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Paper Citation Record · LEDGER

Checkpoint Merging via Bayesian Optimization in LLM Pretraining

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2403.19390.

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

pith.paper-citation-record.v1
2403.19390 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 2026-08-07T14:33:45.716553Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T16:48:39.721562Z

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 792949d9-2653-4ad7-ab01-3a5f4db5f1ea · inbound

Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities cites this paper.

Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities Checkpoint Merging via Bayesian Optimization in LLM Pretraining

Reference 132

Resolution
verified exact
arxiv_id, observed 2026-05-17T22:16:04.542620Z

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=2026-05-17T22:16:04.386706Z digest=sha256:4a534e5d4c10fae2bcd4f4ced065b4c15380c6517f30fbffc75f94f7c154d76b

Observation 08f1d924-210a-4655-b52f-6b0d2e0c7bef · inbound

Composable Cross-prompt Essay Scoring by Merging Models cites this paper.

Composable Cross-prompt Essay Scoring by Merging Models Checkpoint Merging via Bayesian Optimization in LLM Pretraining

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-07T14:33:45.716553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:33:45.716553Z digest=sha256:53c15f3e980e4a8f14a25ac8470c8dbea7d55a283946ff148f0877b88d836add

Observation 04763bb9-e42d-4b7d-98ae-5d4af2c4573e · inbound

WSM: Decay-Free Learning Rate Schedule via Checkpoint Merging for LLM Pre-training cites this paper.

WSM: Decay-Free Learning Rate Schedule via Checkpoint Merging for LLM Pre-training Checkpoint Merging via Bayesian Optimization in LLM Pretraining

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-06T14:49:40.278771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T14:49:40.278771Z digest=sha256:e013653009a1aba8fbd7f9509a7ad81313f322a0de3e349d53bf76d2b860e458

Observation 10300319-6727-4932-887f-5f9fda44b7fb · inbound

ReLoRA: Knowledge-Reusing Adaptation for Fast Rollout of Evolving LLM Services cites this paper.

ReLoRA: Knowledge-Reusing Adaptation for Fast Rollout of Evolving LLM Services Checkpoint Merging via Bayesian Optimization in LLM Pretraining

Reference 24

Resolution
verified exact
arxiv_id, observed 2026-06-30T15:44:48.862895Z

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=2026-06-30T15:06:08.515588Z digest=sha256:da8c26f32797a956a6c0db2d83d472b52441e64843644dd10a366eca22452d00

Observation 2664816c-c256-4434-952a-102edce464c3 · inbound

Optimizing Visual Generative Models via Distribution-wise Rewards cites this paper.

Optimizing Visual Generative Models via Distribution-wise Rewards Checkpoint Merging via Bayesian Optimization in LLM Pretraining

Reference 20

Resolution
verified exact
arxiv_id, observed 2026-07-03T16:48:39.723111Z

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=2026-07-03T16:39:12.711424Z digest=sha256:35e17b377618171027700154421b66e4cf7d19a52f9b7657698ab02a9004c884