Tootfinder

Chance-constrained Flow Matching for High-Fidelity Constraint-aware Generation
Jinhao Liang, Yixuan Sun, Anirban Samaddar, Sandeep Madireddy, Ferdinando Fioretto
https://arxiv.org/abs/2509.25157

Chance-constrained Flow Matching for High-Fidelity Constraint-aware Generation
Generative models excel at synthesizing high-fidelity samples from complex data distributions, but they often violate hard constraints arising from physical laws or task specifications. A common remedy is to project intermediate samples onto the feasible set; however, repeated projection can distort the learned distribution and induce a mismatch with the data manifold. Thus, recent multi-stage procedures attempt to defer projection to clean samples during sampling, but they increase algorithmic…

System design 101 (or even before): never use the email as the PK for a user account. What if the user needs to change their email?
Better make it a regular column with a UNIQUE constraint.

Simultaneous Quantization and Reduction of Constrained Systems
Jianhao M. Yang
https://arxiv.org/abs/2509.22747 https://arxiv.org/pdf/2509.22747

Simultaneous Quantization and Reduction of Constrained Systems
We present a novel framework for quantizing constrained quantum systems in which the processes of quantization and constraint enforcement are performed simultaneously. The approach is based on an extension of the stationary action principle, incorporating an information-theoretic term arising from vacuum fluctuations. Constraints are included directly in the Lagrangian via Lagrange multipliers, allowing the subsequent variational procedure to yield the quantum dynamics without ambiguity regardi…

Influence-Guided Concolic Testing of Transformer Robustness
Chih-Duo Hong, Yu Wang, Yao-Chen Chang, Fang Yu
https://arxiv.org/abs/2509.23806 https://arxiv.…

Influence-Guided Concolic Testing of Transformer Robustness
Concolic testing for deep neural networks alternates concrete execution with constraint solving to search for inputs that flip decisions. We present an {influence-guided} concolic tester for Transformer classifiers that ranks path predicates by SHAP-based estimates of their impact on the model output. To enable SMT solving on modern architectures, we prototype a solver-compatible, pure-Python semantics for multi-head self-attention and introduce practical scheduling heuristics that temper const…

Exponentially slow Mixing arising from Entropic Repulsion in $p$-SOS model
Seokun Choi
https://arxiv.org/abs/2509.23120 https://arxiv.org/pdf/2509.23120

Exponentially slow Mixing arising from Entropic Repulsion in $p$-SOS model
We investigate the Glauber dynamics of the generalized (2+1)-dimensional $p$-SOS model under a hard floor constraint. This setting induces entropic repulsion: the integer-valued interface height is forced to remain above the wall and consequently rises to a typical height $H(p,L)$ that depends on both the parameter $p$ and the system size $L$. In the classical SOS model ($p=1$), \cite{caputo2016scaling, caputo2014dynamics} derived an exponential lower bound for the mixing time, demonstrating th…

Taming Variability: Randomized and Bootstrapped Conformal Risk Control for LLMs
Lingyou Pang, Lei Huang, Jianyu Lin, Tianyu Wang, Alexander Aue, Carey E. Priebe
https://arxiv.org/abs/2509.23007

Taming Variability: Randomized and Bootstrapped Conformal Risk Control for LLMs
We transform the randomness of LLMs into precise assurances using an actuator at the API interface that applies a user-defined risk constraint in finite samples via Conformal Risk Control (CRC). This label-free and model-agnostic actuator manages ship/abstain/escalate actions based solely on a scalar score from opaque outputs. We enhance CRC's computational efficiency and robustness through Batched Bootstrap CRC (BB-CRC) and Randomized Batched Weighted-Average CRC (RBWA-CRC), reducing calibrati…

