Tootfinder

Robust Multiagent Collaboration Through Weighted Max-Min T-Joins
Sharareh Alipour
https://arxiv.org/abs/2602.07720 https://arxiv.org/pdf/2602.07720 https://arxiv.org/html/2602.07720
arXiv:2602.07720v1 Announce Type: new
Abstract: Many multiagent tasks -- such as reviewer assignment, coalition formation, or fair resource allocation -- require selecting a group of agents such that collaboration remains effective even in the worst case. The \emph{weighted max-min $T$-join problem} formalizes this challenge by seeking a subset of vertices whose minimum-weight matching is maximized, thereby ensuring robust outcomes against unfavorable pairings.
We advance the study of this problem in several directions. First, we design an algorithm that computes an upper bound for the \emph{weighted max-min $2k$-matching problem}, where the chosen set must contain exactly $2k$ vertices. Building on this bound, we develop a general algorithm with a \emph{$2 \ln n$-approximation guarantee} that runs in $O(n^4)$ time. Second, using ear decompositions, we propose another upper bound for the weighted max-min $T$-join cost. We also show that the problem can be solved exactly when edge weights belong to $\{1,2\}$.
Finally, we evaluate our methods on real collaboration datasets. Experiments show that the lower bounds from our approximation algorithm and the upper bounds from the ear decomposition method are consistently close, yielding empirically small constant-factor approximations. Overall, our results highlight both the theoretical significance and practical value of weighted max-min $T$-joins as a framework for fair and robust group formation in multiagent systems.
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Large eddy simulation of turbulent swirl-stabilized flames using the front propagation formulation: impact of the resolved flame thickness
Ruochen Guo, Yunde Su, Yuewen Jiang
https://arxiv.org/abs/2602.21940 https://arxiv.org/pdf/2602.21940 https://arxiv.org/html/2602.21940
arXiv:2602.21940v1 Announce Type: new
Abstract: This work extends the front propagation formulation (FPF) combustion model to large eddy simulation (LES) of swirl-stabilized turbulent premixed flames and investigates the effects of resolved flame thickness on the predicted flame dynamics. The FPF method is designed to mitigate the spurious propagation of under-resolved flames while preserving the reaction characteristics of filtered flame fronts. In this study, the model is extended to account for non-adiabatic effects and is coupled with an improved sub-filter flame speed estimation that resolves the inconsistency arising from heat-release effects on local sub-filter turbulence. The performance of the extended FPF method is validated by LES of the TECFLAM swirl-stabilized burner, where the results agree well with experimental measurements. The simulations reveal that the stretching of vortical structures in the outer shear layer leads to the formation of trapped flame pockets, which are identified as the physical mechanism responsible for the secondary temperature peaks observed in the experiment. The prediction of this phenomenon is shown to be strongly dependent on the resolved flame thickness, when the filter size is used for modeling sub-filter flame wrinklings. Without proper modeling of the chemical steepening effects, the thickness of the resolved flame brush is over-predicted, causing the flame consumption rate to be under-estimated. Consequently, the flame brush detaches from the outer shear layer, resulting in a failure to capture the flame pockets and the associated secondary temperature peaks.
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Stolen to add #AltText.
