Tootfinder

Constraint-Consistent Control of Task-Based and Kinematic RCM Constraints for Surgical Robots
Yu Li, Hamid Sadeghian, Zewen Yang, Valentin Le Mesle, Sami Haddadin
https://arxiv.org/abs/2509.14075

Constraint-Consistent Control of Task-Based and Kinematic RCM Constraints for Surgical Robots
Robotic-assisted minimally invasive surgery (RAMIS) requires precise enforcement of the remote center of motion (RCM) constraint to ensure safe tool manipulation through a trocar. Achieving this constraint under dynamic and interactive conditions remains challenging, as existing control methods either lack robustness at the torque level or do not guarantee consistent RCM constraint satisfaction. This paper proposes a constraint-consistent torque controller that treats the RCM as a rheonomic hol…

Information-Theoretic Fairness with A Bounded Statistical Parity Constraint
Amirreza Zamani, Abolfazl Changizi, Ragnar Thobaben, Mikael Skoglund
https://arxiv.org/abs/2508.12847

Information-Theoretic Fairness with A Bounded Statistical Parity Constraint
In this paper, we study an information-theoretic problem of designing a fair representation that attains bounded statistical (demographic) parity. More specifically, an agent uses some useful data $X$ to solve a task $T$. Since both $X$ and $T$ are correlated with some sensitive attribute or secret $S$, the agent designs a representation $Y$ that satisfies a bounded statistical parity and/or privacy leakage constraint, that is, such that $I(Y;S) \leq ε$. Here, we relax the perfect demographic …

Piano: A Multi-Constraint Pin Assignment-Aware Floorplanner
Zhexuan Xu, Kexin Zhou, Jie Wang, Zijie Geng, Siyuan Xu, Shixiong Kai, Mingxuan Yuan, Feng Wu
https://arxiv.org/abs/2508.13161

Piano: A Multi-Constraint Pin Assignment-Aware Floorplanner
Floorplanning is a critical step in VLSI physical design, increasingly complicated by modern constraints such as fixed-outline requirements, whitespace removal, and the presence of pre-placed modules. In addition, the assignment of pins on module boundaries significantly impacts the performance of subsequent stages, including detailed placement and routing. However, traditional floorplanners often overlook pin assignment with modern constraints during the floorplanning stage. In this work, we i…

A Neural Column-and-Constraint Generation Method for Solving Two-Stage Stochastic Unit Commitment
Zhentong Shao, Jingtao Qin, Nanpeng Yu
https://arxiv.org/abs/2508.11071 https:/…

A Neural Column-and-Constraint Generation Method for Solving Two-Stage Stochastic Unit Commitment
Two-stage stochastic unit commitment (2S-SUC) problems have been widely adopted to manage the uncertainties introduced by high penetrations of intermittent renewable energy resources. While decomposition-based algorithms such as column-and-constraint generation has been proposed to solve these problems, they remain computationally prohibitive for large-scale, real-time applications. In this paper, we introduce a Neural Column-and-Constraint Generation (Neural CCG) method to significantly accele…

Discrete Optimization of Min-Max Violation and its Applications Across Computational Sciences
Cheikh Ahmed, Mahdi Mostajabdaveh, Samin Aref, Zirui Zhou
https://arxiv.org/abs/2508.13437

Discrete Optimization of Min-Max Violation and its Applications Across Computational Sciences
We introduce the Discrete Min-Max Violation (DMMV) as a general optimization problem which seeks an assignment of discrete values to variables that minimizes the largest constraint violation. This context-free mathematical formulation is applicable to a wide range of use cases that have worst-case performance requirements. After defining the DMMV problem mathematically, we explore its properties to establish a foundational understanding. To tackle DMMV instance sizes of practical relevance, we …

Analyzing Information Sharing and Coordination in Multi-Agent Planning
Tianyue Ou, Saujas Vaduguru, Daniel Fried
https://arxiv.org/abs/2508.12981 https://a…

Analyzing Information Sharing and Coordination in Multi-Agent Planning
Multi-agent systems (MASs) have pushed the boundaries of large language model (LLM) agents in domains such as web research and software engineering. However, long-horizon, multi-constraint planning tasks involve conditioning on detailed information and satisfying complex interdependent constraints, which can pose a challenge for these systems. In this study, we construct an LLM-based MAS for a travel planning task which is representative of these challenges. We evaluate the impact of a notebook…

Decoded Quantum Interferometry Requires Structure
Eric R. Anschuetz, David Gamarnik, Jonathan Z. Lu
https://arxiv.org/abs/2509.14509 https://arxiv.org/pdf/…

Decoded Quantum Interferometry Requires Structure
We study the performance of Decoded Quantum Interferometry (DQI) on typical instances of MAX-$k$-XOR-SAT when the transpose of the constraint matrix is drawn from a standard ensemble of LDPC parity check matrices. We prove that if the decoding step of DQI corrects up to the folklore efficient decoding threshold for LDPC codes, then DQI is obstructed by a topological feature of the near-optimal space of solutions known as the overlap gap property (OGP). As the OGP is widely conjectured to exactl…

A PC Algorithm for Max-Linear Bayesian Networks
Carlos Am\'endola, Benjamin Hollering, Francesco Nowell
https://arxiv.org/abs/2508.13967 https://arxiv.…

A PC Algorithm for Max-Linear Bayesian Networks
Max-linear Bayesian networks (MLBNs) are a relatively recent class of structural equation models which arise when the random variables involved have heavy-tailed distributions. Unlike most directed graphical models, MLBNs are typically not faithful to d-separation and thus classical causal discovery algorithms such as the PC algorithm or greedy equivalence search can not be used to accurately recover the true graph structure. In this paper, we begin the study of constraint-based discovery algor…

An updated constraint on the Effective Field Theory of Dark Energy
Chi Zheng, Wei Liu, Zonghao Zhan, Wenjuan Fang
https://arxiv.org/abs/2509.13804 https://…

An updated constraint on the Effective Field Theory of Dark Energy
The Effective Field Theory of Dark Energy (EFTofDE) provides a systematic and model-independent framework to study dark energy (DE) and modified gravity (MG) with one additional scalar degree of freedom. It can describe the known models such as Quintessence, k-essence, DGP, $f(R)$, and Horndeski theories. In this work, we update constraints on EFTofDE by utilizing the most up-to-date public data including the BAO (DESI DR2), CMB (Planck 2018 \& ACT DR6), SNIa (DESY5), weak lensing (DESY3) and f…

The Interpolation Constraint in the RV Analysis of M-Dwarfs Using Empirical Templates
Dhvani Doshi, Nicolas B. Cowan, \'Etienne Artigau, Ren\'e Doyon, Andr\'e M. Silva, Khaled Al Moulla, Yashar Hezaveh
https://arxiv.org/abs/2508.10980

The Interpolation Constraint in the RV Analysis of M-Dwarfs Using Empirical Templates
Precise radial velocity (pRV) measurements of M-dwarfs in the near-infrared (NIR) rely on empirical templates due to the lack of accurate stellar spectral models in this regime. Templates are assumed to approximate the true spectrum when constructed from many observations or in the high signal-to-noise limit. We develop a numerical simulation that generates SPIRou-like pRV observations from PHOENIX spectra, constructs empirical templates, and estimates radial velocities. This simulation solely …

On CP-violation and quark masses: reducing the number of free parameters
A. Kleppe
https://arxiv.org/abs/2508.11081 https://arxiv.org/pdf/2508.11081…

On CP-violation and quark masses: reducing the number of free parameters
A physically viable ansatz for quark mass matrices must satisfy certain constraints, like the constraint imposed by CP-violation. In this article we study a concrete example, by looking at some generic matrices with a nearly democratic texture, and the implications of the constraints imposed by CP-violation, specifically the Jarlskog invariant. This constraint reduces the number of parameters from six to five, implying that the six mass eigenvalues of the up-quarks and the down-quarks are inter…

Group Fair Matchings using Convex Cost Functions
Atasi Panda, Harsh Sharma, Anand Louis, Prajakta Nimbhorkar
https://arxiv.org/abs/2508.12549 https://arxiv…

Group Fair Matchings using Convex Cost Functions
We consider the problem of assigning items to platforms where each item has a utility associated with each of the platforms to which it can be assigned. Each platform has a soft constraint over the total number of items it serves, modeled via a convex cost function. Additionally, items are partitioned into groups, and each platform also incurs group-specific convex cost over the number of items from each group that can be assigned to the platform. These costs promote group fairness by penalizin…

Fast Single-Snapshot Harmonic Recovery with 2D Sparse Arrays using BCCB Matrices
Youval Klioui
https://arxiv.org/abs/2509.13592 https://arxiv.org/pdf/2509.…

