arXiv AI
Jun 11
GPO: Learning from Critical Steps to Improve LLM Reasoning
arXiv:2509. 16456v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used in various domains, showing impressive potential on different tasks.
SEER: Supervised Learning to Control Energetic Reasoning
arXiv:2607. 16523v1 Announce Type: new Abstract: One of the main strengths of Constraint Programming is the ability to reduce the search space via propagation.
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arXiv Machine Learning
Sep 16
Learning efficient representations of complex constraints for scalable optimization
The paper introduces PolyFormer, a physics-informed machine learning framework that learns compact polytopic representations of complex constraints. By transforming constraint-induced geometry into efficient polytopic reformulations, PolyFormer reduces optimization complexity and enables the use of standard solvers. Evaluations on large‑scale resource aggregation, network‑constrained optimization, and uncertainty‑aware optimization show up to 6,400‑fold speedups and 99.87% memory savings while keeping feasibility and objective errors low.
arXiv AI
Jun 6
Step-by-Step Optimization-like Reasoning in LLMs over Expanding Search Spaces
arXiv:2606. 05464v1 Announce Type: new Abstract: Verifiable reward training has improved mathematical and coding reasoning, but these domains capture only part of step-by-step decision making.
arXiv AI
Jul 24
Identifying Good Rules for Efficient SAT Encodings of Single-Constant Multiplication Using Machine Learning
arXiv:2607. 21188v1 Announce Type: new Abstract: The Single Constant Multiplication problem is a fundamental NP-hard optimization task in hardware design, which seeks to decompose a fixed constant using only additions, subtractions, and bit-shifts.
arXiv Machine Learning
Jun 25
MiniOpt: Reasoning to Model and Solve General Optimization Problems with Limited Resources
arXiv:2606. 25832v1 Announce Type: new Abstract: Achieving strong optimization generalization across diverse optimization problems while requiring limited training resources remains a challenging problem for optimization-oriented large language models (LLMs).
arXiv AI
Aug 18
Position: Certified Correctness in Neural Constraint Reasoning Requires Symbolic Integration
arXiv:2608. 14569v1 Announce Type: new Abstract: Neural solvers for constraint satisfaction problems have achieved remarkable in-distribution accuracy, yet they suffer from a fundamental limitation persistent constraint violations occur under distribution shifts even when the model reports high confidence.
arXiv AI
2d ago
Auto-Formalizing Neuro-Symbolic Predictors
arXiv:2610.01519v1 Announce Type: cross Abstract: Neuro-Symbolic (NeSy) predictors incorporate prior knowledge into the prediction process of neural networks, ensuring that outputs satisfy specified...
arXiv AI
Aug 26
Constrained Hyperparameter Optimization for Streaming Data
arXiv:2608.24712v1 Announce Type: cross Abstract: Optimization of hyperparameters is a critical factor to obtain optimal model performance. While existing research has predominantly concentrated on b...
Hugging Face Trending Papers
Jun 24
MiniOpt: Reasoning to Model and Solve General Optimization Problems with Limited Resources
Achieving strong optimization generalization across diverse optimization problems while requiring limited training resources remains a challenging problem for optimization-oriented large language models (LLMs). Existing approaches typically rely on large-scale supervised datasets, costly reasoning annotations, and expensive intermediate step verification, resulting in substantial training overhead.
arXiv AI
Jul 7
A Systematic Survey on Large Language Models for Evolutionary Optimization: From Modeling to Solving
arXiv:2509. 08269v5 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly integrated with evolutionary computation to support optimization tasks.
arXiv AI
Aug 21
Learning Early-to-Final Solution Consistency for MILP Acceleration
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 AI
Jul 22
MILP-Evo: Closed-Loop Fully Automatic Design of MILP Solvers
arXiv:2607. 18252v1 Announce Type: new Abstract: Machine learning methods have shown that data-driven policies can accelerate mixed-integer linear programming (MILP) solvers, but many such approaches remain difficult to inspect, adapt, and deploy because the learned policy is represented as an external predictor or other opaque model.