arXiv:2508.20330v5 Announce Type: replace Abstract: Combinatorial optimization problems are ubiquitous in science and engineering. Still, learning-based approaches to accelerate combinatorial optimization often require solving a large number of difficult instances to collect training data, incurring significant computational cost. Existing learning-based methods require training dedicated models for each problem distribution, for each downstream task, severely limiting their scalability and generalization. We introduce Forge: Foundational Optimization Representations from Graph Embeddings, a f
The proliferation of complex combinatorial optimization problems across various fields is driving the need for more efficient and generalizable learning-based solutions, particularly as AI becomes more integrated into scientific and engineering workflows.
This development proposes a foundational AI approach that could significantly reduce the computational cost and improve the generalization of learning for optimization, making AI-driven problem-solving more scalable and accessible.
Traditional methods requiring extensive retraining for each new optimization problem or distribution could be replaced by more versatile foundational models, streamlining the application of AI to complex tasks.
- · AI researchers and developers
- · Companies with complex supply chains
- · Engineering and scientific sectors
- · Cloud computing providers
- · Developers of highly specialized, narrow AI optimization tools
- · Organizations relying on manual or heuristic-based optimization
- · Sectors unwilling to adopt foundational AI models
Forge improves the efficiency and generalizability of AI for solving combinatorial optimization problems across diverse domains.
This could accelerate scientific discovery, optimize industrial processes, and reduce R&D costs by making complex AI modeling more accessible.
The increased efficiency in optimization might lead to new classes of solvable problems, fostering innovation in areas like drug discovery, materials science, and logistics.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG