Paper Citation Record · LEDGER

Foundational Large Language Models for Materials Research

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2412.09560.

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

pith.paper-citation-record.v1
2412.09560 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 2026-08-08T06:32:00.761636+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:45:51.267103Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T19:17:18.638122Z

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 de72df31-bb33-4798-b9c9-fcf4e62cf108 · inbound

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools cites this paper.

A Survey of AI for Materials Science: Foundation Models, LLM Agents, Datasets, and Tools Foundational Large Language Models for Materials Research

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T22:45:51.267103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c788867-c45b-4785-a7e7-246cde77b10a · inbound

Perovskite-R1: a domain-specialized large language model for intelligent discovery of precursor additives and experimental design cites this paper.

Perovskite-R1: a domain-specialized large language model for intelligent discovery of precursor additives and experimental design Foundational Large Language Models for Materials Research

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-22T00:30:49.234038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4da29440-b34a-46cc-b5ad-979f06f28b93 · inbound

Scale-Dependent Input Representation and Confidence Estimation for LLMs in Materials Property Prediction cites this paper.

Scale-Dependent Input Representation and Confidence Estimation for LLMs in Materials Property Prediction Foundational Large Language Models for Materials Research

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-12T11:01:31.616002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 174f3f0d-f8ad-45ad-a784-84090a8890c7 · inbound

From Blind Guess to Informed Judgment: Teaching LLMs to Evaluate Materials by Building Knowledge-Augmented Preference Signals cites this paper.

From Blind Guess to Informed Judgment: Teaching LLMs to Evaluate Materials by Building Knowledge-Augmented Preference Signals Foundational Large Language Models for Materials Research

Reference 19

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T07:33:13.349548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 09b89d18-e485-4dc7-afae-f36de9ccfd02 · inbound

MatMind: A Structure-Activity Knowledge-Driven Generative Foundation Model for Materials Science cites this paper.

MatMind: A Structure-Activity Knowledge-Driven Generative Foundation Model for Materials Science Foundational Large Language Models for Materials Research

Reference 28

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T19:17:18.639472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ac8bf16d-7565-4faa-ad09-259bb225c91d · inbound

Symbolic Augmentation Closes a Canonical-Equivalence Blind Spot in Neural Fact-Checkers cites this paper.

Symbolic Augmentation Closes a Canonical-Equivalence Blind Spot in Neural Fact-Checkers Foundational Large Language Models for Materials Research

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-02T13:40:19.502537Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.