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Paper Citation Record · LEDGER

Bayesian optimal experimental design with Wasserstein information criteria

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2504.10092.

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

pith.paper-citation-record.v1
2504.10092 v2

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 2026-08-10T06:31:04.303077+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T14:38:11.478562Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-08T12:14:51.348715Z

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 3def2175-3eab-4681-82e8-59e62af5002e · inbound

Beyond Expected Information Gain: Stable Bayesian Optimal Experimental Design with Integral Probability Metrics and Plug-and-Play Extensions cites this paper.

Beyond Expected Information Gain: Stable Bayesian Optimal Experimental Design with Integral Probability Metrics and Plug-and-Play Extensions Bayesian optimal experimental design with Wasserstein information criteria

Reference 5

Resolution
verified exact
arxiv_id, observed 2026-05-28T02:03:51.681556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-08T14:18:19.330577Z digest=sha256:fdf5a3a22331f749082b207c6f7645b07a4e03da99d64a8b22a6361b847fb525

Observation 892c1149-6955-44f2-b95a-fd8b00a362e9 · inbound

Active Learning for Communication Structure Optimization in LLM-Based Multi-Agent Systems cites this paper.

Active Learning for Communication Structure Optimization in LLM-Based Multi-Agent Systems Bayesian optimal experimental design with Wasserstein information criteria

Reference 33

Resolution
verified exact
arxiv_id, observed 2026-05-28T02:03:51.681556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-08T04:05:24.618750Z digest=sha256:7c3cda8545273414ab514902b446b6c0b474a2e936153055cfe8da82fec4a1de

Observation 34f019da-2ea2-4b72-9204-6f855f5195fc · inbound

Active Learning for Communication Structure Optimization in LLM-Based Multi-Agent Systems cites this paper.

Active Learning for Communication Structure Optimization in LLM-Based Multi-Agent Systems Bayesian optimal experimental design with Wasserstein information criteria

Reference 33

Resolution
verified exact
arxiv_id, observed 2026-05-28T02:03:51.681556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-11T01:46:15.489550Z digest=sha256:a0eecd60283d2840ac7ad7ffac942c12d52cbbfe628720a6d9b80c7d4162b796

Observation d2e1563a-1dc1-4c8c-a6ad-b8a576161851 · inbound

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems cites this paper.

A Convex Approximation Framework for Neural Likelihood-Based Bayesian Inverse Problems Bayesian optimal experimental design with Wasserstein information criteria

Reference 10

Resolution
verified exact
local_arxiv, observed 2026-07-08T12:14:51.350515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-07-08T12:12:52.958003Z digest=sha256:e34b086a76f4f2e1fd1ddfd3f94d245f790d11c695dc22f4e3284cc4d0561e7f

Observation fbd6fdc8-0bd8-401b-b886-eeac7ceb3077 · inbound

Uncertainty quantification in mechanics: A unified Bayesian perspective cites this paper.

Uncertainty quantification in mechanics: A unified Bayesian perspective Bayesian optimal experimental design with Wasserstein information criteria

Reference 193

Resolution
unresolved
no resolver link, observed 2026-08-01T14:38:11.478562Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T14:38:11.478562Z digest=sha256:2607528072161630a1e116a6b22caca9414627f6fb9a9620e8ed7cbe73f24404