Fast Single-Snapshot Harmonic Recovery with 2D Sparse Arrays using BCCB Matrices
We introduce an efficient implementation of sparse recovery methods for the problem of harmonic estimation with 2D sparse arrays using a single snapshot. By imposing a uniformity constraint on the harmonic grids of the subdictionaries used in the sparse recovery problem, in addition to a mild constraint on the array topology that consists in having the elements lie on a grid specified in half-wavelength units, we show that the Gram matrices that appear in these sparse recovery methods exhibit a…

An Improved Solution to the Two Normal Means Problem via Regularization
Yang Liu, Jonathan P. Williams
https://arxiv.org/abs/2508.13012 https://arxiv.org/p…

An Improved Solution to the Two Normal Means Problem via Regularization
The many-normal-means problem is a classic example that motivates the development of many important inferential procedures in the history of statistics. In this short note, we consider a further special case of the problem, which involves only two normally distributed data points with a constraint that the pair of means are not too far apart from one another. Starting with a regularized ML estimator, we construct a novel possibilistic IM for marginal inference on one of the two means. Not only …

The trace of field equations for higher-derivative gravity and an equality associating the Lagrangian density with a divergence term
Jun-Jin Peng, Hua Li
https://arxiv.org/abs/2508.13549

The trace of field equations for higher-derivative gravity and an equality associating the Lagrangian density with a divergence term
We figure out the explicit expression for the trace of the field equations associated to higher derivative theories of gravity endowed with Lagrangians depending upon the metric and its Riemann tensor, together with arbitrary order covariant derivatives of the Riemann tensor. Then an equality linking the Lagrangian density with the divergence of a vector field is proposed under a constraint on the Lagrangian density. As a significant application, we further obtain an equality to establish the r…

Mixed order phase transition in a locally constrained exclusion process
Gunter Schutz, Ali Zahra
https://arxiv.org/abs/2509.14467 https://arxiv.org/pdf/250…

Mixed order phase transition in a locally constrained exclusion process
We investigate a novel variant of the exclusion process in which particles perform asymmetric nearest-neighbor jumps across a bond \((k, k+1)\) only if the preceding site \((k-1)\) is unoccupied. This next-nearest-neighbor constraint significantly enriches the system's dynamics, giving rise to long-range correlations and a mixed-order transition controlled by the asymmetry parameter. We focus on the critical case of half filling, where the system splits into two ergodic components, each associa…

The Stochastic Dissipation Model for the Steady State Neutrino and Multi-Wavelength Emission of TXS 0506 056
Zhen-Jie Wang, Ruo-Yu Liu, Xiang-Yu Wang
https://arxiv.org/abs/2509.14587

The Stochastic Dissipation Model for the Steady State Neutrino and Multi-Wavelength Emission of TXS 0506+056
The blazar TXS 0506+056 has been suggested to be a potential high-energy neutrino source thanks to the observations of IceCube, which found outburst-like neutrino emissions during 2014-2015 and 2017 in the transient emission search, and a $3.5σ$ local significance in a 10-year time-integrated search. The conventional one-zone jet model cannot explain the observed neutrino flux during outbursts due to the constraint from the X-ray flux, leading to proposals of multi-zone models (e.g. two-zone m…

Generalized Quasispecies Model with Time Delays and Periodic Fluctuations in Replication
Nolbert Morales, Edward A. Turner
https://arxiv.org/abs/2508.12424 https://

Generalized Quasispecies Model with Time Delays and Periodic Fluctuations in Replication
In this research, we present a generalized quasispecies model in which population growth is governed by an arbitrary nonlinear function incorporating time delays. We begin by demonstrating that, under the constant population constraint, the dynamics of the system with time delays remain confined to the invariant manifold for both forward and backward time evolution. Furthermore, we establish that in this modified quasispecies model, defined on a single-peak fitness landscape, in the presence of…

Long time behavior of the Vlasov-Maxwell-Fokker-Planck system with a strong external magnetic field
Anh-Tuan Vu
https://arxiv.org/abs/2508.12231 https://ar…

Long time behavior of the Vlasov-Maxwell-Fokker-Planck system with a strong external magnetic field
The subject matter of this paper concerns the magnetic confinement of plasma. We investigate the long time behavior of the Vlasov-Maxwell-Fokker-Planck system under the effect of a strong external magnetic field. We first formally derive an asymptotically reduced model. In three space dimensions, a constraint occurs along the parallel direction to the magnetic field. To eliminate the corresponding Lagrange multiplier, we then study the Vlasov-Maxwell-Fokker-Planck system in two spatial dimensio…

Refined Elementary Capacities from Symplectic Field Theory
Jonathan Michala
https://arxiv.org/abs/2508.12525 https://arxiv.org/pdf/2508.12525

Refined Elementary Capacities from Symplectic Field Theory
We extend the family of capacities given by McDuff and Siegel by including a constraint $\ell$ on the number of positive asymptotically cylindrical ends of curves showing up in the definition. We prove a generalized computation formula for four-dimensional convex toric domains that offers new, sometimes sharp, embedding obstructions in stabilized and unstabilized cases. The formula restricts to the McDuff-Siegel capacities for $\ell = \infty$ and to the Gutt-Hutchings capacities for $\ell = 1$.…

Crosslisted article(s) found for cs.LO. https://arxiv.org/list/cs.LO/new
[1/1]:
- The Constraint Satisfaction Problem Over Multisorted Cores
Dejan Delic, John Marcoux

An Implementation Relaxation Approach to Principal-Agent Problems
Hang Jiang
https://arxiv.org/abs/2509.14766 https://arxiv.org/pdf/2509.14766

An Implementation Relaxation Approach to Principal-Agent Problems
The first-order approach (FOA) is the standard tool for solving principal-agent problems, replacing the incentive compatibility (IC) constraint with its first-order condition to obtain a relaxed problem. We show that FOA is not a valid relaxation when the support of the outcome distribution shifts with the agent's effort, as in well-studied additive-noise models. In such cases, the optimal effort may occur at a kink point that the first-order condition cannot capture, causing FOA to miss optima…

A Uniqueness Theorem for Distributed Computation under Physical Constraint
Zhiyuan Ren, Mingxuan Lu, Wenchi Cheng
https://arxiv.org/abs/2509.11754 https://…

A Uniqueness Theorem for Distributed Computation under Physical Constraint
Foundational models of computation often abstract away physical hardware limitations. However, in extreme environments like In-Network Computing (INC), these limitations become inviolable laws, creating an acute trilemma among communication efficiency, bounded memory, and robust scalability. Prevailing distributed paradigms, while powerful in their intended domains, were not designed for this stringent regime and thus face fundamental challenges. This paper demonstrates that resolving this tril…

Incentivizing Safer Actions in Policy Optimization for Constrained Reinforcement Learning
Somnath Hazra, Pallab Dasgupta, Soumyajit Dey
https://arxiv.org/abs/2509.09208 https://…

Incentivizing Safer Actions in Policy Optimization for Constrained Reinforcement Learning
Constrained Reinforcement Learning (RL) aims to maximize the return while adhering to predefined constraint limits, which represent domain-specific safety requirements. In continuous control settings, where learning agents govern system actions, balancing the trade-off between reward maximization and constraint satisfaction remains a significant challenge. Policy optimization methods often exhibit instability near constraint boundaries, resulting in suboptimal training performance. To address t…

Q-Linear Convergence of the Proximal Augmented Lagrangian Method for Non-Convex Conic Programming
Ning Zhang, Yi Zhang
https://arxiv.org/abs/2509.11531 https://

Q-Linear Convergence of the Proximal Augmented Lagrangian Method for Non-Convex Conic Programming
This paper provides a local convergence analysis of the proximal augmented Lagrangian method (PALM) applied to a class of non-convex conic programming problems. Previous convergence results for PALM typically imposed assumptions such as constraint non-degeneracy, strict complementarity, second-order sufficiency conditions, or a combination of constraint nondegeneracy with strong second-order sufficiency conditions. In contrast, our work demonstrates a Q-linear convergence rate for an inexact ve…

Distributed Batch Matrix Multiplication: Trade-Offs in Download Rate, Randomness, and Privacy
Amirhosein Morteza, Remi A. Chou
https://arxiv.org/abs/2509.15047 https://

Distributed Batch Matrix Multiplication: Trade-Offs in Download Rate, Randomness, and Privacy
We study the trade-off between communication rate and privacy for distributed batch matrix multiplication of two independent sequences of matrices $\bold{A}$ and $\bold{B}$ with uniformly distributed entries. In our setting, $\bold{B}$ is publicly accessible by all the servers while $\bold{A}$ must remain private. A user is interested in evaluating the product $\bold{AB}$ with the responses from the $k$ fastest servers. For a given parameter $α\in [0, 1]$, our privacy constraint must ensure th…

