Constraint Satisfaction

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5 papers in the last four weeks, up 25% on the four weeks before. 0.0% of all new papers.

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Latest papers 57

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DiBS: Diffusion-Informed Branch Selection
Jun 2, 2026Bo Liu, Yuan Xie, Yuan Gao +4Diffusion Guidance Constraint Satisfaction
1Jilin University · 2The Chinese University of Hong Kong, Shenzhen · 3Southwest University +3
Bridging Auxiliary Constraints to Resolve Instruction Following in Large Reasoning Models
Jun 2, 2026Zhengyi Zhao, Shubo Zhang, Huimin Wang +7LLM Prompting Instruction Following
The Chinese University of Hong Kong · Tencent Jarvis Lab · Westlake University +2
Repair Before Veto: Repair-Augmented Constraint Learning for Contextual Decisions
Jun 1, 2026Yifan WangConstraint Satisfaction Algorithmic Recourse
Department of Mechanical Engineering, McGill University, QC, H3A 2T7, Canada
Conditioned free-energy density of proteins using unbalanced solutions to constraint satisfaction problems
May 31, 2026Pratik Worah, Subhash Khot, Srinivasa VaradhanFree Energy Calculation Semidefinite Programming
Satisfiability Solving with LLMs: A Matched-Pair Evaluation of Reasoning Capability
May 27, 2026Leizhen Zhang, Shuhan Chen, Sheng ChenLLM Evaluation Constraint Satisfaction
University of Louisiana at Lafayette, Lafayette, USA · East China Normal University, Shanghai, China
Blocked Gibbs meets Diffusion Transformers: Unsupervised Learning for Constraint Optimization
May 24, 2026Yudong W. Xu, Wenhao Li, Xiaoyu Wang +2Diffusion Transformer Constrained Optimization
University of Toronto · Vector Institute
Can Transformers Learn to Verify During Backtracking Search?
May 21, 2026Yin Jun Phua, Tony Ribeiro, Tuan Nguyen +1Tree Search Transformer Attention
Institute of Science Tokyo, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan · Centrale Nantes, CNRS, Laboratoire des Sciences du Num´erique de Nantes, LS2N, UMR 6004, F-44000 Nantes, France · National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, Tokyo 101-8430, Japan +2
Targeting Clause Type Distributions: a Picklock for Random Satisfiability Problems
May 19, 2026J. Schwardt, J. C. BudichMetaheuristic Optimization Constraint Satisfaction
Max Planck Institute for the Physics of Complex Systems, Nöthnitzer Str. 38, 01187 Dresden, Germany · Institute of Theoretical Physics, Technische Universität Dresden and Würzburg-Dresden Cluster of Excellence ctd.qmat, 01062 Dresden, Germany
Streamlined Constraint Reasoning via CNN Pattern Recognition on Enumerated Solutions
May 19, 2026Patrick SpracklenLLM-Based Program Synthesis Constraint Satisfaction
Learning How to Cube
May 15, 2026Ferhat Erata, Sam Kouteili, Thanos Typaldos +4Transformer Constraint Satisfaction
Yale University · AWS Agentic AI
Transformers Linearly Represent Highly Structured World Models
May 13, 2026Roman Kniazev, Nathanaël FijalkowCircuit Discovery World Models
LaBRI, CNRS · University of Bordeaux · France
Dsat: A Native SAT Solver for Discrete Logic
May 10, 2026Yaofang Zhang, Ken Zhou, Adnan DarwicheConstraint Satisfaction
Department of Computer Science, University of California, Los Angeles
Agentic MIP Research: Accelerated Constraint Handler Generation
May 9, 2026Liding Xu, Yugeng Zhou, Sebastian PokuttaMixed-Integer Linear Programming LLM-Based Program Synthesis
Zuse Institute Berlin, Berlin, Germany. · Independent Researcher. · Zuse Institute Berlin +1
Lattice Deduction Transformers
May 9, 2026Liam Davis, Leopold Haller, Alberto Alfarano +1Recurrent Transformers Neuro-Symbolic Reasoning
Amherst College · Axiom · Barnard College, Columbia University
Inference of Qualitative Models from Steady-State Data via Weighted MaxSMT
May 8, 2026Ondřej Huvar, Nikola Beneš, Martin Jonáš +2Gene Regulatory Network Inference Constraint Satisfaction
Masaryk University, Brno, Czechia
Unsat Core Prediction through Polarity-Aware Representation Learning over Clause-Literal Hypergraphs
May 6, 2026Zhenchao Sun, Shuai Ma, Ping Lu +1Hypergraph Neural Networks Constraint Satisfaction
SKLCCSE Lab, Beihang University, Beijing, China.
Residual Drift Dominates Contradiction in Multi-Turn Constraint Reasoning
Apr 28, 2026Sebastien KawadaConstraint Satisfaction
Kaons Los Angeles, United States
Constraint-Based Analysis of Reasoning Shortcuts in Neurosymbolic Learning
Apr 25, 2026Akihiro Takemura, Katsumi Inoue, Masaaki NishinoSymbolic Reasoning Constraint Satisfaction
National Institute of Informatics, Tokyo, Japan · Communication Science Laboratories, NTT, Inc., Japan
HardNet++: Nonlinear Constraint Enforcement in Neural Networks
Apr 21, 2026Andrea Goertzen, Kaveh Alim, Youngjae Min +1Nonlinear MPC Neural Network Optimization
Massachusetts Institute of Technology
Transfer Learning from Foundational Optimization Embeddings to Unsupervised SAT Representations
Apr 16, 2026Koyena Pal, Serdar KadiogluUnsupervised Representation Learning Transfer Learning
AI Center of Excellence, Fidelity Investments, USA · Khoury College of Computer Sciences, Northeastern University, USA · Department of Computer Science, Brown University, USA
SatIR: Scalable High-Recall Constraint-Satisfaction-Based Information Retrieval for Clinical Trials Matching
Apr 10, 2026Zikai Zhou, Yufei Jin, Yilin Xu +3Healthcare Satisfiability Modulo Theories
Department of Computer Science, Stanford University, Stanford, CA, USA · Samueli Electrical and Computer Engineering, UCLA, Los Angeles, CA, USA · Department of Computer Science, Emory University, Atlanta, GA, USA +1
Implementing Cumulative Functions with Generalized Cumulative Constraints
Aug 3, 2025Pierre Schaus, Charles Thomas, Roger KameugneConstraint Satisfaction Constraint Programming
UCLouvain, ICTEAM, Louvain-la-Neuve, Belgium · University of Maroua, Maroua, Cameroon
3D Software Synthesis Driven by Constraint-Expressive Intermediate Representation
Jul 24, 2025Shuqing Li, Anson Y. Lam, Yun Peng +2Program Synthesis 3D Scene Generation
The Chinese University of Hong Kong Hong Kong, China · Renmin University of China Beijing, China
HardCore Generation: Generating Hard UNSAT Problems for Data Augmentation
Sep 27, 2024Joseph Cotnareanu, Zhanguang Zhang, Hui-Ling Zhen +2Synthetic Data Augmentation Synthetic Benchmark Generation
McGill University Montreal, Canada · Huawei Noah’s Ark Lab Montreal, Canada · Huawei Noah’s Ark Lab Hong Kong, China +1
Stable Marriage Problems with Ties and Incomplete Preferences: An Empirical Comparison of ASP, SAT, ILP, CP, and Local Search Methods
Aug 11, 2021Selin Eyupoglu, Muge Fidan, Yavuz Gulesen +5Answer Set Programming Constraint Satisfaction
Computer Science, Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey · Economics, Faculty of Arts and Social Sciences, Sabanci University, Istanbul, Turkey
PyCSP3: Modeling Combinatorial Constrained Problems in Python
Sep 1, 2020Christophe Lecoutre, Nicolas SzczepanskiConstrained Optimization Constraint Satisfaction
University of Artois CRIL CNRS, UMR 8188[cs.AI] France
A binarized-domains arc-consistency algorithm for TCSPs: its computational analysis and its use as a filtering procedure in solution search algorithms
Feb 22, 2020Amar IsliConstraint Satisfaction Temporal Consistency
University of Sciences and Technology Houari Boumedi`ene · Faculty of Computer Science · Department of Artiﬁcial Intelligence and Data Science +2

