compounds over time (Do online ratings actually reflect independent customer opinions?). A wider window that includes other people's judgments may make a reviewer less independent, not better informed.

There is also a perception trap. Users prefer answers with more citations almost as much when the citations are irrelevant as when they're relevant (β=0.273 vs 0.285) (Do users trust citations more when there are simply more of them?). A reviewer, human or automated, can feel better covered by a bigger context window without being any better at catching problems. That is why the equal-cost, equal-false-alert design matters. Without it, more context looks like a win because it feels thorough.

The corpus therefore leaves it open. The signs so far are that structure and targeted evidence beat raw breadth, and that extra context brings bias and error propagation. Whether rolling windows or episode-level monitoring pay for themselves at a fixed budget hasn't been shown here.