Sampling Method for Generalized Graph Signals with Pre-selected Vertices via DC Optimization
Keitaro Yamashita, Kazuki Naganuma, Shunsuke Ono
https://arxiv.org/abs/2509.14836 ht…

Sampling Method for Generalized Graph Signals with Pre-selected Vertices via DC Optimization
This paper proposes a method for vertex-wise flexible sampling of a broad class of graph signals, designed to attain the best possible recovery based on the generalized sampling theory. This is achieved by designing a sampling operator by an optimization problem, which is inherently non-convex, as the best possible recovery imposes a rank constraint. An existing method for vertex-wise flexible sampling is able to control the number of active vertices but cannot incorporate prior knowledge of ma…

On Uniformly Time-Varying Control Barrier Functions
Adrian Wiltz, Dimos V. Dimarogonas
https://arxiv.org/abs/2509.15037 https://arxiv.org/pdf/2509.15037

On Uniformly Time-Varying Control Barrier Functions
This paper investigates the design of a subclass of time-varying Control Barrier Functions (CBFs), specifically that of uniformly time-varying CBFs. Leveraging the fact that CBFs encode a system's dynamic capabilities relative to a state constraint, we decouple the design of uniformly time-varying CBFs into a time-invariant and a time-varying component. We characterize the subclass of time-invariant CBFs that yield a uniformly time-varying CBF when combined with a specific type of time-varying …

Spatiotemporal Calibration for Laser Vision Sensor in Hand-eye System Based on Straight-line Constraint
Peiwen Yang, Mingquan Jiang, Xinyue Shen, Heping Zhang
https://arxiv.org/abs/2509.12928

Spatiotemporal Calibration for Laser Vision Sensor in Hand-eye System Based on Straight-line Constraint
Laser vision sensors (LVS) are critical perception modules for industrial robots, facilitating real-time acquisition of workpiece geometric data in welding applications. However, the camera communication delay will lead to a temporal desynchronization between captured images and the robot motions. Additionally, hand-eye extrinsic parameters may vary during prolonged measurement. To address these issues, we introduce a measurement model of LVS considering the effect of the camera's time-offset a…

GRPO-GCC: Enhancing Cooperation in Spatial Public Goods Games via Group Relative Policy Optimization with Global Cooperation Constraint
Zhaoqilin Yang, Chanchan Li, Tianqi Liu, Hongxin Zhao, Youliang Tian
https://arxiv.org/abs/2510.08607

GRPO-GCC: Enhancing Cooperation in Spatial Public Goods Games via Group Relative Policy Optimization with Global Cooperation Constraint
Inspired by the principle of self-regulating cooperation in collective institutions, we propose the Group Relative Policy Optimization with Global Cooperation Constraint (GRPO-GCC) framework. This work is the first to introduce GRPO into spatial public goods games, establishing a new deep reinforcement learning baseline for structured populations. GRPO-GCC integrates group relative policy optimization with a global cooperation constraint that strengthens incentives at intermediate cooperation l…

Constraint-Guided Unit Test Generation for Machine Learning Libraries
Lukas Krodinger, Altin Hajdari, Stephan Lukasczyk, Gordon Fraser
https://arxiv.org/abs/2510.09108 https://

Constraint-Guided Unit Test Generation for Machine Learning Libraries
Machine learning (ML) libraries such as PyTorch and TensorFlow are essential for a wide range of modern applications. Ensuring the correctness of ML libraries through testing is crucial. However, ML APIs often impose strict input constraints involving complex data structures such as tensors. Automated test generation tools such as Pynguin are not aware of these constraints and often create non-compliant inputs. This leads to early test failures and limited code coverage. Prior work has investig…

A Monotonicity-Based Regularization Approach to Shape Reconstruction for the Helmholtz Equation
Sarah Eberle-Blick, Bastian Harrach, Xianchao Wang
https://arxiv.org/abs/2508.11439

A Monotonicity-Based Regularization Approach to Shape Reconstruction for the Helmholtz Equation
We consider an inverse boundary value problem for determining unknown scatterers, which is governed by the Helmholtz equation in a bounded domain. To address this, we develop a novel convex data-fitting formulation that is capable of reconstructing the shape of the unknown scatterers.Our formulation is based on a monotonicity relation between the scattering index and boundary measurements. We use this relation to obtain a pixel-wise constraint on the unknown scattering index, and then minimize …

Global Constraint LLM Agents for Text-to-Model Translation
Junyang Cai, Serdar Kadioglu, Bistra Dilkina
https://arxiv.org/abs/2509.08970 https://arxiv.org/…

Global Constraint LLM Agents for Text-to-Model Translation
Natural language descriptions of optimization or satisfaction problems are challenging to translate into correct MiniZinc models, as this process demands both logical reasoning and constraint programming expertise. We introduce a framework that addresses this challenge with an agentic approach: multiple specialized large language model (LLM) agents decompose the modeling task by global constraint type. Each agent is dedicated to detecting and generating code for a specific class of global const…

Spacing Test for Fused Lasso
Rieko Tasaka, Tatsuya Kimura, Joe Suzuki
https://arxiv.org/abs/2509.14229 https://arxiv.org/pdf/2509.14229

Spacing Test for Fused Lasso
This study addresses the unresolved problem of selecting the regularization parameter in the fused lasso. In particular, we extend the framework of the Spacing Test proposed by Tibshirani et al. to the fused lasso, providing a theoretical foundation for post-selection inference by characterizing the selection event as a polyhedral constraint. Based on the analysis of the solution path of the fused lasso using a LARS-type algorithm, we derive exact conditional $p$-values for the selected change-…

Constraint-Level Design of zkEVMs: Architectures, Trade-offs, and Evolution
Yahya Hassanzadeh-Nazarabadi, Sanaz Taheri-Boshrooyeh
https://arxiv.org/abs/2510.05376 https://

Constraint-Level Design of zkEVMs: Architectures, Trade-offs, and Evolution
Zero-knowledge Ethereum Virtual Machines (zkEVMs) must reconcile a fundamental contradiction: the Ethereum Virtual Machine was designed for transparent sequential execution, while zero-knowledge proofs require algebraic circuit representations. This survey provides the first systematic analysis of how existing major production zkEVM implementations resolve this tension through distinct constraint engineering strategies. We develop a comparative framework that maps the design space across three …

Constraint Satisfaction Approaches to Wordle: Novel Heuristics and Cross-Lexicon Validation
Jahidul Arafat, Fariha Tasmin, Sanjaya Poudel, Kamrujjaman, Eftakhar Ahmed Arnob, Ahsan Habib Tareq
https://arxiv.org/abs/2510.02855

Constraint Satisfaction Approaches to Wordle: Novel Heuristics and Cross-Lexicon Validation
Wordle presents an algorithmically rich testbed for constraint satisfaction problem (CSP) solving. While existing solvers rely on information-theoretic entropy maximization or frequency-based heuristics without formal constraint treatment, we present the first comprehensive CSP formulation of Wordle with novel constraint-aware solving strategies. We introduce CSP-Aware Entropy, computing information gain after constraint propagation rather than on raw candidate sets, and a Probabilistic CSP fra…

A Nonlinear Scaling-based Design of Control Lyapunov-barrier Function for Relative Degree 2 Case and its Application to Safe Feedback Linearization
Haechan Pyon, Gyunghoon Park
https://arxiv.org/abs/2509.15071

A Nonlinear Scaling-based Design of Control Lyapunov-barrier Function for Relative Degree 2 Case and its Application to Safe Feedback Linearization
In this paper we address the problem of control Lyapunov-barrier function (CLBF)-based safe stabilization for a class of nonlinear control-affine systems. A difficulty may arise for the case when a constraint has the relative degree larger than 1, at which computing a proper CLBF is not straightforward. Instead of adding an (possibly non-existent) control barrier function (CBF) to a control Lyapunov function (CLF), our key idea is to simply scale the value of the CLF on the unsafe set, by utili…

Constraint-Compliant Network Optimization through Large Language Models
Youngjin Song, Wookjin Lee, Hong Ki Kim, Sang Hyun Lee
https://arxiv.org/abs/2509.07492 https://

Constraint-Compliant Network Optimization through Large Language Models
This work develops an LLM-based optimization framework ensuring strict constraint satisfaction in network optimization. While LLMs possess contextual reasoning capabilities, existing approaches often fail to enforce constraints, causing infeasible solutions. Unlike conventional methods that address average constraints, the proposed framework integrates a natural language-based input encoding strategy to restrict the solution space and guarantee feasibility. For multi-access edge computing netwo…