I think we can actually prove that this constraint is the *only* constraint that can preserve freedom:
1. There will exist actors in a system who will wish to take advantage of others. Evolution drives survival and one strategy for increasing survival in an altruistic society is to become a parasite.
2. Expecting exploitative dynamics, a system needs to have a set of rules to manage exploitation.
3. If the set of rules is static it will lack the requisite variety necessary to manage the infinite possible behavior of humans so the system will fail.
4. If the system is dynamic then it must have a rule set about how it's own rules are updated. This would make the system recursive, which makes the system at least as complex as mathematics. Any system at least as complex as mathematics is necessarily either incomplete or inconsistent (Gödel's incompleteness theorem). If the system is incomplete, then constraints can be evaded which then allow a malicious agent to seize control of the system and update the rules for their own benefit. If constraints are incomplete, then a malicious agent can take advantage of others within the system.
5. Therefore, no social system can possibly protect freedom unless there exists a single metasystemic constraint (that the system must be optional) allowing for the system to be abandoned when compromised.
Oh, you might say, but this just means you have to infinitely abandon systems. Sure, but there's an evolutionary advantage to cooperation so there's evolutionary pressure to *not* be a malicious actor. So a malicious actor being able to compromise the whole system is likely to be a much more rare event. Compromising a system is a lot of work, so the first thing a malicious actor would want to do is preserve that work. They would want to lock you in. The most important objective to a malicious actor compromising a system would be to violate that metasystemic constraint, or all of their work goes out the window when everyone leaves.
And now you understand why borders exist, why fascists are obsessed with maintaining categories like gender, race, ethnicity, etc. This is why even Democrats like Newsom are on board with putting houseless people in concentration camps. And this is why the most important thing anarchists promote is the ability to choose not to be part of any of that.

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…

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…

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 …

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…

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 …

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…

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 …

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…

Efficient Approximation Algorithms for Fair Influence Maximization under Maximin Constraint
Xiaobin Rui, Zhixiao Wang, Chen Peng, Qiangpeng Fang, Wei Chen
https://arxiv.org/abs/2509.26579

Efficient Approximation Algorithms for Fair Influence Maximization under Maximin Constraint
Aiming to reduce disparities of influence across different groups, Fair Influence Maximization (FIM) has recently garnered widespread attention. The maximin constraint, a common notion of fairness adopted in the FIM problem, imposes a direct and intuitive requirement that asks the utility (influenced ratio within a group) of the worst-off group should be maximized. Although the objective of FIM under maximin constraint is conceptually straightforward, the development of efficient algorithms wit…

from my link log —
Should CSS be a constraint system instead?
https://pavpanchekha.com/blog/why-css-bad.html
saved 2025-12-06 https://

From singular to regular: revisiting thermodynamics of Bardeen-AdS black holes
Meng-Sen Ma, Yun He, Xiao-Ming Wang, Huai-Fan Li
https://arxiv.org/abs/2510.06576 https://

From singular to regular: revisiting thermodynamics of Bardeen-AdS black holes
We explore the thermodynamic properties of the regular Bardeen-AdS black hole obtained by imposing an additional constraint on a singular "mother" black hole. This constraint eliminates the physical singularity but leads to the breakdown of the standard first law of black hole thermodynamics. The singular black hole exhibits a reentrant phase transition similar to that of the higher-dimensional Kerr-AdS black hole. The Bardeen-AdS black hole exhibits $P-V$ criticalities similar to that of the R…

Optimal placement of wind farms via quantile constraint learning
Wenxiu Feng, Antonio Alc\'antara, Carlos Ruiz
https://arxiv.org/abs/2510.01093 https://

Optimal placement of wind farms via quantile constraint learning
Wind farm placement arranges the size and the location of multiple wind farms within a given region. The power output is highly related to the wind speed on spatial and temporal levels, which can be modeled by advanced data-driven approaches. To this end, we use a probabilistic neural network as a surrogate that accounts for the spatiotemporal correlations of wind speed. This neural network uses ReLU activation functions so that it can be reformulated as mixed-integer linear set of constraints …

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…

“My expectation is that within 10 years,
almost all new data centers will be built in space
purely because of the constraint that we’re facing on energy terrestrially”
https://spectrum.ieee.org/nvidia-h100-space?share_id=9034975

NVIDIA's H100 GPU Takes AI Processing to Space
Could space-based data centers be the answer to Earth's energy and cooling challenges? NVIDIA's H100 GPU is leading the charge in orbit.

