The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
Linear models with an $L_1$-norm penalty are still the leading approach for high‑dimensional tasks, yet many existing solvers are slow, ineffective, and hard to parallelise, making them unsuitable for large industry‑scale corpora. The paper evaluates several recent state‑of‑the‑art methods and shows that older techniques outperform them in general use. It also demonstrates that a simple baseline—LBFGS applied to a sub‑gradient with minor tweaks—yields strong performance and is easier to support and scale in production.
arXiv:2608. 19953v1 Announce Type: new Abstract: Mixed-Integer Linear Programming (MILP) is a fundamental problem class in operations research and combinatorial optimization, with broad applications to industrial decision-making.
arXiv:2608. 00019v1 Announce Type: new Abstract: Deploying large language models (LLMs) for operations research (OR) tasks remains challenging because correctness depends on a coherent modeling process, not merely a correct final answer.
arXiv:2607. 07863v1 Announce Type: new Abstract: In physically dominated machining processes, experimental datasets are small, expensive, and material-specific; in this regime, data curation, evaluation design, and the form of physics integration can matter as much as the learning algorithm.
Mixed-Integer Linear Programming (MILP) is a fundamental problem class in operations research and combinatorial optimization, with broad applications to industrial decision-making. Owing to their NP-hardness, however, modern solvers may struggle to find high-quality solutions for challenging MILP instances within practical time limits.
arXiv:2605. 25246v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used for optimization modeling and solver-code generation, yet practical operations research and optimization problems often require a harder capability: designing scalable algorithms that exploit problem structure and outperform direct formulation-and-solve baselines.