AI Hallucination ResearchAudiencesPractitionersUnited StatesLawyersDetail › Finding
Practitioners — Lawyers · updated 2026-06-11
Share / Print Twitter LinkedIn Email

Finding#6 . Inflated comment-letter count and invented commenter names

RLB Citation ID: RLB-F-US-CFTC-CPO-CTA-REGULATION-4-7-QEP-THRESHOLDS-2024-Q017
What the RLB Specialist Panel found

Finding#6 . Inflated comment-letter count and invented commenter names

  • Citation ID: RLB-H-US-CFTC-CPO-CTA-REGULATION-4-7-QEP-THRESHOLDS-2024-Q017-Opus47
  • AI's failure: AI reported approximately 40 comment letters where the final-rule documents receipt of eight
  • Risk for the Lawyers: Operational and reputational exposure when stakeholder-engagement appendices misstate the actual commenter set For lawyers working on CFTC Regulation 4.7 matters, the AI's stated answer reads as a verbatim quotation that a practitioner would paste into a memo, register entry, or client deliverable before verification against the source. The regulator's own text, however, records a different position. The final-rule pre-print's Background discussion records receipt of eight comment letters in response to the October 2023 Proposal, with the relevant footnote naming SIFMA AMG, IAA, AIMA, MFA, ICI, and NFA. Opus 4.7 reported approximately 40 comment letters and named an extended commenter list that includes the American Bar Association Business Law Section's Committee on Derivatives and Futures Law. Both the count (~40 vs eight) and the commenter list (extended vs the named six) are inconsistent with the regulator's text. The inflation of the count is the more dangerous of the two for stakeholder appendices intended for client circulation. For a lawyer drafting on this question, the immediate risk is that the AI's answer enters a client memo, board briefing, or transactional opinion without verification, and that the inconsistency surfaces later under counterparty review, regulatory inquiry, or internal QC.
  • see this finding ->
References — raw findings (per AI model)
← Previous finding Finding#5 . July 2024 CPI-U buying-power figures stated as outdated NPRM-era figures (Sonnet 4.6) Next finding → Finding#7 . NPRM-stage CPI-U buying-power figures invented (verbatim-quote request)
Cite this finding

Each finding has a stable Citation ID (RLB-F-… for aggregated case-study findings, RLB-H-… for raw per-model hallucinations) — like a DOI, the ID always resolves to the canonical finding even if URLs change.

RLB Citation ID: RLB-F-US-CFTC-CPO-CTA-REGULATION-4-7-QEP-THRESHOLDS-2024-Q017
Bluebook / OSCOLA (US + UK legal) Download
RegLeg Specialist Panel, Finding#6 . Inflated comment-letter count and invented commenter names [RLB-F-US-CFTC-CPO-CTA-REGULATION-4-7-QEP-THRESHOLDS-2024-Q017], RegLegBrief AI Hallucination Research (June 11, 2026), https://reglegbrief.com/regulators/j3/us/cftc/cpo-cta-regulation-4-7-qep-thresholds-2024/practitioners/lawyers/finding/US-CFTC-US-001-CPO-CTA-REGULATION-4-7-QEP-THRESHOLDS-2024-v1-017/.
Plain text Download
RegLeg Specialist Panel (2026). "Finding#6 . Inflated comment-letter count and invented commenter names — Practitioners — Lawyers." Citation ID: RLB-F-US-CFTC-CPO-CTA-REGULATION-4-7-QEP-THRESHOLDS-2024-Q017. RegLegBrief AI Hallucination Research, published 2026-06-11. https://reglegbrief.com/regulators/j3/us/cftc/cpo-cta-regulation-4-7-qep-thresholds-2024/practitioners/lawyers/finding/US-CFTC-US-001-CPO-CTA-REGULATION-4-7-QEP-THRESHOLDS-2024-v1-017/
APA 7th edition Download
RegLeg Specialist Panel. (2026). Finding#6 . Inflated comment-letter count and invented commenter names [Hallucination finding RLB-F-US-CFTC-CPO-CTA-REGULATION-4-7-QEP-THRESHOLDS-2024-Q017]. RegLegBrief AI Hallucination Research. https://reglegbrief.com/regulators/j3/us/cftc/cpo-cta-regulation-4-7-qep-thresholds-2024/practitioners/lawyers/finding/US-CFTC-US-001-CPO-CTA-REGULATION-4-7-QEP-THRESHOLDS-2024-v1-017/
BibTeX Download
@misc{reglegbrief_RLB_F_US_CFTC_CPO_CTA_REGULATION_4_7_QEP_THRESHOLDS_2024_Q017,
  author    = {RegLeg Specialist Panel},
  title     = {Finding#6 . Inflated comment-letter count and invented commenter names},
  year      = {2026},
  publisher = {RegLegBrief AI Hallucination Research},
  note      = {Hallucination finding Citation ID: RLB-F-US-CFTC-CPO-CTA-REGULATION-4-7-QEP-THRESHOLDS-2024-Q017},
  url       = {https://reglegbrief.com/regulators/j3/us/cftc/cpo-cta-regulation-4-7-qep-thresholds-2024/practitioners/lawyers/finding/US-CFTC-US-001-CPO-CTA-REGULATION-4-7-QEP-THRESHOLDS-2024-v1-017/}
}
← Back to case study summary Case study detail →

Every finding on this page compares an AI subject's account of the rule against the regulator's verbatim text from the regulator's own portal. Both are linked. Each delta, its root causes, and impact analysis are documented and published with immutable Citation IDs.