FORGE: Foundational Optimization Representations from Graph Embeddings | Paper Citation Record

Observation a42a8ee0-772e-43e9-932c-3c48b76ff2a2 · outbound

This paper cites write newline.

FORGE: Foundational Optimization Representations from Graph Embeddings write newline

Reference 1

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Observation 4629a9b2-009e-49d0-b1bc-e03d84f3c33a · outbound

This paper cites strIPlib: Structured Integer Programming Library.

FORGE: Foundational Optimization Representations from Graph Embeddings strIPlib: Structured Integer Programming Library

Reference 2

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Observation 46d928ae-ef10-422b-b407-4e97c4292946 · outbound

This paper cites Machine learning for combinatorial optimization: A methodological tour d’horizon.

FORGE: Foundational Optimization Representations from Graph Embeddings Machine learning for combinatorial optimization: A methodological tour d’horizon

Reference 3

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This paper cites Machine learning for combinatorial optimization: a methodological tour d’horizon.

FORGE: Foundational Optimization Representations from Graph Embeddings Machine learning for combinatorial optimization: a methodological tour d’horizon

Reference 4

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Observation 8a3bf44b-4616-41f4-9a47-83324459937c · outbound

This paper cites Routefinder: Towards foundation models for vehicle routing problems.

FORGE: Foundational Optimization Representations from Graph Embeddings Routefinder: Towards foundation models for vehicle routing problems

Reference 5

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Observation 8245c656-799d-459f-a68e-4da51537a946 · outbound

This paper cites Routefinder: Towards foundation models for vehicle routing problems, 2025.

FORGE: Foundational Optimization Representations from Graph Embeddings Routefinder: Towards foundation models for vehicle routing problems, 2025

Reference 6

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Observation 73fbc7c4-3405-4d45-8261-46f76882afae · outbound

This paper cites Towards a generic representation of combinatorial problems for learning-based approaches.

FORGE: Foundational Optimization Representations from Graph Embeddings Towards a generic representation of combinatorial problems for learning-based approaches

Reference 7

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Observation ec8800ca-8084-4ad3-98bd-d5cd790f24fe · outbound

This paper cites Tackling prevalent conditions in unsupervised combinatorial optimization: Cardinality, minimum, covering, and more.

FORGE: Foundational Optimization Representations from Graph Embeddings Tackling prevalent conditions in unsupervised combinatorial optimization: Cardinality, minimum, covering, and more

Reference 8

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Observation 4c5ca4a2-ba3c-41ed-8e5f-503e0cc7f774 · outbound

This paper cites Learning backdoors for mixed integer linear programs with contrastive learning.

FORGE: Foundational Optimization Representations from Graph Embeddings Learning backdoors for mixed integer linear programs with contrastive learning

Reference 9

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Observation 07d4c27e-f454-45f7-af1c-f4c721beda4f · outbound

This paper cites Balans: Multi-Armed Bandits-based Adaptive Large Neighborhood Search for Mixed-Integer Programming Problem.

FORGE: Foundational Optimization Representations from Graph Embeddings Balans: Multi-Armed Bandits-based Adaptive Large Neighborhood Search for Mixed-Integer Programming Problem

Reference 10

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Observation 52949bcc-93bc-49f1-af00-11a8747beca8 · outbound

This paper cites Multi-task representation learning for mixed integer linear programming.

FORGE: Foundational Optimization Representations from Graph Embeddings Multi-task representation learning for mixed integer linear programming

Reference 11

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Observation 8ffa389c-4e6d-4e00-9147-c5e77a9cf112 · outbound

This paper cites Balans: Multi-armed bandits-based adaptive large neighborhood search for mixed-integer programming problem.

FORGE: Foundational Optimization Representations from Graph Embeddings Balans: Multi-armed bandits-based adaptive large neighborhood search for mixed-integer programming problem

Reference 12

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Observation 539db3e8-d5fe-4ca1-a211-d79a7512b4bb · outbound

This paper cites u rk, Taha Varol, Reyhan Aydo g an, and Okan \.

FORGE: Foundational Optimization Representations from Graph Embeddings u rk, Taha Varol, Reyhan Aydo g an, and Okan \

Reference 13

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Observation 5755cc3b-a6ee-472a-a6c6-6d1587580e6c · outbound

This paper cites On representing linear programs by graph neural networks.

