Tootfinder

Pareto-Optimal Linear Programming
Bart van Rossum, Twan Dollevoet
https://arxiv.org/abs/2509.18073 https://arxiv.org/pdf/2509.18073

Pareto-Optimal Linear Programming
Pareto-optimality plays a central role in evaluating the efficiency of solutions to allocation problems, such as house allocation, school choice, and kidney exchange. We introduce a general linear programming problem subject to Pareto-optimality conditions, which we call Max-Pareto. Using the novel result that Pareto-optimal bipartite matchings are fractionally Pareto-optimal, we prove that Max-Pareto is $\mathcal{NP}$-complete. We propose a bilinear programming formulation of Max-Pareto, and e…

The Pareto Frontier of Resilient Jet Tagging
Rikab Gambhir, Matt LeBlanc, Yuanchen Zhou
https://arxiv.org/abs/2509.19431 https://arxiv.org/pdf/2509.19431…

The Pareto Frontier of Resilient Jet Tagging
Classifying hadronic jets using their constituents' kinematic information is a critical task in modern high-energy collider physics. Often, classifiers are designed by targeting the best performance using metrics such as accuracy, AUC, or rejection rates. However, the use of a single metric can lead to the use of architectures that are more model-dependent than competitive alternatives, leading to potential uncertainty and bias in analysis. We explore such trade-offs and demonstrate the consequ…

GaussianPSL: A novel framework based on Gaussian Splatting for exploring the Pareto frontier in multi-criteria optimization
Phuong Mai Dinh, Van-Nam Huynh
https://arxiv.org/abs/2509.17889

GaussianPSL: A novel framework based on Gaussian Splatting for exploring the Pareto frontier in multi-criteria optimization
Multi-objective optimization (MOO) is essential for solving complex real-world problems involving multiple conflicting objectives. However, many practical applications - including engineering design, autonomous systems, and machine learning - often yield non-convex, degenerate, or discontinuous Pareto frontiers, which involve traditional scalarization and Pareto Set Learning (PSL) methods that struggle to approximate accurately. Existing PSL approaches perform well on convex fronts but tend to …

Graph atomic cluster expansion for foundational machine learning interatomic potentials
Yury Lysogorskiy, Anton Bochkarev, Ralf Drautz
https://arxiv.org/abs/2508.17936 https://

Graph atomic cluster expansion for foundational machine learning interatomic potentials
Foundational machine learning interatomic potentials that can accurately and efficiently model a vast range of materials are critical for accelerating atomistic discovery. We introduce universal potentials based on the graph atomic cluster expansion (GRACE) framework, trained on several of the largest available materials datasets. Through comprehensive benchmarks, we demonstrate that the GRACE models establish a new Pareto front for accuracy versus efficiency among foundational interatomic pote…

FraPPE: Fast and Efficient Preference-based Pure Exploration
Udvas Das, Apurv Shukla, Debabrota Basu
https://arxiv.org/abs/2508.16487 https://arxiv.org/pdf…

FraPPE: Fast and Efficient Preference-based Pure Exploration
Preference-based Pure Exploration (PrePEx) aims to identify with a given confidence level the set of Pareto optimal arms in a vector-valued (aka multi-objective) bandit, where the reward vectors are ordered via a (given) preference cone $\mathcal{C}$. Though PrePEx and its variants are well-studied, there does not exist a computationally efficient algorithm that can optimally track the existing lower bound for arbitrary preference cones. We successfully fill this gap by efficiently solving the …

Pareto-optimal Tradeoffs Between Communication and Computation with Flexible Gradient Tracking
Yan Huang, Jinming Xu, Li Chai, Jiming Chen, Karl H. Johansson
https://arxiv.org/abs/2509.18129

Are We Scaling the Right Thing? A System Perspective on Test-Time Scaling
Youpeng Zhao, Jinpeng LV, Di Wu, Jun Wang, Christopher Gooley
https://arxiv.org/abs/2509.19645 https://…

Are We Scaling the Right Thing? A System Perspective on Test-Time Scaling
Test-time scaling (TTS) has recently emerged as a promising direction to exploit the hidden reasoning capabilities of pre-trained large language models (LLMs). However, existing scaling methods narrowly focus on the compute-optimal Pareto-frontier, ignoring the simple fact that compute-optimal is not always system-optimal. In this work, we propose a system-driven perspective on TTS, analyzing how reasoning models scale against practical metrics, such as latency and cost-per-token. By evaluating…

Preference-Guided Multi-Objective UI Adaptation
Yao Song, Christoph Gebhardt, Yi-Chi Liao, Christian Holz
https://arxiv.org/abs/2509.18960 https://arxiv.or…

Proximately Envy-Free and Efficient Allocation of Mixed Manna
Siddharth Barman, Paritosh Verma
https://arxiv.org/abs/2509.18673 https://arxiv.org/pdf/2509.…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[3/5]:
- Locally Pareto-Optimal Interpretations for Black-Box Machine Learning Models
Aniruddha Joshi, Supratik Chakraborty, S Akshay, Shetal Shah, Hazem Torfah, Sanjit Seshia

Crosslisted article(s) found for cs.LO. https://arxiv.org/list/cs.LO/new
[1/1]:
- Locally Pareto-Optimal Interpretations for Black-Box Machine Learning Models
Aniruddha Joshi, Supratik Chakraborty, S Akshay, Shetal Shah, Hazem Torfah, Sanjit Seshia

Modelling peaks over thresholds in panel data: a two-level grouped panel generalized Pareto regression
Zefan Liu, Natalia Nolde
https://arxiv.org/abs/2509.15023 https://

Modelling peaks over thresholds in panel data: a two-level grouped panel generalized Pareto regression
Panel data arise in a wide range of application areas, and developing modelling methods for extreme values under such a setup is essential for reliable risk assessment and management. When choosing to model the marginal distributions of univariate extremes, one may wish to balance the flexibility in capturing the heterogeneity among margins and the efficiency of estimation. This can be achieved through a combination of regression techniques and assuming a latent group structure based on paramet…

Multi-Agent Inverse Reinforcement Learning for Identifying Pareto-Efficient Coordination -- A Distributionally Robust Approach
Luke Snow, Vikram Krishnamurthy
https://arxiv.org/abs/2509.08956

Multi-Agent Inverse Reinforcement Learning for Identifying Pareto-Efficient Coordination -- A Distributionally Robust Approach
Multi-agent inverse reinforcement learning (IRL) aims to identify Pareto-efficient behavior in a multi-agent system, and reconstruct utility functions of the individual agents. Motivated by the problem of detecting UAV coordination, how can we construct a statistical detector for Pareto-efficient behavior given noisy measurements of the decisions of a multi-agent system? This paper approaches this IRL problem by deriving necessary and sufficient conditions for a dataset of multi-agent system dy…

🔄 Available in Fast and Auto modes with continuous upgrades based on user feedback
🎯 Sets new record on Pareto Intelligence frontier according to Artificial Analysis
🛠️ Native tool use capabilities with real-time search integration
📊 Democratizing advanced #AI technology for broader accessibility and innovation
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MoGU V2: Toward a Higher Pareto Frontier Between Model Usability and Security
Yanrui Du, Fenglei Fan, Sendong Zhao, Jiawei Cao, Ting Liu, Bing Qin
https://arxiv.org/abs/2509.06807

