Across more than 350 language models, when two models both answer a leaderboard question wrongly they give the same wrong answer 60% of the time on one dataset, and larger, more accurate models have highly correlated errors even across different architectures and providers.

SocioLens project,

Claim LD09-SL-C25 (v1.0)

Provenance and status

Claim type: Estimate
Publication status: Eligible for publication, with a qualifier
Verification state: Checked against the abstract of the primary source only. Who checked this claim is not recorded for it; claim checks so far were AI-led, and no person has checked claims against their sources.
Does not license: Effects on real decisions
Limitations: Official abstract only; benchmark tasks; agreement baselines matter (LD09-SL-C26)
Source (as cited): Kim, Garg, Peng & Garg, “Correlated Errors in Large Language Models,” ICML 2025, arXiv:2506.07962
Source type: Peer-reviewed conference paper (recorded as “Peer-reviewed conference paper”)
Institutional position: Independent academic
Maturity of the evidence: Emerging evidence
Recorded date: 15 October 2026
Currentness / correction: This is the current version. No correction or concern is recorded on its sources.

Sources this version cites

Kim, Garg, Peng & Garg, “Correlated Errors in Large Language Models,” ICML 2025, arXiv:2506.07962 (Peer-reviewed conference paper)
Read the original

Version

This page shows version v1.0. Its record never changes, even when newer evidence arrives; the site may add a note to the page or withhold a figure not cleared for public use.

This pinned version is also the current version.

Versions: v1.0 · current

Raw record: every recorded field, with its coded values

All 25 canonical Claim fields, verbatim, with the coded values derived from them. The plain-language labels above are read from these.

Claim type (coded): ESTIMATE (as printed: “ESTIMATE”)
Publication status (coded): ELIGIBLE (QUALIFIED) (as printed: “ELIGIBLE WITH QUALIFIER”)
Verification state (coded): CONTENT_VERIFIED_PRIMARY (ABSTRACT_ONLY)
Source type (coded): PEER_REVIEWED_CONFERENCE_PAPER
Maturity (coded): EMERGING

#	Canonical field	Canonical value (verbatim)	Normalized (Claim Value Vocabulary v1.0.2)
1	CLAIM ID	LD09-SL-C25	—
2	PUBLIC-FACING CLAIM	Across more than 350 language models, when two models both answer a leaderboard question wrongly they give the same wrong answer 60% of the time on one dataset, and larger, more accurate models have highly correlated errors even across different architectures and providers.	—
3	CLAIM TYPE	ESTIMATE	base_types: ESTIMATE
4	RESEARCH STRAND	C — Systems, decisions and shared knowledge	—
5	MATURITY	EMERGING	states: EMERGING
6	SOURCE	Kim, Garg, Peng & Garg, “Correlated Errors in Large Language Models,” ICML 2025, arXiv:2506.07962	source_ids: SRC-SL-KIM-ET-AL-CORRELATED-ERRORS-ICML-2025
7	SOURCE TYPE	Peer-reviewed conference paper	classes: PEER_REVIEWED_CONFERENCE_PAPER
8	INSTITUTIONAL POSITION	Independent academic	—
9	SCOPE	Leaderboard tasks and a résumé-screening task	—
10	DESIGN	Analysis of agreement between models’ errors	—
11	SAMPLE / DATASET	More than 350 language models on two leaderboards	—
12	MODEL / TOOL / VERSION	Language models from many providers	—
13	COMPARATOR	Pairs of models from different architectures and providers	—
14	MAIN RESULT	60% agreement when both err on one dataset; correlation higher among larger, more accurate models; downstream effects in model-as-judge and hiring settings	—
15	INFERENTIAL TARGET	Whether using models from different providers avoids shared errors	—
16	DOES NOT LICENSE	Effects on real decisions	—
17	VERIFICATION STATE	CONTENT VERIFIED ABSTRACT	state: CONTENT_VERIFIED_PRIMARY · qualifiers: ABSTRACT_ONLY · verified_by: UNKNOWN
18	LIMITATIONS	Official abstract only; benchmark tasks; agreement baselines matter (LD09-SL-C26)	—
19	CONTRADICTORY / SUPPORTING SOURCES	Diversity restored by pooling vendors in idea generation: LD09-SL-C17	—
20	GEOGRAPHY / LANGUAGE	Not checked / English	—
21	DISTRIBUTIONAL LEDGER	Not recorded	—
22	R01 / FIELD-MAP LINKS	LD-09, D3	—
23	WHAT WOULD CHANGE THIS CLAIM?	Evidence that model diversity reduces shared errors in deployed decisions	—
24	PUBLICATION STATUS	ELIGIBLE WITH QUALIFIER	state: ELIGIBLE · qualifiers: QUALIFIED
25	RECORDED DATE	2026-10-15	—

