How detailed should an AI's 'show your work' log be so reviewers catch real mistakes without drowning in detail?

This explores how finely an AI agent's reasoning trace should be broken down so that mistakes get caught without review turning into a slog. The corpus's short answer: check at the level of individual steps, but don't make humans do that checking. The more useful finding is that "faster review" and "better review" can come apart. In one study, a redesigned agent-trace interface helped people find errors much faster but didn't make them any more accurate. People who missed errors also became more confident in their wrong answers Does a faster trace interface actually improve error detection?. A cleaner view of the trace can make review feel thorough when it isn't.

The case for step-level granularity is strong. Scoring only the final answer, or averaging confidence across a whole trace, hides the specific moment where reasoning broke down. Step-level confidence catches those breakdowns, and it also lets a bad trace be stopped early instead of run to the end Does step-level confidence outperform global averaging for trace filtering?. On long agent tasks, adding checks on intermediate states raised success from 32% to 87%, because most failures turned out to be process violations, not wrong final answers Where do reasoning agents actually fail during long traces?. An agentic reviewer that worked through proofs and experiments line by line caught mathematical errors that had passed expert human review at top venues Can inference scaling help reviewers catch errors humans miss?.

Fine granularity only becomes expensive when it blocks the work. One approach runs verifiers alongside generation instead of inside it. They pull out checkable state from the trace and step in only when something is violated, so correct runs pay almost no extra latency Can verifiers monitor reasoning without slowing generation down?. A related design principle splits model judgment from deterministic checks that can be executed automatically. That way the expensive, careful reading is saved for the parts that actually need judgment Can separating judgment from verification improve research paper reliability?. You also don't have to choose a single granularity. Branching search trees naturally produce coarse, strategy-level signals near the root and fine-grained detail signals near the leaves Does tree depth automatically produce supervision at multiple granularities?.

There is one catch: step-by-step isn't enough on its own. Some errors only exist across a sequence of actions. Each step looks fine in isolation, but together they break a rule. Checks that look at one action at a time structurally can't see these, so the reviewer needs some memory of what came before Can stateless checks ever catch sequence-level constraint violations?. The errors themselves are also getting harder to spot. Weaker models fail visibly by deleting content, while frontier models corrupt it quietly and leave the surface looking intact Does model capability change how documents degrade?. Quick skimming works less and less well as models improve.

A practical takeaway: don't decide where to spend review effort based on how long a trace is. Trace length mostly reflects how close a problem is to the model's training data, not how hard the problem is Does longer reasoning actually mean harder problems?. Taken together, the corpus suggests automated checks at the step and state level running in the background, with memory across steps, and humans shown only the flagged moments. The corpus doesn't directly test which display granularity works best for human reviewers. That part is still an open question.