https://mstdn.social/@maskedscheduler/116307742757163764

Replaced article(s) found for econ.TH. https://arxiv.org/list/econ.TH/new
[1/1]:
- Human Misperception of Generative-AI Alignment: A Laboratory Experiment
Kevin He, Ran Shorrer, Mengjia Xia
https://arxiv.org/abs/2502.14708 https://mastoxiv.page/@arXiv_econTH_bot/114040735507903818
- Optimal Procurement Design: A Reduced-Form Approach
Kun Zhang
https://arxiv.org/abs/2504.15555 https://mastoxiv.page/@arXiv_econTH_bot/114386070253243701
- Nondistortionary belief elicitation
Marcin P\k{e}ski, Colin Stewart
https://arxiv.org/abs/2506.12167 https://mastoxiv.page/@arXiv_econTH_bot/114698133040888130
- Exploring the conditions for sustainability with open-ended innovation
Debora Princepe, Cristobal Qui\~ninao, Cristina D\'iaz Faloh, Pablo A. Marquet, Matteo Marsili
https://arxiv.org/abs/2510.01085 https://mastoxiv.page/@arXiv_physicssocph_bot/115303700316225913
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UrbanFM: Scaling Urban Spatio-Temporal Foundation Models
Wei Chen, Yuqian Wu, Junle Chen, Xiaofang Zhou, Yuxuan Liang
https://arxiv.org/abs/2602.20677 https://arxiv.org/pdf/2602.20677 https://arxiv.org/html/2602.20677
arXiv:2602.20677v1 Announce Type: new
Abstract: Urban systems, as dynamic complex systems, continuously generate spatio-temporal data streams that encode the fundamental laws of human mobility and city evolution. While AI for Science has witnessed the transformative power of foundation models in disciplines like genomics and meteorology, urban computing remains fragmented due to "scenario-specific" models, which are overfitted to specific regions or tasks, hindering their generalizability. To bridge this gap and advance spatio-temporal foundation models for urban systems, we adopt scaling as the central perspective and systematically investigate two key questions: what to scale and how to scale. Grounded in first-principles analysis, we identify three critical dimensions: heterogeneity, correlation, and dynamics, aligning these principles with the fundamental scientific properties of urban spatio-temporal data. Specifically, to address heterogeneity through data scaling, we construct WorldST. This billion-scale corpus standardizes diverse physical signals, such as traffic flow and speed, from over 100 global cities into a unified data format. To enable computation scaling for modeling correlations, we introduce the MiniST unit, a novel split mechanism that discretizes continuous spatio-temporal fields into learnable computational units to unify representations of grid-based and sensor-based observations. Finally, addressing dynamics via architecture scaling, we propose UrbanFM, a minimalist self-attention architecture designed with limited inductive biases to autonomously learn dynamic spatio-temporal dependencies from massive data. Furthermore, we establish EvalST, the largest-scale urban spatio-temporal benchmark to date. Extensive experiments demonstrate that UrbanFM achieves remarkable zero-shot generalization across unseen cities and tasks, marking a pivotal first step toward large-scale urban spatio-temporal foundation models.
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A Modal de Finetti Theorem: Exchangeability under S4 and S5
Daniel Zantedeschi
https://arxiv.org/abs/2603.27547 https://arxiv.org/pdf/2603.27547 https://arxiv.org/html/2603.27547
arXiv:2603.27547v1 Announce Type: new
Abstract: We introduce modal exchangeability, a symmetry principle for probability measures on Kripke frames: invariance under those automorphisms of the frame that preserve the accessibility relation and fix a designated world. This principle characterizes when an agent's uncertainty over possible-world valuations respects the modal structure. We establish representation theorems that determine the probabilistic consequences of modal exchangeability for S4 and S5 frames. Under S5, where accessibility is an equivalence relation, the classical de Finetti theorem is recovered: valuations are conditionally i.i.d. given a single directing measure. Under S4, where accessibility is a preorder, the accessible cluster decomposes into orbits of the stabilizer group, and valuations within each orbit are conditionally i.i.d. with an orbit-specific directing measure. A rigidity constraint emerges: each directing measure must be constant across its orbit. Rigidity is not assumed but forced by symmetry; it is a theorem, not a modeling choice. The proofs are constructive, requiring only dependent choice (ZF DC), and yield computable representations for recursively presented frames. Rigidity has direct epistemic content: rational agents whose uncertainty respects modal structure cannot assign different latent parameters to worlds within the same orbit. The framework connects probabilistic representation theory to the S4/S5 distinction central to epistemic and temporal logic, with consequences for hyperintensional belief and rational learning under partial information.