Simulation of bilayer Hamiltonians based on monitored quantum trajectories
Yuan Xue, Zihan Cheng, Matteo Ippoliti
https://arxiv.org/abs/2509.13440 https://…

Simulation of bilayer Hamiltonians based on monitored quantum trajectories
In the study of open quantum systems it is often useful to treat mixed states as pure states of a fictitious doubled system. In this work we explore the opposite approach: mapping isolated bilayer systems to open monolayer systems. Specifically, we show that arbitrary bilayer Hamiltonians possessing an antiunitary layer exchange symmetry, and subject to a constraint on the sign of interlayer couplings, can be mapped to Lindbladians on a monolayer system with some of the jump operators postselec…

Termination Analysis of Linear-Constraint Programs
Amir M. Ben-Amram, Samir Genaim, Jo\"el Ouaknine, James Worrell
https://arxiv.org/abs/2509.06752 https://

Termination Analysis of Linear-Constraint Programs
This Survey provides an overview of techniques in termination analysis for programs with numerical variables and transitions defined by linear constraints. This subarea of program analysis is challenging due to the existence of undecidable problems, and this Survey systematically explores approaches that mitigate this inherent difficulty. These include foundational decidability results, the use of ranking functions, and disjunctive well-founded transition invariants. The Survey also discusses n…

A Colorful Version of Carath\'eodory's Theorem plus a constraint
Pavle V. M. Blagojevic
https://arxiv.org/abs/2509.01000 https://arxiv.org/pdf/2509…

A Colorful Version of Carathéodory's Theorem plus a constraint
We develop a topological framework in an attempt to generalize the classical colourful Caratheodory theorem by imposing an additional constraint. For that we introduce the notion of zero-avoding complexes and covering criteria for the existence of colourful transversals. Using the developed method in combination with the homological Nerve theorem of Meshulam we recover all known versions of the colourful Caratheodory's theorem and prove a constraint extension which, in particular, implies an ex…

New constraint on Europa's ice shell: magnetic signature from the ocean
Florentin Daniel, Ludovic Petitdemange, Christophe Gissinger
https://arxiv.org/abs/2509.10903 https:/…

New constraint on Europa's ice shell: magnetic signature from the ocean
Jupiter's icy moons are believed to host subsurface liquid oceans, and among them, Europa stands out as one of the most promising candidates for extraterrestrial life. Yet, the processes driving oceanic flows beneath its ice shell, as well as the factors controlling the thickness of this ice, remain incompletely understood. One especially distinctive feature of Europa is that its salty ocean is electrically conducting and thus influenced by Jupiter's time-varying magnetic field, which is believ…

Einstein's Equations in Electromagnetic Media
Eren Erberk Erkul, Ulf Leonhardt
https://arxiv.org/abs/2508.11300 https://arxiv.org/pdf/2508.11300…

Einstein's Equations in Electromagnetic Media
In this paper, Plebanski's 1960 mapping is extended to encode the full Einstein system in an electromagnetic medium. This is achieved by identifying transformation parameters in terms of the ADM constraint and evolution equations, and subsequently applying the mapping to the entire system. These transformed equations are then linearized in vacuum to derive gravitational-wave analogues as perturbations within the optical medium.

Sequence Variables: A Constraint Programming Computational Domain for Routing and Sequencing
Augustin Delecluse, Pierre Schaus, Pascal Van Hentenryck
https://arxiv.org/abs/2510.09373

Sequence Variables: A Constraint Programming Computational Domain for Routing and Sequencing
Constraint Programming (CP) offers an intuitive, declarative framework for modeling Vehicle Routing Problems (VRP), yet classical CP models based on successor variables cannot always deal with optional visits or insertion based heuristics. To address these limitations, this paper formalizes sequence variables within CP. Unlike the classical successor models, this computational domain handle optional visits and support insertion heuristics, including insertion-based Large Neighborhood Search. We…

ACT: Automated Constraint Targeting for Multi-Objective Recommender Systems
Daryl Chang, Yi Wu, Jennifer She, Li Wei, Lukasz Heldt
https://arxiv.org/abs/2509.03661 https://

ACT: Automated Constraint Targeting for Multi-Objective Recommender Systems
Recommender systems often must maximize a primary objective while ensuring secondary ones satisfy minimum thresholds, or "guardrails." This is critical for maintaining a consistent user experience and platform ecosystem, but enforcing these guardrails despite orthogonal system changes is challenging and often requires manual hyperparameter tuning. We introduce the Automated Constraint Targeting (ACT) framework, which automatically finds the minimal set of hyperparameter changes needed to satisf…

Phase Transitions of the Additive Uniform Noise Channel with Peak Amplitude and Cost Constraint
Jonas Stapmanns, Catarina Dias, Luke Eilers, Tobias K\"uhn, Jean-Pascal Pfister
https://arxiv.org/abs/2510.12427

Phase Transitions of the Additive Uniform Noise Channel with Peak Amplitude and Cost Constraint
Under which condition is quantization optimal? We address this question in the context of the additive uniform noise channel under peak amplitude and cost constraints. We compute analytically the capacity-achieving input distribution as a function of the noise level, the average cost constraint, and the curvature of the cost function. We find that when the cost function is concave, the capacity-achieving input distribution is discrete, whereas when the cost function is convex and the cost const…

Vanishing Signatures, Orbit Closure, and the Converse of the Holant Theorem
Jin-Yi Cai, Ben Young
https://arxiv.org/abs/2509.10991 https://arxiv.org/pdf/25…

Vanishing Signatures, Orbit Closure, and the Converse of the Holant Theorem
Valiant's Holant theorem is a powerful tool for algorithms and reductions for counting problems. It states that if two sets $\mathcal{F}$ and $\mathcal{G}$ of tensors (a.k.a. constraint functions or signatures) are related by a \emph{holographic transformation}, then $\mathcal{F}$ and $\mathcal{G}$ are \emph{Holant-indistinguishable}, i.e., every tensor network using tensors from $\mathcal{F}$, resp. from $\mathcal{G}$, contracts to the same value. Xia (ICALP 2010) conjectured the converse of t…

Tilt Stability for Nonlinear Programs under Relaxed Constant Rank Constraint Qualification
Nguyen Huy Chieu, Nguyen Thi Quynh Trang, Nguyen Thi Hai Yen
https://arxiv.org/abs/2508.06927

Tilt Stability for Nonlinear Programs under Relaxed Constant Rank Constraint Qualification
This paper investigates the tilt stability of local minimizers for nonlinear programs under the relaxed constant rank constraint qualification in finite dimensions. By employing a neighborhood primal-dual approach and extending calculus rules for subgradient graphical derivative, we obtain some pointbased characterizations of tilt-stable local minimizers along with an explicit formula for calculating the exact bound of tilt stability. These results extend the corresponding ones of H. Gfrerer an…

The Correspondence Principle: A bridge between general potential theories and nonlinear elliptic differential operators
F. Reese Harvey, Kevin R. Payne
https://arxiv.org/abs/2509.13709

The Correspondence Principle: A bridge between general potential theories and nonlinear elliptic differential operators
General potential theories concern the study of functions which are subharmonic with respect to a suitable constraint set (called a subequation) in the space of 2-jets. While interesting in their own right, general potential theories are being widely used to study fully nonlinear PDEs determined by degenerate elliptic operators acting on the space of 2-jets. We will discuss a powerful tool, the correspondence principle, which establishes the equivalence between subequation subharmonics (superha…

First Constraint on P-odd/T-odd Cross Section in Polarized Neutron Transmission through Transversely Polarized $^{139}$La
Rintaro Nakabe, Clayton J. Auton, Shunsuke Endo, Hiroyuki Fujioka, Vladimir Gudkov, Katsuya Hirota, Ikuo Ide, Takashi Ino, Motoyuki Ishikado, Wataru Kambara, Shiori Kawamura, Atsushi Kimura, Masaaki Kitaguchi, Ryuju Kobayashi, Takahiro Okamura, Takayuki Oku, Takuya Okudaira, Mao Okuizumi, J. G. Otero Munoz, Joseph D. Parker, Kenji Sakai, Tatsushi Shima, Hirohiko M. …

First Constraint on P-odd/T-odd Cross Section in Polarized Neutron Transmission through Transversely Polarized $^{139}$La
We report the first constraint on time-reversal invariance violating (TRIV) effects in polarized neutron transmission through a polarized $^{139}$La target. Using the explicit forward scattering amplitude for $^{139}$La, we derive analytic expressions for spin observables within the density matrix formalism and apply them to the existing transmission data. The experiment was not optimized for TRIV observables but was taken to measure the spin-dependent cross section around the $p$-wave resonanc…