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…

I feel as though I should illustrate the difference that this one single constraint can make by two examples.
The rules of Simon Says are maximally authoritarian. You must perform any action ordered, with the only restriction that the authority must say "Simon says" first. Were you forced to stay in this system, it would be the most despotic autocracy possible. But it's not. It's a silly game because you can leave at any time.
Let's flip this and imagine a room. During a specific period of time you will have absolute control over everything in this room. In this room you have total freedom. This is not even the limited freedom, the coordinated freedom, the compromising freedom of civil society. You could, without consequence, perform any action you wish in this room. You could say anything, destroy or steal any object, order any individual to perform any action, kill any person in the room with you and take anything they own. This is the sovereign freedom, the absolute freedom, of dictators and kings. The only restriction is that you are not allowed to leave the room while you have this freedom. In fact, you really only have this level of freedom because the room is actually empty other than for you. I am, of course, talking about a form of torture still common in the US: solitary confinement.

Pinching Antenna Systems (PASS) for Cell-Free Communications
Haochen Li
https://arxiv.org/abs/2510.03628 https://arxiv.org/pdf/2510.03628

Pinching Antenna Systems (PASS) for Cell-Free Communications
A pinching antenna system (PASS) assisted cell-free communication system is proposed. A sum rate maximization problem under the BS power budget constraint and PA deployment constraint is formulated. To tackle the proposed non-convex optimization problem, an alternating optimization (AO) algorithm is developed. In particular, the digital beamforming sub-problem is solved using the weighted minimum mean square error (WMMSE) method, whereas the pinching beamforming sub-problem is handled via a pen…

This is the best critique of SF's Family Zoning Plan I've seen, from Fernando Martí, who knows his stuff.
“Zoning is not the primary constraint to construction today — not in SF where it costs $800,000 or more to build a unit of housing. Nor is environmental review or planning approvals. If these were the constraints to production, then we wouldn’t have tens of thousands of already approved market-rate units unable to get construction financing”

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…

Two-cardinal Kurepa Hypotheses
Fanxin Wu
https://arxiv.org/abs/2510.08860 https://arxiv.org/pdf/2510.08860

Two-cardinal Kurepa Hypotheses
We consider the two-cardinal Kurepa Hypothesis $\mathsf{KH}(κ,λ)$. We observe that if $κ\leqλ<μ$ are infinite cardinals then $\lnot\mathsf{KH}(κ,λ)\land\mathsf{KH}(κ,μ)\rightarrow\mathsf{KH}(λ^+,μ)$, and show that in some sense this is the only $\mathsf{ZFC}$ constraint. The case of singular $λ$ and its relation to Chang's Conjecture and scales is discussed. We also extend an independence result about Kurepa and Aronszajn trees due to Cummings to the case of successors of singular c…

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…

Nine lower bound conjectures on streaming approximation algorithms for CSPs
Noah G. Singer
https://arxiv.org/abs/2510.10714 https://arxiv.org/pdf/2510.1071…

Nine lower bound conjectures on streaming approximation algorithms for CSPs
In this column, we overview recent progress by many authors on understanding the approximability of constraint satisfaction problems (CSPs) in low-space streaming models. Inspired by this recent progress, we collate nine conjectural lower bounds against streaming algorithms for CSPs, some of which appear here for the first time.

Improved Dark Photon Sensitivity from the Dark SRF Experiment
Saarik Kalia, Zhen Liu, Bianca Giaccone, Oleksandr Melnychuk, Roman Pilipenko, Asher Berlin, Anson Hook, Sergey Belomestnykh, Crispin Contreras-Martinez, Daniil Frolov, Timergali Khabiboulline, Yuriy Pischalnikov, Sam Posen, Oleg Pronitchev, Vyacheslav Yakovlev, Anna Grassellino, Roni Harnik, Alexander Romanenko

Improved Dark Photon Sensitivity from the Dark SRF Experiment
We report the refined dark-photon exclusion bound from Dark SRF's pathfinder run. Our new result is driven by improved theoretical modeling of frequency instability in high-quality resonant experiments. Our analysis leads to a constraint that is an order of magnitude stronger than previously reported (corresponding to a signal-to-noise ratio that is four orders of magnitude larger). This result represents the world-leading constraint on non-dark-matter dark photons over a wide range of masses b…

