Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
DARWIN 1.5: Large Language Models as Materials Science Adapted Learners
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2412.11970.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T20:38:22.688424Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T17:21:10.447495Z
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 854f98e8-b41f-40ce-861b-6c9e07d596dc · inbound
ChemPile: A 250GB Diverse and Curated Dataset for Chemical Foundation Models cites this paper.
ChemPile: A 250GB Diverse and Curated Dataset for Chemical Foundation Models DARWIN 1.5: Large Language Models as Materials Science Adapted Learners
Reference 63
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:38:22.688424Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 65f71517-8d93-4fef-a68e-98d57de3dd63 · inbound
TopoMAS: Large Language Model Driven Topological Materials Multiagent System cites this paper.
TopoMAS: Large Language Model Driven Topological Materials Multiagent System DARWIN 1.5: Large Language Models as Materials Science Adapted Learners
Reference 37
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:02:31.678781Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2d228bd7-bc3e-4a5a-be8e-8edef56ab9d6 · inbound
AtomWorld: A Benchmark for Evaluating Spatial Reasoning in Large Language Models on Crystalline Materials cites this paper.
AtomWorld: A Benchmark for Evaluating Spatial Reasoning in Large Language Models on Crystalline Materials DARWIN 1.5: Large Language Models as Materials Science Adapted Learners
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T11:26:06.773274Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ddc62ec9-6261-44f3-9566-f1c723ce0616 · inbound
Composition-Weighted Symbolic Regression for General-Purpose Property Prediction cites this paper.
Composition-Weighted Symbolic Regression for General-Purpose Property Prediction DARWIN 1.5: Large Language Models as Materials Science Adapted Learners
Reference 34
- Resolution
- verified exact
- arxiv_id, observed 2026-05-09T06:05:36.533603Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 41fe96b3-8933-438d-a608-335ad9735217 · inbound
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry cites this paper.
From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry DARWIN 1.5: Large Language Models as Materials Science Adapted Learners
Reference 274
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T17:21:10.452674Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.