MoGU V2: Toward a Higher Pareto Frontier Between Model Usability and Security
As Large Language Models (LLMs) increasingly permeate human life, their security has emerged as a critical concern, particularly their ability to maintain harmless responses to malicious instructions. Although extensive methods have improved LLMs' security, they often lead to conservative, rejection-oriented responses that compromise practical usability. This presents a key challenge: how to advance the Pareto frontier between LLMs' usability and security, rather than necessitate a trade-off be…

Using utility graphs to search for Pareto-optimal outcomes in complex, interdependent issue negotiations
Valentin Robu, Mark Klein
https://arxiv.org/abs/2509.10885 https://

Using utility graphs to search for Pareto-optimal outcomes in complex, interdependent issue negotiations
This paper studies how utility graphs decomposition algorithms can be used to effectively search for Pareto-efficient outcomes in complex automated negotiation. We propose a number of algorithms that can efficiently handle high-dimensional utility graphs, and test them on a variety of utility graph topologies, generated based on state of the art methods for analysing complex graphs. We show that we can achieve exponential speed-up, for many structures, even for very large utility graphs. To our…

Parameterized Algorithms for Computing Pareto Sets
Joshua K\"onen, Heiko R\"oglin, Tarek Stuck
https://arxiv.org/abs/2509.06124 https://arxiv.org…

Parameterized Algorithms for Computing Pareto Sets
Dynamic programming over tree decompositions is a common technique in parameterized algorithms. In this paper, we study whether this technique can also be applied to compute Pareto sets of multiobjective optimization problems. We first derive an algorithm to compute the Pareto set for the multicriteria s-t cut problem and show how this result can be applied to a polygon aggregation problem arising in cartography that has recently been introduced by Rottmann et al. (GIScience 2021). We also show…

APFEx: Adaptive Pareto Front Explorer for Intersectional Fairness
Priyobrata Mondal, Faizanuddin Ansari, Swagatam Das
https://arxiv.org/abs/2509.13908 https://

APFEx: Adaptive Pareto Front Explorer for Intersectional Fairness
Ensuring fairness in machine learning models is critical, especially when biases compound across intersecting protected attributes like race, gender, and age. While existing methods address fairness for single attributes, they fail to capture the nuanced, multiplicative biases faced by intersectional subgroups. We introduce Adaptive Pareto Front Explorer (APFEx), the first framework to explicitly model intersectional fairness as a joint optimization problem over the Cartesian product of sensiti…

Many optimization problems assume exact inputs and total orders.
But what if uncertainty affects f — or if it returns a tuple, or a set instead of a single value?
Our new preprint explores how to handle such cases systematically:
Optimization under uncertainty — understanding orders and testing programs with specifications
👥 Botta, Jansson & Richter
📄

Causal-Guided Dimension Reduction for Efficient Pareto Optimization
Dinithi Jayasuriya, Divake Kumar, Sureshkumar Senthilkumar, Devashri Naik, Nastaran Darabi, Amit Ranjan Trivedi
https://arxiv.org/abs/2510.09941

Causal-Guided Dimension Reduction for Efficient Pareto Optimization
Multi-objective optimization of analog circuits is hindered by high-dimensional parameter spaces, strong feedback couplings, and expensive transistor-level simulations. Evolutionary algorithms such as Non-dominated Sorting Genetic Algorithm II (NSGA-II) are widely used but treat all parameters equally, thereby wasting effort on variables with little impact on performance, which limits their scalability. We introduce CaDRO, a causal-guided dimensionality reduction framework that embeds causal di…

On the universal calibration of Pareto-type linear combination tests
Parijat Chakraborty, F. Richard Guo, Kerby Shedden, Stilian Stoev
https://arxiv.org/abs/2509.12066 https://

On the universal calibration of Pareto-type linear combination tests
It is often of interest to test a global null hypothesis using multiple, possibly dependent, $p$-values by combining their strengths while controlling the Type I error. Recently, several heavy-tailed combinations tests, such as the harmonic mean test and the Cauchy combination test, have been proposed: they map $p$-values into heavy-tailed random variables before combining them in some fashion into a single test statistic. The resulting tests, which are calibrated under the assumption of indepe…

Per-antenna power constraints: constructing Pareto-optimal precoders with cubic complexity under non-negligible noise conditions
Sergey Petrov, Samson Lasaulce, Merouane Debbah
https://arxiv.org/abs/2508.09646

Per-antenna power constraints: constructing Pareto-optimal precoders with cubic complexity under non-negligible noise conditions
Precoding matrix construction is a key element of the wireless signal processing using the multiple-input and multiple-output model. It is established that the problem of global throughput optimization under per-antenna power constraints belongs, in general, to the class of monotonic optimization problems, and is unsolvable in real-time. The most widely used real-time baseline is the suboptimal solution of Zero-Forcing, which achieves a cubic complexity by discarding the background noise coeffi…

Proportional and Pareto-Optimal Allocation of Chores with Subsidy
Jugal Garg, Eklavya Sharma, Xiaowei Wu
https://arxiv.org/abs/2510.10335 https://arxiv.org…

Proportional and Pareto-Optimal Allocation of Chores with Subsidy
We consider the problem of allocating $m$ indivisible chores among $n$ agents with possibly different weights, aiming for a solution that is both fair and efficient. Specifically, we focus on the classic fairness notion of proportionality and efficiency notion of Pareto-optimality. Since proportional allocations may not always exist in this setting, we allow the use of subsidies (monetary compensation to agents) to ensure agents are proportionally-satisfied, and aim to minimize the total subsid…

Analysis of quantities determining the critical inverse temperature in the annealed Potts model with Pareto vertex weights
A. J. E. M. Janssen
https://arxiv.org/abs/2508.21409 h…

Analysis of quantities determining the critical inverse temperature in the annealed Potts model with Pareto vertex weights
We consider in this work the crucial quantity $t_c$ that determines the critical inverse temperature $β_c$ in the $q$-state Potts model on sparse rank-1 random graphs where the vertices are equipped with a Pareto weight density $(τ-1)\,w^{-τ}\,{\cal X}_{[1,\infty)}(w)$. It is shown in \cite{ref1} that this $t_c$ is the unique positive zero of a function ${\cal K}$ that is obtained by an appropriate combination of the stationarity condition and the criticality condition for the case the exter…

Multiplicative Turing Ensembles, Pareto's Law, and Creativity
Alexander Kolpakov, Aidan Rocke
https://arxiv.org/abs/2510.04167 https://arxiv.org/pdf/25…

Multiplicative Turing Ensembles, Pareto's Law, and Creativity
We study integer-valued multiplicative dynamics driven by i.i.d. prime multipliers and connect their macroscopic statistics to universal codelengths. We introduce the Multiplicative Turing Ensemble (MTE) and show how it arises naturally - though not uniquely - from ensembles of probabilistic Turing machines. Our modeling principle is variational: taking Elias' Omega codelength as an energy and imposing maximum entropy constraints yields a canonical Gibbs prior on integers and, by restriction, o…

