Can reviewers access what they know when checking LLM outputs?

A reviewer can know about LLM errors, be looking at the output, and still miss the error, because what they know is not reachable at the moment of review. The abstract states the account directly: "error detection is more effective when oversight-relevant information is accessible to users at the moment of review." Across "two randomized lab-in-the-field experiments with 640 customer-facing employees," self-generated explanations "improve error detection and strengthen recall of verification-relevant reasoning," and cues that reactivate that reasoning "help sustain detection under repeated LLM use." The discussion names the contribution as "retrievability as a distinct precondition for effective oversight," and says oversight "can fail when relevant information is not accessible at the moment of review, even in settings where reviewers have received that information and are actively reviewing the output."

The paper gives two mechanisms, "generative encoding and cue-supported reactivation." Producing your own explanation builds the memory of why an output should or should not be trusted, and a later cue brings that memory back when a new output arrives. The introduction sets up the gap this fills. Errors persist "even when reviewers are aware of LLM fallibility and prominent system warnings are displayed," and a reviewer "recently trained on a type of LLM error, or has personally encountered it, may still miss the next instance." Having been told and being able to call it up are different things. The paper positions retrievability against two existing accounts: capability, where failure comes from limited "literacy, expertise, or mental models," and engagement, where failure comes from "insufficient scrutiny of seemingly reliable systems." It calls retrievability "distinct and complementary" to both, not a replacement for either.

Against the nearest notes, this one adds a third place to look. Do users worldwide trust confident AI outputs even when wrong? sits on the engagement side, since users follow confident output. The introduction's own "fluent, confident, and largely correct LLM output invites acceptance rather than detailed scrutiny" is that account. This paper does not dispute it. It asks what a reviewer who does scrutinize has to scrutinize with. Can organizations lose scrutiny capacity while keeping oversight forms? locates the failure in the institution, where the capacity is gone. On this vault's reading, retrievability is a per-reviewer version of the same worry that needs no institutional erosion: the capacity exists and is not accessible when needed. That reading is the vault's, not the paper's. How do competent systems quietly undermine safety oversight? names weakened skepticism as one quiet mechanism, and retrievability would be a separate route to the same missed error.

What the excerpt does not give. No effect sizes, no task or error types, and no comparison conditions, so it cannot say whether self-generating an explanation beats being handed one. It does not describe the cue used or how long "repeated LLM use" lasted, and the abstract's "daily retrieval cues" is a practical recommendation, not a described protocol. The sample is customer-facing employees, and nothing here tests high-stakes settings like the fabricated court citations the introduction mentions. It also does not pit the three accounts against one another; it asserts complementarity. At the strength supported, the design question shifts from "was the reviewer trained and told to look" to "can the reviewer reach the relevant reasoning at review time," and the paper's suggested remedies, "lightweight onboarding self-explanations and daily retrieval cues," are cheap enough to try.