GenAI4ELab/papercli-papers huggingface dataset:
Constrained Molecule Generation Modelled Using the Grammar Constraint
Authors: David Saikali; Gilles Pesant
Venue: AAAI 2026 (main)

Abstract: Drug discovery is a very time-consuming and costly endeavour due to its huge design space and to the lengthy and failure-fraught process of bringing a product to market. Automating the generation of candidate molecules exhibiting some of the desired properties can help. Among the standard formats to encode molecules, SMILES is a widespread string representation. We propose a constraint programming model showcasing the grammar constraint to express the design space of organic molecules using the SMILES notation.  We show how some common physicochemical properties --- such as molecular weight and lipophilicity --- and structural features can be expressed as constraints in the model. We also contribute a weighted counting algorithm for the grammar constraint, allowing us to use a belief propagation heuristic to guide the generation.  Our experiments indicate that such a heuristic is key to driving the search towards desired molecules.
Assignment Problems in Cost Function Networks
Authors: Guidio Sewa; David Allouche; Simon de Givry; George Katsirelos; Pierre Montalbano; Thomas Schiex
Venue: AAAI 2026 (main)

Abstract: To efficiently solve exact discrete optimization problems, branch and bound algorithms require tight bounds. In constraint programming, for optimization, soft arc consistencies typically derive much stronger bounds than those offered by domain or bound consistencies applied to a cost variable. The reason is that soft local consistencies exchange marginal cost information between variables whereas domain consistencies rely only on shrinking domains, which is less informative. However, CP solvers equipped with soft arc consistencies have so far offered limited support for efficient processing of global constraints.

In this work, we show how we can efficiently enforce soft local consistency over the AllDifferent constraint, relying on algorithms for the Linear Assignment Problem (LAP). We implement this propagator in toulbar2, the state-of-the-art weighted CP solver exploiting soft local consistencies for bounding. We show that, equipped with this new propagator, toulbar2 outperforms state-of-the-art domain consistency-based CP as well as integer programming solvers for the Quadratic Assignment Problem and shows better performance for miniCOP instances of the 2024 XCSP competition with AllDifferent constraints.
A GPU-based Constraint Programming Solver
Authors: Pierre Talbot
Venue: AAAI 2026 (main)

Abstract: Machine learning has tremendously benefited from graphics processing units (GPUs) to accelerate training and inference by several orders of magnitude. However, this success has not been replicated in general and exact combinatorial optimization. Our key contribution is to propose a general-purpose discrete constraint programming solver fully implemented on GPU. It is based on integer interval bound propagation and backtracking search. The two main ingredients are (1) ternary constraint network optimized for GPU architectures, and (2) an on-demand subproblems generation strategy. Our constraint solving algorithm is significantly simpler than those found in optimized CPU constraint solvers, yet is competitive with sequential solvers in the MiniZinc 2024 challenge.
Certified Branch-and-Bound MaxSAT Solving
Authors: Dieter Vandesande; Jordi Coll; Bart Bogaerts
Venue: AAAI 2026 (main)

Abstract: Over the past few decades, combinatorial solvers have seen remarkable performance improvements, enabling their practical use in real-world applications.
In some of these applications, ensuring the correctness of the solver's output is critical.
However, the complexity of modern solvers makes them susceptible to bugs in their source code.
In the domain of satisfiability checking (SAT), this issue has been addressed through proof logging, where the solver generates a formal proof of the correctness of its answer.
For more expressive problems like MaxSAT, the optimization variant of SAT, proof logging had not seen a comparable breakthrough until recently. 

