Paper Citation Record · LEDGER

Latent Bayesian Optimization via Autoregressive Normalizing Flows

As of 18 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 2 inbound Pith citation observations for arXiv:2504.14889.

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

pith.paper-citation-record.v1
2504.14889 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:44:34.297954Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T09:45:42.783390Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T22:12:50.769368Z

Reference resolution

12 of 12 outbound references displayed

verified exact0
verified fuzzy8
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d402ca05-1071-44a7-9512-54fa9f075323 · outbound

This paper cites Light sky blue areas indicate regions with lower density compared to the darker regions.

Latent Bayesian Optimization via Autoregressive Normalizing Flows Light sky blue areas indicate regions with lower density compared to the darker regions

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:44:34.477988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 46955813-709d-4b32-abee-30adf7373a83 · outbound

This paper cites an unresolved cited work.

Latent Bayesian Optimization via Autoregressive Normalizing Flows Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:44:34.443388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4f7cc546-fa4e-4d58-ae39-79ad8bb164fe · outbound

This paper cites The table presents scores and standard deviations across 6 evaluation metrics, with each score representing the mean of 5 independent runs.

Latent Bayesian Optimization via Autoregressive Normalizing Flows The table presents scores and standard deviations across 6 evaluation metrics, with each score representing the mean of 5 independent runs

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:44:34.513071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation df8dc39e-db44-4abb-b971-b38b7370cf85 · outbound

This paper cites an unresolved cited work.

Latent Bayesian Optimization via Autoregressive Normalizing Flows Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:44:34.461226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e995bec5-ae16-4ef8-b8bc-1f010574f452 · outbound

This paper cites From the table, our SeqFlow model achieves better perfor- mance with fewer parameters compared to the baseline model.

Latent Bayesian Optimization via Autoregressive Normalizing Flows From the table, our SeqFlow model achieves better perfor- mance with fewer parameters compared to the baseline model

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:44:34.426376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 284e5eb7-7714-4192-a67b-5054dc19756f · outbound

This paper cites Both the oracle budget and the number of initial data were set to 10,000.

Latent Bayesian Optimization via Autoregressive Normalizing Flows Both the oracle budget and the number of initial data were set to 10,000

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:44:34.408897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 7ec4c647-bd87-45a2-a14f-1c9a4239517e · outbound

This paper cites Each task’s performance is averaged over five trials.

Latent Bayesian Optimization via Autoregressive Normalizing Flows Each task’s performance is averaged over five trials

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:44:34.392131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 763182cc-c75e-4f63-843e-4aac94658756 · outbound

This paper cites Figures 8, 9, and 10 display the results for the (100, 500), (10,000, 10,000), and (10,000, 70,000) oracle settings, respectively.

Latent Bayesian Optimization via Autoregressive Normalizing Flows Figures 8, 9, and 10 display the results for the (100, 500), (10,000, 10,000), and (10,000, 70,000) oracle settings, respectively

Reference 500

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:44:34.495202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 09ff8e28-c54a-434c-ada1-ff3818de04cf · outbound

This paper cites A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences.

Latent Bayesian Optimization via Autoregressive Normalizing Flows A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-16T11:44:34.238541Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bbab8034-f17d-45d4-a311-44224a0b41b4 · outbound

This paper cites Variational inference with normalizing flows.

Latent Bayesian Optimization via Autoregressive Normalizing Flows Variational inference with normalizing flows

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:44:34.529405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 46d03996-8e96-4da0-87ea-f863ef8609ed · outbound

This paper cites High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning.

Latent Bayesian Optimization via Autoregressive Normalizing Flows High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-16T11:44:34.244774Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 912d4cf1-ddac-49a9-972d-6eaf8922b0f7 · outbound

This paper cites ver et al.,.

Latent Bayesian Optimization via Autoregressive Normalizing Flows ver et al.,

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:44:34.374287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

Observation 61b69e70-789f-428b-930c-667b30d21aa9 · inbound

A Language-Guided Bayesian Optimization for Efficient LoRA Hyperparameter Search cites this paper.

A Language-Guided Bayesian Optimization for Efficient LoRA Hyperparameter Search Latent Bayesian Optimization via Autoregressive Normalizing Flows

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T09:45:42.783390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d1a4ac89-f8d9-4fd4-8873-314191cb5635 · inbound

Accelerating Redshift-Conditioned Galaxy Image Synthesis with One-step Generative Modeling cites this paper.

Accelerating Redshift-Conditioned Galaxy Image Synthesis with One-step Generative Modeling Latent Bayesian Optimization via Autoregressive Normalizing Flows

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-05-19T22:12:50.775541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.