--- title: Authority decay: how knowledge ages inside a company slug: authority-decay category: Research tags: Authority, Decay, Research date: 2026-04-21 read_time: 9 min read word_count: 1280 canonical: https://quelvio.com/blog/authority-decay machine_url: https://quelvio.com/ai/blog/authority-decay publisher: Quelvio ---
# Authority decay: how knowledge ages inside a company
Research · April 21, 2026 · 9 min read
Patterns we see across the customer corpora Quelvio indexes — how engineering knowledge loses authority over 18 months, and what restores it.
When a document is written and shipped as the canonical reference on a topic, its authority score peaks in roughly the first month. After that, what happens to it depends almost entirely on what the rest of the corpus does, not on the document itself. Most documents stay untouched after publish — that's the baseline. The question we keep asking, looking at indexed customer corpora over time, is: what does an untouched canonical document's authority look like 6, 12, and 18 months later?
What we see in the data is a consistent shape across engineering and product corpora, with one important variable — what happens *around* the document. The decay curve isn't a property of the document. It's a property of the corpus.
## The shape of the curve
An untouched engineering document with reasonable initial authority shows three distinct phases as it ages.
**Months 0–3 — peak.** Authority is at its highest. Recent claims tend to be referenced often, the contributors are still active in the domain, and competing documents haven't accumulated. The decay rate during this window is essentially flat.
**Months 3–9 — slow drift.** Authority erodes at a measurable but moderate rate. The domain has moved on slightly; some claims in the document are no longer the team's current view. Inbound references plateau. New documents in the same domain start to appear, but no individual one has yet displaced the original.
**Months 9–18 — the cliff.** This is the phase that surprises most customers when we show it to them. Authority falls off sharply, not gradually. Almost always, this cliff coincides with a specific event in the corpus — a new document was written by an active owner, a claim divergence was detected, or a Slack thread quietly took on the canonical role. The cliff is not the document aging; it is the corpus electing a different authority in the same domain.
## What accelerates decay
Three patterns in the surrounding corpus reliably accelerate authority decay for an unrelated, untouched document.
**Owner turnover.** When the contributor who shipped a document leaves the company or moves out of the domain, the document's authority starts compounding downward over the next two quarters even if nothing else changes. The system is correct to do this — institutional knowledge attached to a person who is no longer accountable for the topic is genuinely lower-confidence than knowledge attached to a current owner. The decay rate roughly doubles in the six months following a clear ownership handoff.
**Domain churn.** Some domains move faster than others. In engineering corpora, infrastructure and platform domains tend to be high-churn — new tools, new providers, new architectural decisions every quarter. Authority decays roughly 2–3× faster in high-churn domains than in stable ones. This is why two documents shipped on the same day can have very different authority profiles a year later, depending on which domain they live in.
**Silent supersession.** A new document writing about the same topic with current ownership and divergent claims drives the steepest decay. This is the *cliff* event in the curve above. When it happens, the older document's authority can drop by half within a month. The corpus has elected a new authority on the topic, and the cross-reference graph picks it up almost immediately.
## What restores authority
Authority decay is not one-way. Three actions reliably restore authority on a document that has fallen behind.
**Re-attribution by an active owner.** When a current domain owner edits, comments on, or explicitly endorses an older document, authority recovers measurably. The amount of recovery is roughly proportional to the editor's current domain authority. This is what makes the system stable rather than ratcheting downward — a senior owner stepping in and saying *"this is still right"* restores canonical status.
**Inbound reference activity.** When other documents in the corpus start linking back to an older document — especially documents written by active domain owners — authority recovers. The system reads inbound references as votes, weighted by the author's current authority.
**Explicit lifecycle marking.** When a tenant administrator marks a document as canonical in the dashboard, the lifecycle label overrides the natural decay. We expose this in the dashboard precisely because there are documents — security policies, retention rules, compliance baselines — whose authority should not be subject to the corpus's churn. The system respects the explicit marking but still records the implicit signals in the background, so the administrator can see when the corpus is starting to disagree with the override.
## Why this matters for retrieval
Authority decay is not just an interesting curve. It is the load-bearing reason a retrieval system can return *the current view* rather than *whatever was indexed first*. Without modeling decay, every retrieval pipeline gradually drifts toward returning the documents that ranked highly when they were first added — even after the corpus around them has moved on.
The version of the cliff event that hurts most in production is the slow-deploy case. A 2024 deployment runbook had high authority for nine months and then quietly lost it to a Slack thread in early 2026. The runbook is still in the corpus, still indexed, still ranks well on keyword overlap. An agent retrieving "deployment process" without authority decay returns the 2024 runbook, confidently. An agent with authority decay returns the Slack thread, with the runbook labeled `SUPERSEDED` and one-click-away for the reader who wants the historical view.
Modeling decay is what lets the system maintain *what's currently true* over the long arc of a company's evolution, not just *what was true when we indexed it.*
[Looking at your own curves] The authority-decay view is on the dashboard roadmap for the next Brain release. Until then, customers can see per-document authority by running `quelvio source <query-id>` in v0.3.0 — every chunk in the result prints its current authority value alongside its lifecycle label.
Tags: Authority, Decay, Research