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Paper Citation Record · LEDGER

FORGE: Foundational Optimization Representations from Graph Embeddings

As of 16 August 2026, this Paper Citation Record lists 73 of 73 outbound references and 1 inbound Pith citation observation for arXiv:2508.20330.

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

pith.paper-citation-record.v1
2508.20330 v5

Coverage vector

measured 73 of 73 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:51:23.415820Z

measured 74 of 74 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T12:01:08.000282Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T12:05:22.431996Z

Reference resolution

73 of 73 outbound references displayed

  • verified exact2
  • verified fuzzy51
  • unresolved20
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

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

This paper cites write newline.

FORGE: Foundational Optimization Representations from Graph Embeddings write newline

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-15T16:51:23.091587Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T16:51:23.091587Z digest=sha256:283b4370f1c66b982610b2949c8dbb4006c7a4f2fa90bffbc034c901734543a5

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:51:24.569429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.097365Z digest=sha256:ca6160acc8b5e55909c672db2b467057bb65f4957a0ccb0d92500c1d6c406eec

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

Resolution
unresolved
no resolver link, observed 2026-08-15T16:51:23.102143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T16:51:23.102143Z digest=sha256:8acbf992c49985588653cd50154341e4e095483386da0d4f95083072c1e55dd8

Observation b89ea44b-2dc9-4021-b463-640a1a32606b · 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 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:51:24.556101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.107074Z digest=sha256:712c0391fb28eead7aa92479c2fdd9e1d642acdae9b03bbf739307f1add85d93

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:51:24.542779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.111503Z digest=sha256:768848b48fe9ea2d003ef2ae88ebf933bef24d8c9134ec79244e1eecb9a71e4e

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

Resolution
unresolved
no resolver link, observed 2026-08-15T16:51:23.115935Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T16:51:23.115935Z digest=sha256:83e30cad3ae00bbc02f3733892bc8b8ab4ec62fd540886e216c060c271ce2975

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:51:24.528850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.120510Z digest=sha256:5b8c95efd84b62c78c8216724543d7827051b18d5e09385b0932367a4d41749f

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:51:24.515188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.125483Z digest=sha256:30e4f5ca169de4aea571c229ba9c6b61e677671a192513e88a8161e5c51374bf

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:51:24.501025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.129643Z digest=sha256:e932c0d684dba934b824d0905edae9c8e44a3a25ae97408ba0e9f6752f088add

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

Resolution
unresolved
no resolver link, observed 2026-08-15T16:51:23.133878Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T16:51:23.133878Z digest=sha256:80e693a0f1658c17f357f595d93eb35d5c645b604c30cb5669248c59f2276aa6

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:51:24.487322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.138297Z digest=sha256:9f8005784548d7616a540d1218e038fbf601dfbd2113ecb840563bc2c3872921

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:51:24.473238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.142775Z digest=sha256:2ddccd60ebb7bba898b7df4394c92d786a14d77974b91a79c47b87179548eb47

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:51:24.459022Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.147032Z digest=sha256:fce85eb858ce604fb48d39b1c453da55c3deaf63c82a9d36fb260f7dcb96e8d9

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:51:24.444757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.151201Z digest=sha256:4047b485ada63b44c7d63f6cca18cece5ff4bbe1194ec133ae2859bd956cd0f2

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:51:24.431272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.155311Z digest=sha256:17f14b9feab467032ade5d00075d7cf193085ecd2610cf844c42c83193ad7ea8

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:51:24.417538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.164197Z digest=sha256:4fc489d9b8000ee8e68911ff15536c77389c126a8b3daa6d064e348e21f59b6b

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

Resolution
unresolved
no resolver link, observed 2026-08-15T16:51:23.167972Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T16:51:23.167972Z digest=sha256:02137955301ab7ce243db522e667aaeb1838aba21149083615185a84ef0c5e59

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

Resolution
unresolved
no resolver link, observed 2026-08-15T16:51:23.172280Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T16:51:23.172280Z digest=sha256:ff6b517eb67366f48d60f78e08546e593113f73d3fd25d6ccb88af7861e0735b

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:51:24.402396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.176669Z digest=sha256:5434e8a92886b9005b72de70e474640cea9fa1d794af1165b04ee5ca0d025878

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:51:24.388337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.180965Z digest=sha256:b7151b598d204cd31036bb80c6c8a2cb48344ffdc8453f4132873ddbf8de18c4

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.185111Z digest=sha256:f94d7b0766d42fe301de45783b258e9b1aaf02b065bf18f7dd5102dab348a0bd

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:51:24.361227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.190096Z digest=sha256:bbfb242f16e3a6ed968344a5285ed407ad5374243c2d50144943f8d71ccc3621

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:51:24.347604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.194760Z digest=sha256:c7e0841130cdc6bffb5e9351e1780eea862115fcc0c3be620d29d771f06c3512