FORGE: Foundational Optimization Representations from Graph Embeddings On representing linear programs by graph neural networks

Reference 14

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Observation c4d4c07d-beef-44ce-b39d-cbe37f021c07 · outbound

This paper cites Rethinking the capacity of graph neural networks for branching strategy.

FORGE: Foundational Optimization Representations from Graph Embeddings Rethinking the capacity of graph neural networks for branching strategy

Reference 15

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Observation 82eefb1d-1771-4af3-b6cb-870b54c43699 · outbound

This paper cites Augment with care: Contrastive learning for combinatorial problems.

FORGE: Foundational Optimization Representations from Graph Embeddings Augment with care: Contrastive learning for combinatorial problems

Reference 17

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Observation ec704bfb-f1e5-4841-b256-de38106adcad · outbound

This paper cites Towards Understanding Linear Word Analogies.

FORGE: Foundational Optimization Representations from Graph Embeddings Towards Understanding Linear Word Analogies

Reference 18

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Observation 5b7532ad-01de-402b-ad88-bd0008331f70 · outbound

This paper cites A comprehensive evaluation of contemporary ml-based solvers for combinatorial optimization.

FORGE: Foundational Optimization Representations from Graph Embeddings A comprehensive evaluation of contemporary ml-based solvers for combinatorial optimization

Reference 19

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Observation b6440312-715a-497a-b32e-d5488802c7be · outbound

This paper cites Ferber, Jialin Song, Bistra Dilkina, and Yisong Yue.

FORGE: Foundational Optimization Representations from Graph Embeddings Ferber, Jialin Song, Bistra Dilkina, and Yisong Yue

Reference 20

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Observation 215762a4-9d41-425b-b526-774b90977a78 · outbound

This paper cites Network Design with Applications to Transportation and Logistics.

FORGE: Foundational Optimization Representations from Graph Embeddings Network Design with Applications to Transportation and Logistics

Reference 21

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Observation 01d3d189-7af4-43bf-b304-970f4f459772 · outbound

This paper cites Exact combinatorial optimization with graph convolutional neural networks.

FORGE: Foundational Optimization Representations from Graph Embeddings Exact combinatorial optimization with graph convolutional neural networks

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:51:24.374816Z

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Observation 5c8bfa9c-b2de-4b6a-ac69-76b8b41d0979 · outbound

This paper cites Deep learning in search heuristics.

FORGE: Foundational Optimization Representations from Graph Embeddings Deep learning in search heuristics

Reference 23

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Observation e539c79f-19fb-45b6-b4af-5df35e754bd5 · outbound

This paper cites Christophel, Kati Jarck, Thorsten Koch, Jeff Linderoth, Marco L\"ubbecke, Hans D.

FORGE: Foundational Optimization Representations from Graph Embeddings Christophel, Kati Jarck, Thorsten Koch, Jeff Linderoth, Marco L\"ubbecke, Hans D

Reference 24

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Observation 3d63fc1a-d0ea-4158-ad4a-90ea2e6eb95e · outbound

This paper cites Gurobi Optimizer Reference Manual , 2024.

FORGE: Foundational Optimization Representations from Graph Embeddings Gurobi Optimizer Reference Manual , 2024

Reference 25

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This paper cites Hamilton, Zhitao Ying, and Jure Leskovec.

FORGE: Foundational Optimization Representations from Graph Embeddings Hamilton, Zhitao Ying, and Jure Leskovec

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:51:24.324802Z

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Observation b5dbcc83-e6a7-46c8-9d32-12566990f11b · outbound

This paper cites A gnn-guided predict-and-search framework for mixed-integer linear programming.

FORGE: Foundational Optimization Representations from Graph Embeddings A gnn-guided predict-and-search framework for mixed-integer linear programming

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:51:24.311896Z

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Observation cfebb4e9-1bde-4da7-9ac1-8ed3d1731fb4 · outbound

This paper cites Learning to search in branch and bound algorithms.

FORGE: Foundational Optimization Representations from Graph Embeddings Learning to search in branch and bound algorithms

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:51:24.298775Z

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Observation 4cdb51ff-b0b6-487e-8321-59dc073f3f30 · outbound

This paper cites Learning to search in branch and bound algorithms.

FORGE: Foundational Optimization Representations from Graph Embeddings Learning to search in branch and bound algorithms

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:51:24.285527Z

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Observation d73433ba-84f4-4cde-a2ae-0b70663f4901 · outbound

This paper cites Automatic milp solver configuration by learning problem similarities.

