Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Less can be more for predicting properties with large language models
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2406.17295.
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 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T20:38:22.717865Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T06:09:36.546846Z
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 409aa136-7ef6-4069-88db-ea2b513d1be1 · inbound
Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry cites this paper.
Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry Less can be more for predicting properties with large language models
Reference 78
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T16:00:23.135235Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6cf9ebb1-6886-4259-9da8-bf3eae700f85 · 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 Less can be more for predicting properties with large language models
Reference 70
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:38:22.717865Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3677416f-2fe0-4183-b7fb-930687abb866 · inbound
Discovery and recovery of crystalline materials with property-conditioned transformers cites this paper.
Discovery and recovery of crystalline materials with property-conditioned transformers Less can be more for predicting properties with large language models
Reference 40
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T20:04:40.386848Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dcfa5a29-bae3-47a0-9702-8a735b087a1e · inbound
Conditional Generative Models Enable Targeted Exploration of MAX Phase Design Space cites this paper.
Conditional Generative Models Enable Targeted Exploration of MAX Phase Design Space Less can be more for predicting properties with large language models
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T10:21:27.924303Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 6e7d3185-6057-44e6-ba23-b86719444264 · 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 Less can be more for predicting properties with large language models
Reference 16
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T11:01:31.608756Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation d5731ef5-e569-438d-8cfe-2365e1ba0bce · 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 Less can be more for predicting properties with large language models
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T19:17:18.616717Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 54626e30-5185-4631-a3ff-6a9a9be94a08 · inbound
Atomistic Language Models Understand and Generate Materials cites this paper.
Atomistic Language Models Understand and Generate Materials Less can be more for predicting properties with large language models
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T06:09:36.554610Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.