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…

Single-Deviation Stability in Additively Separable Hedonic Games with Constrained Coalition Sizes
Martin Bullinger, Adam Dunajski, Edith Elkind, Matan Gilboa
https://arxiv.org/abs/2510.12641

Single-Deviation Stability in Additively Separable Hedonic Games with Constrained Coalition Sizes
We study stability in additively separable hedonic games when coalition sizes have to respect fixed size bounds. We consider four classic notions of stability based on single-agent deviations, namely, Nash stability, individual stability, contractual Nash stability, and contractual individual stability. For each stability notion, we consider two variants: in one, the coalition left behind by a deviator must still be of a valid size, and in the other there is no such constraint. We provide a ful…

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…

Spinning into Quantum Geometry: Dirac and Wheeler-DeWitt Dynamics from Stochastic Helicity
Partha Nandi, Partha Ghose, Francesco Petruccione
https://arxiv.org/abs/2510.10836 htt…

Spinning into Quantum Geometry: Dirac and Wheeler-DeWitt Dynamics from Stochastic Helicity
Spin networks in loop quantum gravity provide a kinematical picture of quantum geometry but lack a natural mechanism for dynamical Dirac-type evolution, while the Wheeler--DeWitt equation typically enters only as an imposed constraint. We propose a stochastic framework in which each spin-network edge carries helicity-resolved amplitudes -- two-state internal labels that undergo Poisson-driven flips. The resulting coupled master equations, after analytic continuation and the introduction of a fu…

Towards Automated and Predictive Network-Level Energy Profiling in Reconfigurable IoT Systems
Mohammud J. Bocus, Senhui Qiu, Robert J. Piechocki, Kerstin Eder
https://arxiv.org/abs/2510.09842

Towards Automated and Predictive Network-Level Energy Profiling in Reconfigurable IoT Systems
Energy efficiency has emerged as a defining constraint in the evolution of sustainable Internet of Things (IoT) networks. This work moves beyond simulation-based or device-centric studies to deliver measurement-driven, network-level smart energy analysis. The proposed system enables end-to-end visibility of energy flows across distributed IoT infrastructures, uniting Bluetooth Low Energy (BLE) and Visible Light Communication (VLC) modes with environmental sensing and E-ink display subsystems un…

I feel as though I should illustrate the difference that this one single constraint can make by two examples.
The rules of Simon Says are maximally authoritarian. You must perform any action ordered, with the only restriction that the authority must say "Simon says" first. Were you forced to stay in this system, it would be the most despotic autocracy possible. But it's not. It's a silly game because you can leave at any time.
Let's flip this and imagine a room. During a specific period of time you will have absolute control over everything in this room. In this room you have total freedom. This is not even the limited freedom, the coordinated freedom, the compromising freedom of civil society. You could, without consequence, perform any action you wish in this room. You could say anything, destroy or steal any object, order any individual to perform any action, kill any person in the room with you and take anything they own. This is the sovereign freedom, the absolute freedom, of dictators and kings. The only restriction is that you are not allowed to leave the room while you have this freedom. In fact, you really only have this level of freedom because the room is actually empty other than for you. I am, of course, talking about a form of torture still common in the US: solitary confinement.

Risk-Budgeted Control Framework for Balanced Performance and Safety in Autonomous Vehicles
Pei Yu Chang, Vishnu Renganathan, Qadeer Ahmed
https://arxiv.org/abs/2510.10442 https:…

Risk-Budgeted Control Framework for Balanced Performance and Safety in Autonomous Vehicles
This paper presents a risk-budgeted monitor with a control framework that certifies safety for autonomous driving. In this process, a sliding window is proposed to monitor for insufficient barrier residuals or nonzero tail risk, ensuring system safety. When the safety margin deteriorates, it triggers switching the safety constraint from a performance-based relaxed-control barrier function (R-CBF) to a conservative conditional value at risk (CVaR-CBF) to address the safety concern. This switchin…

Encoding Numeric Computations and Infusing Heuristic Knowledge Using Integrity Constraints in stableKanren
Xiangyu Guo, Ajay Bansal
https://arxiv.org/abs/2510.04049 https://

