Pith. sign in

Paper Citation Record · LEDGER

When Large Language Model Meets Optimization

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2405.10098.

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

pith.paper-citation-record.v1
2405.10098 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:34:30.117730Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T20:15:48.383989Z

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 0a242d4f-a770-4f0d-9b9e-7f06d5c5d978 · inbound

Accelerating Quantum Eigensolver Algorithms With Machine Learning cites this paper.

Accelerating Quantum Eigensolver Algorithms With Machine Learning When Large Language Model Meets Optimization

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-05-23T20:15:48.387364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-23T20:13:39.576927Z digest=sha256:af50622da28f44edc22f686054b90d5b4eb2083209f917c831c343e54a6e4fff

Observation 18946ef5-b68c-4256-923b-a4baf9187b29 · inbound

Decision Information Meets Large Language Models: The Future of Explainable Operations Research cites this paper.

Decision Information Meets Large Language Models: The Future of Explainable Operations Research When Large Language Model Meets Optimization

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-07T19:51:22.244036Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T19:51:22.244036Z digest=sha256:ceb9c5bebb9ce6c81aab0c65a740927bacd9805d529d50b5339e86720646b964

Observation a5393245-4e8f-406b-834b-9a9b1ec02bce · inbound

Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows cites this paper.

Optimization Problem Solving Can Transition to Evolutionary Agentic Workflows When Large Language Model Meets Optimization

Reference 2021

Resolution
unresolved
no resolver link, observed 2026-08-15T23:34:30.117730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T23:34:30.117730Z digest=sha256:fa52b9beb5a5bbe6cfbec0ce7a443dbcdc366c17dc359238657347f302f62a09

Observation 87738fa5-2955-4823-bcb2-424db09c4915 · inbound

RideAgent: An LLM-Enhanced Optimization Framework for Automated Taxi Fleet Operations cites this paper.

RideAgent: An LLM-Enhanced Optimization Framework for Automated Taxi Fleet Operations When Large Language Model Meets Optimization

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-15T22:44:19.181647Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:44:19.181647Z digest=sha256:c5c778df3fcf00af2bed24d5a1002284f973ae34e5651db4cdc6c7b296130cbc

Observation 71108e99-fa7a-46c6-8a6a-0cd267742b4d · inbound

Instruction Learning Paradigms: A Dual Perspective on White-box and Black-box LLMs cites this paper.

Instruction Learning Paradigms: A Dual Perspective on White-box and Black-box LLMs When Large Language Model Meets Optimization

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-07T00:50:48.731007Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T00:50:48.731007Z digest=sha256:e87adbcbaeeabd554833c78c9ee62773b4709097c645915da84547d869aabf5d

Observation 13dd25b8-0099-4ef0-814c-a01558ad51b7 · inbound

CHAL: Council of Hierarchical Agentic Language cites this paper.

CHAL: Council of Hierarchical Agentic Language When Large Language Model Meets Optimization

Reference 75

Resolution
verified exact
arxiv_id, observed 2026-05-14T19:59:25.831096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-14T19:59:13.378797Z digest=sha256:0956dd7cdb600b7d2f8d51bf7b6eda1eea49168171aee3fca9d982cbed770ad6