Generalizable Pareto-Optimal Offloading with Reinforcement Learning in Mobile Edge Computing
Ning Yang, Junrui Wen, Meng Zhang, Ming Tang
https://arxiv.org/abs/2509.10474 https:…

Generalizable Pareto-Optimal Offloading with Reinforcement Learning in Mobile Edge Computing
Mobile edge computing (MEC) is essential for next-generation mobile network applications that prioritize various performance metrics, including delays and energy efficiency. However, conventional single-objective scheduling solutions cannot be directly applied to practical systems in which the preferences (i.e., the weights of different objectives) are often unknown or challenging to specify in advance. In this study, we formulate a multi-objective offloading problem for MEC with multiple edges…

Pareto-optimality of Majoranas in hybrid platforms
Juan Daniel Torres Luna, Sebastian Miles, A. Mert Bozkurt, Chun-Xiao Liu, Antonio L. R. Manesco, Anton R. Akhmerov, Michael Wimmer
https://arxiv.org/abs/2510.07406

Pareto-optimality of Majoranas in hybrid platforms
To observe Majorana bound states, and especially to use them as a qubit, requires careful optimization of competing quality metrics. We systematically compare Majorana quality in proximitized semiconductor nanowires and quantum dot chains. Using multi-objective optimization, we analyze the fundamental trade-offs between topological gap and localization length, two key metrics that determine MBS coherence and operational fidelity. We demonstrate that these quantities cannot be simultaneously opt…

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality
Chaiwon Kim, Jongyeong Lee, Min-hwan Oh
https://arxiv.org/abs/2510.12152 https://

Follow-the-Perturbed-Leader for Decoupled Bandits: Best-of-Both-Worlds and Practicality
We study the decoupled multi-armed bandit (MAB) problem, where the learner selects one arm for exploration and one arm for exploitation in each round. The loss of the explored arm is observed but not counted, while the loss of the exploited arm is incurred without being observed. We propose a policy within the Follow-the-Perturbed-Leader (FTPL) framework using Pareto perturbations. Our policy achieves (near-)optimal regret regardless of the environment, i.e., Best-of-Both-Worlds (BOBW): constan…

When is it (im)possible to respect all individuals' preferences under uncertainty?
Kensei Nakamura
https://arxiv.org/abs/2508.12542 https://arxiv.org/p…

When is it (im)possible to respect all individuals' preferences under uncertainty?
When aggregating Subjective Expected Utility preferences, the Pareto principle leads to an impossibility result unless the individuals have a common belief. This paper examines the source of this impossibility in more detail by considering the aggregation of a general class of incomplete preferences that can represent gradual ambiguity perceptions. Our result shows that the planner cannot avoid ignoring some individuals unless there is a probability distribution that all individuals agree is mo…

A parallel algorithm for generating Pareto-optimal radiosurgery treatment plans
Joakim da Silva, Daniel Hern\'andez Escobar, Tor Kjellsson Lindblom, H{\aa}kan Nordstr\"om, Jens Sj\"olund
https://arxiv.org/abs/2509.08602

A parallel algorithm for generating Pareto-optimal radiosurgery treatment plans
Using inverse planning tools to create radiotherapy treatment plans is an iterative process, where clinical trade-offs are explored by changing the relative importance of different objectives and rerunning the optimizer until a desirable plan is found. We seek to optimize hundreds of radiosurgery treatment plans, corresponding to different weightings of objectives, fast enough to incorporate interactive Pareto navigation of clinical trade-offs into the clinical workflow. We apply the alternatin…

Geometric Brownian motion with random observation time as generalization of the double Pareto distribution
Ken Yamamoto, Takashi Bando, Hirokazu Yanagawa, Yorhihiro Yamazaki
https://arxiv.org/abs/2509.24201

Geometric Brownian motion with random observation time as generalization of the double Pareto distribution
We study the probability distribution of the value of geometric Brownian motion at the stochastic observation time. It is known that the exponentially distributed observation time yields the distribution called the double Pareto distribution, and this study aims to generalize this distribution. First, we provide a calculation formula for the moment of the observed value of geometric Brownian motion using the moment-generating function of the observation time distribution. Next, the probability …

De trias energetica leidt niet alleen in Nederland tot tegenvallende resultaten... Tijd voor een nieuwe versie: fossiel (aardolie, aardgas en kolen) vervangen door electrificatie en warmte. Isoleren: tot het Pareto optimale niveau. Daar zou KGG/EZK met z'n economische achtergrond toch heel erg warm voor moeten lopen...

The "Fabric First" Trap: Decades Of Studies Show Electrification Wins Every Time - CleanTechnica
Over-insulating buildings through “fabric first” wastes money and delays decarbonization; electrification with moderate insulation gets much bigger results.

Bridge Sampling Diagnostics
Giorgio Micaletto, Aki Vehtari
https://arxiv.org/abs/2508.14487 https://arxiv.org/pdf/2508.14487…

Bridge Sampling Diagnostics
In Bayesian statistics, the marginal likelihood is used for model selection and averaging, yet it is often challenging to compute accurately for complex models. Approaches such as bridge sampling, while effective, may suffer from issues of high variability of the estimates. We present how to estimate Monte Carlo standard error (MCSE) for bridge sampling, and how to diagnose the reliability of MCSE estimates using Pareto-$\hat{k}$ and block reshuffling diagnostics without the need to repeatedly …

An Italian Gender Equality Index
Lorenzo Panebianco
https://arxiv.org/abs/2509.17140 https://arxiv.org/pdf/2509.17140…

An Italian Gender Equality Index
Following the works on the Gender Equality Index (GEI), we propose a composite indicator to measure the gender gap across Italian regions. Our approach differs from the original GEI in both the selection of indicators and the aggregation methodology. Specifically, the choice of indicators is inspired by the both the GEI and the WeWorld Index Italia, while the aggregation relies on an original variation of the Mazziotta-Pareto Index. Finally, we apply our results drawing 2023 open data.

