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Paper Citation Record · LEDGER

A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2406.10833.

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

pith.paper-citation-record.v1
2406.10833 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T20:29:52.717760Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T04:32:32.686171Z

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 9b1ea579-96a8-41ae-bcbd-9bb1f0e7a697 · inbound

Ensembles of Low-Rank Expert Adapters cites this paper.

Ensembles of Low-Rank Expert Adapters A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery

Reference 90

Resolution
unresolved
no resolver link, observed 2026-08-09T20:29:52.717760Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T20:29:52.717760Z digest=sha256:a6ed9696f7a51c32980bdba460010f4cf6c170bf21bac3375d28323eb7d92ba1

Observation e8f12ecf-f847-4df1-ada7-584633875646 · inbound

Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning cites this paper.

Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery

Reference 252

Resolution
verified exact
arxiv_id, observed 2026-05-23T04:32:32.689378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-23T04:30:38.804702Z digest=sha256:fbeaad9fbd994837976be18fb41abfcc5a660edf4dd258983cfe38e7ac570b86

Observation d636335c-91b7-49f6-9b7e-b9e4f9bb7466 · inbound

ScienceMeter: Tracking Scientific Knowledge Updates in Language Models cites this paper.

ScienceMeter: Tracking Scientific Knowledge Updates in Language Models A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-07T12:34:47.226927Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:34:47.226927Z digest=sha256:ff652bf5e3e02cebbccc8a6971ee0b0692cf7a32d35687d46d00d4c7d7ff2b93

Observation b5a48d0e-96b2-47ab-837b-9a9a873b4ed1 · inbound

PhySense: Principle-Based Physics Reasoning Benchmarking for Large Language Models cites this paper.

PhySense: Principle-Based Physics Reasoning Benchmarking for Large Language Models A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-07T12:22:22.263718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:22:22.263718Z digest=sha256:6e8e8ef44a05e1592d29d991bf46e22147732093dfa7b52b1db7880882465fcc

Observation 2105a71c-2d65-46ce-9d55-289a4efdb43b · inbound

GenoMAS: A Multi-Agent Framework for Scientific Discovery via Code-Driven Gene Expression Analysis cites this paper.

GenoMAS: A Multi-Agent Framework for Scientific Discovery via Code-Driven Gene Expression Analysis A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery

Reference 62

Resolution
verified exact
arxiv_id, observed 2026-05-22T00:40:51.440770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-22T00:37:11.945418Z digest=sha256:cb094aede3f2af03f89d1b26aa925e9825fbd30a57887804f3e5761b9f931c86

Observation cf5b6e1a-6946-4ef0-a895-0a0fd87a7836 · inbound

How Far Are AI Scientists from Changing the World? cites this paper.

How Far Are AI Scientists from Changing the World? A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery

Reference 205

Resolution
unresolved
no resolver link, observed 2026-08-06T10:55:15.180034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T10:55:15.180034Z digest=sha256:942cdee922c243c002bf96157275e6108120924a2ac71885b194a112b56e1c63

Observation 9eef16d0-5c9b-43b3-812c-524ff8c1fdbb · inbound

VASP Agent: An Agentic Framework for Autonomous First-principles Calculations cites this paper.

VASP Agent: An Agentic Framework for Autonomous First-principles Calculations A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-03T14:45:40.341654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T14:45:40.341654Z digest=sha256:107ae23220ba8e15a020f0f1ff440f498ba1f5ed958bd0d495b6ac2368281ef5

Observation ce1258c3-4446-4e0a-b6a7-f4df7395314b · inbound

Hypothesis-and-Refinement Learning of Organic Structures from Multimodal Spectroscopic Data cites this paper.

Hypothesis-and-Refinement Learning of Organic Structures from Multimodal Spectroscopic Data A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-01T11:44:10.100729Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T11:44:10.100729Z digest=sha256:9b1722678f295e71c018066f6ee0829e22567e415b9d243e9788c21258f2697e