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DiBS: Diffusion-Informed Branch Selection

Bo Liu, Yuan Xie, Yuan Gao, Xiaolong Luo+3

1Jilin University · 2The Chinese University of Hong Kong, Shenzhen · 3Southwest University+3

Diffusion Guidance Constraint Satisfaction

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Sudoku is a representative constraint satisfaction problem that requires global structural reasoning under strict discrete constraints. The existing works of solving Sudoku mainly focus on two dominant approaches, i.e., traditional heuristic and deep learning solver. However, they suffer from two complementary limitations: learning-based solvers lack hard correctness guarantees, while complete symbolic solvers are still prone to long-tail search. To address these shortcomings, we propose a novel diffusion model-guided approach, termed as DiBS, for the branch selection search process. Specifically, DiBS keeps the symbolic solver complete and uses the diffusion model as a branch-ordering guide. The core method is ranking candidate values under the current partial assignment and lightweight consistency signal. Furthermore, we provide an in-depth theoretical proof to reveal how it works and why it works. Experiments on the challenging Royle 17-clue Sudoku benchmark show that our DiBS substantially reduces search cost relative to strong heuristic baselines, especially in nodes, backtracks, and long-tail percentiles. Besides, these results confirm that learned global guidance is effective on hard instances where branch-order mistakes are most expensive. All codes are available at https://github.com/shanxierdan/DiBS.

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