Paper Citation Record · LEDGER

Relational In-Context Learning via Synthetic Pre-training with Structural Prior

As of 6 August 2026, this Paper Citation Record lists 6 of 6 outbound references and 9 inbound Pith citation observations for arXiv:2603.03805.

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

pith.paper-citation-record.v1
2603.03805 v5

Coverage vector

measured 6 of 6 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T19:05:20.221158Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T13:14:30.940081Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-02T06:06:41.441129Z

Reference resolution

6 of 6 outbound references displayed

verified exact0
verified fuzzy0
unresolved5
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 970afa07-9d72-4bde-af01-a0a09e38c883 · outbound

This paper cites an unresolved cited work.

Relational In-Context Learning via Synthetic Pre-training with Structural Prior Unresolved cited work

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T19:05:19.606764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3508abb1-15bd-4027-975b-2a3032f6592f · outbound

This paper cites To capture this, we augment the latent initialization step.

Relational In-Context Learning via Synthetic Pre-training with Structural Prior To capture this, we augment the latent initialization step

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-02T19:05:19.695759Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 827205c2-f7ff-4f51-9b47-a9d3fa90bbae · outbound

This paper cites – Relational Tasks (RDBs):∼1.2M datasets, comprising: * Small Prior (1-hop DFS):∼800k.

Relational In-Context Learning via Synthetic Pre-training with Structural Prior – Relational Tasks (RDBs):∼1.2M datasets, comprising: * Small Prior (1-hop DFS):∼800k

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-02T19:05:19.818342Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b902fed-b757-4cf8-b9a2-edcc9f62dad3 · outbound

This paper cites Since DFS produces feature sets of variable length depending on the schema depth, we employ a standardization strategy to maintain consistent input dimensions.

Relational In-Context Learning via Synthetic Pre-training with Structural Prior Since DFS produces feature sets of variable length depending on the schema depth, we employ a standardization strategy to maintain consistent input dimensions

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-02T19:05:20.037269Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3366e359-4b8c-4286-aa26-0f76e69b6a5c · outbound

This paper cites To maximize data utility, we employ a multi-target sampling strategy.

Relational In-Context Learning via Synthetic Pre-training with Structural Prior To maximize data utility, we employ a multi-target sampling strategy

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-02T19:05:20.125528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4074a13e-d122-4c92-93aa-3b0f3a6f19ab · outbound

This paper cites row token.

Relational In-Context Learning via Synthetic Pre-training with Structural Prior row token

Reference 6

Resolution: malformed identifier; no resolver link, observed 2026-08-02T19:05:20.221158Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation e3145a58-51fa-4928-9056-b29a51b9f83a · inbound

KumoRFM-2: Scaling Foundation Models for Relational Learning cites this paper.

KumoRFM-2: Scaling Foundation Models for Relational Learning Relational In-Context Learning via Synthetic Pre-training with Structural Prior

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-05-11T09:46:05.983827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 314de3cc-80e9-4831-8f82-71f6300c9e9e · inbound

KGPFN: Unlocking the Potential of Knowledge Graph Foundation Model via In-Context Learning cites this paper.

KGPFN: Unlocking the Potential of Knowledge Graph Foundation Model via In-Context Learning Relational In-Context Learning via Synthetic Pre-training with Structural Prior

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-06-30T20:35:02.826757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation afdac293-3d5f-48aa-b9ad-d46ca6984181 · inbound

Shaping the Prior: How Synthetic Task Distributions Determine Tabular Foundation Model Quality cites this paper.

Shaping the Prior: How Synthetic Task Distributions Determine Tabular Foundation Model Quality Relational In-Context Learning via Synthetic Pre-training with Structural Prior

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-05-20T12:43:17.593711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6b0b6b28-1d03-4da1-9325-ec82d5d2efd4 · inbound

RelGT-AC: A Relational Graph Transformer for Autocomplete Tasks in Relational Databases cites this paper.

RelGT-AC: A Relational Graph Transformer for Autocomplete Tasks in Relational Databases Relational In-Context Learning via Synthetic Pre-training with Structural Prior

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-07-02T02:36:27.506449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3e415f33-4000-4f7c-a9ec-55548e2657ee · inbound

OpenRFM: Dissecting Relational In-Context Learning cites this paper.

OpenRFM: Dissecting Relational In-Context Learning Relational In-Context Learning via Synthetic Pre-training with Structural Prior

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-07-02T06:06:41.442456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f5a343d3-364d-4f73-8e51-97737c046377 · inbound

Statistically Indistinguishable, Operationally Distinct: A Formal Barrier for Tabular Foundation Models cites this paper.

Statistically Indistinguishable, Operationally Distinct: A Formal Barrier for Tabular Foundation Models Relational In-Context Learning via Synthetic Pre-training with Structural Prior

Reference 16

Resolution: metadata mismatch; local_arxiv, observed 2026-06-30T09:34:34.755361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8ef35c59-561c-468e-906a-86596598f024 · inbound

Parameter-Free Encoders Remain Viable for RDB Foundation Models cites this paper.

Parameter-Free Encoders Remain Viable for RDB Foundation Models Relational In-Context Learning via Synthetic Pre-training with Structural Prior

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-11T11:47:14.742492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1878cf31-3e1f-402b-9f36-4a132810ec4b · inbound

Parameter-Free Encoders Remain Viable for RDB Foundation Models cites this paper.

Parameter-Free Encoders Remain Viable for RDB Foundation Models Relational In-Context Learning via Synthetic Pre-training with Structural Prior

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-02T08:36:04.443267Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22573878-d2f7-4724-bcde-8d10bdeed785 · inbound

PluRel-to-RDB-PFN: Schema-Guided Synthetic Relational Pretraining cites this paper.

PluRel-to-RDB-PFN: Schema-Guided Synthetic Relational Pretraining Relational In-Context Learning via Synthetic Pre-training with Structural Prior

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T13:14:30.940081Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.