The EU’s Code of Practice for general-purpose AI models with systemic risk requires state-of-the-art model evaluations with internal validity, external validity and reproducibility, elicitation that minimises under-elicitation and sandbagging, and independent external evaluation, unless the model is “similarly safe or safer” or no suitable evaluator is found after a public call such as one open for 20 business days; the Model Report with evaluation results goes to the AI Office, signatories commit to publish summarised versions of their framework and Model Reports if and insofar as necessary to assess or mitigate systemic risks, and external evaluators keep control over publishing their own findings.

SocioLens project,

Claim LD06-SL-C24 (v1.0)

Provenance and status

Claim type: Fact
Publication status: Eligible for publication, with qualifiers: legal scope
Verification state: Checked against the primary source. 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: Any claim about how signatories have applied it
Limitations: Practice is unknown: no signatory Model Report or AI Office assessment was found
Source (as cited): European Commission, “The General-Purpose AI Code of Practice: Safety and Security chapter,” final version, 10 July 2025
Source type: Government document (recorded as “EU code of practice”)
Institutional position: EU (Commission-facilitated code written by independent chairs)
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

European Commission, “The General-Purpose AI Code of Practice: Safety and Security chapter,” final version, 10 July 2025 (Government document)
No checked link to the original yet.

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): FACT (as printed: “FACT”)
Publication status (coded): ELIGIBLE (LEGAL_SCOPE) (as printed: “ELIGIBLE WITH LEGAL-SCOPE QUALIFIER”)
Verification state (coded): CONTENT_VERIFIED_PRIMARY
Source type (coded): GOVERNMENT_PRIMARY
Maturity (coded): EMERGING

#	Canonical field	Canonical value (verbatim)	Normalized (Claim Value Vocabulary v1.0.2)
1	CLAIM ID	LD06-SL-C24	—
2	PUBLIC-FACING CLAIM	The EU’s Code of Practice for general-purpose AI models with systemic risk requires state-of-the-art model evaluations with internal validity, external validity and reproducibility, elicitation that minimises under-elicitation and sandbagging, and independent external evaluation, unless the model is “similarly safe or safer” or no suitable evaluator is found after a public call such as one open for 20 business days; the Model Report with evaluation results goes to the AI Office, signatories commit to publish summarised versions of their framework and Model Reports if and insofar as necessary to assess or mitigate systemic risks, and external evaluators keep control over publishing their own findings.	—
3	CLAIM TYPE	FACT	base_types: FACT
4	RESEARCH STRAND	C — Evaluation in governance, audits and red-teaming	—
5	MATURITY	EMERGING	states: EMERGING
6	SOURCE	European Commission, “The General-Purpose AI Code of Practice: Safety and Security chapter,” final version, 10 July 2025	source_ids: SRC-SL-EU-GPAI-CODE-OF-PRACTICE-SAFETY-SECURITY-2025-07-10
7	SOURCE TYPE	EU code of practice	classes: GOVERNMENT_PRIMARY
8	INSTITUTIONAL POSITION	EU (Commission-facilitated code written by independent chairs)	—
9	SCOPE	Signatory providers of general-purpose AI models with systemic risk	—
10	DESIGN	Voluntary code supporting compliance with the AI Act	—
11	SAMPLE / DATASET	Not applicable	—
12	MODEL / TOOL / VERSION	Not applicable	—
13	COMPARATOR	None	—
14	MAIN RESULT	Measure 3.2 and Appendix 3: evaluation methods listed (benchmarks, red-teaming, uplift studies and others), rigour defined as internal validity, external validity and reproducibility, at least 20 business days suggested for most evaluations, evaluator access that can include model internals; Measure 7: Model Report to the AI Office with at least five random input–output samples per evaluation; Measure 7.4: external evaluators’ reports included in the Model Report while they “maintain control over the publication of their findings”; Measure 10.2: summarised Framework and Model Report(s) published “if and insofar as necessary” to assess or mitigate systemic risks	—
15	INFERENTIAL TARGET	How the EU writes evaluation validity into a compliance instrument	—
16	DOES NOT LICENSE	Any claim about how signatories have applied it	—
17	VERIFICATION STATE	CONTENT VERIFIED	state: CONTENT_VERIFIED_PRIMARY · verified_by: UNKNOWN
18	LIMITATIONS	Practice is unknown: no signatory Model Report or AI Office assessment was found	—
19	CONTRADICTORY / SUPPORTING SOURCES	Compare the partial, time-bounded state evaluation in LD06-SL-C23	—
20	GEOGRAPHY / LANGUAGE	EU / English	—
21	DISTRIBUTIONAL LEDGER	Not analysed in this claim	—
22	R01 / FIELD-MAP LINKS	LD-06, D4b	—
23	WHAT WOULD CHANGE THIS CLAIM?	Evidence on how signatories apply the external-evaluation requirement and its exceptions	—
24	PUBLICATION STATUS	ELIGIBLE WITH LEGAL-SCOPE QUALIFIER	state: ELIGIBLE · qualifiers: LEGAL_SCOPE
25	RECORDED DATE	2026-10-15	—