In this paper, we show how to achieve proof logging for state-of-the-art techniques in Branch-and-Bound MaxSAT solving. 
This includes certifying look-ahead methods used in such algorithms as well as advanced clausal encodings of pseudo-Boolean constraints based on so-called Multi-Valued Decision Diagrams (MDDs).
We implement these ideas in MaxCDCL, the dominant branch-and-bound solver, and experimentally demonstrate that proof logging is feasible with limited overhead, while proof checking remains a challenge.
Generative Branching for Mixed-Integer Linear Programming
Authors: Ruobing Wang; Xin Li; Yangchuan Wang; Zijian Zhang; Mingzhong Wang
Venue: AAAI 2026 (main)

Abstract: Branch-and-bound (B&B) is a fundamental algorithmic framework for solving Mixed-Integer Linear Programming (MILP) problems, where branching decisions critically affect solver efficiency. Recent learning-based methods apply imitation learning to select branching variables, but their deterministic predictions limit exploration and generalization. In this paper, we propose a novel framework that formulates branching variable selection as a conditional generative process, exploring deep-level decision features. Our approach leverages diffusion models to enable diverse and exploratory branching score generation, while consistency modeling distills this process into efficient one-step inference conditioned on the B&B state. This mode allows our method to achieve both high-quality and fast branching decisions, significantly improving the overall performance of branch-and-bound solvers. Extensive experiments on challenging cross-scale and cross-category benchmarks demonstrate that our framework consistently outperforms state-of-the-art imitation learning baselines, delivering substantial improvements in solution quality, computational efficiency, and inference speed.
Cubing for Tuning
Authors: Haoze Wu; Clark Barrett; Nina Narodytska
Venue: AAAI 2026 (main)

Abstract: We are exploring the problem of building an automated reasoning procedure that adaptively tunes the high-level solving strategy for a given problem. There are two main distinctive characteristics of our approach: tuning is performed solely online, unlike the common use of tuning as an offline process; and tuning data comes exclusively from the given instance, so we do not rely on the availability of similar benchmarks and can work with unique challenging instances. Our approach builds on top of the divide-and-conquer paradigm that naturally serves partitioned sub-problems for an automated tuning algorithm to obtain a good solving strategy. We demonstrate performance improvement on two classes of important problems--SAT-solving and neural network verification--and show that our method can learn unconventional solving strategies in some cases.
Co-Layout: LLM-driven Co-optimization for Interior Layout
Authors: Chucheng Xiang; Ruchao Bao; Biyin Feng; Wenzheng Wu; Zhongyuan Liu; Yirui Guan; Ligang Liu
Venue: AAAI 2026 (main)

Abstract: We present a novel framework for automated interior design that combines large language models (LLMs) with grid-based integer programming to jointly optimize room layout and furniture placement. Given a textual prompt, the LLM-driven agent workflow extracts structured design constraints related to room configurations and furniture arrangements. These constraints are encoded into a unified grid-based representation inspired by ``Modulor". Our formulation accounts for key design requirements, including corridor connectivity, room accessibility, spatial exclusivity, and user-specified preferences. To improve computational efficiency, we adopt a coarse-to-fine optimization strategy that begins with a low-resolution grid to solve a simplified problem and guides the solution at the full resolution. Experimental results across diverse scenarios demonstrate that our joint optimization approach significantly outperforms existing two-stage design pipelines in solution quality, and achieves notable computational efficiency through the coarse-to-fine strategy.
CSP4SDG: Constraint and Information-Theory Based Role Identification in Social Deduction Games with LLM-Enhanced Inference
Authors: Kaijie Xu; Fandi Meng; Clark Verbrugge; Simon Mark Lucas
Venue: AAAI 2026 (main)

Abstract: In Social Deduction Games (SDGs) such as Avalon, Mafia, and Werewolf, players conceal their identities and deliberately mislead others, making hidden-role inference a central and demanding task. Accurate role identification, which forms the basis of an agent's belief state, is therefore the keystone for both human and AI performance. We introduce CSP4SDG, a probabilistic, constraint–satisfaction framework that analyses gameplay objectively. Game events and dialogue are mapped to four linguistically agnostic constraint classes—evidence, phenomena, assertions, and hypotheses. Hard constraints prune impossible role assignments, while weighted soft constraints score the remainder; information-gain weighting links each hypothesis to its expected value under entropy reduction, and a simple closed-form scoring rule guarantees that truthful assertions converge to classical hard logic with minimum error. The resulting posterior over roles is fully interpretable and updates in real time. Experiments on three public datasets show that CSP4SDG (i) outperforms LLM-based baselines in every inference scenario, and (ii) boosts LLMs when supplied as an auxiliary "reasoning tool." Our study validates that principled probabilistic reasoning with information theory is a scalable alternative—or complement—to heavy-weight neural models for SDGs.
Scalable Mixed-Integer Optimization with Neural Constraints via Dual Decomposition
Authors: Shuli Zeng; Sijia Zhang; Feng Wu; Shaojie Tang; Xiangyang Li
Venue: AAAI 2026 (main)

