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Paper Citation Record · LEDGER

Towards Optimizing with Large Language Models

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2310.05204.

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

pith.paper-citation-record.v1
2310.05204 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:08:53.608128Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T16:18:05.335815Z

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 f124c993-595c-4833-97a9-0a4882b4f2a6 · inbound

Using Large Language Models for Parametric Shape Optimization cites this paper.

Using Large Language Models for Parametric Shape Optimization Towards Optimizing with Large Language Models

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-11T18:19:53.970612Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:19:53.970612Z digest=sha256:10b1f290be5063f3110667dd3fb29d5d96ed8abbde847c16fa1c3f745dc76e04

Observation d348343f-f701-43e9-8560-b329081a26cd · inbound

Monte Carlo Tree Search for Comprehensive Exploration in LLM-Based Automatic Heuristic Design cites this paper.

Monte Carlo Tree Search for Comprehensive Exploration in LLM-Based Automatic Heuristic Design Towards Optimizing with Large Language Models

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-10T20:26:29.183022Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T20:26:29.183022Z digest=sha256:38e968018cfc7bc5b6f2bdcdd07dc62ca486253711606a31e06410075423d8ea

Observation 69ef2aa6-c634-4f46-a7cd-2eca241b8b4e · inbound

MoE$^2$: Optimizing Collaborative Inference for Edge Large Language Models cites this paper.

MoE$^2$: Optimizing Collaborative Inference for Edge Large Language Models Towards Optimizing with Large Language Models

Reference 67

Resolution
unresolved
no resolver link, observed 2026-08-10T20:13:14.766332Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T20:13:14.766332Z digest=sha256:7962d4ee2dc2e940241ecfe3a28a87ac21cd862f24f8a92037ab9e0bfaa463d4

Observation f1339edb-aa0c-4737-9013-f461c78e4ac6 · inbound

Generalizing Large Language Model Usability Across Resource-Constrained cites this paper.

Generalizing Large Language Model Usability Across Resource-Constrained Towards Optimizing with Large Language Models

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-15T22:08:53.608128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:08:53.608128Z digest=sha256:29f8506b53c40d5da97e87d7aa637b052565c81b611d92fd82211bc97bd909ca

Observation 1efc000d-03c1-4f0c-b92a-1d5d98b8fd68 · inbound

On the Convergence of Large Language Model Optimizer for Black-Box Network Management cites this paper.

On the Convergence of Large Language Model Optimizer for Black-Box Network Management Towards Optimizing with Large Language Models

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-06T20:33:42.602505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:33:42.602505Z digest=sha256:4b2acd3deea2c0dfd4ebda5c8931d21af6ef21ca7525fffb621bae5d42965978

Observation 9ce51074-5f80-4902-9056-2343a9f7c0b7 · inbound

Symbiotic Agents: A Novel Paradigm for Trustworthy AGI-driven Networks cites this paper.

Symbiotic Agents: A Novel Paradigm for Trustworthy AGI-driven Networks Towards Optimizing with Large Language Models

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-15T18:23:46.127855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T18:23:46.127855Z digest=sha256:63cbc555d529de3c6db578142fd4a7f6f29bd1d25fa1129c89ae06dfb9e6af8a

Observation 07c99837-4769-4638-b1e8-a822e510acc0 · 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 Towards Optimizing with Large Language Models

Reference 111

Resolution
unresolved
no resolver link, observed 2026-08-04T20:55:41.530807Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T20:55:41.530807Z digest=sha256:6ca24525f3756d596834eb8a43ddab4b56b05021e3f2ea3213fd50768ae76e3f

Observation 168412c6-e8f9-4561-be64-aed2b25284dd · inbound

OPT-Engine: Benchmarking the Limits of LLMs in Optimization Modeling via Complexity Scaling cites this paper.

OPT-Engine: Benchmarking the Limits of LLMs in Optimization Modeling via Complexity Scaling Towards Optimizing with Large Language Models

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-16T16:18:05.338503Z

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.

source=pdf_text observed=2026-05-16T16:17:43.055124Z digest=sha256:f2c5606d0652ae6ec91f31acea5fcc33888c666cb32287b8337ce38de1c4c467

Observation e3436e0f-bf12-471d-8b86-35d5a180060a · inbound

TurboAgent: An LLM-Driven Autonomous Multi-Agent Framework for Turbomachinery Aerodynamic Design cites this paper.

TurboAgent: An LLM-Driven Autonomous Multi-Agent Framework for Turbomachinery Aerodynamic Design Towards Optimizing with Large Language Models

Reference 42

Resolution
verified exact
arxiv_id, observed 2026-05-11T05:16:02.330353Z

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.

source=pdf_text observed=2026-05-10T18:14:38.180193Z digest=sha256:98334c40ba29b3e6a1f17436305c0efbd0e4e0a06797c59670b3596ae267b0d2