Classical circuit complexity characterizes parallel computation in purely combinatorial terms, ignoring the physical constraints that govern real hardware. The standard classes $\mathbf{NC}$, $\mathbf{AC}$, and $\mathbf{TC}$ treat unlimited fan-in, free interconnection, and polynomial gate counts as feasible -- assumptions that conflict with geometric, energetic, and thermodynamic realities. We introduce the family of realizable circuit classes $\mathbf{RC}_d$, which model computation embedded in physical $d$-dimensional space. Each circuit in $\mathbf{RC}_d$ obeys conservative realizability laws: volume scales as $\mathcal{O}(t^d)$, cross-boundary information flux is bounded by $\mathcal{O}(t^{d-1})$ per unit time, and growth occurs through local, physically constructible edits. These bounds apply to all causal systems, classical or quantum. Within this framework, we show that algorithms with runtime $ω(n^{d/(d-1)})$ cannot scale to inputs of maximal entropy, and that any $d$-dimensional parallel implementation offers at most a polynomial speed-up of degree $(d-1)$ over its optimal sequential counterpart. In the limit $d\to\infty$, $\mathbf{RC}_\infty(\mathrm{polylog})=\mathbf{NC}$, recovering classical parallelism as a non-physical idealization. By unifying geometry, causality, and information flow, $\mathbf{RC}_d$ extends circuit complexity into the physical domain, revealing universal scaling laws for computation.

Competition 2025, following those published in 2022 [2], 2023 [3], and 2024 [4]. The website containing all detailed results of this international competition is available at: https://www.cril.univ-artois.fr/XCSP25 The organization of this 2025 competition involved the following tasks: adjusting general details (dates, tracks, .. . These instances can be found in this archive. Some (usually minor) differences may exist when compiling the models presented in this document and those that can be found in this archive. Remember that the complete description, Version 3.2, of the format (XCSP For the 2025 competition, 33 problems have been selected. They are succinctly presented in Table 1.1. For each problem, the type of the involved (global) constraints is indicated. At this point, do note that making a good selection of problems/instances is a difficult task. When table is followed by (), it means that starred tables are involved. It is always interesting to see how constraint solvers behave when the instances of a problem become harder and harder. This is what we call the scaling behavior of solvers.

In Social Deduction Games (SDGs) such as Avalon, Mafia, and W erewolf, 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.

Federated learning on clients with noisy labels is a challenging problem, as such clients can infiltrate the global model, impacting the overall generalizability of the system. Existing methods proposed to handle noisy clients assume that a sufficient number of clients with clean labels are available, which can be leveraged to learn a robust global model while dampening the impact of noisy clients. This assumption fails when a high number of heterogeneous clients contain noisy labels, making the existing approaches ineffective. In such scenarios, it is important to locally regularize the clients before communication with the global model, to ensure the global model isn't corrupted by noisy clients. While pre-trained self-supervised models can be effective for local regularization, existing centralized approaches relying on pretrained initialization are impractical in a federated setting due to the potentially large size of these models, which increases communication costs. In that line, we propose a regularization objective for client models that decouples the pre-trained and classification models by enforcing similarity between close data points within the client. We leverage the representation space of a self-supervised pretrained model to evaluate the closeness among examples. This regularization, when applied with the standard objective function for the downstream task in standard noisy federated settings, significantly improves performance, outperforming existing state-of-the-art federated methods in multiple computer vision and medical image classification benchmarks. Unlike other techniques that rely on self-supervised pretrained initialization, our method does not require the pretrained model and classifier backbone to share the same architecture, making it architecture-agnostic.

The core principle of PlaCo is to provide a high-level interface for specifying robot control problems, while internally reformulating them into the QP formulation introduced in equation (1) expected by efficient numerical solvers. This section illustrates how common robotics problems naturally reduce to this form. First, Section III-A recalls the equivalence between least-squares objectives and the standard QP formulation. Section III-B extends this formulation to the case of multiple objectives. Section III-C discusses how to incorporate hard and soft constraints into the QP framework. Section III-D introduces integrated decision variables, which allow system dynamics to be embedded directly into the QP problem. Finally, Section III-E presents how QR factorization is used to reduce the dimensionality of the optimization problem. An usage example is provided in Appendix A to illustrate the problem specification process in PlaCo. A. From least-squares to standard QP formulation A least-squares minimization problem is formulated as min