Field 22 references resolved against Field Map v0.2: LD-09 (LIVING_DEBATE); D3 (DOMAIN)

The sources as recorded, with their record IDs and coded source types:

Pinned Sources (field 6, version-specific)

“Kim, Garg, Peng & Garg, “Correlated Errors in Large Language Models,” ICML 2025, arXiv:2506.07962” → Kim et al. (2025), “Correlated Errors in Large Language…” (SRC-SL-KIM-ET-AL-CORRELATED-ERRORS-ICML-2025--v1.0) — Kim, Garg, Peng & Garg, “Correlated Errors in Large Language Models,” ICML 2025, arXiv:2506.07962; class PEER_REVIEWED_CONFERENCE_PAPER (explicit public provenance pin)

Cited by Living Debate versions

Individual gain, collective narrowing? Does AI make science, creative work and shared knowledge more alike? (v0.1) LD-09

Cite this page

SocioLens project. (2026, October 15). Across more than 350 language models, when two models both answer a leaderboard question wrongly they give the same wrong answer 60% of the time on one dataset, and larger, more accurate models have highly correlated errors even across different architectures and providers [Claim LD09-SL-C25, version v1.0]. SocioLens. https://sociolens-web.pages.dev/claims/ld09-sl-c25/v1.0/

@misc{sociolens_ld09_sl_c25_v1_0,
  author = {{SocioLens project}},
  title = {Across more than 350 language models, when two models both answer a leaderboard question wrongly they give the same wrong answer 60\% of the time on one dataset, and larger, more accurate models have highly correlated errors even across different architectures and providers.},
  year = {2026},
  date = {2026-10-15},
  howpublished = {\url{https://sociolens-web.pages.dev/claims/ld09-sl-c25/v1.0/}},
  note = {Claim LD09-SL-C25, version v1.0}
}

The link points to this version, whose record never changes. For a finding, cite the original study as well: SocioLens summarizes research and does not replace it.

Research-of-record provenance

Materialized by sociolens-materializer 0.6.0 at 2026-09-28T20:00:00.000Z; normalization contract SocioLens — Claim Value Vocabulary v1.0.2 — Pre-Spike Materialization Contract (v1.0.2). Content seal SHA-256 d2ba0afec675d4cecc1994c601db448c669bd305c96057e5a73258d141d983cc.

Repository-authored inputs (not Drive exports)

SocioLens claim — LD09-SL-C25 v1.0 (v1.0) — written in the SocioLens repository by Project Lead (Claude, AI) on 2026-10-15; no Drive identity exists. Authority: AGENTS.md "Authority" (the Project Lead decides research content within the research-integrity rules) and the Owner's choice in the SocioLens project chat on 15 October 2026 to build the LD-09 debate; a new Claim, first version, written from the project's LD-09 source notes, checked against full texts or official abstracts. Block “ENTRY 25 — LD09-SL-C25” SHA-256 7e1921d65516278e0128ddb0d71cc5b3a06746fe97bc2a1742a6ea27aa0e4555; fixture fixtures/authored/claims/LD09-SL-C25--v1.0.

Builder decisions (not Drive data)

VERSION = v1.0 — basis: Claim version taken from the repository-authored claim fixture version (SocioLens claim — LD09-SL-C25 v1.0); the first version of a new Claim of the LD-09 debate
CURRENT_POINTER = true — basis: repository-authored claim "SocioLens claim — LD09-SL-C25 v1.0" (Project Lead (Claude, AI), 2026-10-15); basis: AGENTS.md "Authority" (the Project Lead decides research content within the research-integrity rules) and the Owner's choice in the SocioLens project chat on 15 October 2026 to build the LD-09 debate; a new Claim, first version, written from the project's LD-09 source notes, checked against full texts or official abstracts
FIELD_6_SOURCE_REFERENCE = SRC-SL-KIM-ET-AL-CORRELATED-ERRORS-ICML-2025--v1.0 — basis: field 6 of ENTRY 25 — LD09-SL-C25 cites "Kim, Garg, Peng & Garg, “Correlated Errors in Large Language Models,” ICML 2025, arXiv:2506.07962"; the only field-6 segment of this ClaimVersion's repository-authored ledger block.
SOURCE_PROVENANCE_PIN = SRC-SL-KIM-ET-AL-CORRELATED-ERRORS-ICML-2025--v1.0 — basis: explicit version-pinned public provenance of this ClaimVersion (Amendment v1.2 §6): the pinned field-6 Source record "Kim, Garg, Peng & Garg, “Correlated Errors in Large Language Models,” ICML 2025, arXiv:2506.07962".