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EAG-PT: Emission-Aware Gaussians and Path Tracing for Indoor Scene Reconstruction and Editing
Xijie Yang, Mulin Yu, Changjian Jiang, Kerui Ren, Tao Lu, Jiangmiao Pang, Dahua Lin, Bo Dai, Linning Xu
https://arxiv.org/abs/2601.23065 https://arxiv.org/pdf/2601.23065 https://arxiv.org/html/2601.23065
arXiv:2601.23065v1 Announce Type: new
Abstract: Recent reconstruction methods based on radiance field such as NeRF and 3DGS reproduce indoor scenes with high visual fidelity, but break down under scene editing due to baked illumination and the lack of explicit light transport. In contrast, physically based inverse rendering relies on mesh representations and path tracing, which enforce correct light transport but place strong requirements on geometric fidelity, becoming a practical bottleneck for real indoor scenes. In this work, we propose Emission-Aware Gaussians and Path Tracing (EAG-PT), aiming for physically based light transport with a unified 2D Gaussian representation. Our design is based on three cores: (1) using 2D Gaussians as a unified scene representation and transport-friendly geometry proxy that avoids reconstructed mesh, (2) explicitly separating emissive and non-emissive components during reconstruction for further scene editing, and (3) decoupling reconstruction from final rendering by using efficient single-bounce optimization and high-quality multi-bounce path tracing after scene editing. Experiments on synthetic and real indoor scenes show that EAG-PT produces more natural and physically consistent renders after editing than radiant scene reconstructions, while preserving finer geometric detail and avoiding mesh-induced artifacts compared to mesh-based inverse path tracing. These results suggest promising directions for future use in interior design, XR content creation, and embodied AI.
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Conventional Accelerator Magnets
Stephane Sanfilippo
https://arxiv.org/abs/2602.19808 https://arxiv.org/pdf/2602.19808 https://arxiv.org/html/2602.19808
arXiv:2602.19808v1 Announce Type: new
Abstract: This course introduces conventional magnets used in particle accelerators, focusing on both normal-conducting copper coil magnets and permanent magnets (PMs). It covers magnet classification, design principles, material selection, and mechanical constraints. Advantages and limitations of PMs compared to copper coil magnets are discussed. Key construction steps and cooling methods are presented. The course also includes magnetic field measurement techniques and quality control. Practical examples from PSI and CERN illustrate the concepts.
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Solving Problems of Unknown Difficulty
Nicholas Wu
https://arxiv.org/abs/2604.00156 https://arxiv.org/pdf/2604.00156 https://arxiv.org/html/2604.00156
arXiv:2604.00156v1 Announce Type: new
Abstract: This paper studies how uncertainty about problem difficulty shapes problem-solving strategies. I develop a dynamic model where an agent solves a problem by brainstorming approaches of unknown quality and allocating a fixed effort budget among them. Success arrives from spending effort pursuing good approaches, at a rate determined by the unknown problem difficulty. The agent balances costly exploration (expanding the set of approaches) with exploitation (pursuing existing approaches). Failures could signal either a bad idea or a hard problem, and this uncertainty generates novel dynamics: optimal search alternates between trying new approaches and revisiting previously abandoned ones. I then examine a principal-agent environment, where moral hazard arises on the intensive margin: how the agent explores. Dynamic commitment leads contracts to frontload incentives, which can be counteracted by the presence of learning. The framework reflects scientific discovery, product development, and other creative work, providing insights into innovation and organizational design.
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“This provisional implementation of the trade agreement with Mercosur is anti-democratic. The EU, which always talks about the principles of the rule of law, is here violating its own decision-making process,” said Dutch lawmaker Jessika van Leeuwen, who hails from the European People’s Party, von der Leyen’s own group.
https://www.politico.eu/article/brussels-implements-eu-mercosur-trade-deal/