Encoding Numeric Computations and Infusing Heuristic Knowledge Using Integrity Constraints in stableKanren
This paper presents examples of using integrity constraints in stableKanren to encode numeric computations for problem solving. Then, we use one of the examples to introduce multiple ways to infuse heuristic knowledge and reduce solving time. stableKanren is an extension of miniKanren that supports normal logic programs under stable model semantics. stableKanren further supports numeric computation by constructing a constraint store for integrity constraints. There are three ways to extend a re…

A CSP approach to Graph Sandwich Problems
Manuel Bodirsky, Santiago Guzm\'an-Pro
https://arxiv.org/abs/2510.09128 https://arxiv.org/pdf/2510.09128

A CSP approach to Graph Sandwich Problems
The \emph{Sandwich Problem} (SP) for a graph class $\calC$ is the following computational problem. The input is a pair of graphs $(V,E_1)$ and $(V,E_2)$ where $E_1\subseteq E_2$, and the task is to decide whether there is an edge set $E$ where $E_1\subseteq E \subseteq E_2$ such that the graph $(V,E)$ belongs to $\calC$. In this paper we show that many SPs correspond to the constraint satisfaction problem (CSP) of an infinite $2$-edge-coloured graph $H$. We then notice that several known comple…

Constraining the new contributions to electron $g-2$ in a radiative neutrino mass model
Bayu Dirgantara, J. Julio
https://arxiv.org/abs/2510.08504 https://…

Constraining the new contributions to electron $g-2$ in a radiative neutrino mass model
We examine electron and muon anomalous magnetic dipole moments within a radiative neutrino mass model featuring TeV-scale scalar leptoquarks $S(3,1,-1/3)$ and $R(3,2,1/6)$. We utilize textures with decoupling electron and muon sectors, so that both electron and muon anomalous magnetic dipole moments could receive internal chiral enhancements from different heavy up-type quarks while in the same time evading the stringent $μ\to eγ$ constraint. A successful fit to neutrino oscillation data requ…

Physically Valid Biomolecular Interaction Modeling with Gauss-Seidel Projection
Siyuan Chen, Minghao Guo, Caoliwen Wang, Anka He Chen, Yikun Zhang, Jingjing Chai, Yin Yang, Wojciech Matusik, Peter Yichen Chen
https://arxiv.org/abs/2510.08946

Physically Valid Biomolecular Interaction Modeling with Gauss-Seidel Projection
Biomolecular interaction modeling has been substantially advanced by foundation models, yet they often produce all-atom structures that violate basic steric feasibility. We address this limitation by enforcing physical validity as a strict constraint during both training and inference with a uniffed module. At its core is a differentiable projection that maps the provisional atom coordinates from the diffusion model to the nearest physically valid conffguration. This projection is achieved usin…

Non-unitary Time Evolution via the Chebyshev Expansion Method
\'Aron Holl\'o, D\'aniel Varjas, Cosma Fulga, L\'aszl\'o Oroszl\'any, Viktor K\"onye
https://arxiv.org/abs/2510.10643

Non-unitary Time Evolution via the Chebyshev Expansion Method
The Chebyshev expansion method is a well-established technique for computing the time evolution of quantum states, particularly in Hermitian systems with a bounded spectrum. Here, we show that the applicability of the Chebyshev expansion method extends well beyond this constraint: It remains valid across the entire complex plane and is thus suitable for arbitrary non-Hermitian matrices. We identify that numerical rounding errors are the primary source of errors encountered when applying the met…

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

Replaced article(s) found for physics.app-ph. https://arxiv.org/list/physics.app-ph/new
[1/1]:
- Generalized causality constraint based on duality symmetry reveals untapped potential of sound ab...
Qu, Yang, Huang, Liu, Dong, Li, Sheng, Abrahams, Fang

Consistent gauge theories for the slave particle representation of the strongly correlated $t$-$J$ model
Xi Luo, Tao Shi, Yue Yu, Long Liang
https://arxiv.org/abs/2510.09264 htt…

Consistent gauge theories for the slave particle representation of the strongly correlated $t$-$J$ model
This note aims to clarify the confusion and inconsistency, and to address the incompleteness in our recent work [1,2]. In order to avoid the ill-defined nature of the free propagator of the gauge field in the ordered states of the $t$-$J$ model, we adopted a gauge fixing that was not of the Becchi-Rouet-Stora-Tyutin (BRST) exact form in our previous work [2]. This led to the situation where Dirac's second-class constraints, namely, the slave particle number constraint and the Ioffe-Larkin curre…

