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Paper Citation Record · LEDGER

ComplianceGate: Classifier-Gated Multi-Tier LLM Routing for Inference in Regulated Industries

As of 22 August 2026, this Paper Citation Record lists 8 of 8 outbound references and 0 inbound Pith citation observations for arXiv:2606.31163.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2606.31163 v2

Coverage vector

measured 8 of 8 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-02T20:16:17.328340Z

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

8 of 8 outbound references displayed

  • verified exact7
  • verified fuzzy0
  • unresolved1
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ad378ff6-9c98-4a6c-ad58-233f9856a14c · outbound

This paper cites FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance.

ComplianceGate: Classifier-Gated Multi-Tier LLM Routing for Inference in Regulated Industries FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance

Reference 1

Resolution
verified exact
local_arxiv, observed 2026-07-02T20:17:21.097258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-07-02T20:16:17.328340Z digest=sha256:cedf0b2b64fbc703a4e052fc5005955c8a636e096a79511273c280bf11d286b4

Observation 14ce09d0-d201-46dc-98c3-ee6a69d3a589 · outbound

This paper cites RouteLLM: Learning to Route LLMs with Preference Data.

ComplianceGate: Classifier-Gated Multi-Tier LLM Routing for Inference in Regulated Industries RouteLLM: Learning to Route LLMs with Preference Data

Reference 2

Resolution
verified exact
local_arxiv, observed 2026-07-02T20:17:21.093640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-07-02T20:16:17.328340Z digest=sha256:010601de368dca3b039f4f79859f2b1cbdb36c3e3adf6d47765ba91c264252ea

Observation 6693918d-31a3-40f9-b8cd-d56e86645206 · outbound

This paper cites AutoMix: Automatically Mixing Language Models.

ComplianceGate: Classifier-Gated Multi-Tier LLM Routing for Inference in Regulated Industries AutoMix: Automatically Mixing Language Models

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-07-02T20:17:21.091082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-07-02T20:16:17.328340Z digest=sha256:1e0f89da57077b3a83657b640f48ee18d230b0f49cd1b7f104e344c7bcc9dc32

Observation 1cede858-c685-4ad8-82c0-280dc5aa473f · outbound

This paper cites Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing.

ComplianceGate: Classifier-Gated Multi-Tier LLM Routing for Inference in Regulated Industries Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-07-02T20:17:21.080725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-07-02T20:16:17.328340Z digest=sha256:1632affd17f287f6429ae3dfead8170bf7f1ca1435a988d33e6cc52562ad9f31

Observation 11091da6-0973-4c61-8047-6b024cb75b8f · outbound

This paper cites Mixtral of Experts.

ComplianceGate: Classifier-Gated Multi-Tier LLM Routing for Inference in Regulated Industries Mixtral of Experts

Reference 5

Resolution
verified exact
local_arxiv, observed 2026-07-02T20:17:21.091503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-07-02T20:16:17.328340Z digest=sha256:b7ff8900d4673c6326845eef354ba423a4980ddd129fe3acd22737d581051a69

Observation d67b57e9-76ce-4811-a653-b669b7842991 · outbound

This paper cites Qwen3 Technical Report.

ComplianceGate: Classifier-Gated Multi-Tier LLM Routing for Inference in Regulated Industries Qwen3 Technical Report

Reference 6

Resolution
verified exact
local_arxiv, observed 2026-07-02T20:17:21.085181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-07-02T20:16:17.328340Z digest=sha256:9aa6edfa5fe2cefbc10c7b12cdf886b1f6fa7bb997318e8aa5addf7c5cfb0792

Observation 553bbf1e-0e11-496e-8657-de00d7837767 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

ComplianceGate: Classifier-Gated Multi-Tier LLM Routing for Inference in Regulated Industries Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 7

Resolution
verified exact
local_arxiv, observed 2026-07-02T20:17:21.094132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-07-02T20:16:17.328340Z digest=sha256:1488f847f26ab93f01637211f065928aa17ba28334a6a040bd85ec42dbbc0191

Observation 7e6ffc71-5e94-4201-983f-ef870f0d46dc · outbound

This paper cites an unresolved cited work.

ComplianceGate: Classifier-Gated Multi-Tier LLM Routing for Inference in Regulated Industries Unresolved cited work

Reference 8

Resolution
unresolved
raw_fallback, observed 2026-07-05T20:31:25.377667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-07-02T20:16:17.328340Z digest=sha256:ec12b89a3e4337c175cbf5a1682824eb4e26cfacc788019faf9e1e3860b4a96f

Pith citing papers

No inbound Pith citation observations are available.