Paper Citation Record · LEDGER

Learning Causal Graphs at Scale: A Foundation Model Approach

As of 16 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:2506.18285.

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

pith.paper-citation-record.v1
2506.18285 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:00:10.945137Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

14 of 14 outbound references displayed

verified exact1
verified fuzzy3
unresolved9
parse uncertain1
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e3c5c4e2-e6e2-4f58-8438-df8385747e29 · outbound

This paper cites Gradient-Based Neural DAG Learning.

Learning Causal Graphs at Scale: A Foundation Model Approach Gradient-Based Neural DAG Learning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T19:00:10.870190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed990953-fd75-4313-8254-cf9ac7c7cd83 · outbound

This paper cites Hyperparameters, including sparsity constraint coefficients and thresholds, are extensively tuned 15 to optimize SHD performance.

Learning Causal Graphs at Scale: A Foundation Model Approach Hyperparameters, including sparsity constraint coefficients and thresholds, are extensively tuned 15 to optimize SHD performance

Reference 3

Resolution: verified exact; raw_fallback, observed 2026-08-15T19:00:11.166554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 12ce7a86-dad9-4ff2-9306-86d297565572 · outbound

This paper cites Transformer learns the cross-task prior and regularization for in-context learning.

Learning Causal Graphs at Scale: A Foundation Model Approach Transformer learns the cross-task prior and regularization for in-context learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T19:00:10.889049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9c77f1eb-c359-47ee-a205-97261dcc5ef8 · outbound

This paper cites Causality for Large Language Models.

Learning Causal Graphs at Scale: A Foundation Model Approach Causality for Large Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T19:00:10.919460Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6b755d5-cfe7-4aac-a2d8-bb250b20548e · outbound

This paper cites Towards causal foundation model: on duality between optimal balancing and attention.

Learning Causal Graphs at Scale: A Foundation Model Approach Towards causal foundation model: on duality between optimal balancing and attention

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:00:11.440043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 47a070b1-25c2-4c7f-a011-8f00485c9d85 · outbound

This paper cites We leave such theoretical investigations to a future work.

Learning Causal Graphs at Scale: A Foundation Model Approach We leave such theoretical investigations to a future work

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:00:11.422672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f3bad25d-90cf-48ee-bd52-c233c4c4f898 · outbound

This paper cites an unresolved cited work.

Learning Causal Graphs at Scale: A Foundation Model Approach Unresolved cited work

Reference 14

Resolution: parse uncertain; raw_fallback, observed 2026-08-15T19:00:11.404069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 335d843f-de49-4999-924b-62383fd4a859 · outbound

This paper cites Identifiability of Causal Graphs using Functional Models.

Learning Causal Graphs at Scale: A Foundation Model Approach Identifiability of Causal Graphs using Functional Models

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-15T19:00:10.906965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc94149b-124b-43bc-b128-a4f8243f2a69 · outbound

This paper cites Supervised Whole DAG Causal Discovery.

Learning Causal Graphs at Scale: A Foundation Model Approach Supervised Whole DAG Causal Discovery

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-15T19:00:10.876752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 257aae5d-e01a-4878-a326-d403c99f0943 · outbound

This paper cites Neural Interpretable PDEs: Harmonizing Fourier Insights with Attention for Scalable and Interpretable Physics Discovery.

Learning Causal Graphs at Scale: A Foundation Model Approach Neural Interpretable PDEs: Harmonizing Fourier Insights with Attention for Scalable and Interpretable Physics Discovery

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-15T19:00:10.883289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3ef1d857-94a5-48d9-add4-cd01b493588f · outbound

This paper cites Integrating Large Language Model for Improved Causal Discovery.

Learning Causal Graphs at Scale: A Foundation Model Approach Integrating Large Language Model for Improved Causal Discovery

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-15T19:00:10.858390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 290c7487-fdce-4998-9839-062a0b598fb7 · outbound

This paper cites Asymptotic theory of in-context learning by linear attention.arXiv preprint arXiv:2405.11751,.

Learning Causal Graphs at Scale: A Foundation Model Approach Asymptotic theory of in-context learning by linear attention.arXiv preprint arXiv:2405.11751,

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-15T19:00:10.900866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62ec1d04-c151-4fb4-b708-4f652df36be6 · outbound

This paper cites Large Language Models for Causal Discovery: Current Landscape and Future Directions.

Learning Causal Graphs at Scale: A Foundation Model Approach Large Language Models for Causal Discovery: Current Landscape and Future Directions

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T19:00:10.913017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f9468ba-4ff4-4513-8ca7-04959a77eacd · outbound

This paper cites Meta-dag: Meta causal discovery via bilevel optimization.

Learning Causal Graphs at Scale: A Foundation Model Approach Meta-dag: Meta causal discovery via bilevel optimization

Reference 2025

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:00:11.457018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.