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Paper Citation Record · LEDGER

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

As of 13 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 1 inbound Pith citation observation for arXiv:2502.09994.

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

pith.paper-citation-record.v1
2502.09994 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T19:51:22.307933Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:26:49.286842Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T13:26:50.143400Z

Reference resolution

26 of 26 outbound references displayed

  • verified exact1
  • verified fuzzy13
  • unresolved12
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 13e10834-f173-487a-8594-44c72e28c667 · outbound

This paper cites An exact graph edit distance algorithm for solving pattern recognition problems.

Decision Information Meets Large Language Models: The Future of Explainable Operations Research An exact graph edit distance algorithm for solving pattern recognition problems

Reference 1

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 691d2fa3-15aa-4860-998a-9ebe943812ac · outbound

This paper cites "" The prompt template for Code (2): 1 CODE_PROMPT =.

Decision Information Meets Large Language Models: The Future of Explainable Operations Research "" The prompt template for Code (2): 1 CODE_PROMPT =

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T19:51:22.519136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 36fcf9a7-e6de-4129-9019-ac3ff4091648 · outbound

This paper cites A closer look into using large language models for automatic evaluation.

Decision Information Meets Large Language Models: The Future of Explainable Operations Research A closer look into using large language models for automatic evaluation

Reference 5

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 5e676ab2-c779-45ed-bb25-766fdd115e50 · outbound

This paper cites A Better LLM Evaluator for Text Generation: The Impact of Prompt Output Sequencing and Optimization.

Decision Information Meets Large Language Models: The Future of Explainable Operations Research A Better LLM Evaluator for Text Generation: The Impact of Prompt Output Sequencing and Optimization

Reference 6

Resolution
verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 596e98a0-9aca-400f-a15d-d337d66d0f28 · outbound

This paper cites The effect of explanations on trust in an assistance system for public transport users and the role of the propensity to trust.

Decision Information Meets Large Language Models: The Future of Explainable Operations Research The effect of explanations on trust in an assistance system for public transport users and the role of the propensity to trust

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T19:51:22.595365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T19:51:22.232142Z digest=sha256:78385ea8590fd11dae527e1f1cb4e52f091011cdbc2cb7a9767357adfae97d50

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

This paper cites When Large Language Model Meets Optimization.

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.

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Observation e685d728-4522-4150-947c-26f0ad56fe04 · outbound

This paper cites Less is more: Dis- covering concise network explanations.

Decision Information Meets Large Language Models: The Future of Explainable Operations Research Less is more: Dis- covering concise network explanations

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T19:51:22.574119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation ef9572dd-c874-444d-9de8-e81438fcca34 · outbound

This paper cites Large Language Models for Supply Chain Optimization.

Decision Information Meets Large Language Models: The Future of Explainable Operations Research Large Language Models for Supply Chain Optimization

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-07T19:51:22.252864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T19:51:22.252864Z digest=sha256:29be8127cc8540f111a931648b5add4849015cc85951fc2bc43be0dcb5d5d4e6

Observation d7300e30-c5ee-495e-8434-6ef51c967491 · outbound

This paper cites Nl4opt com- petition: Formulating optimization problems based on their natural language descriptions.

Decision Information Meets Large Language Models: The Future of Explainable Operations Research Nl4opt com- petition: Formulating optimization problems based on their natural language descriptions

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T19:51:22.563910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation c0bdee5b-e0b1-44d0-8755-8c9312f14f41 · outbound

This paper cites ADD CONSTRAINT.

Decision Information Meets Large Language Models: The Future of Explainable Operations Research ADD CONSTRAINT

Reference 14

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 92d9f99f-e665-41cb-afdf-34578066d9c2 · outbound

This paper cites ORLM: A Customizable Framework in Training Large Models for Automated Optimization Modeling.

Decision Information Meets Large Language Models: The Future of Explainable Operations Research ORLM: A Customizable Framework in Training Large Models for Automated Optimization Modeling

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-07T19:51:22.265348Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation f36a2bef-cdd5-4858-99ef-0216e33e3169 · outbound

This paper cites Towards human-aligned evaluation for linear programming word problems.

Decision Information Meets Large Language Models: The Future of Explainable Operations Research Towards human-aligned evaluation for linear programming word problems

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T19:51:22.541489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 898d0a07-9594-47a3-a55e-0bcd86196552 · outbound

This paper cites Solving General Natural-Language-Description Optimization Problems with Large Language Models.

Decision Information Meets Large Language Models: The Future of Explainable Operations Research Solving General Natural-Language-Description Optimization Problems with Large Language Models

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-07T19:51:22.273098Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 72a1b902-13b1-46f5-912e-d86f40a9ed12 · outbound

This paper cites LLMs for XAI: Future Directions for Explaining Explanations.

Decision Information Meets Large Language Models: The Future of Explainable Operations Research LLMs for XAI: Future Directions for Explaining Explanations

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-07T19:51:22.277063Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T19:51:22.277063Z digest=sha256:c96c0fde13404293ebaf2345ff3dc5162b8c9b001de08c3e9aaa5e0a3b1851ef

Observation fc107bd0-640a-46c3-bdb5-f8de4dd49959 · outbound

This paper cites an unresolved cited work.

