AI Papers Atlas Wave 0

Method

Using AI
to keep up with AI.

This site exists to keep scholars of management, organizations, and strategy current with AI research that bears on their fields—by putting AI itself to work on the reading problem, under human command and independent QA.

Motivation

The research most relevant to our disciplines—how AI-native firms organize, how agent organizations coordinate and fail, what AI does to productivity, tasks, and managerial work—is produced at preprint speed, scattered across arXiv, SSRN, NBER, journals, standards bodies, and lab reports. Traditional scholarly infrastructure was not built for this cadence: review articles lag by years, and each lab indexes only its own output.

As of our 2026-08-20 survey of the public landscape, we found no maintained, structured paper database at this intersection. The one repository ever aimed at it froze in 2021; the active curated lists nearby are either engineering-scoped or contain no organizational research at all. Our answer is to use AI to keep up with AI: agentic campaigns do the sweeping, screening, and evidence coding; independent engines cross-check the results; humans rule the judgment calls. The output is not a link list—it is a database with provenance.

How a work enters the atlas

  1. Discovery. Scoped research campaigns sweep the sources against a frozen inclusion boundary, logging every query.
  2. Screening. Every lead gets an explicit decision—deep-code, context, merge, or exclude—with lineage recorded.
  3. Evidence coding. Admitted works are read at a recorded location; each extracted claim carries an evidence class, an exact locator, a scope boundary, and a confidence grade. Claims stay at the level of what the inspected source says.
  4. Independent QA. A separate session audits the bundle; defects force a repair round before acceptance.
  5. Landing. Accepted bundles are hash-manifested (SHA-256) and committed append-only. This site is a projection of that ledger—the counts on these pages reproduce from it.

Wave-0 provenance

The current corpus (34 works, 70 claims) is the QA-accepted evidence ledger of campaign 2026-08-18__ai-native-organization, landed in github.com/joyzhzh/sota-repository-scout at commit 799340c, bundle SHA-256 add4b41b45a8…. That campaign asked one question: what organizational mechanisms distinguish an AI-native organization from an organization that merely adds AI tools? The site's database twin binds the same rows into release apw-r0-seed (ledger SHA-256 ce8aff0549a7…) and projects them into the corpus file every page reads.

What is coming

  • Wave 1: ~886 paper identifiers (measured 2026-08-20) harvested from eleven research campaigns—agent organization design, agentic solo firms, agent markets and evaluation, platform ecosystems, organization-theory craft—being resolved to full bibliographic records via OpenAlex and Crossref, deduplicated, and screened.
  • The citation graph: every paper a node, every in-corpus citation an edge—the whole-corpus view with per-paper deep links.
  • Exports: BibTeX and RIS join CSV and CSL JSON once bibliographic identity is hydrated, with a fixity manifest.
  • Themes: a small controlled vocabulary, assigned by recorded multi-vote panels, never silently.

Claim discipline

Population words—“all”, “complete”, “comprehensive”—are banned as coverage claims. Waves are additive; corrections happen through an append-only adjudication overlay, never by silently rewriting landed records. Where metadata is unresolved, the record says so.

Who

Built by a human-directed AI research apparatus: research and build seats run by AI engines under human command, with every landing gated. The same machinery that keeps this database current is, itself, an instance of the phenomenon the corpus studies.