Paper Citation Record · LEDGER

Large Language Models for Causal Discovery: Current Landscape and Future Directions

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2402.11068.

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

pith.paper-citation-record.v1
2402.11068 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:42:31.443670Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T09:07:47.105174Z

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 49238333-ac26-4d59-a7f1-6045af9845a8 · inbound

Position: The ML Community Must Build an AI-Augmented Peer-Review Ecosystem cites this paper.

Position: The ML Community Must Build an AI-Augmented Peer-Review Ecosystem Large Language Models for Causal Discovery: Current Landscape and Future Directions

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T05:22:49.629166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0e43b16-9937-406e-a690-a72811e0ed65 · inbound

Evaluating and Improving Robustness in Large Language Models: A Survey and Future Directions cites this paper.

Evaluating and Improving Robustness in Large Language Models: A Survey and Future Directions Large Language Models for Causal Discovery: Current Landscape and Future Directions

Reference 177

Resolution: unresolved; no resolver link, observed 2026-08-07T05:42:31.443670Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0eee858b-b666-4c42-a479-337cbb6694bf · inbound

When Planning Fails Despite Correct Execution: On Epistemic Calibration for LLM-Based Multi-Agent Systems cites this paper.

When Planning Fails Despite Correct Execution: On Epistemic Calibration for LLM-Based Multi-Agent Systems Large Language Models for Causal Discovery: Current Landscape and Future Directions

Reference 74

Resolution: verified exact; arxiv_id, observed 2026-05-25T04:26:37.836250Z

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.

Observation 413ca0c5-299c-4979-888c-a7f819c4536d · inbound

KRCA: An Efficient Root Cause Analysis System in Hyper-scale Microservice Systems via Agentic AI cites this paper.

KRCA: An Efficient Root Cause Analysis System in Hyper-scale Microservice Systems via Agentic AI Large Language Models for Causal Discovery: Current Landscape and Future Directions

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-07-03T09:07:47.106817Z

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.

Observation 47d48f6d-b78f-4696-b742-86dd7b7216d7 · inbound

KRCA: An Efficient Root Cause Analysis System in Hyper-scale Microservice Systems via Agentic AI cites this paper.

KRCA: An Efficient Root Cause Analysis System in Hyper-scale Microservice Systems via Agentic AI Large Language Models for Causal Discovery: Current Landscape and Future Directions

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-02T09:07:56.565582Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81b6333a-4c19-4437-b8a1-5dc5d81a0681 · inbound

KRCA: An Efficient Root Cause Analysis System in Hyper-scale Microservice Systems via Agentic AI cites this paper.

KRCA: An Efficient Root Cause Analysis System in Hyper-scale Microservice Systems via Agentic AI Large Language Models for Causal Discovery: Current Landscape and Future Directions

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-04T04:36:50.544717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.