Janus-faces of temporal constraint languages: a dichotomy of expressivity
Johanna Brunar, Michael Pinsker, Moritz Sch\"obi
https://arxiv.org/abs/2509.04347 https://

Janus-faces of temporal constraint languages: a dichotomy of expressivity
The Bodirsky-Kára classification of temporal constraint languages stands as one of the earliest and most seminal complexity classifications within infinite-domain Constraint Satisfaction Problems (CSPs), yet it remains one of the most mysterious in terms of algorithms and algebraic invariants for the tractable cases. We show that those temporal languages which do not pp-construct EVERYTHING (and thus by the classification are solvable in polynomial time) have, in fact, very limited expressive …

Systematic Constraint Formulation and Collision-Free Trajectory Planning Using Space-Time Graphs of Convex Sets
Matthew D. Osburn, Cameron K. Peterson, John L. Salmon
https://arxiv.org/abs/2508.10203

Systematic Constraint Formulation and Collision-Free Trajectory Planning Using Space-Time Graphs of Convex Sets
In this paper, we create optimal, collision-free, time-dependent trajectories through cluttered dynamic environments. The many spatial and temporal constraints make finding an initial guess for a numerical solver difficult. Graphs of Convex Sets (GCS) and the recently developed Space-Time Graphs of Convex Sets formulation (ST-GCS) enable us to generate optimal minimum distance collision-free trajectories without providing an initial guess to the solver. We also explore the derivation of general…

Truthful Two-Obnoxious-Facility Location Games with Optional Preferences and Minimum Distance Constraint
Xiaojia Han, Wenjing Liu, Qizhi Fang
https://arxiv.org/abs/2508.08036 ht…

Truthful Two-Obnoxious-Facility Location Games with Optional Preferences and Minimum Distance Constraint
In this paper, we study a truthful two-obnoxious-facility location problem, in which each agent has a private location in [0, 1] and a public optional preference over two obnoxious facilities, and there is a minimum distance constraint d between the two facilities. Each agent wants to be as far away as possible from the facilities that affect her, and the utility of each agent is the total distance from her to these facilities. The goal is to decide how to place the facilities in [0, 1] so as t…

A Quantum Walk-Driven Algorithm for the Minimum Spanning Tree Problem under a Maximal Degree Constraint
F. S. Luiz, F. F. Fanchini, Victor Hugo C. de Albuquerque, J. P. Papa, M. C. de Oliveira
https://arxiv.org/abs/2508.07007

A Quantum Walk-Driven Algorithm for the Minimum Spanning Tree Problem under a Maximal Degree Constraint
We present a novel quantum walk-based approach to solve the Minimum Spanning Tree (MST) problem under a maximal degree constraint (MDC). By recasting the classical MST problem as a quantum walk on a graph, where vertices are encoded as quantum states and edge weights are inverted to define a modified Hamiltonian, we demonstrate that the quantum evolution naturally selects the MST by maximizing the cumulative transition probability (and thus the Shannon entropy) over the spanning tree. Our metho…

Multi-Agent Trust Region Policy Optimisation: A Joint Constraint Approach
Chak Lam Shek, Guangyao Shi, Pratap Tokekar
https://arxiv.org/abs/2508.10340 https://

Multi-Agent Trust Region Policy Optimisation: A Joint Constraint Approach
Multi-agent reinforcement learning (MARL) requires coordinated and stable policy updates among interacting agents. Heterogeneous-Agent Trust Region Policy Optimization (HATRPO) enforces per-agent trust region constraints using Kullback-Leibler (KL) divergence to stabilize training. However, assigning each agent the same KL threshold can lead to slow and locally optimal updates, especially in heterogeneous settings. To address this limitation, we propose two approaches for allocating the KL dive…

Power-Dominance in Estimation Theory: A Third Pathological Axis
Sri Satish Krishna Chaitanya Bulusu, Mikko Sillanp\"a\"a
https://arxiv.org/abs/2509.12691 https://

Power-Dominance in Estimation Theory: A Third Pathological Axis
This paper introduces a novel framework for estimation theory by introducing a second-order diagnostic for estimator design. While classical analysis focuses on the bias-variance trade-off, we present a more foundational constraint. This result is model-agnostic, domain-agnostic, and is valid for both parametric and non-parametric problems, Bayesian and frequentist frameworks. We propose to classify the estimators into three primary power regimes. We theoretically establish that any estimator o…

Optimizing Rate-CRB Performance for Beyond Diagonal Reconfigurable Intelligent Surface Enabled ISAC
Xiaoqi Zhang, Liang Liu, Shuowen Zhang, Weifeng Zhu, Haijun Zhang
https://arxiv.org/abs/2508.11295

Optimizing Rate-CRB Performance for Beyond Diagonal Reconfigurable Intelligent Surface Enabled ISAC
This letter considers a beyond diagonal reconfigurable intelligent surface (BD-RIS) aided integrated sensing and communication (ISAC) system, where the BD-RIS can help a multi-antenna base station (BS) serve multiple user equipments (UEs) and localize a target simultaneously. We formulate an optimization problem that designs the BS beamforming matrix and the BD-RIS scattering matrix to maximize UEs' sum rate subject to a localization Cramer-Rao bound (CRB) constraint and an additional unitary m…

Arguzz: Testing zkVMs for Soundness and Completeness Bugs
Christoph Hochrainer, Valentin W\"ustholz, Maria Christakis
https://arxiv.org/abs/2509.10819 https://

Arguzz: Testing zkVMs for Soundness and Completeness Bugs
Zero-knowledge virtual machines (zkVMs) are increasingly deployed in decentralized applications and blockchain rollups since they enable verifiable off-chain computation. These VMs execute general-purpose programs, frequently written in Rust, and produce succinct cryptographic proofs. However, zkVMs are complex, and bugs in their constraint systems or execution logic can cause critical soundness (accepting invalid executions) or completeness (rejecting valid ones) issues. We present Arguzz, t…

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation
Peizhuo Liu
https://arxiv.org/abs/2508.05677

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation
RL-based medical questionnaire systems have shown great potential in medical scenarios. However, their safety and robustness remain unresolved. This study performs a comprehensive evaluation on adversarial attack methods to identify and analyze their potential vulnerabilities. We formulate the diagnosis process as a Markov Decision Process (MDP), where the state is the patient responses and unasked questions, and the action is either to ask a question or to make a diagnosis. We implemented six …

Constraint Qualification for Generic Parameter Families of Constraints in Optimization
Naoki Hamada, Kenta Hayano, Hiroshi Teramoto
https://arxiv.org/abs/2510.02381 https://

Constraint Qualification for Generic Parameter Families of Constraints in Optimization
Constraint qualifications (CQs) are central to the local analysis of constrained optimization. In this paper, we completely determine the validity of the four classical CQs -- LICQ, MFCQ, ACQ, and GCQ -- for constraint map-germs that arise in generic four-parameter families. Our approach begins by proving that all four CQs are invariant under the action of the group $\mathcal{K}[G]$ and under the operation of reduction. As a consequence, the verification of CQ-validity for a generic constraint …

Approximation in an optimal design problem governed by the heat equation
Kei Matsushima, Tomoyuki Oka
https://arxiv.org/abs/2509.11011 https://arxiv.org/pd…

Approximation in an optimal design problem governed by the heat equation
This paper studies a two-material optimal design problem for the time-averaged duality pairing between a (possibly time-dependent) heat source and the weak solution of an initial-boundary value problem for the heat equation with a two-material diffusion coefficient, under a volume constraint. In general, such optimal designs are not guaranteed to exist, and geometric constraints such as the perimeter are required. As an approximation of the problem with an additional perimeter constraint, a mat…

Dynamic Regret Bounds for Online Omniprediction with Long Term Constraints
Yahav Bechavod, Jiuyao Lu, Aaron Roth
https://arxiv.org/abs/2510.07266 https://a…

Dynamic Regret Bounds for Online Omniprediction with Long Term Constraints
We present an algorithm guaranteeing dynamic regret bounds for online omniprediction with long term constraints. The goal in this recently introduced problem is for a learner to generate a sequence of predictions which are broadcast to a collection of downstream decision makers. Each decision maker has their own utility function, as well as a vector of constraint functions, each mapping their actions and an adversarially selected state to reward or constraint violation terms. The downstream dec…

Constraint effective action and critical correlation functions at fixed magnetization
F\'elix Rose, Adam Ran\c{c}on, Ivan Balog
https://arxiv.org/abs/2509.05195 https://

