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Paper Citation Record · LEDGER

Query Understanding in the Age of Large Language Models

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2306.16004.

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

pith.paper-citation-record.v1
2306.16004 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:21:09.359135Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T18:40:14.653043Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 11a6a21a-0419-4b74-9002-04882f5f2711 · inbound

Standard Applicability Judgment and Cross-jurisdictional Reasoning: A RAG-based Framework for Medical Device Compliance cites this paper.

Standard Applicability Judgment and Cross-jurisdictional Reasoning: A RAG-based Framework for Medical Device Compliance Query Understanding in the Age of Large Language Models

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-06T23:21:09.359135Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:21:09.359135Z digest=sha256:9ab552d356f73250b0bb8dd0f5934af1d381200042f945d9e769d3afd8e26206

Observation 2ca69d4e-50f9-4836-8d68-c22488096f19 · inbound

Explainable Information Retrieval in the Audit Domain cites this paper.

Explainable Information Retrieval in the Audit Domain Query Understanding in the Age of Large Language Models

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-06T20:12:05.570094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:12:05.570094Z digest=sha256:a42a5dedf3458bc7c96e2694a33f5001b7accad980191e5d05fa4b47357d1cc5

Observation dc7ffec7-8f27-4b90-8303-b5d522a5e87e · inbound

Agentic Multi-Source Grounding for Enhanced Query Intent Understanding: A DoorDash Case Study cites this paper.

Agentic Multi-Source Grounding for Enhanced Query Intent Understanding: A DoorDash Case Study Query Understanding in the Age of Large Language Models

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-15T18:40:14.656645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-15T18:39:19.589578Z digest=sha256:396b5cf8f8d9b168bb4093d0fa8f0ccfa3058ab1542fd4fa396e91c66b23ec5e

Observation 5f270166-20e5-4fc1-bbf4-9d714584e944 · inbound

When More Reformulations Hurt: Avoiding Drift using Ranker Feedback cites this paper.

When More Reformulations Hurt: Avoiding Drift using Ranker Feedback Query Understanding in the Age of Large Language Models

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-11T16:06:40.531004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-09T18:40:35.840350Z digest=sha256:fd1d0e591f5c3fe741502aea579c37d615c2b261411839dc1c93161a789c594d

Observation 84d7ea81-c540-4a2b-b6f6-7041899f4e16 · inbound

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach cites this paper.

SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach Query Understanding in the Age of Large Language Models

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-04T01:47:04.667006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T01:47:04.667006Z digest=sha256:fc97bd9ddaf2b681dbae35ed01252e3e828f918c184bc4c3fece7ff8abc0e44d