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

Resolution
unresolved
no resolver link, observed 2026-08-15T16:51:23.198830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T16:51:23.198830Z digest=sha256:6c3079ee8e5b360d815c52a155964c7046dd323e5e7370a7fd729e8c69999c35

Observation dc430b25-26c5-43aa-80c3-472305babc08 · outbound

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.202983Z digest=sha256:d66df2b0ce3f295555181917b3d4ed5f746223997cc10f69b6a5fda5ef3c948c

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.207033Z digest=sha256:c1192206d24f55259a3e146e903feb3cf24190e0f1968b57fc5434d483d5ad54

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.211432Z digest=sha256:bf8709a178e80c234e207f0f1521d936d312c71e19b56884e06de1cd8086f704

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.215748Z digest=sha256:616dbfa2335852929658467a7bffe3390f189e8c1f15ca3b76bbd2d2e5e93f55

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.220041Z digest=sha256:a142604beead106517d61ea1e040f4550056cf7c38983d3c08e5680a7b1f1366

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.224119Z digest=sha256:d11a0e261c0f64899a7a5f34f5d3a44e98093342266f583c1ce76da0632f08e0

Observation 293c92a0-f987-4988-89b6-3a0c4b25c96b · outbound

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.228141Z digest=sha256:439200e70ae5825bc67371ed7eaa30e8e4842b3c851f3e823a9d338532c46c27

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

Resolution
unresolved
no resolver link, observed 2026-08-15T16:51:23.232201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T16:51:23.232201Z digest=sha256:3f0bc6c28989e13a59c510416a9b6a26c807cdbc42f023dbe1997595561a460f

Observation 3ade3dc5-d169-407a-9ef4-0c5c95582362 · outbound

This paper cites ISAC - instance-specific algorithm configuration.

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

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-15T16:51:23.236540Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T16:51:23.236540Z digest=sha256:b1de43fa636c35ef7c7346eb1e78d216bba9290d856989c7d7fb3e5dfb68c4ec

Observation f75e95f4-7547-4d8d-ab27-4103217639c6 · outbound

This paper cites Algorithm selection and scheduling.

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

Reference 35

Resolution
verified exact
doi, observed 2026-08-15T16:51:23.478999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.240760Z digest=sha256:8d5e515cced4b5e7241a652bdb177e2151b5427b86f4e5fc2fcbcf4c7217d992

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.245175Z digest=sha256:951f5dc925d077d1367ba6ac6769c1ba512420b55b8fa4d22d559ea3150467ed

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.249246Z digest=sha256:d836d0933dba59eaadb03fe63434e62474be41835a0a04aab12f25f5a03806cc

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.254061Z digest=sha256:d5ed07181d342c55f613dcb58f07a99e48505fefd4de3b7efe3fd84c291d1882

Observation 80ddd002-2626-4c2a-a23b-91c621dc37e9 · outbound

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.258269Z digest=sha256:993b330ee699f31d3694aa19e72af6fe69f879ce0b545fdfd2b6479251dd28a4

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.263498Z digest=sha256:446709be883c94291c708cc6b37c7a410a341a3baee7a064fc5be6ab7af7a6cd

Observation 5a807567-3f26-4f9a-aa3b-67c4cbacc355 · outbound

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:51:24.162272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.267882Z digest=sha256:5a0eda9dcdec8b9cf3547adb5ac83ff0176c4de06a4d5a92101acf3f7636fb03

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:51:24.148439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.271818Z digest=sha256:8c8e8f1cb468ee1928112fcf542f0b220e3a219d1bff58622b7af7c69410e3c1

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:51:24.134598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.275807Z digest=sha256:2392f6e13ffd866409f9ca58dff46323a92afa6e8207c21d2c58b1af738b3bca

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.280024Z digest=sha256:ac95da5a107b2ac713733b2b31f94c0a7860f0ba513b0e34c7fced59ff00e30e

Observation 3f839a6e-0cdf-429b-a3c9-ae8b162cb475 · outbound

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.284080Z digest=sha256:be4762a00f3c7b50d384a32117833886704e218338b4381f25f537ec72ea8488

Observation cd185d07-cd9f-45f4-977a-1cc45bbfea0b · outbound

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

Resolution
unresolved
no resolver link, observed 2026-08-15T16:51:23.288151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T16:51:23.288151Z digest=sha256:b96235f64579e92b94e97d01fe3e2f477c47b96ce970111bc4be84f860e67e47

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

Resolution
unresolved
no resolver link, observed 2026-08-15T16:51:23.293031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T16:51:23.293031Z digest=sha256:f50fa6b81a9fbb2174c0561f51c2a669421c4008dc47eaf461897cf594258ca8

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.297516Z digest=sha256:5272317075705e9633b9114984a3958a3409f0fd7d5861fd077f77a075e5d275

