Modeling scheduling problems with conditional time intervals and cumulative functions has become a common approach when using modern commercial constraint programming solvers. This paradigm enables the modeling of a wide range of scheduling problems, including those involving producers and consumers. However, it is unavailable in existing open-source solvers and practical implementation details remain undocumented. In this work, we present an implementation of this modeling approach using a single, generic global constraint called the Generalized Cumulative. We also introduce a novel time-table filtering algorithm specifically designed to handle tasks defined on conditional time-intervals. Experimental results demonstrate that this approach, combined with the new filtering algorithm, performs competitively with existing solvers enabling the modeling of producer and consumer scheduling problems and effectively scales to large-scale problems.

We present a new approach for learning to solve SMT formulas. We phrase the challenge of solving SMT formulas as a tree search problem where at each step a transformation is applied to the input formula until the formula is solved.

We present a new approach for learning to solve SMT formulas. We phrase the challenge of solving SMT formulas as a tree search problem where at each step a transformation is applied to the input formula until the formula is solved.

Constraint Programming (CP) offers an intuitive, declarative framework for modeling V ehicle Routing Problems (VRP), yet classical CP models based on successor variables cannot always deal with optional visits or insertion based heuristics. T o address these limitations, this paper formalizes sequence variables within CP . Unlike the classical successor models, this computational domain handle optional visits and support insertion heuristics, including insertion-based Large Neighborhood Search. We provide a clear definition of their domain, update operations, and introduce consistency levels for constraints on this domain. An implementation is described with the underlying data structures required for integrating sequence variables into existing trail-based CP solvers. Furthermore, global constraints specifically designed for sequence variables and vehicle routing are introduced. Finally, the effectiveness of sequence variables is demonstrated by simplifying problem modeling and achieving competitive computational performance on the Dial-a-Ride Problem.

Constraint Programming and its high-level modeling languages have long been recognized for their potential to achieve the holy grail of problem-solving. However, the complexity of modeling languages, the large number of global constraints, and the art of creating good models have often hindered non-experts from choosing CP to solve their combinatorial problems. While generating an expert-level model from a natural-language description of a problem would be the dream, we are not yet there. We propose a tutoring system called CP-Model-Zoo, exploiting expert-written models accumulated through the years. CP-Model-Zoo retrieves the closest source code model from a database based on a user's natural language description of a combinatorial problem. It ensures that expert-validated models are presented to the user while eliminating the need for human data labeling. Our experiments show excellent accuracy in retrieving the correct model based on a user-input description of a problem simulated with different levels of expertise.

Failure-Directed Search (FDS) is a significant complete generic search algorithm used in Constraint Programming (CP) to efficiently explore the search space, proven particularly effective on scheduling problems. This paper analyzes FDS's properties, showing that minimizing the size of its search tree guided by ranked branching decisions is closely related to the Multi-armed bandit (MAB) problem. Building on this insight, MAB reinforcement learning algorithms are applied to FDS, extended with problem-specific refinements and parameter tuning, and evaluated on the two most fundamental scheduling problems, the Job Shop Scheduling Problem (JSSP) and Resource-Constrained Project Scheduling Problem (RCPSP). The resulting enhanced FDS, using the best extended MAB algorithm and configuration, performs 1.7 times faster on the JSSP and 2.1 times faster on the RCPSP benchmarks compared to the original implementation in a new solver called OptalCP, while also being 3.5 times faster on the JSSP and 2.1 times faster on the RCPSP benchmarks than the current state-of-the-art FDS algorithm in IBM CP Optimizer 22.1. Furthermore, using only a 900-second time limit per instance, the enhanced FDS improved the existing state-of-the-art lower bounds of 78 of 84 JSSP and 226 of 393 RCPSP standard open benchmark instances while also completely closing a few of them.