Complexity Bounds for Smooth Convex Multiobjective Optimization
Phillipe R. Sampaio
https://arxiv.org/abs/2509.13550 https://arxiv.org/pdf/2509.13550

Complexity Bounds for Smooth Convex Multiobjective Optimization
We study the oracle complexity of finding $\varepsilon$-Pareto stationary points in smooth multiobjective optimization with $m$ objectives. The progress metric is the Pareto stationarity gap $\mathcal{G}(x)$ (the norm of an optimal convex combination of gradients). Our contributions are fourfold. (i) For strongly convex objectives, any span first-order method (iterates lie in the span of past gradients) exhibits linear convergence no faster than $\exp(-Θ(T/\sqrtκ))$ after $T$ oracle calls, wh…

RDD: Pareto Analysis of the Rate-Distortion-Distinguishability Trade-off
Andriy Enttsel, Alex Marchioni, Andrea Zanellini, Mauro Mangia, Gianluca Setti, Riccardo Rovatti
https://arxiv.org/abs/2509.24805

RDD: Pareto Analysis of the Rate-Distortion-Distinguishability Trade-off
Extensive monitoring systems generate data that is usually compressed for network transmission. This compressed data might then be processed in the cloud for tasks such as anomaly detection. However, compression can potentially impair the detector's ability to distinguish between regular and irregular patterns due to information loss. Here we extend the information-theoretic framework introduced in [1] to simultaneously address the trade-off between the three features on which the effectiveness…

Self-supervised diffusion model fine-tuning for costate initialization using Markov chain Monte Carlo
Jannik Graebner, Ryne Beeson
https://arxiv.org/abs/2510.02527 https://

Self-supervised diffusion model fine-tuning for costate initialization using Markov chain Monte Carlo
Global search and optimization of long-duration, low-thrust spacecraft trajectories with the indirect method is challenging due to a complex solution space and the difficulty of generating good initial guesses for the costate variables. This is particularly true in multibody environments. Given data that reveals a partial Pareto optimal front, it is desirable to find a flexible manner in which the Pareto front can be completed and fronts for related trajectory problems can be found. In this wor…

SwiReasoning: Switch-Thinking in Latent and Explicit for Pareto-Superior Reasoning LLMs
Dachuan Shi, Abedelkadir Asi, Keying Li, Xiangchi Yuan, Leyan Pan, Wenke Lee, Wen Xiao
https://arxiv.org/abs/2510.05069

SwiReasoning: Switch-Thinking in Latent and Explicit for Pareto-Superior Reasoning LLMs
Recent work shows that, beyond discrete reasoning through explicit chain-of-thought steps, which are limited by the boundaries of natural languages, large language models (LLMs) can also reason continuously in latent space, allowing richer information per step and thereby improving token efficiency. Despite this promise, latent reasoning still faces two challenges, especially in training-free settings: 1) purely latent reasoning broadens the search distribution by maintaining multiple implicit …

Crosslisted article(s) found for eess.IV. https://arxiv.org/list/eess.IV/new
[1/1]:
- Adaptive Pareto-Optimal Token Merging for Edge Transformer Models in Semantic Communication
Omar Erak, Omar Alhussein, Hatem Abou-Zeid, Mehdi Bennis

Improved Dominance Filtering for Unions and Minkowski Sums of Pareto Sets
Konstantinos Karathanasis, Spyros Kontogiannis, Christos Zaroliagis
https://arxiv.org/abs/2508.20689 ht…

Improved Dominance Filtering for Unions and Minkowski Sums of Pareto Sets
A key task in multi-objective optimization is to compute the Pareto subset or frontier $P$ of a given $d$-dimensional objective space $F$; that is, a maximal subset $P\subseteq F$ such that every element in $P$ is not-dominated (it is not worse in all criteria) by any element in $F$. This process, called dominance-filtering, often involves handling objective spaces derived from either the union or the Minkowski sum of two given partial objective spaces which are Pareto sets themselves, and cons…

Adaptive Pareto-Optimal Token Merging for Edge Transformer Models in Semantic Communication
Omar Erak, Omar Alhussein, Hatem Abou-Zeid, Mehdi Bennis
https://arxiv.org/abs/2509.09168

Adaptive Pareto-Optimal Token Merging for Edge Transformer Models in Semantic Communication
Large-scale transformer models have emerged as a powerful tool for semantic communication systems, enabling edge devices to extract rich representations for robust inference across noisy wireless channels. However, their substantial computational demands remain a major barrier to practical deployment in resource-constrained 6G networks. In this paper, we present a training-free framework for adaptive token merging in pretrained vision transformers to jointly reduce inference time and transmissi…

A Possibility Frontier Approach to Diverse Talent Selection
Neil Natarajan, Kadeem Noray
https://arxiv.org/abs/2510.06119 https://arxiv.org/pdf/2510.06119

A Possibility Frontier Approach to Diverse Talent Selection
Organizations (e.g., talent investment programs, schools, firms) are perennially interested in selecting cohorts of talented people. And organizations are increasingly interested in selecting diverse cohorts. Except in trivial cases, measuring the tradeoff between cohort diversity and talent is computationally difficult. Thus, organizations are presently unable to make Pareto-efficient decisions about these tradeoffs. We introduce an algorithm that approximates upper bounds on cohort talent and…

AI-SearchPlanner: Modular Agentic Search via Pareto-Optimal Multi-Objective Reinforcement Learning
Lang Mei, Zhihan Yang, Chong Chen
https://arxiv.org/abs/2508.20368 https://

AI-SearchPlanner: Modular Agentic Search via Pareto-Optimal Multi-Objective Reinforcement Learning
Recent studies have explored integrating Large Language Models (LLMs) with search engines to leverage both the LLMs' internal pre-trained knowledge and external information. Specially, reinforcement learning (RL) has emerged as a promising paradigm for enhancing LLM reasoning through multi-turn interactions with search engines. However, existing RL-based search agents rely on a single LLM to handle both search planning and question-answering (QA) tasks in an end-to-end manner, which limits thei…

Simulation of a generalized asset exchange model with investment and income mechanisms
Jan Tobochnik, Harvey Gould, William Klein
https://arxiv.org/abs/2508.05810 https://

Simulation of a generalized asset exchange model with investment and income mechanisms
An agent-based model of the economy is generalized to incorporate investment and guaranteed income mechanisms in addition to the exchange and distribution mechanisms considered in earlier models. We find realistic wealth distributions and realistic values of the Gini coefficients and the Pareto index. We also show that although the system reaches a steady state, the system is not in thermal equilibrium. The nonequilibrium behavior is associated with the multiplicative noise generated by the inv…

🌍 The climate crisis demands urgent action. But which actions are best?
Decision makers face tough trade-offs:
Policy A lowers emissions at home but increases reliance on imports.
Policy B cuts emissions long-term but raises unemployment short-term.
Policy C boosts jobs now but increases emissions in the near term.
None of these choices are simple. A policy that looks good locally may increase global emissions, or its effects may depend on what other countries d…

Replaced article(s) found for cs.DM. https://arxiv.org/list/cs.DM/new
[1/1]:
- Disk and Partial Disk Inspection: Worst- to Average-Case and Pareto Upper Bounds
James Conley, Konstantinos Georgiou

Replaced article(s) found for cs.IT. https://arxiv.org/list/cs.IT/new
[1/1]:
- PAC codes with Bounded-Complexity Sequential Decoding: Pareto Distribution and Code Design
Mohsen Moradi, Hessam Mahdavifar

Chinese ModernBERT with Whole-Word Masking
Zeyu Zhao, Ningtao Wang, Xing Fu, Yu Cheng
https://arxiv.org/abs/2510.12285 https://arxiv.org/pdf/2510.12285

Chinese ModernBERT with Whole-Word Masking
Encoder-only Transformers have advanced along three axes -- architecture, data, and systems -- yielding Pareto gains in accuracy, speed, and memory efficiency. Yet these improvements have not fully transferred to Chinese, where tokenization and morphology differ markedly from English. We introduce Chinese ModernBERT, a from-scratch Chinese encoder that couples: (i) a hardware-aware 32k BPE vocabulary tailored to frequent Chinese affixes/compounds, lowering the embedding budget; (ii) whole-word …