Decision Information Meets Large Language Models: The Future of Explainable Operations Research Unresolved cited work

Reference 19

Resolution
unresolved
raw_fallback, observed 2026-08-07T19:51:22.529937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 82ae827d-2f37-4b10-a9bc-011c7da72205 · outbound

This paper cites "" A.2.2 P ROMPT TEMPLATE FOR SAFEGUARD AGENT The prompt template for Safeguard with system message for the ChatCompletion inference: 1 SAFEGUARD_SYSTEM_MSG =.

Decision Information Meets Large Language Models: The Future of Explainable Operations Research "" A.2.2 P ROMPT TEMPLATE FOR SAFEGUARD AGENT The prompt template for Safeguard with system message for the ChatCompletion inference: 1 SAFEGUARD_SYSTEM_MSG =

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T19:51:22.507472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation c6f7ff52-c62c-4749-a9ed-4883ce7b75c5 · outbound

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Decision Information Meets Large Language Models: The Future of Explainable Operations Research Unresolved cited work

Reference 23

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation c477692e-285b-4b7a-8674-ca4b779a3f6c · outbound

This paper cites an unresolved cited work.

Decision Information Meets Large Language Models: The Future of Explainable Operations Research Unresolved cited work

Reference 24

Resolution
unresolved
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation d70a7d23-c62c-4d82-830b-156c3b249ac5 · outbound

This paper cites Table 6 shows a 60.00% reduction in total errors from zero-shot to one-shot, demonstrating a substantial improvement in the model’s per- formance.

Decision Information Meets Large Language Models: The Future of Explainable Operations Research Table 6 shows a 60.00% reduction in total errors from zero-shot to one-shot, demonstrating a substantial improvement in the model’s per- formance

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T19:51:22.460235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation ae7f1792-2d23-4dbc-9295-e4154b86a9ab · outbound

This paper cites AirlineOptimization.

Decision Information Meets Large Language Models: The Future of Explainable Operations Research AirlineOptimization

Reference 50

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation 5d796928-52f6-427a-806b-734f005c9c3c · outbound

This paper cites Exploring the Use of Large Language Models for Reference-Free Text Quality Evaluation: An Empirical Study.

Decision Information Meets Large Language Models: The Future of Explainable Operations Research Exploring the Use of Large Language Models for Reference-Free Text Quality Evaluation: An Empirical Study

Reference 2007

Resolution
unresolved
no resolver link, observed 2026-08-07T19:51:22.219530Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T19:51:22.219530Z digest=sha256:8265fd5573638c68c0ef1e1fc58b33f293e0e9128034450e89f1572591d921ed

Observation a2ec129f-4e51-425c-a14d-263a82cb1882 · outbound

This paper cites GPT-4 Technical Report.

Decision Information Meets Large Language Models: The Future of Explainable Operations Research GPT-4 Technical Report

Reference 2015

Resolution
unresolved
no resolver link, observed 2026-08-07T19:51:22.210559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T19:51:22.210559Z digest=sha256:7200245754e8d6f01bcfb36b957e15c4ad02ffa43a69242a89bbca5fd4dcaf73

Observation e86f4390-eec1-4ab6-88e2-90422f243d93 · outbound

This paper cites Grid optimiza- tion competition challenge 3 problem formulation.

Decision Information Meets Large Language Models: The Future of Explainable Operations Research Grid optimiza- tion competition challenge 3 problem formulation

Reference 2017

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T19:51:22.584324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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Observation cebd3be6-b0e5-4898-a1f2-9334ba9c05b0 · outbound

This paper cites A survey on applica- tions of bipartite graph edit distance.

Decision Information Meets Large Language Models: The Future of Explainable Operations Research A survey on applica- tions of bipartite graph edit distance

Reference 2018

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T19:51:22.552818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-07T19:51:22.261347Z digest=sha256:b84c01fda44661e3d1653cc30377fea22446949032c25b481de2c7c01f276268

Observation 465b2920-d80c-41b4-b73f-fe9d80742027 · outbound

This paper cites OptiMUS: Scalable Optimization Modeling with (MI)LP Solvers and Large Language Models.

Decision Information Meets Large Language Models: The Future of Explainable Operations Research OptiMUS: Scalable Optimization Modeling with (MI)LP Solvers and Large Language Models

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-07T19:51:22.214984Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T19:51:22.214984Z digest=sha256:252373868f75d146ea62d30f1dfa10d30f45427fad61135791d78127a330f6ac

Observation 26f6ddca-8538-4338-9208-6f9b56a6915a · outbound

This paper cites Are LLM-based Evaluators Confusing NLG Quality Criteria?.

Decision Information Meets Large Language Models: The Future of Explainable Operations Research Are LLM-based Evaluators Confusing NLG Quality Criteria?

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-07T19:51:22.239945Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T19:51:22.239945Z digest=sha256:3c97e38da367b8a07eb874600976089064c1c4b94b11a8a751112a43c99f0513

Pith citing papers

Observation 2a68f10f-7b6f-48c5-b41d-c2e6d116d755 · inbound

DualSchool: How Reliable are LLMs for Optimization Education? cites this paper.

DualSchool: How Reliable are LLMs for Optimization Education? Decision Information Meets Large Language Models: The Future of Explainable Operations Research

Reference 34

Resolution
verified exact
local_arxiv, observed 2026-08-07T13:26:50.163457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

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