Papers with optimization challenges

2 papers
OptiVerse: A Comprehensive Benchmark towards Optimization Problem Solving (2026.findings-acl)

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Challenge: Existing benchmarks focus on Mathematical Programming and Combinatorial Optimization, hindering comprehensive evaluation.
Approach: They propose a benchmarking tool that compares 1,000 curated optimization problems across three difficulty levels.
Outcome: The proposed model improves performance on hard problems while maintaining 27% accuracy.
Empowering Diffusion Models on the Embedding Space for Text Generation (2024.naacl-long)

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Challenge: Recent work adapts diffusion models to textual data by diffusing on the embedding space.
Approach: They propose an embedding diffusion model based on Transformer to solve the problem of embeddable space and denoising model.
Outcome: The proposed model is more efficient than previous methods on seminal text generation tasks and is superior to existing models.

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