There are 3 fundamental freedoms outlined in Dawn of Everything:
(1) the freedom to move away or relocate from one’s surroundings;
(2) the freedom to ignore or disobey commands issued by others; and
(3) the freedom to shape entirely new social realities, or shift back and forth between different ones.
I think these can all be captured in one statement when reframed as a system constraint: for a system to be free, participation must be optional for all members.
People must be part of *some* system. Even individualistic survivalism is itself a system (if not a very good one). Then there is a corollary as well: any system that is not free, that is not optional, can turn optional systems into mandatory ones, and thus (adopted from the MLK quote) un-freedom anywhere is a threat to freedom everywhere.
Edit:
I'm gonna drop the #Philosophy tag on here because apparently that's where I went with it. Challenges and push-back welcome.
Edit:
Aaaaand Its a blog post
https://anarchoccultism.org/building-zion/an-algorithm-for-liberation
As usual, comments, typos, and questions are always welcome.

The tetrahedral Horn problem and asymptotics of U(n) 6j symbols
Anton Alekseev, Matthias Christandl, Thomas C. Fraser
https://arxiv.org/abs/2510.04877 https://

The tetrahedral Horn problem and asymptotics of U(n) 6j symbols
Horn's problem is concerned with characterizing the eigenvalues $(a,b,c)$ of Hermitian matrices $(A,B,C)$ satisfying the constraint $A+B=C$ and forming the edges of a triangle in the space of Hermitian matrices. It has deep connections to tensor product invariants, Littlewood-Richardson coefficients, geometric invariant theory and the intersection theory of Schubert varieties. This paper concerns the tetrahedral Horn problem which aims to characterize the tuples of eigenvalues $(a,b,c,d,e,f)$ o…

Existence of global weak solutions to a parabolic $p$-Laplacian problem with convective term
Angelica Pia Di Feola, Michael Ruzicka
https://arxiv.org/abs/2510.05847 https://

Existence of global weak solutions to a parabolic $p$-Laplacian problem with convective term
For a given bounded domain $Ω\subset \mathbb R^3$, with $C^2$ boundary, and a given instant of time $T>0$, we prove the existence of a global weak solution on $(0,T)$, which satisfies a maximum principle, to a parabolic $p$-Laplacian system with convective term without divergence constraint for any $p\in (1,2)$.

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…

Homomorphism Problems in Graph Databases and Automatic Structures
R\'emi Morvan
https://arxiv.org/abs/2510.07422 https://arxiv.org/pdf/2510.07422

Homomorphism Problems in Graph Databases and Automatic Structures
This thesis investigates the central role of homomorphism problems (structure-preserving maps) in two complementary domains: database querying over finite, graph-shaped data, and constraint solving over (potentially infinite) structures. Building on the well-known equivalence between conjunctive query evaluation and homomorphism existence, the first part focuses on conjunctive regular path queries, a standard extension of conjunctive queries that incorporates regular-path predicates. We study t…

Replaced article(s) found for cs.ET. https://arxiv.org/list/cs.ET/new
[1/1]:
- Lagrange Oscillatory Neural Networks for Constraint Satisfaction and Optimization
Corentin Delacour, Bram Haverkort, Filip Sabo, Nadine Azemard, Aida Todri-Sanial

Geometric Queries on Closed Implicit Surfaces for Walk on Stars
Tianyu Huang
https://arxiv.org/abs/2510.07275 https://arxiv.org/pdf/2510.07275

Geometric Queries on Closed Implicit Surfaces for Walk on Stars
Walk on stars (WoSt) is currently one of the most advanced Monte Carlo solvers for PDEs. Unfortunately, the lack of reliable geometric query approaches has hindered its applicability to boundaries defined by implicit surfaces. This work proposes a geometric query framework over closed implicit surfaces for WoSt, under the scope of walkin' Robin. Our key observation is that all WoSt queries can be formulated as constrained global optimization or constraint satisfaction problems. Based on our for…