Abstract: Embedding deep neural networks (NNs) into mixed-integer programs (MIPs) is attractive for decision making with learned constraints, yet state-of-the-art monolithic linearisations blow up in size and quickly become intractable. In this paper, we introduce a novel dual-decomposition framework that relaxes the single coupling equality u=x with an augmented Lagrange multiplier and splits the problem into a vanilla MIP and a constrained NN block. Each part is tackled by the solver that suits it best-branch and cut for the MIP subproblem, first-order optimisation for the NN subproblem, so the model remains modular, the number of integer variables never grows with network depth, and the per-iteration cost scales only linearly with the NN size. On the public SurrogateLIB benchmark, our method proves scalable, modular, and adaptable: it runs 120x faster than an exact Big-M formulation on the largest test case; the NN sub-solver can be swapped from a log-barrier interior step to a projected-gradient routine with no code changes; and swapping the MLP for an LSTM backbone still completes the full optimisation in 47s without any bespoke adaptation.
Right Branches Matter in Failure-based Variable Ordering Heuristics
Authors: Yang Zhang; Hongbo Li
Venue: AAAI 2026 (main)

Abstract: Failure-based variable ordering heuristics (VOH) are efficient general-purpose search heuristics for solving constraint satisfaction problems (CSP). They learn from the failures detected during the search and select the variables that are most likely to fail. The current failure-based VOHs, i.e. the failure-rate-based (FRBA) and failure-length-based (FLBA), focus on only the failures detected in left branches. In this paper, we investigate how the failure information from right branches affects the performance of the failure-based VOHs. Four strategies utilizing the failure information of right branches are proposed to refine the failure-based VOHs. Our experiments performed with the benchmark instances used in the recent MiniZinc challenges show that utilizing the failures detected in right branches enhances the performance of the failure-based VOHs. The refined version combining all the proposed strategies generally gets the best performance. It demonstrates remarkable superiority over several general-purpose VOHs, including activity-based search, conflict-history search, refined weighted degree, pick/dom, and the existing FRBA, which are considered state-of-the-art. Our study demonstrates that right branches matter in failure-based VOHs.
Improving Exact Algorithm for Pseudo Boolean Optimization with Two New Phase Selection Heuristics
Authors: Yujiao Zhao; Yizhan Xiang; Jiangnan Li; Yiyuan Wang; Minghao Yin
Venue: AAAI 2026 (main)

Abstract: Pseudo-Boolean optimization (PBO) problem involves optimizing a linear objective function under linear inequality constraints defined over Boolean variables. PBO is widely used for modeling many combinational optimization problems, particularly in some real-world scenarios. In core-guided CDCL-based exact solvers, the way branching variables are assigned, known as phase selection, significantly affects the solving efficiency. This paper introduces two strategies to enhance solver performance by improving phase selection. Firstly, we design a new phase selection strategy that actively guides variables in the objective function toward assignments closer to the optimal solution. Secondly, to prevent the solver from becoming trapped in local solutions, we propose a reinforcement learning-based rephase mechanism that dynamically updates and resets variable phases. We integrate two phase selection strategies into two state-of-the-art PBO solvers and compare them against top-performing solvers from the PB competitions, using benchmarks from these competitions for assessment. The experimental results show that our solvers outperform the winning solver from the competitions.
Relational Verification for Cost-Aware Quantum Program Optimization
Authors: Ziming Zhao; Tingting Li; Zhaoxuan Li; Jianwei Yin
Venue: AAAI 2026 (main)

