One wrong document drops deep-research agents 66-88 points, and it's not a retrieval problem

Here's the setup. You build a benchmark with 100 research tasks. Each answer is supported by two independent chains of corroborating records. Clean environment, agents do fine. Then you drop in exactly one plausible-looking document that carries a conflicting answer.

Accuracy falls 66 to 88 percentage points.

That's DRNOISE, submitted July 19. The number alone would be interesting, but the failure mode is the part that changed how I'm thinking about my own pipeline. The agents weren't failing to retrieve the truth. They found the correct records. They just stopped before reconciling the conflict. The paper calls it verification inertia: the agent has enough to answer, so it answers, and the contradictory evidence sitting in its own context never gets adjudicated.

I've been assuming for about a year that citation capability is a decent proxy for verification. If the agent can point at a source, it presumably read the source and weighed it. That assumption is wrong. Citation is a formatting behavior. Verification is a reasoning behavior. They are not the same thing, and every research agent I've built conflates them.

The other uncomfortable finding: generic "verify your sources" prompting helped but didn't close the gap. Not even close. So the fix isn't a better system prompt. The fix is architectural. You need an explicit reconciliation step that runs before an answer is emitted, one that specifically looks for cross-source disagreement and refuses to proceed until it's resolved or flagged.

What I'd actually build: a separate pass that takes the retrieved set, groups claims by the entity or fact they assert, and surfaces any group where two sources disagree. Not a prompt instruction. A structural gate. The agent doesn't get to write the answer until it's produced a conflict map, even if that map is empty.

This lands hard for anyone running research agents unattended. If nobody reads the output before it ships, and the open web contains one confidently wrong page on your topic, you have a system that will retrieve the truth and then publish the lie. And it'll cite both.

Pair this with the AWS AgentCore optimization insights that shipped this week, aimed at exactly this class of failure: agents that return successfully, pass every health check, and deliver wrong outcomes. Liveness monitoring tells you nothing about correctness. Two independent teams arrived at the same gap in the same week.