Achieving Pareto Optimality in Games via Single-bit Feedback
Seref Taha Kiremitci, Ahmed Said Donmez, Muhammed O. Sayin
https://arxiv.org/abs/2509.25921 https://

Achieving Pareto Optimality in Games via Single-bit Feedback
Efficient coordination in multi-agent systems often incurs high communication overhead or slow convergence rates, making scalable welfare optimization difficult. We propose Single-Bit Coordination Dynamics for Pareto-Efficient Outcomes (SBC-PE), a decentralized learning algorithm requiring only a single-bit satisfaction signal per agent each round. Despite this extreme efficiency, SBC-PE guarantees convergence to the exact optimal solution in arbitrary finite games. We establish explicit regret…

Joint modeling of low and high extremes using a multivariate extended generalized Pareto distribution
Noura Alotaibi, Matthew Sainsbury-Dale, Philippe Naveau, Carlo Gaetan, Rapha\"el Huser
https://arxiv.org/abs/2509.05982

Joint modeling of low and high extremes using a multivariate extended generalized Pareto distribution
In most risk assessment studies, it is important to accurately capture the entire distribution of the multivariate random vector of interest from low to high values. For example, in climate sciences, low precipitation events may lead to droughts, while heavy rainfall may generate large floods, and both of these extreme scenarios can have major impacts on the safety of people and infrastructure, as well as agricultural or other economic sectors. In the univariate case, the extended generalized P…

Spatial Modeling and Risk Zoning of Global Extreme Precipitation via Graph Neural Networks and r-Pareto Processes
Zimu Wang, Yifan Wu, Daning Bi
https://arxiv.org/abs/2509.10362

Spatial Modeling and Risk Zoning of Global Extreme Precipitation via Graph Neural Networks and r-Pareto Processes
Extreme precipitation events occurring over large spatial domains pose substantial threats to societies because they can trigger compound flooding, landslides, and infrastructure failures across wide areas. A hybrid framework for spatial extreme precipitation modeling and risk zoning is proposed that integrates graph neural networks with r-Pareto processes (GNN-rP). Unlike traditional statistical spatial extremes models, this approach learns nonlinear, nonstationary dependence structures from p…

Pareto-optimal Non-uniform Language Generation
Moses Charikar, Chirag Pabbaraju
https://arxiv.org/abs/2510.02795 https://arxiv.org/pdf/2510.02795

Pareto-optimal Non-uniform Language Generation
Kleinberg and Mullainathan (2024) recently proposed an interesting model for language generation in the limit: Given a countable collection of languages, and an adversary enumerating the strings of some language $L$ from the collection, the objective is to generate new strings from the target language, such that all strings generated beyond some finite time are valid. Li, Raman and Tewari (2024) and Charikar and Pabbaraju (2024) showed strong non-uniform generation guarantees in this model, giv…

Pareto-Grid-Guided Large Language Models for Fast and High-Quality Heuristics Design in Multi-Objective Combinatorial Optimization
Minh Hieu Ha, Hung Phan, Tung Duy Doan, Tung Dao, Dao Tran, Huynh Thi Thanh Binh
https://arxiv.org/abs/2507.20923

Pareto-Grid-Guided Large Language Models for Fast and High-Quality Heuristics Design in Multi-Objective Combinatorial Optimization
Multi-objective combinatorial optimization problems (MOCOP) frequently arise in practical applications that require the simultaneous optimization of conflicting objectives. Although traditional evolutionary algorithms can be effective, they typically depend on domain knowledge and repeated parameter tuning, limiting flexibility when applied to unseen MOCOP instances. Recently, integration of Large Language Models (LLMs) into evolutionary computation has opened new avenues for automatic heuristi…

Robust Econometrics for Growth-at-Risk
Tobias Adrian, Yuya Sasaki, Yulong Wang
https://arxiv.org/abs/2508.00263 https://arxiv.org/pdf/2508.00263

Robust Econometrics for Growth-at-Risk
The Growth-at-Risk (GaR) framework has garnered attention in recent econometric literature, yet current approaches implicitly assume a constant Pareto exponent. We introduce novel and robust econometrics to estimate the tails of GaR based on a rigorous theoretical framework and establish validity and effectiveness. Simulations demonstrate consistent outperformance relative to existing alternatives in terms of predictive accuracy. We perform a long-term GaR analysis that provides accurate and in…

Ranking Methods for Skyline Queries
Micka\"el Martin-Nevot (AMU), Lotfi Lakhal (AMU)
https://arxiv.org/abs/2507.21860 https://arxiv.org/pdf/2507.21860…

Ranking Methods for Skyline Queries
{Multi-criteria decision analysis in databases has been actively studied, especially through the Skyline operator. Yet, few approaches offer a relevant comparison of Pareto optimal, or Skyline, points for high cardinality result sets. We propose to improve the dp-idp method, inspired by tf-idf, a recent approach computing a score for each Skyline point, by introducing the concept of dominance hierarchy. As dp-idp lacks efficiency and does not ensure a distinctive rank, we introduce the RankSky …

Fracture toughness and auxeticity in disordered metamaterials
Hannes Holey, Andrea Lorenzo Henri Sergio Detry, Silvia Bonfanti, Roberto Guerra, Anshul D. S. Parmar, Jacopo Fiocchi, Ausonio Tuissi, Michael Zaiser, Stefano Zapperi
https://arxiv.org/abs/2508.06505

Fracture toughness and auxeticity in disordered metamaterials
Auxetic metamaterials are commonly thought to exhibit favorable mechanical properties, notably high energy absorption. Here we investigate disordered metamaterials obtained from random beam networks by optimizing simultaneously auxeticity and the energy absorbed before fracture. By giving different weights to these optimization targets, we demonstrate that the optimal configurations are connected along a Pareto front where high auxeticity implies comparatively low energy absorption and vice ver…

Multi-Objective Optimizations of High Gradient C-band Photoinjector for High Bunch Charge Applications
M. Kaemingk, P. M. Anisimov, J. M. Maxson, J. B. Rosenzweig, E. I. Simakov, H. Xu
https://arxiv.org/abs/2509.11014

Multi-Objective Optimizations of High Gradient C-band Photoinjector for High Bunch Charge Applications
The high gradients potentially achievable in distributed-coupling C-band photoinjectors make them attractive for many high brightness applications. Here we discuss optimization results for a 1.6 cell C-band photoinjector with a 240 MV/m peak field at the cathode that delivers a 250 pC electron bunch charge. We use a Multi-Objective Genetic Algorithm (MOGA), obtaining a Pareto front of emittance vs. bunch length. We also perform MOGA optimizations including an aperture to retain only a bright be…

Multi-objective Evolutionary Algorithms (MOEAs) in PMEDM - A Comparative Study in Pareto Frontier
Mohsen Asghari Ilani, Yaser Mike Banad
https://arxiv.org/abs/2509.01775 https:/…

