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Paper Citation Record · LEDGER

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

As of 20 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 2026-08-19T06:32:44.657259+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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-07-02T20:16:17.328340Z digest=sha256:4d5d64cc471c8ff0a2e4eb1da720e5132a095a6e7185c90c2e24c23c563abbaf

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-07-02T20:16:17.328340Z digest=sha256:0349a50efb68ccadcadd1ae35f8d3bc2b0aabf1b9161357bf7ff298b51c2c9a8

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-07-02T20:16:17.328340Z digest=sha256:58f45a213ce8627d2b59afd6cf03d05443c4b6bf44f9bdbb63d6afa9d0cbcba4

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-07-02T20:16:17.328340Z digest=sha256:db36d7488de9ace7562ff832561056323086cb4e4b033aaf59f8e83b77df58d7

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-07-02T20:16:17.328340Z digest=sha256:185b2798e34c3814a1bedd7f6c1aec96ec4e40f2a906c9c3ed2f61b4863a2a6d

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-07-02T20:16:17.328340Z digest=sha256:1e6c500b4d1fb1ebab78659f9050131d0bfc331650fc0be5b7129486773c41d7

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-07-02T20:16:17.328340Z digest=sha256:5f5e9794fdb16b673d6f97f9a20f49c45bc6789c31411cf015411a3def6d8a21

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 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-07-02T20:16:17.328340Z digest=sha256:0eea3b08e95e57205673ab464815f1af4348fbd83bd05fce1e3e2df6446b5883

Pith citing papers

No inbound Pith citation observations are available.