Abstract: Optimizing quantum programs is key to mitigating noise, reducing error-correction overhead, and improving performance on both near-term and fault-tolerant devices. Existing heuristic and learning-based optimizers, however, lack formal guarantees and risk semantic errors in the presence of entanglement and measurement. We present RelOpt, a semantics-preserving optimizer that enforces relational correctness between original and optimized programs. RelOpt is built on a lightweight intermediate language (QCore) with a relational operational semantics supporting partial-trace equivalence, measurement-distribution preservation, and approximate correctness. Optimization is guided by a multi-objective cost model that considers gate count, circuit depth, and error-correction cost. Only rewrite rules that are formally verified against user-specified contracts are applied. The engine combines symbolic simulation, SMT reasoning, and cost analysis to achieve safe and effective optimizations. On standard benchmarks such as QFT, Grover, and QAOA, RelOpt consistently outperforms Qiskit, t|ket>, and learning-based optimizers across multiple cost metrics while maintaining formal guarantees. By integrating formal verification with cost-aware compilation, RelOpt establishes a foundation for trustworthy and hardware-adaptive quantum toolchains.
Exact Optimization for Minimum Dominating Sets
Authors: Enqiang Zhu; Qiqi Bao; Yu Zhang; Chanjuan Liu; Pu Wu
Venue: AAAI 2026 (main)

Abstract: The Minimum Dominating Set (MDS) problem is a well-established combinatorial optimization problem with numerous real-world applications. Its NP-hard nature makes it increasingly difficult to obtain exact solutions as the graph size grows. This paper introduces ParDS, an exact algorithm developed to address the MDS problem within the branch-and-bound framework. ParDS features two key innovations: an advanced linear programming technique that yields tighter lower bounds and a set of novel reduction rules that dynamically simplify instances throughout the solving process. Compared to the leading exact algorithms presented at IJCAI 2023 and 2024, ParDS demonstrates theoretically superior lower-bound quality. Experimental results on standard benchmark datasets highlight several significant advantages of ParDS: it achieves fastest solving times in 70% of graph categories, especially on large, sparse graphs, delivers a speed-up of up to 3,411 times on the fastest individual instance, and successfully solves 16 out of 43 instances that other algorithms were unable to resolve within the 5-hour time limit. 
These findings establish ParDS as a state-of-the-art solution for exactly solving the MDS problem
T-SKM-Net: Trainable Neural Network Framework for Linear Constraint Satisfaction via Sampling Kaczmarz-Motzkin Method
Authors: Haoyu Zhu; Yao Zhang; Jiashen Ren; Qingchun Hou
Venue: AAAI 2026 (main)

Abstract: Neural network constraint satisfaction is crucial for safety-critical applications such as power system optimization, robotic path planning, and autonomous driving. However, existing constraint satisfaction methods face efficiency-applicability trade-offs, with hard constraint methods suffering from either high computational complexity or restrictive assumptions on constraint structures. The Sampling Kaczmarz-Motzkin (SKM) method is a randomized iterative algorithm for solving large-scale linear inequality systems with favorable convergence properties, but its argmax operations introduce non-differentiability, posing challenges for neural network applications. This work proposes the Trainable Sampling Kaczmarz-Motzkin Network (T-SKM-Net) framework and, for the first time, systematically integrates SKM-type methods into neural network constraint satisfaction. The framework transforms mixed constraint problems into pure inequality problems through null space transformation, employs SKM for iterative solving, and maps solutions back to the original constraint space, efficiently handling both equality and inequality constraints. We provide theoretical proof of post-processing effectiveness in expectation and end-to-end trainability guarantees based on unbiased gradient estimators, demonstrating that despite non-differentiable operations, the framework supports standard backpropagation. On the DCOPF case118 benchmark, our method achieves 4.27ms/item GPU serial forward inference with 0.0025% max optimality gap with post-processing mode and 5.25ms/item with 0.0008% max optimality gap with joint training mode, delivering over 25× speedup compared to the pandapower solver while maintaining zero constraint violations under given tolerance.
Beyond Static: Related Questions Retrieval Through Conversations in Community Question Answering
Authors: Xiao Ao; Jie Zou; Yibiao Wei; Peng Wang; Weikang Guo
Venue: AAAI 2026 (main)

