ARXIV:2604.15448 · AI FOR OPTIMIZATION AND DECISION PROBLEMS · SUBMITTED 20 APR · 20:24 UTC · FRESHNESS STALE

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Transfer Learning from Foundational Optimization Embeddings to Unsupervised SAT Representations

Koyena Pal · Serdar Kadioglu · arXiv

Leveraging pre-trained optimization embeddings to unlock unsupervised insights in Boolean satisfiability problems.

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Opportunity summary

Pain Leveraging pre-trained optimization embeddings to unlock unsupervised insights in Boolean satisfiability problems.

Evidence 0 refs | 3 sources | 50% coverage

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PROBLEM

Leveraging pre-trained optimization embeddings to unlock unsupervised insights in Boolean satisfiability problems. These embeddings were shown to enable cross-domain transfer and reduce reliance on solver-generated labels.

METHOD

Full abstract

Foundational optimization embeddings have recently emerged as powerful pre-trained representations for mixed-integer programming (MIP) problems. These embeddings were shown to enable cross-domain transfer and reduce reliance on solver-generated labels. In this work, we investigate whether such representations generalize beyond optimization to decision problems, focusing on Boolean satisfiability (SAT). We adapt the foundational optimization architecture to SAT by mapping CNF formulas into the same bipartite constraint-variable graph representation used for MIPs. This allows direct reuse of the pre-trained embedding model without architectural changes or supervised fine-tuning. Our results show that these embeddings capture structural regularities in SAT instances and support unsupervised tasks such as instance clustering and distribution identification. We demonstrate, for the first time, that foundational optimization embeddings can transfer to constraint satisfaction domains. Our findings is a step toward a unified representational framework for both optimization and decision problems.

RESULT

ScienceToStartup currently rates this 5.0/10 on the public viability pass. These embeddings were shown to enable cross-domain transfer and reduce reliance on solver-generated labels. Code availability is flagged in the production record; the public…

WHY NOW

AI for Optimization and Decision Problems moved forward this cycle; last verified April 2026. Public score 5.0/10. Production flags indicate code availability.

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Opportunity summary

Score5.0

PainLeveraging pre-trained optimization embeddings to unlock unsupervised insights in Boolean satisfiability problems.

Evidence0 refs | 3 sources | 50% coverage

Blockerno shell-level blocker reported

Analysis summary

Leveraging pre-trained optimization embeddings to unlock unsupervised insights in Boolean satisfiability problems.

VerifiedSource: PDF linkedVerifiedPaperPack: citation fields availablePartialProof: unverified proof status

Competitive landscape

Leveraging pre-trained optimization embeddings to unlock unsupervised insights in Boolean satisfiability problems.

Segment

AI for Optimization and Decision Problems

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5.0/10 public viability

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Leveraging pre-trained optimization embeddings to unlock unsupervised insights in Boolean satisfiability problems.

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AI for Optimization and Decision Problems

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No public code link in the paper record yet

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5.0/10 public viability

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not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Transfer Learning from Foundational Optimization Embeddings to Unsupervised SAT Representations

Transfer Learning from Foundational Optimization Embeddings to Unsupervised SAT Representations

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