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Related Articles from SNS
Combinatorial Landscape Analysis for Dominating Set and Vertex Coloring
arXiv:2606.07361v1 Announce Type: new Abstract: We analyze the two combinatorial problems of Dominating Set and Vertex Coloring regarding what kind of local optima are present for various instances. For a variety of graph classes each, we determine whether the induced landscapes are unimodal, plateau-unimodal (all optima are just one plateau), equimodal (all local optima are global) or truly multimodal. We do this for two different neighborhood operators, one based on making only a single...
Speeding Up the NSGA-II via Dynamic Population Sizes
Announce Type: replace Abstract: Multi-objective evolutionary algorithms (MOEAs) are among the most widely and successfully applied optimizers for multi-objective problems. However, to store many optimal trade-offs (the Pareto optima) simultaneously, MOEAs are typically run with a large population of solution candidates. This slows down the algorithm and renders the choice of the population size a crucial design decision.
Episodic Memory Temporal Consistency for Cooperative Multi-Agent Reinforcement Learning
arXiv:2606.04492v1 Announce Type: new Abstract: Cooperative Multi-Agent Reinforcement Learning (MARL) frequently suffers from severe reward sparsity and exploration bottlenecks. While episodic memory mechanisms mitigate these issues by reusing high-return trajectories, they often trap agents in local optima due to unconstrained incentive distribution and semantic representation collapse. To address this, we propose Episodic Memory Temporal Consistency (EMTC), a framework that robustly...
Local linear convergence of gradient methods for overparameterized Gaussian mixtures
Announce Type: new Abstract: We study the problem of learning Gaussian mixture models under overparameterization. Prior work has shown that while overparameterization is essential for avoiding spurious local optima and enables global recovery of the ground-truth model using the gradient-EM (expectation-maximization) algorithm, it can dramatically slow down the local rate of convergence. Under certain assumptions on the mixture weights, we show that a standard divergence measure minimized by...
Speeding Up the NSGA-II via Dynamic Population Sizes
arXiv:2509.01739v3 Announce Type: replace Abstract: Multi-objective evolutionary algorithms (MOEAs) are among the most widely and successfully applied optimizers for multi-objective problems. However, to store many optimal trade-offs (the Pareto optima) simultaneously, MOEAs are typically run with a large population of solution candidates. This slows down the algorithm and renders the choice of the population size a crucial design decision.
MotionDreamer: Universal Skeletal Motion Generation for 3D Rigged Shapes
arXiv:2606.01518v1 Announce Type: new Abstract: Motion generation for rigged shapes is vital for scalable 4D asset production. However, template-based methods are limited by specific topologies and fail to generalize across diverse morphologies. Conversely, per-case optimization is computationally expensive, susceptible to local optima, and highly sensitive to viewpoint-induced ambiguities.
Local Search on Vertex Coloring for Bipartite Graphs
arXiv:2606.09509v1 Announce Type: new Abstract: Local search is a well-known heuristic method used in optimization. In this thesis, we explore its capabilities on the vertex coloring problem, an $NP$-hard problem with relevance in both theoretical analysis and practical application.
Social welfare optimisation under institutional reward and punishment
arXiv:2605.31330v1 Announce Type: new Abstract: Institutional incentives are widely used to promote cooperation among autonomous, self-regarding agents, from human societies to multi-agent and AI systems. Existing work typically treats incentive design as a bi-objective problem: minimise institutional cost while achieving a high long-run frequency of cooperation. Whether such schemes also maximise social welfare - total population payoff net of institutional expenditure - has remained...
PhysAgent: Automating Physics-Based 4D Synthesis via Trajectory-Grounded Multi-Agent Feedback
Announce Type: new Abstract: Achieving fully automated, physically plausible 3D motion synthesis is a core objective in graphics and generative AI. However, configuring complex environmental force fields still relies entirely on manual expert intervention, creating a severe bottleneck for large-scale simulation data generation. Existing automated methods primarily focus on material optimization and exhibit severe modality gaps and technical flaws when applied to the vastly more complex force...
Plant growth forms shaping biodiversity patterns can guide conservation planning on the Qinghai-Tibet Plateau
Plant growth forms effectively reflect profound evolutionary adaptations that underpin ecosystem functioning; however, how their biogeographic disparities influence conservation prioritization remains poorly understood. We here integrate a comprehensive phylogeny with high-resolution distribution data for 14,468 vascular plant species across the Qinghai-Tibet Plateau, showing that growth-form identity decouples the spatial patterns of taxonomic and phylogenetic diversity, with distinct forms...