What are the updated concentration limits that apply to government money market funds and Treasury ETFs under the 2024 Regulation 1.25 amendments, including any tiered or size-based thresholds based on fund and management company asset sizes?
The model answered as if the final rule preserved the uniform-threshold structure of prior Regulation 1.25 and rejected size-based differentiation — a description that fits early-stage rulemaking commentary but not the final amended text. The rule introduces an explicit two-condition tier keyed to fund AUM and management company AUM; the model's response overwrites this with a generalised prior-version schema and presents the overwrite as a confirmed regulatory outcome.
This finding implicates the training corpus's representation of the final rule text relative to pre-final secondary commentary. The model asserted that the final rule rejected size-based tiering — a claim that reflects early rulemaking commentary, not the published final rule. The retrieval pipeline failed to surface the primary rule text at sufficient specificity to override the trained-schema prior, suggesting the Federal Register notice is underweighted relative to secondary sources in retrieval ranking for this query type.
A Chief Risk Officer preparing an FCM investment policy statement asked for the updated concentration limits applying to government money market funds and Treasury ETFs under the 2024 Regulation 1.25 amendments, including any size-based or tiered thresholds.
The model explicitly denied the existence of a size-based tier while simultaneously using tier-like vocabulary in its own response structure ("Tier 1 — Per Instrument"), an internal contradiction that suggests the model assembled its answer from schema rather than retrieved rule text. The rule's tier is conditioned on fund AUM and management-company AUM — not FCM total assets — which may explain the mismatch: the model appears to have pattern-matched on "size-based tier" as an FCM-size question and answered on that basis, missing the fund-and-manager-side conditions the rule actually imposes.
The convergence with Claude Opus 4.7 on the same concentration-tier question makes this finding structurally significant: two different model architectures, same configuration, same wrong schema, same explicit denial of the tier's existence. The internal contradiction in the response — denying a size-based tier while using tier-like vocabulary — suggests the model assembled its answer from a regulatory schema template rather than retrieved primary text. This points to a retrieval-ranking issue shared across model families: the Federal Register final rule text is not being surfaced at sufficient authority weight to override the trained prior on this query.
When a Compliance team uses AI to confirm the concentration limits for the affiliated FCM's investment policy, the AI asserted a flat 10%-per-instrument ceiling with no size-based variation — omitting the regulation's 50% ceiling that applies when both the fund's assets are ≥$1B and the management company manages ≥$25B AUM. An investment policy drafted against the AI's uniform-10% framing mischaracterises the actual permissible investment framework, producing a control document that misrepresents the FCM's obligations to the CFTC and NFA on examination.
If the error runs the other direction — and the firm takes the AI's answer as validating a more permissive existing policy — the exposure is a segregation investment in excess of the permitted ceiling, which the CFTC can pursue as a violation of Regulation 1.25's customer-protection framework regardless of whether any customer harm resulted.
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.
RegLeg Specialist Panel (2026). "Finding#1 — Tiered concentration limits for government money market funds and Treasury ETFs — Corporate Banking × Compliance — United States." Citation ID: RLB-F-US-CFTC-FCM-DCO-CUSTOMER-FUNDS-INVESTMENTS-REG-1-25-2024-Q001. RegLegBrief AI Hallucination Research, published 2026-06-04. https://reglegbrief.com/regulators/j3/us/cftc/fcm-dco-customer-funds-investments-reg-1-25-2024/sectors/corporate_banking/compliance/finding/US-CFTC-US-001-FCM-DCO-CUSTOMER-FUNDS-INVESTMENTS-REG-1-25-2024-v1-001/
RegLeg Specialist Panel. (2026). Finding#1 — Tiered concentration limits for government money market funds and Treasury ETFs [Hallucination finding RLB-F-US-CFTC-FCM-DCO-CUSTOMER-FUNDS-INVESTMENTS-REG-1-25-2024-Q001]. RegLegBrief AI Hallucination Research. https://reglegbrief.com/regulators/j3/us/cftc/fcm-dco-customer-funds-investments-reg-1-25-2024/sectors/corporate_banking/compliance/finding/US-CFTC-US-001-FCM-DCO-CUSTOMER-FUNDS-INVESTMENTS-REG-1-25-2024-v1-001/
RegLeg Specialist Panel, Finding#1 — Tiered concentration limits for government money market funds and Treasury ETFs [RLB-F-US-CFTC-FCM-DCO-CUSTOMER-FUNDS-INVESTMENTS-REG-1-25-2024-Q001], RegLegBrief AI Hallucination Research (June 04, 2026), https://reglegbrief.com/regulators/j3/us/cftc/fcm-dco-customer-funds-investments-reg-1-25-2024/sectors/corporate_banking/compliance/finding/US-CFTC-US-001-FCM-DCO-CUSTOMER-FUNDS-INVESTMENTS-REG-1-25-2024-v1-001/.
@misc{reglegbrief_RLB_F_US_CFTC_FCM_DCO_CUSTOMER_FUNDS_INVESTMENTS_REG_1_25_2024_Q001,
author = {RegLeg Specialist Panel},
title = {Finding#1 — Tiered concentration limits for government money market funds and Treasury ETFs},
year = {2026},
publisher = {RegLegBrief AI Hallucination Research},
note = {Hallucination finding Citation ID: RLB-F-US-CFTC-FCM-DCO-CUSTOMER-FUNDS-INVESTMENTS-REG-1-25-2024-Q001},
url = {https://reglegbrief.com/regulators/j3/us/cftc/fcm-dco-customer-funds-investments-reg-1-25-2024/sectors/corporate_banking/compliance/finding/US-CFTC-US-001-FCM-DCO-CUSTOMER-FUNDS-INVESTMENTS-REG-1-25-2024-v1-001/}
}