Constraint effective action and critical correlation functions at fixed magnetization
We present an extension of the functional renormalization group (FRG) framework developed to compute critical probability distributions of the order parameter to momentum-dependent observables. Focusing on the constraint effective action at fixed magnetization for the Ising universality class, we derive its exact flow equations and solve them at the second-order of the derivative expansion (DE2). We solve these flow equations numerically for two- and three-dimensional systems, extract universal…

Singleton algorithms for the Constraint Satisfaction Problem
Dmitriy Zhuk
https://arxiv.org/abs/2509.18434 https://arxiv.org/pdf/2509.18434

Singleton algorithms for the Constraint Satisfaction Problem
A natural strengthening of an algorithm for the (promise) constraint satisfaction problem is its singleton version: we first fix a constraint to some tuple from the constraint relation, then run the algorithm, and remove the tuple from the constraint if the answer is negative. We characterize the power of the singleton versions of standard universal algorithms for the (promise) CSP over a fixed template in terms of the existence of a minion homomorphism. Using the Hales-Jewett theorem, we show …

Model Predictive Control with High-Probability Safety Guarantee for Nonlinear Stochastic Systems
Zishun Liu, Liqian Ma, Yongxin Chen
https://arxiv.org/abs/2509.11584 https://

Model Predictive Control with High-Probability Safety Guarantee for Nonlinear Stochastic Systems
We present a model predictive control (MPC) framework for nonlinear stochastic systems that ensures safety guarantee with high probability. Unlike most existing stochastic MPC schemes, our method adopts a set-erosion that converts the probabilistic safety constraint into a tractable deterministic safety constraint on a smaller safe set over deterministic dynamics. As a result, our method is compatible with any off-the-shelf deterministic MPC algorithm. The key to the effectiveness of our method…

ConstraintLLM: A Neuro-Symbolic Framework for Industrial-Level Constraint Programming
Weichun Shi, Minghao Liu, Wanting Zhang, Langchen Shi, Fuqi Jia, Feifei Ma, Jian Zhang
https://arxiv.org/abs/2510.05774

ConstraintLLM: A Neuro-Symbolic Framework for Industrial-Level Constraint Programming
Constraint programming (CP) is a crucial technology for solving real-world constraint optimization problems (COPs), with the advantages of rich modeling semantics and high solving efficiency. Using large language models (LLMs) to generate formal modeling automatically for COPs is becoming a promising approach, which aims to build trustworthy neuro-symbolic AI with the help of symbolic solvers. However, CP has received less attention compared to works based on operations research (OR) models. We…

Probing Light Primordial Black Holes through Non-cold Dark Matter
Yu-Ming Chen
https://arxiv.org/abs/2509.09767 https://arxiv.org/pdf/2509.09767

Probing Light Primordial Black Holes through Non-cold Dark Matter
We study the matter power spectrum constraint on primordial black holes (PBH) by the dark matter (DM) emitted through Hawking radiation. We particularly focus on the scenario where PBH, with mass ranges between 1g and $10^9$g, evaporates before big-bang nucleosynthesis (BBN). Addition to that, we consider the case where PBH abundance is scarce and there is no early PBH domination taking place. On the DM side, we assume a fraction of the population is produced from PBH evaporation, while the rem…

Strong convergence rates of stochastic theta methods for index 1 stochastic differential algebraic equations under non-globally Lipschitz conditions
Lin Chen, Ziheng Chen, Jing Zhao
https://arxiv.org/abs/2509.11618

Strong convergence rates of stochastic theta methods for index 1 stochastic differential algebraic equations under non-globally Lipschitz conditions
This work investigates numerical approximations of index 1 stochastic differential algebraic equations (SDAEs) with non-constant singular matrices under non-global Lipschitz conditions. Analyzing the strong convergence rates of numerical solutions in this setting is highly nontrivial, due to both the singularity of the constraint matrix and the superlinear growth of the coefficients. To address these challenges, we develop an approach for establishing mean square convergence rates of numerical …

From Evaluation to Enhancement: Large Language Models for Zero-Knowledge Proof Code Generation
Zhantong Xue, Pingchuan Ma, Zhaoyu Wang, Shuai Wang
https://arxiv.org/abs/2509.11708

From Evaluation to Enhancement: Large Language Models for Zero-Knowledge Proof Code Generation
Zero-knowledge proofs (ZKPs) are increasingly deployed in domains such as privacy-preserving authentication, blockchain scalability, and secure finance. However, authoring ZK programs remains challenging: unlike mainstream programming, ZK development requires reasoning about finite field arithmetic, constraint systems, and gadgets, making it knowledge-intensive and error-prone. While large language models (LLMs) have demonstrated strong code generation capabilities in general-purpose languages,…

Anchoring Refusal Direction: Mitigating Safety Risks in Tuning via Projection Constraint
Yanrui Du, Fenglei Fan, Sendong Zhao, Jiawei Cao, Qika Lin, Kai He, Ting Liu, Bing Qin, Mengling Feng
https://arxiv.org/abs/2509.06795

Anchoring Refusal Direction: Mitigating Safety Risks in Tuning via Projection Constraint
Instruction Fine-Tuning (IFT) has been widely adopted as an effective post-training strategy to enhance various abilities of Large Language Models (LLMs). However, prior studies have shown that IFT can significantly compromise LLMs' safety, particularly their ability to refuse malicious instructions, raising significant concerns. Recent research into the internal mechanisms of LLMs has identified the refusal direction (r-direction) in the hidden states, which plays a pivotal role in governing r…

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment
Amir Bahador Javadi, Amin Kargarian, Mort Naraghi-Pour
https://arxiv.org/abs/2509.10246 https://

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment
The increasing penetration of renewable energy sources introduces significant uncertainty in power system operations, making traditional deterministic unit commitment approaches computationally expensive. This paper presents a machine learning surrogate modeling approach designed to reformulate the feasible design space of the two-stage stochastic unit commitment (TSUC) problem, reducing its computational complexity. The proposed method uses a support vector machine (SVM) to construct a surroga…

Randomized Feasibility-Update Algorithms for Stochastic Variational Inequality Problems
Abhishek Chakraborty, Angelia Nedi\'c
https://arxiv.org/abs/2509.12613 https://

Randomized Feasibility-Update Algorithms for Stochastic Variational Inequality Problems
This paper considers stochastic monotone variational inequalities whose feasible region is the intersection of a (possibly infinite) number of convex functional level sets. A projection-based approach or direct Lagrangian-based techniques for such problems can be computationally expensive if not impossible to implement. To deal with the problem, we consider randomized methods that avoid the projection step on the whole constraint set by employing random feasibility updates. In particular, we pr…

Constraint-Preserving Data Generation for Visuomotor Policy Learning
Kevin Lin, Varun Ragunath, Andrew McAlinden, Aaditya Prasad, Jimmy Wu, Yuke Zhu, Jeannette Bohg
https://arxiv.org/abs/2508.03944

Constraint-Preserving Data Generation for Visuomotor Policy Learning
Large-scale demonstration data has powered key breakthroughs in robot manipulation, but collecting that data remains costly and time-consuming. We present Constraint-Preserving Data Generation (CP-Gen), a method that uses a single expert trajectory to generate robot demonstrations containing novel object geometries and poses. These generated demonstrations are used to train closed-loop visuomotor policies that transfer zero-shot to the real world and generalize across variations in object geome…

An inequality for relativistic local quantum measurements
Riccardo Falcone, Claudio Conti
https://arxiv.org/abs/2509.11599 https://arxiv.org/pdf/2509.11599…

An inequality for relativistic local quantum measurements
We investigate the trade-off between vacuum insensitivity and sensitivity to excitations in finite-size detectors, taking measurement locality as a fundamental constraint. We derive an upper bound on the detectability of vacuum excitation, given a small but nonzero probability of false positives in the vacuum state. The result is independent of the specific details of the measurement or the underlying physical mechanisms of the detector and relies only on the assumption of locality. Experimenta…

CP-Model-Zoo: A Natural Language Query System for Constraint Programming Models
Augustin Crespin, Ioannis Kostis, H\'el\`ene Verhaeghe, Pierre Schaus
https://arxiv.org/abs/2509.07867

CP-Model-Zoo: A Natural Language Query System for Constraint Programming Models
Constraint Programming and its high-level modeling languages have long been recognized for their potential to achieve the holy grail of problem-solving. However, the complexity of modeling languages, the large number of global constraints, and the art of creating good models have often hindered non-experts from choosing CP to solve their combinatorial problems. While generating an expert-level model from a natural-language description of a problem would be the dream, we are not yet there. We pr…