Multi-objective Evolutionary Algorithms (MOEAs) in PMEDM - A Comparative Study in Pareto Frontier
Electrical discharge machining (EDM) is a crucial process in precision manufacturing, leveraging electro-thermal energy to remove material without electrode contact. In this study, we delve into the realm of Machine Learning (ML) to enhance the efficiency and precision of EDM, particularly focusing on Powder-Mixed Electrical Discharge Machining (PMEDM) with the integration of a vibration system. We comprehensively evaluate four leading ML models - Deep Neural Network (DNN), Extreme Gradient Boo…

Crosslisted article(s) found for eess.SP. https://arxiv.org/list/eess.SP/new
[1/1]:
- Multi-Agent Inverse Reinforcement Learning for Identifying Pareto-Efficient Coordination -- A Dis...
Luke Snow, Vikram Krishnamurthy

When Can Communication Lead to Efficiency?
Itai Arieli, Yakov Babichenko, Atulya Jain, Rann Smorodinsky
https://arxiv.org/abs/2510.12508 https://arxiv.org/…

When Can Communication Lead to Efficiency?
We study games with incomplete information and characterize when a feasible outcome is Pareto efficient. We show that any outcome with excessive randomization over actions is inefficient. Generically, efficiency requires that the total number of actions taken across states be strictly less than the sum of the number of players and states. We then examine the efficiency of equilibrium outcomes in communication models. Generically, a cheap talk outcome is efficient only if it is pure. When the se…

Computing Approximately Proportional Allocations of Indivisible Goods: Beyond Additive and Monotone Valuations
Martin Jupakkal Andersen, Ioannis Caragiannis, Anders Bo Ipsen, Alexander S{\o}ltoft
https://arxiv.org/abs/2508.12453

Computing Approximately Proportional Allocations of Indivisible Goods: Beyond Additive and Monotone Valuations
Although approximate notions of envy-freeness-such as envy-freeness up to one good (EF1)-have been extensively studied for indivisible goods, the seemingly simpler fairness concept of proportionality up to one good (PROP1) has received far less attention. For additive valuations, every EF1 allocation is PROP1, and well-known algorithms such as Round-Robin and Envy-Cycle Elimination compute such allocations in polynomial time. PROP1 is also compatible with Pareto efficiency, as maximum Nash welf…

Crosslisted article(s) found for cs.CY. https://arxiv.org/list/cs.CY/new
[1/1]:
- Learning Pareto-Optimal Pandemic Intervention Policies with MORL
Marian Chen, Miri Zilka

Fast and the Furious: Hot Starts in Pursuit-Evasion Games
Gabriel Smithline, Scott Nivison
https://arxiv.org/abs/2510.10830 https://arxiv.org/pdf/2510.1083…

Fast and the Furious: Hot Starts in Pursuit-Evasion Games
Effectively positioning pursuers in pursuit-evasion games without prior knowledge of evader locations remains a significant challenge. A novel approach that combines game-theoretic control theory with Graph Neural Networks is introduced in this work. By conceptualizing pursuer configurations as strategic arrangements and representing them as graphs, a Graph Characteristic Space is constructed via multi-objective optimization to identify Pareto-optimal configurations. A Graph Convolutional Netwo…

Multi-Objective Multi-Agent Path Finding with Lexicographic Cost Preferences
Pulkit Rustagi, Kyle Hollins Wray, Sandhya Saisubramanian
https://arxiv.org/abs/2510.07276 https://

Multi-Objective Multi-Agent Path Finding with Lexicographic Cost Preferences
Many real-world scenarios require multiple agents to coordinate in shared environments, while balancing trade-offs between multiple, potentially competing objectives. Current multi-objective multi-agent path finding (MO-MAPF) algorithms typically produce conflict-free plans by computing Pareto frontiers. They do not explicitly optimize for user-defined preferences, even when the preferences are available, and scale poorly with the number of objectives. We propose a lexicographic framework for m…

COM-BOM: Bayesian Exemplar Search for Efficiently Exploring the Accuracy-Calibration Pareto Frontier
Gaoxiang Luo, Aryan Deshwal
https://arxiv.org/abs/2510.01178 https://…

COM-BOM: Bayesian Exemplar Search for Efficiently Exploring the Accuracy-Calibration Pareto Frontier
Selecting an optimal set of exemplars is critical for good performance of in-context learning. However, prior exemplar search methods narrowly optimize for predictive accuracy, critically neglecting model calibration--a key determinant of trustworthiness and safe deployment. In this paper, we formulate exemplar selection as a multi-objective optimization problem, explicitly targeting both the maximization of predictive accuracy and the minimization of expected calibration error. We solve this p…

Crosslisted article(s) found for q-bio.PE. https://arxiv.org/list/q-bio.PE/new
[1/1]:
- Learning Pareto-Optimal Pandemic Intervention Policies with MORL
Marian Chen, Miri Zilka

Replaced article(s) found for cs.DM. https://arxiv.org/list/cs.DM/new
[1/1]:
- Disk and Partial Disk Inspection: Worst- to Average-Case and Pareto Upper Bounds
James Conley, Konstantinos Georgiou

Equivalence by coupling for heterogeneous SIS models*
Jean-Fran\c{c}ois Delmas (CERMICS), Dylan Dronnier (CERMICS), Pierre-Andr\'e Zitt (LAMA)
https://arxiv.org/abs/2509.04898

Equivalence by coupling for heterogeneous SIS models*
We consider the optimal allocation of (perfect) vaccine in an heterogeneous SIS model. Using a coupling approach, we explain how different models for the heterogeneity of the population lead to the same Pareto frontier in the cost/loss valuation of the vaccinations strategies. This covers in particular the elementary continuous representation of discrete models and the measure preserving transformation which appears in graphon theory.

Reduced Order Data-driven Twin Models for Nonlinear PDEs by Randomized Koopman Orthogonal Decomposition and Explainable Deep Learning
D. A. Bistrian
https://arxiv.org/abs/2508.03325

Reduced Order Data-driven Twin Models for Nonlinear PDEs by Randomized Koopman Orthogonal Decomposition and Explainable Deep Learning
This study introduces a data-driven twin modeling framework based on modern Koopman operator theory, offering a significant advancement over classical modal decomposition by accurately capturing nonlinear dynamics with reduced complexity and no manual parameter adjustment. The method integrates a novel algorithm with Pareto front analysis to construct a compact, high-fidelity reduced-order model that balances accuracy and efficiency. An explainable NLARX deep learning framework enables real-tim…

Active Learning and Explainable AI for Multi-Objective Optimization of Spin Coated Polymers
Brendan Young, Brendan Alvey, Andreas Werbrouck, Will Murphy, James Keller, Mattias J. Young, Matthew Maschmann
https://arxiv.org/abs/2509.08988

Active Learning and Explainable AI for Multi-Objective Optimization of Spin Coated Polymers
Spin coating polymer thin films to achieve specific mechanical properties is inherently a multi-objective optimization problem. We present a framework that integrates an active Pareto front learning algorithm (PyePAL) with visualization and explainable AI techniques to optimize processing parameters. PyePAL uses Gaussian process models to predict objective values (hardness and elasticity) from the design variables (spin speed, dilution, and polymer mixture), guiding the adaptive selection of sa…