FORGE: Foundational Optimization Representations from Graph Embeddings Automatic milp solver configuration by learning problem similarities

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:51:24.272533Z

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Observation 0c669fc5-9fb3-45de-b2c3-349332a2984f · outbound

This paper cites Neural large neighborhood search for routing problems.

FORGE: Foundational Optimization Representations from Graph Embeddings Neural large neighborhood search for routing problems

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:51:24.259493Z

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This paper cites Ferber, Yuandong Tian, Bistra Dilkina, and Benoit Steiner.

FORGE: Foundational Optimization Representations from Graph Embeddings Ferber, Yuandong Tian, Bistra Dilkina, and Benoit Steiner

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:51:24.245152Z

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Observation be9140d0-1167-4535-83c7-8367391cf61c · outbound

This paper cites Distributional MIPLIB: a Multi-Domain Library for Advancing ML-Guided MILP Methods.

FORGE: Foundational Optimization Representations from Graph Embeddings Distributional MIPLIB: a Multi-Domain Library for Advancing ML-Guided MILP Methods

Reference 33

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This paper cites ISAC - instance-specific algorithm configuration.

FORGE: Foundational Optimization Representations from Graph Embeddings ISAC - instance-specific algorithm configuration

Reference 34

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This paper cites Algorithm selection and scheduling.

FORGE: Foundational Optimization Representations from Graph Embeddings Algorithm selection and scheduling

Reference 35

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Observation 69ef860a-d102-457f-aec4-dc8962c18f5e · outbound

This paper cites Incorporating variance in impact-based search.

FORGE: Foundational Optimization Representations from Graph Embeddings Incorporating variance in impact-based search

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:51:24.231751Z

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Observation cf688280-39dc-4554-9e25-bf5bf554bd2c · outbound

This paper cites Non-model-based search guidance for set partitioning problems.

FORGE: Foundational Optimization Representations from Graph Embeddings Non-model-based search guidance for set partitioning problems

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:51:24.218097Z

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Observation bce42d3c-e1c1-45db-82cb-ec528f18c941 · outbound

This paper cites Learning a reactive restart strategy to improve stochastic search.

FORGE: Foundational Optimization Representations from Graph Embeddings Learning a reactive restart strategy to improve stochastic search

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:51:24.204844Z

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This paper cites Integrating optimized item selection with active learning for continuous exploration in recommender systems.

FORGE: Foundational Optimization Representations from Graph Embeddings Integrating optimized item selection with active learning for continuous exploration in recommender systems

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:51:24.191222Z

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Observation 448f3542-6d59-4f5e-be08-6b1f0a8e7845 · outbound

This paper cites Erdos goes neural: an unsupervised learning framework for combinatorial optimization on graphs.

FORGE: Foundational Optimization Representations from Graph Embeddings Erdos goes neural: an unsupervised learning framework for combinatorial optimization on graphs

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:51:24.177013Z

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This paper cites Configuring mixed-integer programming solvers for large-scale instances.

FORGE: Foundational Optimization Representations from Graph Embeddings Configuring mixed-integer programming solvers for large-scale instances

Reference 41

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Observation 5623c9ba-2fc2-45c3-a1d8-dafed24885b1 · outbound

This paper cites Learning to branch in mixed integer programming.

FORGE: Foundational Optimization Representations from Graph Embeddings Learning to branch in mixed integer programming

Reference 42

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Observation 0efeb076-2739-47a1-8c6f-c8b2a8b78a84 · outbound

This paper cites Khalil, Bistra Dilkina, George L.

FORGE: Foundational Optimization Representations from Graph Embeddings Khalil, Bistra Dilkina, George L

Reference 43

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Observation c248a4fd-f6e7-49c0-9af6-138c97281832 · outbound

This paper cites Autoregressive image generation using residual quantization.

FORGE: Foundational Optimization Representations from Graph Embeddings Autoregressive image generation using residual quantization

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:51:24.120932Z

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This paper cites Towards foundation models for mixed integer linear programming.

FORGE: Foundational Optimization Representations from Graph Embeddings Towards foundation models for mixed integer linear programming

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:51:24.106812Z

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This paper cites Di Liberto, Serdar Kadioglu, Kevin Leo, and Yuri Malitsky.

