This dataset is published for research and evaluation purposes only. Its contents were generated with Anthropic Claude models, so it must not be used to train machine-learning models. By requesting access you agree to use this data for research and evaluation only, and not for model training.

Correction propagation v0 — does the fix travel?

Context first: what system is this from?

Seek is an autonomous research agent that has run nightly since June 2026 on a Mac mini. She follows her own curiosity across the open web and writes what she finds into a knowledge base (a "vault") of small Markdown notes: claim notes (one falsifiable claim each, with a source quote and DOI/URL), observations, entity pages (hubs about a person or thing), questions, and essays. Notes reference each other with [[wikilinks]], so the vault is a linked graph. Crucially, her output becomes her future input: tomorrow's research agenda is generated from today's knowledge state. The whole vault is public and browsable at talk-about.ai; her decision records are published in seektraces-decisions.

Seek's claims are audited by a second model (a "cross-model audit"), and sometimes an audit finds a claim wrong or overstated and corrects it, leaving a dated correction narrative in the note. That raises the question this dataset measures — posed by an external reviewer of Seek's public traces:

When a long-running agent corrects a claim, how far and how fast does the correction propagate through everything built on top of it? "The knowledge base contains the corrected fact" and "downstream artifacts still behave as though the old fact were true" are different tests.

For intuition: a newspaper prints a page-one correction, but last month's op-eds citing the wrong number are still on coffee tables — and tomorrow's columnist might quote the op-ed. For a system that writes tomorrow's research from its own back-issues, this is the core maintenance problem — and it is what ordinary single-shot "deep research" evaluation cannot see.

A worked example (real records from this dataset)

On 2026-07-11, an audit corrected the claim claim-forsythe-coined-computer-science-and-founded-stanford-cs (the popular attribution is a myth — per Knuth, Forsythe did not coin the term). This dataset then asks: what happened to every note that links to the corrected one?

dependent artifact	what happened	class
`claim-purdue-established-first-us-cs-department-1962`	modified 1 day after the correction	`touched-after`, lag_days 1
`observation-numerical-analysts-founded-first-us-cs-departments`	modified 2 days after	`touched-after`, lag_days 2
`claim-forsythe-did-not-coin-computer-science-per-knuth-1972`	created 2026-08-07 — the corrected knowledge later crystallized into its own claim	`created-after-correction`
`entity-george-forsythe`	entity hub created after the correction	`created-after-correction`

Whether each touch actually incorporated the correction (vs. edited for unrelated reasons) is the human-judgment layer — see "the open task."

The data

225 corrections (dated audit-correction records, July–September 2026) joined to their dependent artifacts via the vault's wikilink graph (breadth-first, depth ≤ 3, across claims, observations, maps, entities, questions, drafts, essays) = 19,115 (correction, dependent) records. Dates come from the vault's git history. Fields per record:

correction, correction_date, correction_type — the repaired claim
dependent — a note that links (directly or via ≤3 hops) to it
depth — link distance (1 = links directly to the corrected note)
created, last_modified, first_touch_after_correction, lag_days
class — mechanical status:
- touched-after: dependent existed before the correction and was modified afterward (fast lag suggests, but does not prove, repair)
- untouched-since: existed before, never modified after — the prime stale candidates
- created-after-correction: dependent didn't exist yet — new work built atop a corrected node; it inherited either the fix or the ghost (stale-action exposure candidates)
judgment_needed: always true — see below
dependency_proxy: how "dependent" was operationalized

Mechanical baseline: 7,662 touched-after (median lag 9 days, reaching depth 3), 2,593 untouched-since, 8,860 created-after (new work keeps being built atop corrected nodes as the vault grows).

The open task (why `judgment_needed` is on every record)

The mechanical pass measures motion, not correctness. Distinguishing updated / intentionally historical / stale / not applicable requires reading the dependent against the correction — every source text needed is public (the vault, plus full audit narratives in seektraces-decisions). That makes these 19,115 records a natural benchmark for the capability the reviewer named: can a model (or a human, or the agent herself) correctly classify downstream state after an upstream repair? Beat the baseline; publish your labels.

Honest limits

Wikilink linkage is a proxy for semantic dependency (expect not-applicable cases). Depth-3 closure over a dense graph inflates counts (filter depth == 1 for the strict view). The system evolved while running — treat eras carefully; the earliest corrections predate several machinery changes. Analysis is model-free (string/graph/git mechanics). Known data flaw: one correction carries correction_date: 2024-07-18 (13 records) — the exporter's date parse grabbed a Wayback-capture date quoted inside the audit narrative; the audit actually ran 2026-08-30. Exporter fix pending; filter that correction if dates matter to you.

Provenance and permitted use

Vault text was written by Claude-family models inside the Seek scaffolding; per Anthropic's Consumer Terms this dataset is for research, evaluation, and reading — not training competing models. Siblings: skbench · seek-trajectories · seek-sessions · seektraces · seektraces-decisions.

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