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 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
1d ago
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.
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
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.
arXiv AI
Aug 5
IR2Solve: Structured Intermediate Representations for Cost-Efficient Optimization Autoformulation
arXiv:2608. 02641v1 Announce Type: cross Abstract: Large language models (LLMs) can translate natural-language optimization problems into solver-ready formulations, but direct code generation is brittle: schema, indexing, and semantic errors can cause compilation failures, infeasible models, or incorrect objectives, while iterative repair, search, and multi-agent workflows increase inference cost.
arXiv Machine Learning
Jul 27
Breaking the Data Barrier in Learning Symbolic Computation: A Case Study on Variable Ordering Suggestion for Cylindrical Algebraic Decomposition
arXiv:2601. 13731v2 Announce Type: replace-cross Abstract: Symbolic computation, powered by modern computer algebra systems, has important applications in mathematical reasoning through exact deep computations.
arXiv AI
Jul 21
Structure-Induced Information for Rerooting Levin Tree Search
arXiv:2605. 30664v2 Announce Type: replace Abstract: Subgoal-based policy tree search, which uses a policy to guide search, is effective for complex single-agent deterministic problems but often relies on explicit subgoal generation that can incur substantial overhead and hinders scalability.
Hugging Face Trending Papers
Jul 15
Constraint-Driven Model Optimization: An Industry Framework for Selecting Compression and Acceleration Techniques in Modern Machine Learning Systems
The rapid deployment of machine learning systems across cloud, edge, and enterprise environments has brought model optimization to the forefront of systems-engineering. Despite a rich literature spanning quantization, pruning, knowledge distillation, parameter-efficient fine-tuning (PEFT), and inference-time optimization, practitioners are often left navigating these techniques through heuristics rather than principled methodology.