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Paper Citation Record · LEDGER

LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2405.09783.

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

pith.paper-citation-record.v1
2405.09783 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:50:55.369421Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T16:17:09.461285Z

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 98d52701-50c9-4c4d-a362-7a82f6b48832 · inbound

Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models cites this paper.

Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery

Reference 89

Resolution
metadata mismatch
arxiv_id, observed 2026-05-15T21:20:59.424547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-15T21:20:59.128986Z digest=sha256:73cd8bb280d06880dcd41482fce8ae7bcb320c93721f79830f212f0d33aaa30c

Observation 0a2c8c47-9550-4bdc-8478-adda18cb0195 · inbound

Optimizing Temperature for Language Models with Multi-Sample Inference cites this paper.

Optimizing Temperature for Language Models with Multi-Sample Inference LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-08T20:01:43.715786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T20:01:43.715786Z digest=sha256:d65dda5411b0f830c7675e96b884231ac98a0ee17756dd708a2e3b2c7a36cb1e

Observation 6a1faba7-91ac-4a6f-b2de-678fa11256db · inbound

MLLM-based Discovery of Intrinsic Coordinates and Governing Equations from High-Dimensional Data cites this paper.

MLLM-based Discovery of Intrinsic Coordinates and Governing Equations from High-Dimensional Data LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-15T20:50:55.369421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T20:50:55.369421Z digest=sha256:b8ca7948c963b5b6e219bb89bf04698c89e40dec89133b1d758de2cd812e058f

Observation 3ed22fd7-4065-4d70-939f-d540f2f07ce6 · 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 LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery

Reference 74

Resolution
verified exact
arxiv_id, observed 2026-05-22T00:40:51.396781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-22T00:37:11.945418Z digest=sha256:a0fcbe07bb57910dcc6ba6b57c81c42082d08207a28f3922696c68dbcd436fce

Observation 4dc1f4bc-e77d-4899-8521-3755f06955bb · inbound

Mimicking the Physicist's Eye:A VLM-centric Approach for Physics Formula Discovery cites this paper.

Mimicking the Physicist's Eye:A VLM-centric Approach for Physics Formula Discovery LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-15T17:06:46.164694Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T17:06:46.164694Z digest=sha256:bb24df1a7acb3c40f2fbbd0485a4261c9acf963f509f4f13ba7b4879408bd981

Observation 66e0bab4-efa3-400f-9b2c-8b808b3e45d2 · inbound

A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving cites this paper.

A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery

Reference 223

Resolution
unresolved
no resolver link, observed 2026-08-04T20:55:41.942882Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T20:55:41.942882Z digest=sha256:b1ed0741864681f36fccf5502323c23ca7e75f070ff56601c4ff481f3d1c4509

Observation 8bf9eaba-ea82-482f-bf71-fa073721c766 · inbound

EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving cites this paper.

EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery

Reference 30

Resolution
metadata mismatch
arxiv_id, observed 2026-05-18T15:11:32.682143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-05-18T15:08:03.793304Z digest=sha256:6ef821181dd2d8b3322c30371a99176ece365f6994c2a313e09632d98589444e

Observation 133ab480-475e-4952-8a73-07d6bdacf743 · inbound

Experiment-as-Code Labs: A Declarative Stack for AI-Driven Scientific Discovery cites this paper.

Experiment-as-Code Labs: A Declarative Stack for AI-Driven Scientific Discovery LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery

Reference 6

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T17:31:07.917052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-05-08T17:28:41.217810Z digest=sha256:d6c78834bb2bb7c300aea01c4e28d8dc9995eb177647b4374b3042b452aa9330

Observation 1d984f56-1fbb-481b-8fbc-00cdf7868ac4 · inbound

Experiment-as-Code Labs: A Declarative Stack for AI-Driven Scientific Discovery cites this paper.

Experiment-as-Code Labs: A Declarative Stack for AI-Driven Scientific Discovery LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery

Reference 6

Resolution
metadata mismatch
arxiv_id, observed 2026-05-21T00:13:52.868789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-05-21T00:13:07.546472Z digest=sha256:396fe1df912279b6c5ba541014683cea9b056c15c3fd4a09810f53e0769dada0

Observation 65dbd08b-fd7d-470d-b9be-1b92ad7c28bd · inbound

FunctionEvolve: Structure-Guided Symbolic Regression with LLMs cites this paper.

FunctionEvolve: Structure-Guided Symbolic Regression with LLMs LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery

Reference 12

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T16:17:09.462850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-06-27T22:49:40.344762Z digest=sha256:7d60586cc20491e432a7af5f932c55a02ad897015ccb5daaf235a0b0d15722de

Observation 8a067c7b-22d9-4e8b-ba7a-298d8bfeabd0 · inbound

Large language models for partial differential equation workflows cites this paper.

Large language models for partial differential equation workflows LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-05T16:18:49.350413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T16:18:49.350413Z digest=sha256:f7a7e534809bd7f06a43b3b69a51d54b96bcbc19d185a3a40639320eb5f77375