Tabular Prior-Data Fitted Network (TabPFN) is a foundation model designed for small to medium-sized tabular data, which has attracted much attention recently. This paper investigates the application of TabPFN in Combinatorial Optimization (CO) problems. The aim is to lessen challenges in time and data-intensive training requirements often observed in using traditional methods including exact and heuristic algorithms, Machine Learning (ML)-based models, to solve CO problems. Proposing possibly the first ever application of TabPFN for such a purpose, we adapt and fine-tune the TabPFN model to solve the Travelling Salesman Problem (TSP), one of the most well-known CO problems. Specifically, we adopt the node-based approach and the node-predicting adaptation strategy to construct the entire TSP route. Our evaluation with varying instance sizes confirms that TabPFN requires minimal training, adapts to TSP using a single sample, performs better generalization across varying TSP instance sizes, and reduces performance degradation. Furthermore, the training process with adaptation and fine-tuning is completed within minutes. The methodology leads to strong solution quality even without post-processing and achieves performance comparable to other models with post-processing refinement. Our findings suggest that the TabPFN model is a promising approach to solve structured and CO problems efficiently under training resource constraints and rapid deployment requirements.

We aim to solve the problem of temporal-constraint learning from demonstrations to reproduce demonstration-like logic-constrained behaviors. Learning logic constraints is challenging due to the combinatorially large space of possible specifications and the ill-posed nature of non-Markovian constraints. To figure it out, we introduce a novel temporal-constraint learning method, which we call inverse logic-constraint learning (ILCL). Our method frames ICL as a two-player zero-sum game between 1) a genetic algorithm-based temporal-logic mining (GA-TL-Mining) and 2) logic-constrained reinforcement learning (Logic-CRL). GA-TL-Mining efficiently constructs syntax trees for parameterized truncated linear temporal logic (TLTL) without predefined templates. Subsequently, Logic-CRL finds a policy that maximizes task rewards under the constructed TLTL constraints via a novel constraint redistribution scheme. Our evaluations show ILCL outperforms state-of-the-art baselines in learning and transferring TL constraints on four temporally constrained tasks. We also demonstrate successful transfer to real-world peg-in-shallow-hole tasks.

AI has proven highly successful at urban planning analysis -- learning patterns from data to predict future conditions. The next frontier is AI-assisted decision-making: agents that recommend sites, allocate resources, and evaluate trade-offs while reasoning transparently about constraints and stakeholder values. Recent breakthroughs in reasoning AI -- CoT prompting, ReAct, and multi-agent collaboration frameworks -- now make this vision achievable. This position paper presents the Agentic Urban Planning AI Framework for reasoning-capable planning agents that integrates three cognitive layers (Perception, Foundation, Reasoning) with six logic components (Analysis, Generation, Verification, Evaluation, Collaboration, Decision) through a multi-agents collaboration framework. We demonstrate why planning decisions require explicit reasoning capabilities that are value-based (applying normative principles), rule-grounded (guaranteeing constraint satisfaction), and explainable (generating transparent justifications) -- requirements that statistical learning alone cannot fulfill. We compare reasoning agents with statistical learning, present a comprehensive architecture with benchmark evaluation metrics, and outline critical research challenges. This framework shows how AI agents can augment human planners by systematically exploring solution spaces, verifying regulatory compliance, and deliberating over trade-offs transparently -- not replacing human judgment but amplifying it with computational reasoning capabilities.

The reproducibility crisis in scientific computing constrains robotics research. Existing studies reveal that up to 70% of robotics algorithms cannot be reproduced by independent teams, while many others fail to reach deployment because creating shareable software environments remains prohibitively complex. These challenges stem from fragmented, multi-language, and hardware-software toolchains that lead to dependency hell. We present Pixi, a unified package-management framework that addresses these issues by capturing exact dependency states in project-level lockfiles, ensuring bit-for-bit reproducibility across platforms. Its high-performance SAT solver achieves up to 10x faster dependency resolution than comparable tools, while integration of the conda-forge and PyPI ecosystems removes the need for multiple managers. Adopted in over 5,300 projects since 2023, Pixi reduces setup times from hours to minutes and lowers technical barriers for researchers worldwide. By enabling scalable, reproducible, collaborative research infrastructure, Pixi accelerates progress in robotics and AI.

Our healthcare systems are struggling with the ageing population resulting in an increasing demand and rising expenditures while facing a shortage of healthcare professionals at the same time [7, 12]. When a system is under stress and demand exceeds supply, among other strategies, scheduling resources efficiently and planning becomes important [8]. Hospitals are a critical component of the healthcare system, playing a vital role in care coordination, system development, and supporting population health needs [11]. Efficient planning in hospitals is important to utilized the limited resources in the best possible manner. Here approaches from Operations Research can be of benefit to optimize planning problems such as admission planning, bed allocation, nurse scheduling and surgery scheduling [6, 10]. It has been recognized in the past that resources should be planned in an integrated manner to improve the overall outcomes instead of focusing on individual departments or resources [10].