Abstract: In community question answering (cQA) platforms like Stack Overflow, related question retrieval is recognized as a fundamental task that allows users to retrieve related questions to answer user queries automatically. Although many traditional approaches have been proposed for investigating this research field, they mostly rely on static approaches and neglect the interaction property. We argue that the conversational way can well distinguish the fine-grained representations of questions and has great potential to improve the performance of question retrieval. In this paper, we propose a related question retrieval model through conversations, called TeCQR, to locate related questions in cQA. Specifically, we build conversations by utilizing tag-enhanced clarifying questions. In addition, we design a noise tolerance model that evaluates the semantic similarity between questions and tags, enabling the model to effectively handle noisy feedback. Moreover, the tag-enhanced two-stage offline training is proposed to fully exploit the mutual relationships among user queries, questions, and tags to learn their fine-grained representations. Based on the learned representations and contextual conversations, TeCQR incorporates conversational feedback by learning to ask tag-enhanced clarifying questions to retrieve related questions more effectively. Experimental results demonstrate that our model significantly outperforms state-of-the-art baselines.
Extracting Interaction-Aware Monosemantic Concepts in Recommender Systems
Authors: Dor Arviv; Yehonatan Elisha; Oren Barkan; Noam Koenigstein
Venue: AAAI 2026 (main)

Abstract: We present a method for extracting monosemantic neurons, defined as latent dimensions that align with coherent and interpretable concepts, from user and item embeddings in recommender systems. Our approach employs a Sparse Autoencoder (SAE) to reveal semantic structure within pretrained representations. In contrast to work on language models, monosemanticity in recommendation must preserve the interactions between separate user and item embeddings. To achieve this, we introduce a prediction aware training objective that backpropagates through a frozen recommender and aligns the learned latent structure with the model’s user-item affinity predictions. The resulting neurons capture properties such as genre, popularity, and temporal trends, and support post hoc control operations including targeted filtering and content promotion without modifying the base model. Our method generalizes across different recommendation models and datasets, providing a practical tool for interpretable and controllable personalization.
Brownian Bridge Augmented Surrogate Simulation and Injection Planning for Geological CO2 Storage
Authors: Haoyue Bai; Guodong Chen; Wangyang Ying; Xinyuan Wang; Nanxu Gong; Sixun Dong; Giulia Pedrielli; Haoyu Wang; Haifeng Chen; Yanjie Fu
Venue: AAAI 2026 (main)

Abstract: Geological CO2 storage (GCS) involves injecting captured CO2 into deep subsurface formations to support climate goals. The effective management of GCS relies on adaptive injection planning to dynamically control injection rates and well pressures to balance both storage safety and efficiency. Prior literature, including numerical optimization methods and surrogate-optimization methods, is limited by real-world GCS requirements of smooth state transitions and goal-directed planning within limited time. To address these limitations, we propose a Brownian Bridge–augmented framework for surrogate simulation and injection planning in GCS and develop two insights (i) Brownian bridge as smooth state regularizer for better surrogate simulator; (ii) Brownian bridge as goal-time-conditioned planning guidance for better injection planning. Our method has three stages: (i) learning deep Brownian bridge representations with contrastive and reconstructive losses from historical reservoir and utility trajectories, (ii) incorporating Brownian bridge-based next state interpolation for simulator regularization (iii) guiding injection planning with Brownian utility-conditioned trajectories to generate high-quality injection plans. Experimental results across multiple datasets collected from diverse GCS settings demonstrate that our framework consistently improves simulation fidelity and planning effectiveness while maintaining low computational overhead.