# Verifiers

`kaal:entity:verifiers`

**Status.** derived

This node is assembled mechanically from the 4 claims that carry the concept tag `verifiers`. It is a roster of what the corpus says under this term. It is **not** an adjudicated definition: no single statement here has been ruled canonical, and no first-appearance call has been made. Read the claims and judge for yourself.

## Every claim under this term

4 claims across 2 works, 2018 to 2026.

**2018**

- [3128900-025](https://wulfkaal.github.io/claims/3128900-025) [mechanism/argued] -- Verifiers stake proportionally smaller amounts of reputation tokens than workers, because their higher reputation scores make them less likely to be malicious actors.
  > Similarly, verifiers stake (a proportionally smaller) amount of reputation tokens because verifiers have a higher reputation score and are therefore less likely to be malicious actors.
  Wulf A. Kaal, Decentralized Mechanical Turk Through Verified Reputation (2018). SSRN: https://ssrn.com/abstract=3128900

**2026**

- [7598518-015](https://wulfkaal.github.io/claims/7598518-015) [condition/reported] -- Feng et al. (2025) show that verifier-selected synthetic data can prevent collapse, and the paper concedes the core of that result: verifier selection is an established remedy, and the oracle ceiling and the Gaussian setting are established.
  > Intelligence models increasingly learn from data that earlier Intelligence models produced, and when that loop repeats the rare cases disappear first. This paper preregisters a test of whether a reputation-weighted validator pool, filtering the training data of a recursive replacement loop, keeps those rare cases alive. The outcomes remain sealed until a replication of the original model-collapse result succeeds.
  Wulf A. Kaal, Can Reputation Prevent Model Collapse? A Preregistered Test of Reputation-Weighted Data Filtering (2026). SSRN: https://ssrn.com/abstract=7598518
- [7598518-016](https://wulfkaal.github.io/claims/7598518-016) [mechanism/reported] *(failure mode)* -- Yi et al. (2026) report that an imperfect external verifier drives the model toward the verifier's own knowledge center.
  > Intelligence models increasingly learn from data that earlier Intelligence models produced, and when that loop repeats the rare cases disappear first. This paper preregisters a test of whether a reputation-weighted validator pool, filtering the training data of a recursive replacement loop, keeps those rare cases alive. The outcomes remain sealed until a replication of the original model-collapse result succeeds.
  Wulf A. Kaal, Can Reputation Prevent Model Collapse? A Preregistered Test of Reputation-Weighted Data Filtering (2026). SSRN: https://ssrn.com/abstract=7598518
- [7598518-018](https://wulfkaal.github.io/claims/7598518-018) [mechanism/reported] *(failure mode)* -- Qiao et al. (2026) report that verifiers which see only fragmented slices of the reference can accelerate tail loss.
  > Intelligence models increasingly learn from data that earlier Intelligence models produced, and when that loop repeats the rare cases disappear first. This paper preregisters a test of whether a reputation-weighted validator pool, filtering the training data of a recursive replacement loop, keeps those rare cases alive. The outcomes remain sealed until a replication of the original model-collapse result succeeds.
  Wulf A. Kaal, Can Reputation Prevent Model Collapse? A Preregistered Test of Reputation-Weighted Data Filtering (2026). SSRN: https://ssrn.com/abstract=7598518

## Verify

Every claim above resolves to a record carrying a verbatim source quote, the sha256 of the source PDF, and a preformatted citation. Nothing here asks to be taken on trust.

    curl -s https://wulfkaal.github.io/entities/verifiers.md | sha256sum

**Canonical form.** This markdown file is the canonical hashed representation of this entity node. Its sha256 is the content hash.