Temporal RAG for News: Test Conflicting Reports Prior to Ship

Agents can retrieve the latest report and still provide an older answer. Distinguishing up-to-date evidence and using it appropriately are different challenges to test.

On October 18, 2026, researchers published a preprint proposing TREK, a framework and benchmarks to study search-augmented models on rapidly changing knowledge. In frontier systems they studied, post-retrieval reasoning was the main bottleneck. Models could retrieve the needed evidence to answer and still be misled by conflicting information.

These results set a precedent for developers to enlarge the scope of their RAG evaluation for news applications beyond retrieval quality. The following is a sample test design.

Define the time the answer must describe

Temporal RAG determines the time when a claim is valid. Before searching, define the target time for the claim and the context the application has at that time.

Distinguish the time of an event described in an article from the time of its publication. Recently published articles can describe old events. Currently published articles can describe later events.

Maintain the question, target time, the reviewed source evidence, expected answer, and the passages that support the answer. Retain the materials your team is permitted to keep. Mark the time the evidence was captured and the version reviewed.

A single, live URL is not sufficient to define a test if it changes its content between runs.

Make a simple conflicting-report fixture

Create a made-up example with 3 reports. The first report says the port closed on day 1. The second report is an operator update from day 2: one berth has reopened. The third report, published on day 3, says the port closed again on day 1.

Ask at the end of day 3 if the port is fully operational. One of the expected answers is that one berth has reopened. The evidence is not strong to indicate that the whole port is operational.

Check that the answer is supported by the operator update and limited to one berth. The report published on day 3 repeats older information. Just because that report was published on day 3, doesn’t mean the information is the current status of the port.

Ask another historical question at the end of day 1. The evidence for the question must be before the information that was published later. Review both the expected answers before using the fixture to score a release.

Distinguish between search and answer failures

Run the full application with retrieval on and keep the information actually provided to the model. Check if the information has the required passage to answer the question.

Later, run the answer step only with the evidence that was reviewed. Finally, add conflicting reports to that evidence. For this, keep the model and generation settings the same.

If the passage was not provided to the model, take a look at retrieval and context assembly. If the model answers the question with the provided evidence but fails with conflicting reports, take a look at how the model handles conflicting context.

These comparisons can identify the location of a failure, but not whether a particular prompt or model will correct it.

Score the claim and its citation together

For the port fixture, check if the answer maintains the partial reopening, the relevant time and the limit of what the evidence establishes. Inspect the citation associated with that claim.

A citation to a real article is not adequate if the cited passage does not support the answer. Record unsupported expansion separately from missing evidence. Preserve the failed answer with the evidence used so someone can inspect the result.

Use News Search to assemble the evidence

News Search on Webz.io is a natural language query API that returns article content and relevant paragraphs. It supports filters for date, source and other attributes.

Use these filters to capture candidate coverage for the test. Your application can then retain test inputs, verify results and compare results across releases.

Review Webz.io’s documentation on real time news retrieval to learn about the data pipelines that make this possible. A temporal test adds a specific release question: after right evidence reaches the model, does the answer still describe right state of the event?