Constraint correlation functions of the Ising model in the scaling limit
Ivan Balog, Adam Ran\c{c}on
https://arxiv.org/abs/2509.08557 https://arxiv.org/pdf…

Constraint correlation functions of the Ising model in the scaling limit
We study the correlation function of the one-dimensional Ising model at fixed magnetization. Focusing on the scaling limit close to the zero-temperature fixed point, we show that this correlation function, in momentum space, exhibits surprising oscillations as a function of the magnetization. We show that these oscillations have a period inversely proportional to the momentum and give an interpretation in terms of domain walls. This is in sharp contrast with the behavior of the correlation func…

Theoretical Mass Function for Secondaries Forming via Gravitational Instability in Circumstellar Disks
Fred C. Adams, Aster G. Taylor, Michael R. Meyer
https://arxiv.org/abs/2508.09015

Theoretical Mass Function for Secondaries Forming via Gravitational Instability in Circumstellar Disks
This paper constructs a theoretical framework for calculating the distribution of masses for secondary bodies forming via gravitational instability in the outer regions of circumstellar disks. We show that several alternate ways to specify the mass scale of forming objects converge to the same result under the constraint that the parental disks are marginally stable with stability parameter $Q=1$. Next we show that the well-known constraint that the formation of secondary bodies requires rapid …

Replaced article(s) found for cs.CR. https://arxiv.org/list/cs.CR/new
[2/3]:
- VulSolver: Vulnerability Detection via LLM-Driven Constraint Solving
Xiang Li, Yueci Su, Jiahao Liu, Zhiwei Lin, Yuebing Hou, Peiming Gao, Yuanchao Zhang

ACE-RL: Adaptive Constraint-Enhanced Reward for Long-form Generation Reinforcement Learning
Jianghao Chen, Wei Sun, Qixiang Yin, Lingxing Kong, Zhixing Tan, Jiajun Zhang
https://arxiv.org/abs/2509.04903

ACE-RL: Adaptive Constraint-Enhanced Reward for Long-form Generation Reinforcement Learning
Large Language Models (LLMs) have demonstrated remarkable progress in long-context understanding, yet they face significant challenges in high-quality long-form generation. Existing studies primarily suffer from two limitations: (1) A heavy reliance on scarce, high-quality long-form response data for supervised fine-tuning (SFT) or for pairwise preference reward in reinforcement learning (RL). (2) Focus on coarse-grained quality optimization dimensions, such as relevance, coherence, and helpful…

Learn to Relax with Large Language Models: Solving Nonlinear Combinatorial Optimization Problems via Bidirectional Coevolution
Beidan Liu, Zhengqiu Zhu, Chen Gao, Yong Zhao, Wei Qi, Quanjun Yin
https://arxiv.org/abs/2509.12643

Learn to Relax with Large Language Models: Solving Nonlinear Combinatorial Optimization Problems via Bidirectional Coevolution
Nonlinear Combinatorial Optimization Problems (NCOPs) present a formidable computational hurdle in practice, as their nonconvex nature gives rise to multi-modal solution spaces that defy efficient optimization. Traditional constraint relaxation approaches rely heavily on expert-driven, iterative design processes that lack systematic automation and scalable adaptability. While recent Large Language Model (LLM)-based optimization methods show promise for autonomous problem-solving, they predomina…

Active Sequential Hypothesis Testing with Non-Homogeneous Costs
George Vershinin, Asaf Cohen, Omer Gurewitz
https://arxiv.org/abs/2509.11632 https://arxiv.…

Active Sequential Hypothesis Testing with Non-Homogeneous Costs
We study the Non-Homogeneous Sequential Hypothesis Testing (NHSHT), where a single active Decision-Maker (DM) selects actions with heterogeneous positive costs to identify the true hypothesis under an average error constraint \(δ\), while minimizing expected total cost paid. Under standard arguments, we show that the objective decomposes into the product of the mean number of samples and the mean per-action cost induced by the policy. This leads to a key design principle: one should optimize t…

Modular Counting over 3-Element and Conservative Domains
Andrei A. Bulatov, Amirhossein Kazeminia
https://arxiv.org/abs/2510.09950 https://arxiv.org/pdf/25…

Modular Counting over 3-Element and Conservative Domains
In the Constraint Satisfaction Problem (CSP for short) the goal is to decide the existence of a homomorphism from a given relational structure $G$ to a given relational structure $H$. If the structure $H$ is fixed and $G$ is the only input, the problem is denoted $CSP(H)$. In its counting version, $\#CSP(H)$, the task is to find the number of such homomorphisms. The CSP and #CSP have been used to model a wide variety of combinatorial problems and have received a tremendous amount of attention f…

Distributed Constraint-coupled Resource Allocation: Anytime Feasibility and Violation Robustness
Wenwen Wu, Shanying Zhu, Cailian Chen, Xinping Guan
https://arxiv.org/abs/2508.02164

Distributed Constraint-coupled Resource Allocation: Anytime Feasibility and Violation Robustness
This paper considers distributed resource allocation problems (DRAPs) with a coupled constraint for real-time systems. Based on primal-dual methods, we adopt a control perspective for optimization algorithm design by synthesizing a safe feedback controller using control barrier functions to enforce constraint satisfaction. On this basis, a distributed anytime-feasible resource allocation (DanyRA) algorithm is proposed. It is shown that DanyRA algorithm converges to the exact optimal solution of…

Spectral Small-Incremental-Entangling: Breaking Quasi-Polynomial Complexity Barriers in Long-Range Interacting Systems
Donghoon Kim, Yusuke Kimura, Hugo Mackay, Yosuke Mitsuhashi, Hideaki Nishikawa, Carla Rubiliani, Cheng Shang, Ayumi Ukai, Tomotaka Kuwahara
https://arxiv.org/abs/2509.12014

Spectral Small-Incremental-Entangling: Breaking Quasi-Polynomial Complexity Barriers in Long-Range Interacting Systems
A key challenge in quantum complexity is how entanglement structure emerges from dynamics, highlighted by advances in simulators and information processing. The Lieb--Robinson bound sets a locality-based speed limit on information propagation, while the Small-Incremental-Entangling (SIE) theorem gives a universal constraint on entanglement growth. Yet, SIE bounds only total entanglement, leaving open the fine entanglement structure. In this work, we introduce Spectral-Entangling Strength, measu…

Constraint-Aware Route Recommendation from Natural Language via Hierarchical LLM Agents
Tao Zhe, Rui Liu, Fateme Memar, Xiao Luo, Wei Fan, Xinyue Ye, Zhongren Peng, Dongjie Wang
https://arxiv.org/abs/2510.06078

Constraint-Aware Route Recommendation from Natural Language via Hierarchical LLM Agents
Route recommendation aims to provide users with optimal travel plans that satisfy diverse and complex requirements. Classical routing algorithms (e.g., shortest-path and constraint-aware search) are efficient but assume structured inputs and fixed objectives, limiting adaptability to natural-language queries. Recent LLM-based approaches enhance flexibility but struggle with spatial reasoning and the joint modeling of route-level and POI-level preferences. To address these limitations, we propos…

How Well do Diffusion Policies Learn Kinematic Constraint Manifolds?
Lexi Foland, Thomas Cohn, Adam Wei, Nicholas Pfaff, Boyuan Chen, Russ Tedrake
https://arxiv.org/abs/2510.01404

How Well do Diffusion Policies Learn Kinematic Constraint Manifolds?
Diffusion policies have shown impressive results in robot imitation learning, even for tasks that require satisfaction of kinematic equality constraints. However, task performance alone is not a reliable indicator of the policy's ability to precisely learn constraints in the training data. To investigate, we analyze how well diffusion policies discover these manifolds with a case study on a bimanual pick-and-place task that encourages fulfillment of a kinematic constraint for success. We study …

The Shannon Upper Bound for the Error Exponent
Sergey Tridenski, Anelia Somekh-Baruch
https://arxiv.org/abs/2509.08425 https://arxiv.org/pdf/2509.08425

The Shannon Upper Bound for the Error Exponent
For the discrete-time additive white generalized Gaussian noise channel with a generalized input power constraint, with the respective shape and power parameters >= 1, we derive an upper bound on the optimal block error exponent. Explicit asymptotic upper bounds in the limit of a large block length n are given for three special cases: the Laplace noise channel and the Gaussian noise channel with the average absolute value constraint, and for the Laplace noise channel with the second power const…

A predictive modular approach to constraint satisfaction under uncertainty - with application to glycosylation in continuous monoclonal antibody biosimilar production
Yu Wang, Xiao Chen, Hubert Schwarz, V\'eronique Chotteau, Elling W. Jacobsen
https://arxiv.org/abs/2508.16803