Field 22 references resolved against Field Map v0.2: LD-06 (LIVING_DEBATE); D4b (SUB_AREA)

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

Pinned Sources (field 6, version-specific)

“European Commission, “The General-Purpose AI Code of Practice: Safety and Security chapter,” final version, 10 July 2025” → European Commission (2025), “The General-Purpose AI Code…” (SRC-SL-EU-GPAI-CODE-OF-PRACTICE-SAFETY-SECURITY-2025-07-10--v1.0) — European Commission, “The General-Purpose AI Code of Practice: Safety and Security chapter,” final version, 10 July 2025; class GOVERNMENT_PRIMARY (explicit public provenance pin)

Cited by Living Debate versions

The evaluation apparatus: Can benchmarks, leaderboards and audits carry the weight placed on them? (v0.1) LD-06

Cite this page

SocioLens project. (2026, October 15). The EU’s Code of Practice for general-purpose AI models with systemic risk requires state-of-the-art model evaluations with internal validity, external validity and reproducibility, elicitation that minimises under-elicitation and sandbagging, and independent external evaluation, unless the model is “similarly safe or safer” or no suitable evaluator is found after a public call such as one open for 20 business days; the Model Report with evaluation results goes to the AI Office, signatories commit to publish summarised versions of their framework and Model Reports if and insofar as necessary to assess or mitigate systemic risks, and external evaluators keep control over publishing their own findings [Claim LD06-SL-C24, version v1.0]. SocioLens. https://sociolens-web.pages.dev/claims/ld06-sl-c24/v1.0/

@misc{sociolens_ld06_sl_c24_v1_0,
  author = {{SocioLens project}},
  title = {The EU’s Code of Practice for general-purpose AI models with systemic risk requires state-of-the-art model evaluations with internal validity, external validity and reproducibility, elicitation that minimises under-elicitation and sandbagging, and independent external evaluation, unless the model is “similarly safe or safer” or no suitable evaluator is found after a public call such as one open for 20 business days; the Model Report with evaluation results goes to the AI Office, signatories commit to publish summarised versions of their framework and Model Reports if and insofar as necessary to assess or mitigate systemic risks, and external evaluators keep control over publishing their own findings.},
  year = {2026},
  date = {2026-10-15},
  howpublished = {\url{https://sociolens-web.pages.dev/claims/ld06-sl-c24/v1.0/}},
  note = {Claim LD06-SL-C24, 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 22c61fba06d59ed958d41c9ca438b877c5240ee4df644ae39d07a2ceb9b5b672.

Repository-authored inputs (not Drive exports)

SocioLens claim — LD06-SL-C24 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-06 debate; a new Claim, first version, written from the project's LD-06 source notes, checked against full texts or official abstracts. Block “ENTRY 24 — LD06-SL-C24” SHA-256 9ec61e7dc6138b3bb4d0b3b17138ed5438801e1fc918e17d7bc8af6dae047417; fixture fixtures/authored/claims/LD06-SL-C24--v1.0.

Builder decisions (not Drive data)

VERSION = v1.0 — basis: Claim version taken from the repository-authored claim fixture version (SocioLens claim — LD06-SL-C24 v1.0); the first version of a new Claim of the LD-06 debate
CURRENT_POINTER = true — basis: repository-authored claim "SocioLens claim — LD06-SL-C24 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-06 debate; a new Claim, first version, written from the project's LD-06 source notes, checked against full texts or official abstracts
FIELD_6_SOURCE_REFERENCE = SRC-SL-EU-GPAI-CODE-OF-PRACTICE-SAFETY-SECURITY-2025-07-10--v1.0 — basis: field 6 of ENTRY 24 — LD06-SL-C24 cites "European Commission, “The General-Purpose AI Code of Practice: Safety and Security chapter,” final version, 10 July 2025"; the only field-6 segment of this ClaimVersion's repository-authored ledger block.
SOURCE_PROVENANCE_PIN = SRC-SL-EU-GPAI-CODE-OF-PRACTICE-SAFETY-SECURITY-2025-07-10--v1.0 — basis: explicit version-pinned public provenance of this ClaimVersion (Amendment v1.2 §6): the pinned field-6 Source record "European Commission, “The General-Purpose AI Code of Practice: Safety and Security chapter,” final version, 10 July 2025".