Solving Mixed-Integer Programming (MIP) problems often requires substantial computational resources due to their combinatorial nature. Parallelization has emerged as a critical strategy to accelerate solution times and enhance scalability to tackle large, complex instances. This paper investigates the parallelization capabilities of Balans, a recently proposed multi-armed bandits-based adaptive large neighborhood search for MIPs. While Balans's modular architecture inherently supports parallel exploration of diverse parameter configurations, this potential has not been thoroughly examined. To address this gap, we introduce ParBalans, an extension that leverages both solver-level and algorithmic-level parallelism to improve performance on challenging MIP instances. Our experimental results demonstrate that ParBalans exhibits competitive performance compared to the state-of-the-art commercial solver Gurobi, particularly on hard optimization benchmarks.

Ahead of the 34th International Joint Conference on Artificial Intelligence (IJCAI 2025), which will take place in Montréal, Canada, from 16 to 22 August 2025, the Local Arrangements Committee has launched a campaign to showcase the next generation of AI researchers in Canada. Through a series of 90-second videos, we meet students based in Canada and find out a bit about their work. Imane Chafi, PhD candidate at the Polytechnique Montréal, uses AI models to support dentists in designing dental preparations for dental crowns more efficiently and accurately. Liliane-Caroline Demers, Student Communication Coordinator for IJCAI 2025 Local Arrangement Committee and a recent master's graduate from Polytechnique Montréal, researches AI-generated music. Using a neurosymbolic approach that combines machine learning with constraint programming at inference time, she creates music that is both stylistically authentic and structurally coherent.

Indistinguishable objects often occur when modelling problems in constraint programming, as well as in other related paradigms. They occur when objects can be viewed as being drawn from a set of unlabelled objects, and the only operation allowed on them is equality testing. For example, the golfers in the social golfer problem are indistinguishable. If we do label the golfers, then any relabelling of the golfers in one solution gives another valid solution. Therefore, we can regard the symmetric group of size $n$ as acting on a set of $n$ indistinguishable objects. In this paper, we show how we can break the symmetries resulting from indistinguishable objects. We show how symmetries on indistinguishable objects can be defined properly in complex types, for example in a matrix indexed by indistinguishable objects. We then show how the resulting symmetries can be broken correctly. In Essence, a high-level modelling language, indistinguishable objects are encapsulated in "unnamed types". We provide an implementation of complete symmetry breaking for unnamed types in Essence.

While the multi-step dialog model initiated by ChatGPT is now available from a few dozen online or locally run open source and closed source LLMs, it does not cover the need to efficiently extract salient information from an LLMs "parameter-memory" that encapsulates in a heavily compressed form the result of training the model on trillions of documents and multimodal data. Steps in this direction have been taken, relying on ground-truth involving additional information sources (e.g., collections of reference documents or use of web search queries). Among them, we mention work on improving performance of Retrieval Augmented Generation (RAG) systems [7] by recursively embedding, clustering, and summarizing chunks of text for better hierarchical LLM-assisted summarization [15], multi-agent hybrid LLM and local computation aggregators [3] and deductive verifiers of chain of thoughts reasoning [9]. A more direct approach is recursion on LLM queries, by chaining the LLM's distilled output as input to a next step and casting its content and interrelations in the form of logic programs, to automate and focus this information extraction with minimal human input [18, 20]. Like in the case of typical RAG architectures [7, 15], this process can rely on external ground truth but it can also use new LLM client instances as "oracles" deciding the validity of the synthesized rules or facts. With focus on automation of this unmediated salient knowledge extraction from the LLM's parameter memory and its encapsulation in the form of synthesized logic programming code, we will extend in this paper the work initiated in [18, 20] with: new LLM input-output chaining mechanisms new types of generated logic programs new relational representations elicited from LLM output steps 2 On LLM-generated Logic Programs and their Inference Execution Methods scalable execution mechanisms that accommodate very large logic programs at deeper recursion levels soft-unification based execution of LLM-generated logic programs as a principled encapsulation of the RAG retrieval process The rest of the paper is organized as follows. Section 2 overviews the DeepLLM architecture described in [20] and its new extensions supporting the results in this paper.