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Paper

FrontierCO: Real-World and Large-Scale Evaluation of Machine Learning Solvers for Combinatorial Optimization

by Independent / Community 000fa2a11cacb3a78863cdab3ad1ff3046b7bb0e

Free2AITools Nexus Index

65.6

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 76

P: Popularity 51

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

Machine learning (ML) has shown promise for tackling combinatorial optimization (CO), but much of the reported progress relies on small-scale, synthetic benchmarks that fail to capture real-world structure and scale. A core limitation is that ML methods are typically trained and evaluated on synthetic instance generators, leaving open how they perform on irregular, competition-grade, or industrial datasets. We present FrontierCO, a benchmark for evaluating ML-based CO solvers under real-world...

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Semantic Scholar 10 Citations

Paper Information Summary
Entity Passport
Registry ID	000fa2a11cacb3a78863cdab3ad1ff3046b7bb0e
License	ArXiv
Provider	semantic_scholar

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{000fa2a11cacb3a78863cdab3ad1ff3046b7bb0e,
  author = {Unknown},
  title = {FrontierCO: Real-World and Large-Scale Evaluation of Machine Learning Solvers for Combinatorial Optimization Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/000fa2a11cacb3a78863cdab3ad1ff3046b7bb0e}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). FrontierCO: Real-World and Large-Scale Evaluation of Machine Learning Solvers for Combinatorial Optimization [Paper]. Free2AITools. https://api.semanticscholar.org/000fa2a11cacb3a78863cdab3ad1ff3046b7bb0e

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 76

Popularity (P) 51

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for FrontierCO: Real-World and Large-Scale Evaluation of Machine Learning Solvers for Combinatorial Optimization: Authority (A:76), Popularity (P:51), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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📝 Executive Summary

"Machine learning (ML) has shown promise for tackling combinatorial optimization (CO), but much of the reported progress relies on small-scale, synthetic benchmarks that fail to capture real-world structure and scale. A core limitation is that ML methods are typically trained and evaluated on synthetic instance generators, leaving open how they perform on irregular, competition-grade, or industrial datasets. We present FrontierCO, a benchmark for evaluating ML-based CO solvers under real-world..."

❝ Cite Node

@article{Unknown2026FrontierCO:,
  title={FrontierCO: Real-World and Large-Scale Evaluation of Machine Learning Solvers for Combinatorial Optimization},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

🔗 Full Paper

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📊 Research Signals

📈10CitationsSemantic Scholar

🏛️76AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

🗂️automation workflowField

📦Data Source: semantic_scholar

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Source summary: Based on semantic_scholar metadata. Not a recommendation.

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🆔 Identity & Source

source: semantic_scholar
author: Unknown
license: ArXiv
tags: paper, research, academic

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
pipeline tag

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downloads: 0
stars: null
forks: null
citations: 10

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