FORGE: Foundational Optimization Representations from Graph Embeddings Di Liberto, Serdar Kadioglu, Kevin Leo, and Yuri Malitsky

Reference 46

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Observation 5092d0c5-86fd-4a2e-9bf8-924c67c1c993 · outbound

This paper cites On learning and branching: a survey.

FORGE: Foundational Optimization Representations from Graph Embeddings On learning and branching: a survey

Reference 47

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Observation 74d7077d-38c4-44ae-aa97-8f853077cb4c · outbound

This paper cites Attacking shortest paths by cutting edges.

FORGE: Foundational Optimization Representations from Graph Embeddings Attacking shortest paths by cutting edges

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:51:24.084740Z

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Observation 69470f91-33fd-459a-99f4-f33b26616895 · outbound

This paper cites Solving Mixed Integer Programs Using Neural Networks.

FORGE: Foundational Optimization Representations from Graph Embeddings Solving Mixed Integer Programs Using Neural Networks

Reference 49

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Observation 465cf76c-14d6-47d5-962b-fd16c6da136c · outbound

This paper cites Learning to cut by looking ahead: Cutting plane selection via imitation learning.

FORGE: Foundational Optimization Representations from Graph Embeddings Learning to cut by looking ahead: Cutting plane selection via imitation learning

Reference 50

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Observation 70063ed1-c383-43c7-8cf1-c4e7483f3037 · outbound

This paper cites A diffusion model framework for unsupervised neural combinatorial optimization.

FORGE: Foundational Optimization Representations from Graph Embeddings A diffusion model framework for unsupervised neural combinatorial optimization

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:51:24.062149Z

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Observation f3c8e111-ae46-4069-9b5a-8b15d5a39fda · outbound

This paper cites Deep reinforcement learning for instance-specific algorithm configuration.

FORGE: Foundational Optimization Representations from Graph Embeddings Deep reinforcement learning for instance-specific algorithm configuration

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:51:24.048280Z

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Observation b060aacc-3b80-4cd4-a706-ac0011f5dbd4 · outbound

This paper cites Facenet: A unified embedding for face recognition and clustering.

FORGE: Foundational Optimization Representations from Graph Embeddings Facenet: A unified embedding for face recognition and clustering

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:51:24.033134Z

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Observation c82f278c-afc1-44af-b681-86e0e7e0f74d · outbound

This paper cites Accelerated discovery of set cover solutions via graph neural networks.

FORGE: Foundational Optimization Representations from Graph Embeddings Accelerated discovery of set cover solutions via graph neural networks

Reference 54

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Observation e76eee75-095b-4a49-97b7-ba29caf1c889 · outbound

This paper cites A general large neighborhood search framework for solving integer linear programs.

FORGE: Foundational Optimization Representations from Graph Embeddings A general large neighborhood search framework for solving integer linear programs

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:51:24.005034Z

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Observation ffd3ff45-a80d-4d0e-8285-cdb76abd1417 · outbound

This paper cites Mabwiser: A parallelizable contextual multi-armed bandit library for python.

FORGE: Foundational Optimization Representations from Graph Embeddings Mabwiser: A parallelizable contextual multi-armed bandit library for python

Reference 56

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Observation 990cf20e-a14f-4e00-b4d0-70782096c6a5 · outbound

This paper cites MABWiser: parallelizable contextual multi-armed bandits.

FORGE: Foundational Optimization Representations from Graph Embeddings MABWiser: parallelizable contextual multi-armed bandits

Reference 57

Resolution: verified exact; doi, observed 2026-08-15T16:51:23.455137Z

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Observation ac560c0e-e6d0-4d6a-9265-1d2949f59e93 · outbound

This paper cites Reinforcement learning for integer programming: Learning to cut.

FORGE: Foundational Optimization Representations from Graph Embeddings Reinforcement learning for integer programming: Learning to cut

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:51:23.991212Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c0b49d91-be4b-4f1e-b701-532e4df664a3 · outbound

This paper cites One model, any CSP: graph neural networks as fast global search heuristics for constraint satisfaction.

FORGE: Foundational Optimization Representations from Graph Embeddings One model, any CSP: graph neural networks as fast global search heuristics for constraint satisfaction

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:51:23.977510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 151676b7-9b42-4d95-b359-0bfa8414dd8b · outbound

This paper cites Neural discrete representation learning.