A many-objective evolutionary algorithm using indicator-driven weight vector optimization
Xiaojing Han, Yuanxin Li
https://arxiv.org/abs/2510.02709 https://

A many-objective evolutionary algorithm using indicator-driven weight vector optimization
For regular Pareto Fronts (PFs), such as those that are smooth, continuous, and uniformly distributed, using fixed weight vectors is sufficient for multi-objective optimization approaches using decomposition. However, when encountering irregular PFs-including degenerate, disconnected, inverted, etc. Fixed weight vectors can often cause a non-uniform distribution of the sets or even poor optimization results. To address this issue, this study proposes an adaptive many-objective evolutionary algo…

Integrating AI and Ensemble Forecasting: Explainable Materials Planning with Scorecards and Trend Insights for a Large-Scale Manufacturer
Saravanan Venkatachalam
https://arxiv.org/abs/2510.01006

Integrating AI and Ensemble Forecasting: Explainable Materials Planning with Scorecards and Trend Insights for a Large-Scale Manufacturer
This paper presents a practical architecture for after-sales demand forecasting and monitoring that unifies a revenue- and cluster-aware ensemble of statistical, machine-learning, and deep-learning models with a role-driven analytics layer for scorecards and trend diagnostics. The framework ingests exogenous signals (installed base, pricing, macro indicators, life cycle, seasonality) and treats COVID-19 as a distinct regime, producing country-part forecasts with calibrated intervals. A Pareto-a…

Hierarchical Maximum Entropy via the Renormalization Group
Amir R. Asadi
https://arxiv.org/abs/2509.01424 https://arxiv.org/pdf/2509.01424

Hierarchical Maximum Entropy via the Renormalization Group
Hierarchical structures, which include multiple levels, are prevalent in statistical and machine-learning models as well as physical systems. Extending the foundational result that the maximum entropy distribution under mean constraints is given by the exponential Gibbs-Boltzmann form, we introduce the framework of "hierarchical maximum entropy" to address these multilevel models. We demonstrate that Pareto optimal distributions, which maximize entropies across all levels of hierarchical transf…

Maximizing Social Welfare with Side Payments
Ivan Geffner, Caspar Oesterheld, Vincent Conitzer
https://arxiv.org/abs/2508.07147 https://arxiv.org/pdf/2508.…

Maximizing Social Welfare with Side Payments
We examine normal-form games in which players may \emph{pre-commit} to outcome-contingent transfers before choosing their actions. In the one-shot version of this model, Jackson and Wilkie showed that side contracting can backfire: even a game with a Pareto-optimal Nash equilibrium can devolve into inefficient equilibria once unbounded, simultaneous commitments are allowed. The root cause is a prisoner's dilemma effect, where each player can exploit her commitment power to reshape the equilibri…

Robust Tail Index Estimation under Random Censoring via Minimum Density Power Divergence
Nour Elhouda Guesmia, Abdelhakim Necir, Djamel Meraghni
https://arxiv.org/abs/2507.18737

Robust Tail Index Estimation under Random Censoring via Minimum Density Power Divergence
Based on the minimum density power divergence approach, we propose a robust estimator of the tail index for randomly right-censored data from a Pareto-type distribution. We establish its consistency and asymptotic normality. An extensive simulation study is performed to assess the finite-sample behavior of the estimator in comparison with existing ones. The methodology is further illustrated through an application to a real AIDS survival dataset.

Multiobjective Balanced Gradient Flow
Yingdong Yin
https://arxiv.org/abs/2508.01775 https://arxiv.org/pdf/2508.01775

Multiobjective Balanced Gradient Flow
This paper proposes a novel dynamical system called the Multiobjective Balanced Gradient Flow (MBGF), offering a dynamical perspective for normalized gradient methods in a class of multi-objective optimization problems. Under certain assumptions, we prove the existence of solutions for MBGF trajectories and establish their convergence to weak Pareto points in the case of convex objective functions. For both convex and non-convex scenarios, we provide convergence rates of $O(1/t)$ and $O(1/\sqrt…

Cellular, Cell-less, and Everything in Between: A Unified Framework for Utility Region Analysis in Wireless Networks
Renato Luis Garrido Cavalcante, Tomasz Piotrowski, Slawomir Stanczak
https://arxiv.org/abs/2507.23707

Cellular, Cell-less, and Everything in Between: A Unified Framework for Utility Region Analysis in Wireless Networks
We introduce a unified framework for analyzing utility regions of wireless networks, with a focus on the signal-to-interference-noise-ratio (SINR) and achievable rate regions. The framework provides valuable insights into interference patterns of modern network architectures, such as cell-less and extremely large MIMO networks, and it generalizes existing characterizations of the weak Pareto boundary. A central contribution is the derivation of sufficient conditions that guarantee convexity of …

GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning
Lakshya A Agrawal, Shangyin Tan, Dilara Soylu, Noah Ziems, Rishi Khare, Krista Opsahl-Ong, Arnav Singhvi, Herumb Shandilya, Michael J Ryan, Meng Jiang, Christopher Potts, Koushik Sen, Alexandros G. Dimakis, Ion Stoica, Dan Klein, Matei Zaharia, Omar Khattab
https://arx…

GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning
Large language models (LLMs) are increasingly adapted to downstream tasks via reinforcement learning (RL) methods like Group Relative Policy Optimization (GRPO), which often require thousands of rollouts to learn new tasks. We argue that the interpretable nature of language can often provide a much richer learning medium for LLMs, compared with policy gradients derived from sparse, scalar rewards. To test this, we introduce GEPA (Genetic-Pareto), a prompt optimizer that thoroughly incorporates …

Screening with damages and ordeals
Filip Tokarski
https://arxiv.org/abs/2508.04456 https://arxiv.org/pdf/2508.04456…

Screening with damages and ordeals
A welfare-maximizing designer allocates two kinds of goods using two wasteful screening instruments: ordeals, which enter agents' utilities additively, and damages, which harm agents in proportion to their values for the goods. If agents have common valuations for one of the goods, damages always lead to Pareto-dominated mechanisms: any allocation using damages can also be implemented with ordeals alone, while also leaving greater rents to inframarginal types. However, using damages can be opti…

Integrating AI and Ensemble Forecasting: Explainable Materials Planning with Scorecards and Trend Insights for a Large-Scale Manufacturer
Saravanan Venkatachalam
https://arxiv.org/abs/2510.01006

Integrating AI and Ensemble Forecasting: Explainable Materials Planning with Scorecards and Trend Insights for a Large-Scale Manufacturer
This paper presents a practical architecture for after-sales demand forecasting and monitoring that unifies a revenue- and cluster-aware ensemble of statistical, machine-learning, and deep-learning models with a role-driven analytics layer for scorecards and trend diagnostics. The framework ingests exogenous signals (installed base, pricing, macro indicators, life cycle, seasonality) and treats COVID-19 as a distinct regime, producing country-part forecasts with calibrated intervals. A Pareto-a…

Replaced article(s) found for cs.GT. https://arxiv.org/list/cs.GT/new
[1/1]:
- Stackelberg-Pareto Synthesis with Quantitative Reachability Objectives
Thomas Brihaye, V\'eronique Bruy\`ere, Gaspard Reghem

