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Paper Citation Record · LEDGER

Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2306.00258.

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

pith.paper-citation-record.v1
2306.00258 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 2026-08-10T06:31:04.303077+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T00:14:59.636462Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T22:03:20.430132Z

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 035ad494-b200-42d8-be04-16f670d73d2d · inbound

HEP-JEPA: A foundation model for collider physics using joint embedding predictive architecture cites this paper.

HEP-JEPA: A foundation model for collider physics using joint embedding predictive architecture Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-09T00:14:59.636462Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T00:14:59.636462Z digest=sha256:9c30e195d631bd86fa30089b3a02ba8925c48ca4f58c89683f0ef25070a62bd6

Observation 6b46fd48-5b38-4a04-8e49-caaf7b4fda9f · inbound

Towards a Physics Foundation Model cites this paper.

Towards a Physics Foundation Model Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-04T16:33:00.034425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T16:33:00.034425Z digest=sha256:d4882c5df81e0790ef7c46353da205164b1821be57aceddbbf0b4332ceb6c38c

Observation 9fd98845-86eb-464e-a06b-e677c650f1ed · inbound

PI-JEPA: Label-Free Surrogate Pretraining for Coupled Multiphysics Simulation via Operator-Split Latent Prediction cites this paper.

PI-JEPA: Label-Free Surrogate Pretraining for Coupled Multiphysics Simulation via Operator-Split Latent Prediction Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-13T22:03:20.432066Z

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=2026-05-13T22:00:42.613459Z digest=sha256:31d1bcb044289cb0aaa56a2b83c08c94399072670ed510cfdd79bbdc8394b83a

Observation 51907bad-67f4-415d-b172-5073c3b237c1 · inbound

A Hybridizable Neural Time Integrator for Stable Autoregressive Forecasting cites this paper.

A Hybridizable Neural Time Integrator for Stable Autoregressive Forecasting Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-05-10T00:14:46.506559Z

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=2026-05-10T00:12:58.131638Z digest=sha256:69e956a748f8b7ece70c25d97f57793ea3a0ef4c3e2b1d928319945e817565a9

Observation aaef3760-17fd-4aeb-8be5-8fd2350917cb · inbound

TokaMind for Power Grid: Cross-Domain Transfer from Fusion Plasma cites this paper.

TokaMind for Power Grid: Cross-Domain Transfer from Fusion Plasma Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-13T01:57:06.784418Z

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=2026-05-13T00:59:27.096365Z digest=sha256:ed13a7654899302f5f80d25af51c9e8d30dd81f7afcb67af09d55038639dfdaa