Pith. sign in

Paper Citation Record · LEDGER

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

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 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 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:00:10.913017Z

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.

source=arxiv_source observed=2026-08-07T05:22:49.629166Z digest=sha256:aa31d3aaf77bfbd591040e66210560c4f99651c0bf3d5843c8b5bdd3441ed622

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.

source=pdf_text observed=2026-08-07T05:42:31.443670Z digest=sha256:7f8c3090b4c6518d1d807042ca6bce5bcf636a5cee1b4c0a5a10cd5fc0699b99

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

Learning Causal Graphs at Scale: A Foundation Model Approach cites this paper.

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.

source=pdf_text observed=2026-08-15T19:00:10.913017Z digest=sha256:931c09a703ed9297f0e02bc9ccaee961cc13e92271fcdb13437ed5007bfe62cd

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 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-05-25T04:25:26.710488Z digest=sha256:acc86f28ee529a32238f3636feadff266d30f998b4697f0b2db8782cbe116478

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 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-07-03T09:03:23.845468Z digest=sha256:b2ec8aa47fc27f40af1101fcf6fbbc00835cb27e1b6f0ce2ebb13aa6adf45ccf

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.

source=pdf_text observed=2026-08-02T09:07:56.565582Z digest=sha256:2a8e89838c8b56026e7058336c2c132e936e526e339a03fbaafd653d7ef15960

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.

source=pdf_text observed=2026-08-04T04:36:50.544717Z digest=sha256:5e9b98adb25692b5843ba2270997fb069d85382554044f89927021e8f7a819e9