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

Resolution
unresolved
no resolver link, observed 2026-08-15T16:51:23.301810Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T16:51:23.301810Z digest=sha256:3a1b4e8efd98e769f9c20256b517ad83bf3855bca285680abd476a4a67f7310c

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

Resolution
unresolved
no resolver link, observed 2026-08-15T16:51:23.307308Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T16:51:23.307308Z digest=sha256:07ee5fe85f61cf8f9bc276802e6b97628101a7a8652c35d754e5e286a7834829

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.311486Z digest=sha256:6a93b4b52148a62775bbabd52068caee83b485fcb088f4200921cf41c2f39ad2

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.315679Z digest=sha256:cc4622170db01a0f4fcfd1973ad7aebbdb8ae577f50a368c9e54635ab1595adf

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.319920Z digest=sha256:998bc859f473c2ce1fafb50096b742e1c3de4e31c31266edd6d974185aba6d8a

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

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T16:51:24.019333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.324014Z digest=sha256:0c63aec1fd1b9e154e26305a8c843371d7b8a9422aeb63ea3d4f07bc22652d40

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.328092Z digest=sha256:f79d4323c35d924ad765fee3e717421baf52a7b79916783de5ab5ef163459fa5

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

Resolution
unresolved
no resolver link, observed 2026-08-15T16:51:23.332864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T16:51:23.332864Z digest=sha256:8c04315bbf47b49860b971238bf850d7d8f2cf414167604b218f7299f86a4daf

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.337607Z digest=sha256:edbef8ed8fbcae8b0ec414e5ec21af801c27f2f5060a29f3c9f8e3ce06487f91

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.341682Z digest=sha256:c6318c1c11a249bbfda2e909c74179c4f328849fa718aebbbd6f14b66405fb77

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 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.346400Z digest=sha256:5580478cbf4f05c5390accd6e1545f668ce4173ccf0b1381955693c30635a435

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

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T16:51:23.352649Z digest=sha256:fa29e08d394610e6942f91f6f7052ba513cc9787d2c2a90bf4bb76d6cc213652

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 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.356651Z digest=sha256:5e73109357388583842c0f9f84389ccf33b359546a8cf970342e60d1d53c4dde

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 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.360851Z digest=sha256:b78c271ba67163600b961593e6fdbfc5b102c1999d5314858a7630c7a4f112da

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 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.365456Z digest=sha256:7907def0120b5d5f4253f35b885798b295270b2fd94f002b56e6a896ecf3e2f3

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 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.369726Z digest=sha256:1e10afe0a29aa95e07e8934d4fb8ea9e091087b4377cd57a2fab9384ccb104fe

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 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.373882Z digest=sha256:84dd4ffacac44748d7d2fed2913488b07ee47f892316a2d189fa418c4b9f3988

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

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T16:51:23.379122Z digest=sha256:c5d1101c3569dbfd46a4a7651186a8ff9958e9c63bc869733afaf9de2887aa47

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 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.383598Z digest=sha256:4c6d4bcc0692bc5f4a51abd930416d5c4355effc3ac16997b4df3b54aa3824d9

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.

source=arxiv_source observed=2026-08-15T16:51:23.387987Z digest=sha256:f2c90411273d16bf20a8ba60231642b28a88cb24e1ac2eb52630ea552caf86d6

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 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.392727Z digest=sha256:15221d5807eb2006c153f502bc8ff524d89ee4e0ebce48b5cc1f97a4684739fd

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 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.397536Z digest=sha256:2f7a1ddfe4b7e881e8e8327e0df6288ec58eac665fb369404767e4f245ff765e

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 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-15T16:51:23.401986Z digest=sha256:e3477cee0e97269d5896597cd88160a26116c8d682569ff7e5ff5575f5e27456

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.

source=arxiv_source observed=2026-08-15T16:51:23.406783Z digest=sha256:236616581fab53a2c4055fef198787a885b2c49fda75f78462ccb0ef741bc0b1

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.

source=arxiv_source observed=2026-08-15T16:51:23.411469Z digest=sha256:dbaee8042af27e2e8775dac46895889a6815b0c6f7a9cb9dac7821304a221e19

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

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

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source=arxiv_source observed=2026-08-15T16:51:23.415820Z digest=sha256:11f1e37fad01fc44802fc02c4deef98c1911661425251026bd0f016cbaf63582

Pith citing papers

Observation 30b4669a-ddf9-41a8-be6c-0aa97e44bab2 · inbound

Transfer Learning from Foundational Optimization Embeddings to Unsupervised SAT Representations cites this paper.

Transfer Learning from Foundational Optimization Embeddings to Unsupervised SAT Representations FORGE: Foundational Optimization Representations from Graph Embeddings

Reference 17

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arxiv_id, observed 2026-06-19T17:10:31.406495Z

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source=arxiv_source observed=2026-05-10T12:01:08.000282Z digest=sha256:3ec08c310157468acd1dccd850f0784b6496b50cce233d9f262fc053a6a64da3