Speeding Up the NSGA-II via Dynamic Population Sizes
Benjamin Doerr, Martin S. Krejca, Simon Wietheger
https://arxiv.org/abs/2509.01739 https://arxiv.org/p…

Speeding Up the NSGA-II via Dynamic Population Sizes
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) at once, MOEAs are typically run with a large, static population of solution candidates, which can slow down the algorithm. We propose the dynamic NSGA-II (dNSGA-II), which is based on the popular NSGA-II and features a non-static population size. The dNSGA-II starts with a small initial populati…

Replaced article(s) found for math.PR. https://arxiv.org/list/math.PR/new
[1/1]:
- Weakly self-avoiding walk in a pareto-distributed random potential
Wolfgang K\"onig, Nicolas P\'etr\'elis, Renato Soares dos Santos, Willem van Zuijlen

FoMEMO: Towards Foundation Models for Expensive Multi-objective Optimization
Yiming Yao, Fei Liu, Liang Zhao, Xi Lin, Qingfu Zhang
https://arxiv.org/abs/2509.03244 https://

FoMEMO: Towards Foundation Models for Expensive Multi-objective Optimization
Expensive multi-objective optimization is a prevalent and crucial concern in many real-world scenarios, where sample-efficiency is vital due to the limited evaluations to recover the true Pareto front for decision making. Existing works either involve rebuilding Gaussian process surrogates from scratch for each objective in each new problem encountered, or rely on extensive past domain experiments for pre-training deep learning models, making them hard to generalize and impractical to cope with…

Game-Theoretic Understandings of Multi-Agent Systems with Multiple Objectives
Yue Wang
https://arxiv.org/abs/2509.23026 https://arxiv.org/pdf/2509.23026

Game-Theoretic Understandings of Multi-Agent Systems with Multiple Objectives
In practical multi-agent systems, agents often have diverse objectives, which makes the system more complex, as each agent's performance across multiple criteria depends on the joint actions of all agents, creating intricate strategic trade-offs. To address this, we introduce the Multi-Objective Markov Game (MOMG), a framework for multi-agent reinforcement learning with multiple objectives. We propose the Pareto-Nash Equilibrium (PNE) as the primary solution concept, where no agent can unilater…

A Nonmonotone Front Descent Method for Bound-Constrained Multi-Objective Optimization
Pierluigi Mansueto
https://arxiv.org/abs/2509.02409 https://arxiv.org…

A Nonmonotone Front Descent Method for Bound-Constrained Multi-Objective Optimization
We introduce a nonmonotone extension of the Front Descent framework for multiobjective optimization. The method uses novel nonmonotone line searches that allow temporary increases in some objective functions. To our knowledge, this is the first descent algorithm employing nonmonotone strategies to generate point sets approximating the Pareto front. We establish convergence properties for the resulting sequences of sets, analogous to the original framework, and present numerical results confirmi…

An Interactive Framework for Finding the Optimal Trade-off in Differential Privacy
Yaohong Yang, Aki Rehn, Sammie Katt, Antti Honkela, Samuel Kaski
https://arxiv.org/abs/2509.04290

An Interactive Framework for Finding the Optimal Trade-off in Differential Privacy
Differential privacy (DP) is the standard for privacy-preserving analysis, and introduces a fundamental trade-off between privacy guarantees and model performance. Selecting the optimal balance is a critical challenge that can be framed as a multi-objective optimization (MOO) problem where one first discovers the set of optimal trade-offs (the Pareto front) and then learns a decision-maker's preference over them. While a rich body of work on interactive MOO exists, the standard approach -- mode…

Biobjective optimization with M-convex functions
Ellen H. Fukuda, Satoru Iwata, Itsuki Nakagawa
https://arxiv.org/abs/2507.23423 https://arxiv.org/pdf/2507…

Biobjective optimization with M-convex functions
In this paper, we deal with two ingredients that, as far as we know, have not been combined until now: multiobjective optimization and discrete convex analysis. First, we show that the entire Pareto optimal value set can be obtained in polynomial time for biobjective optimization problems with discrete convex functions, in particular, involving an M$^\natural$-convex function and a linear function with binary coefficients. We also observe that a more efficient algorithm can be obtained in the s…

Rethinking Thinking Tokens: LLMs as Improvement Operators
Lovish Madaan, Aniket Didolkar, Suchin Gururangan, John Quan, Ruan Silva, Ruslan Salakhutdinov, Manzil Zaheer, Sanjeev Arora, Anirudh Goyal
https://arxiv.org/abs/2510.01123

Rethinking Thinking Tokens: LLMs as Improvement Operators
Reasoning training incentivizes LLMs to produce long chains of thought (long CoT), which among other things, allows them to explore solution strategies with self-checking. This results in higher accuracy, but inflates context length, token/compute cost, and answer latency. We ask: Can current models leverage their metacognition to provide other combinations on this Pareto frontier, e.g., better accuracy with lower context length and/or latency? Abstractly, we view the model as an improvement op…

Strict Comparisons of Infinite Utility Streams
Michael Greinecker, Michael Nielsen
https://arxiv.org/abs/2507.20567 https://arxiv.org/pdf/2507.20567…

Strict Comparisons of Infinite Utility Streams
There exists a preference relation on infinite utility streams that does not discriminate between different periods, satisfies the Pareto criterion, and so that almost all pairs of utility streams are strictly comparable. Such a preference relation provides a counterexample to a claim in [Zame, William R. ``Can intergenerational equity be operationalized?'' Theoretical Economics 2.2 (2007): 187-202.]

A Partially Derivative-Free Proximal Method for Composite Multiobjective Optimization in the H\"older Setting
V. S. Amaral, P. B. Assun\c{c}\~ao, D. R. Souza
https://arxiv.org/abs/2508.20071

A Partially Derivative-Free Proximal Method for Composite Multiobjective Optimization in the Hölder Setting
This paper presents an algorithm for solving multiobjective optimization problems involving composite functions, where we minimize a quadratic model that approximates $F(x) - F(x^k)$ and that can be derivative-free. We establish theoretical assumptions about the component functions of the composition and provide comprehensive convergence and complexity analysis. Specifically, we prove that the proposed method converges to a weakly $\varepsilon$-approximate Pareto point in at most $\mathcal{O}\l…

Warm-starting Strategies in Scalarization Methods for Multi-Objective Optimization
Stephanie Riedm\"uller (Zuse Institute Berlin), Janina Zittel (Zuse Institute Berlin), Thorsten Koch (Zuse Institute Berlin, Technische Universit\"at Berlin)
https://arxiv.org/abs/2507.21933

Warm-starting Strategies in Scalarization Methods for Multi-Objective Optimization
We explore how warm-starting strategies can be integrated into scalarization-based approaches for multi-objective optimization in (mixed) integer linear programming. Scalarization methods remain widely used classical techniques to compute Pareto-optimal solutions in applied settings. They are favored due to their algorithmic simplicity and broad applicability across continuous and integer programs with an arbitrary number of objectives. While warm-starting has been applied in this context befor…