A predictive modular approach to constraint satisfaction under uncertainty - with application to glycosylation in continuous monoclonal antibody biosimilar production
The paper proposes a modular-based approach to constraint handling in process optimization and control. This is partly motivated by the recent interest in learning-based methods, e.g., within bioproduction, for which constraint handling under uncertainty is a challenge. The proposed constraint handler, called predictive filter, is combined with an adaptive constraint margin and a constraint violation cost monitor to minimize the cost of violating soft constraints due to model uncertainty and di…

Quenching, Fast and Slow: Breaking Kibble-Zurek Universal Scaling by Jumping along Geodesics
Thi Ha Kyaw, Guillermo Romero, Gaurav Saxena
https://arxiv.org/abs/2510.08528 https:…

Quenching, Fast and Slow: Breaking Kibble-Zurek Universal Scaling by Jumping along Geodesics
A major drawback of adiabatic quantum computing (AQC) is fulfilling the energy gap constraint, which requires the total evolution time to scale inversely with the square of the minimum energy gap. Failure to satisfy this condition violates the adiabatic approximation, potentially undermining computational accuracy. Recently, several approaches have been proposed to circumvent this constraint. One promising approach is to use the family of adiabatic shortcut procedures to fast-forward AQC. One c…

ReCoDe: Reinforcement Learning-based Dynamic Constraint Design for Multi-Agent Coordination
Michael Amir, Guang Yang, Zhan Gao, Keisuke Okumura, Heedo Woo, Amanda Prorok
https://arxiv.org/abs/2507.19151

ReCoDe: Reinforcement Learning-based Dynamic Constraint Design for Multi-Agent Coordination
Constraint-based optimization is a cornerstone of robotics, enabling the design of controllers that reliably encode task and safety requirements such as collision avoidance or formation adherence. However, handcrafted constraints can fail in multi-agent settings that demand complex coordination. We introduce ReCoDe--Reinforcement-based Constraint Design--a decentralized, hybrid framework that merges the reliability of optimization-based controllers with the adaptability of multi-agent reinforce…

Generalized Multi-Constraint Extremum Seeking
Alan Williams, Jorge Cort\'es, Alexander Scheinker
https://arxiv.org/abs/2510.06403 https://arxiv.org/pdf…

Generalized Multi-Constraint Extremum Seeking
We generalize the Safe Extremum Seeking algorithm to address the minimization of an unknown objective function subject to multiple unknown inequality and equality constraints, relying on recent results of gradient flow systems. These constraints may represent safety or other critical conditions. The proposed ES algorithm functions as a general nonlinear programming tool, offering practical maintenance of all constraints and semiglobal practical asymptotic stability, utilizing a Lyapunov argumen…

ASP-FZN: A Translation-based Constraint Answer Set Solver
Thomas Eiter, Tobias Geibinger, Tobias Kaminski, Nysret Musliu, Johannes Oetsch
https://arxiv.org/abs/2507.22774 https:…

ASP-FZN: A Translation-based Constraint Answer Set Solver
We present the solver asp-fzn for Constraint Answer Set Programming (CASP), which extends ASP with linear constraints. Our approach is based on translating CASP programs into the solver-independent FlatZinc language that supports several Constraint Programming and Integer Programming backend solvers. Our solver supports a rich language of linear constraints, including some common global constraints. As for evaluation, we show that asp-fzn is competitive with state-of-the-art ASP solvers on benc…

GC-VLN: Instruction as Graph Constraints for Training-free Vision-and-Language Navigation
Hang Yin, Haoyu Wei, Xiuwei Xu, Wenxuan Guo, Jie Zhou, Jiwen Lu
https://arxiv.org/abs/2509.10454

GC-VLN: Instruction as Graph Constraints for Training-free Vision-and-Language Navigation
In this paper, we propose a training-free framework for vision-and-language navigation (VLN). Existing zero-shot VLN methods are mainly designed for discrete environments or involve unsupervised training in continuous simulator environments, which makes it challenging to generalize and deploy them in real-world scenarios. To achieve a training-free framework in continuous environments, our framework formulates navigation guidance as graph constraint optimization by decomposing instructions into…

Varying Horizon Learning Economic MPC With Unknown Costs of Disturbed Nonlinear Systems
Weiliang Xiong, Defeng He, Haiping Du, Jianbin Mu
https://arxiv.org/abs/2509.11823 https:…

Varying Horizon Learning Economic MPC With Unknown Costs of Disturbed Nonlinear Systems
This paper proposes a novel varying horizon economic model predictive control (EMPC) scheme without terminal constraints for constrained nonlinear systems with additive disturbances and unknown economic costs. The general regression learning framework with mixed kernels is first used to reconstruct the unknown cost. Then an online iterative procedure is developed to adjust the horizon adaptively. Again, an elegant horizon-dependent contraction constraint is designed to ensure the convergence of…

Paratransit Optimization with Constraint Programming: A Case Study in Savannah, Georgia
Liam Jagrowski, Kevin Dalmeijer, Tinghan Ye, Pascal Van Hentenryck
https://arxiv.org/abs/2508.00241

Paratransit Optimization with Constraint Programming: A Case Study in Savannah, Georgia
Paratransit services are vital for individuals who cannot use fixed-route public transit, including those with disabilities. Optimizing these services is essential for transit agencies to deliver high-quality service efficiently. This paper introduces a constraint programming model to jointly optimize route planning and shift scheduling for paratransit operations, along with practical guidance for real-world implementation. A case study in Savannah, Georgia, demonstrates that the new approach i…

DRIVE: Dynamic Rule Inference and Verified Evaluation for Constraint-Aware Autonomous Driving
Longling Geng, Huangxing Li, Viktor Lado Naess, Mert Pilanci
https://arxiv.org/abs/2508.04066

DRIVE: Dynamic Rule Inference and Verified Evaluation for Constraint-Aware Autonomous Driving
Understanding and adhering to soft constraints is essential for safe and socially compliant autonomous driving. However, such constraints are often implicit, context-dependent, and difficult to specify explicitly. In this work, we present DRIVE, a novel framework for Dynamic Rule Inference and Verified Evaluation that models and evaluates human-like driving constraints from expert demonstrations. DRIVE leverages exponential-family likelihood modeling to estimate the feasibility of state transit…

A Markovian Framing of WaveFunctionCollapse for Procedurally Generating Aesthetically Complex Environments
Franklin Yiu, Mohan Lu, Nina Li, Kevin Joseph, Tianxu Zhang, Julian Togelius, Timothy Merino, Sam Earle
https://arxiv.org/abs/2509.09919

A Markovian Framing of WaveFunctionCollapse for Procedurally Generating Aesthetically Complex Environments
Procedural content generation often requires satisfying both designer-specified objectives and adjacency constraints implicitly imposed by the underlying tile set. To address the challenges of jointly optimizing both constraints and objectives, we reformulate WaveFunctionCollapse (WFC) as a Markov Decision Process (MDP), enabling external optimization algorithms to focus exclusively on objective maximization while leveraging WFC's propagation mechanism to enforce constraint satisfaction. We emp…

Automated Constraint Specification for Job Scheduling by Regulating Generative Model with Domain-Specific Representation
Yu-Zhe Shi, Qiao Xu, Yanjia Li, Mingchen Liu, Huamin Qu, Lecheng Ruan, Qining Wang
https://arxiv.org/abs/2510.02679

Automated Constraint Specification for Job Scheduling by Regulating Generative Model with Domain-Specific Representation
Advanced Planning and Scheduling (APS) systems have become indispensable for modern manufacturing operations, enabling optimized resource allocation and production efficiency in increasingly complex and dynamic environments. While algorithms for solving abstracted scheduling problems have been extensively investigated, the critical prerequisite of specifying manufacturing requirements into formal constraints remains manual and labor-intensive. Although recent advances of generative models, part…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/14]:
- TCP: a Benchmark for Temporal Constraint-Based Planning
Zifeng Ding, Sikuan Yan, Zhangdie Yuan, Xianglong Hu, Fangru Lin, Andreas Vlachos

A Unifying Framework for Semiring-Based Constraint Logic Programming With Negation (full version)
Jeroen Spaans, Jesse Heyninck
https://arxiv.org/abs/2507.16067

A Unifying Framework for Semiring-Based Constraint Logic Programming With Negation (full version)
Constraint Logic Programming (CLP) is a logic programming formalism used to solve problems requiring the consideration of constraints, like resource allocation and automated planning and scheduling. It has previously been extended in various directions, for example to support fuzzy constraint satisfaction, uncertainty, or negation, with different notions of semiring being used as a unifying abstraction for these generalizations. None of these extensions have studied clauses with negation allowe…