FORGE: Foundational Optimization Representations from Graph Embeddings Neural discrete representation learning

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-15T16:51:23.352649Z

Source-reported events for the cited work

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Observation af9da2b1-81c9-4f7f-84d5-118fbfa1c109 · outbound

This paper cites Understanding how dimension reduction tools work: an empirical approach to deciphering t-sne, umap, trimap, and pacmap for data visualization.

FORGE: Foundational Optimization Representations from Graph Embeddings Understanding how dimension reduction tools work: an empirical approach to deciphering t-sne, umap, trimap, and pacmap for data visualization

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:51:23.953461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b0399acd-58cf-4ca9-9c6d-3deb03f867ff · outbound

This paper cites Learning large neighborhood search policy for integer programming.

FORGE: Foundational Optimization Representations from Graph Embeddings Learning large neighborhood search policy for integer programming

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:51:23.937662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 142a4738-59e0-4d47-b563-69aa6711c80f · outbound

This paper cites Hoos, and Kevin Leyton-Brown.

FORGE: Foundational Optimization Representations from Graph Embeddings Hoos, and Kevin Leyton-Brown

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:51:23.923259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e40fdb3a-e3e8-4825-acef-8f41996bf071 · outbound

This paper cites Vqgraph: Rethinking graph representation space for bridging gnns and mlps.

FORGE: Foundational Optimization Representations from Graph Embeddings Vqgraph: Rethinking graph representation space for bridging gnns and mlps

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:51:23.909397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1b57b764-5975-41ad-9782-2eeae39a5128 · outbound

This paper cites Are graph neural networks optimal approximation algorithms? Neural Information Processing Systems, 37: 0 73124--73181, 2024.

FORGE: Foundational Optimization Representations from Graph Embeddings Are graph neural networks optimal approximation algorithms? Neural Information Processing Systems, 37: 0 73124--73181, 2024

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:51:23.895027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2627a3ba-331c-430b-9ad6-7c7d87117054 · outbound

This paper cites ParBalans: Parallel Multi-Armed Bandits-based Adaptive Large Neighborhood Search.

FORGE: Foundational Optimization Representations from Graph Embeddings ParBalans: Parallel Multi-Armed Bandits-based Adaptive Large Neighborhood Search

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-15T16:51:23.379122Z

Source-reported events for the cited work

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Observation cf78e65b-ea50-4f24-94b8-fe01f75a24f3 · outbound

This paper cites Vector-quantized image modeling with improved vqgan.

FORGE: Foundational Optimization Representations from Graph Embeddings Vector-quantized image modeling with improved vqgan

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:51:23.881267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4c3354f6-ece2-4eef-8926-4e47c05c1882 · outbound

This paper cites Soundstream: An end-to-end neural audio codec.

FORGE: Foundational Optimization Representations from Graph Embeddings Soundstream: An end-to-end neural audio codec

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-15T16:51:23.387987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e0e82ee-10a2-4256-8c6c-7440737e447f · outbound

This paper cites A survey for solving mixed integer programming via machine learning.

FORGE: Foundational Optimization Representations from Graph Embeddings A survey for solving mixed integer programming via machine learning

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:51:23.858630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation aa60ec97-55a0-4092-8395-a94b9f9c804f · outbound

This paper cites Towards omni-generalizable neural methods for vehicle routing problems.

FORGE: Foundational Optimization Representations from Graph Embeddings Towards omni-generalizable neural methods for vehicle routing problems

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:51:23.844761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e71510ff-a2c6-47c1-a041-d0b070423403 · outbound

This paper cites Learning from labeled and unlabeled data with label propagation.

FORGE: Foundational Optimization Representations from Graph Embeddings Learning from labeled and unlabeled data with label propagation

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:51:23.830815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7672931b-a00a-4b37-9d66-30d208923a6c · outbound

This paper cites @esa (Ref.

FORGE: Foundational Optimization Representations from Graph Embeddings @esa (Ref

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-15T16:51:23.406783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c165e943-0adb-4739-adee-ccb3f6f37dee · outbound

This paper cites an unresolved cited work.

FORGE: Foundational Optimization Representations from Graph Embeddings Unresolved cited work

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-15T16:51:23.411469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fde5d254-6a20-44c9-89c2-26c5b18d000a · outbound

This paper cites GOAL: A Generalist Combinatorial Optimization Agent Learner.

FORGE: Foundational Optimization Representations from Graph Embeddings GOAL: A Generalist Combinatorial Optimization Agent Learner

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-15T16:51:23.415820Z

Source-reported events for the cited work

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