The $\alpha$--regression for compositional data: a unified framework for standard, spatially-lagged, and geographically-weighted regression models
Michail Tsagris
https://arxiv.org/abs/2510.12663

The $α$--regression for compositional data: a unified framework for standard, spatially-lagged, and geographically-weighted regression models
Compositional data-vectors of non--negative components summing to unity--frequently arise in scientific applications where covariates influence the relative proportions of components, yet traditional regression approaches struggle with the unit-sum constraint and zero values. This paper revisits the $α$--regression framework, which uses a flexible power transformation parameterized by $α$ to interpolate between raw data analysis and log-ratio methods, naturally handling zeros without imputati…

Global Solutions to Non-Convex Functional Constrained Problems with Hidden Convexity
Ilyas Fatkhullin, Niao He, Guanghui Lan, Florian Wolf
https://arxiv.org/abs/2511.10626 https://arxiv.org/pdf/2511.10626 https://arxiv.org/html/2511.10626
arXiv:2511.10626v1 Announce Type: new
Abstract: Constrained non-convex optimization is fundamentally challenging, as global solutions are generally intractable and constraint qualifications may not hold. However, in many applications, including safe policy optimization in control and reinforcement learning, such problems possess hidden convexity, meaning they can be reformulated as convex programs via a nonlinear invertible transformation. Typically such transformations are implicit or unknown, making the direct link with the convex program impossible. On the other hand, (sub-)gradients with respect to the original variables are often accessible or can be easily estimated, which motivates algorithms that operate directly in the original (non-convex) problem space using standard (sub-)gradient oracles. In this work, we develop the first algorithms to provably solve such non-convex problems to global minima. First, using a modified inexact proximal point method, we establish global last-iterate convergence guarantees with $\widetilde{\mathcal{O}}(\varepsilon^{-3})$ oracle complexity in non-smooth setting. For smooth problems, we propose a new bundle-level type method based on linearly constrained quadratic subproblems, improving the oracle complexity to $\widetilde{\mathcal{O}}(\varepsilon^{-1})$. Surprisingly, despite non-convexity, our methodology does not require any constraint qualifications, can handle hidden convex equality constraints, and achieves complexities matching those for solving unconstrained hidden convex optimization.
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Spatially-informed transformers: Injecting geostatistical covariance biases into self-attention for spatio-temporal forecasting
Yuri Calleo
https://arxiv.org/abs/2512.17696 https://arxiv.org/pdf/2512.17696 https://arxiv.org/html/2512.17696
arXiv:2512.17696v1 Announce Type: new
Abstract: The modeling of high-dimensional spatio-temporal processes presents a fundamental dichotomy between the probabilistic rigor of classical geostatistics and the flexible, high-capacity representations of deep learning. While Gaussian processes offer theoretical consistency and exact uncertainty quantification, their prohibitive computational scaling renders them impractical for massive sensor networks. Conversely, modern transformer architectures excel at sequence modeling but inherently lack a geometric inductive bias, treating spatial sensors as permutation-invariant tokens without a native understanding of distance. In this work, we propose a spatially-informed transformer, a hybrid architecture that injects a geostatistical inductive bias directly into the self-attention mechanism via a learnable covariance kernel. By formally decomposing the attention structure into a stationary physical prior and a non-stationary data-driven residual, we impose a soft topological constraint that favors spatially proximal interactions while retaining the capacity to model complex dynamics. We demonstrate the phenomenon of ``Deep Variography'', where the network successfully recovers the true spatial decay parameters of the underlying process end-to-end via backpropagation. Extensive experiments on synthetic Gaussian random fields and real-world traffic benchmarks confirm that our method outperforms state-of-the-art graph neural networks. Furthermore, rigorous statistical validation confirms that the proposed method delivers not only superior predictive accuracy but also well-calibrated probabilistic forecasts, effectively bridging the gap between physics-aware modeling and data-driven learning.
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Planar Length-Constrained Minimum Spanning Trees
D Ellis Hershkowitz, Richard Z Huang
https://arxiv.org/abs/2510.09002 https://arxiv.org/pdf/2510.09002

Planar Length-Constrained Minimum Spanning Trees
In length-constrained minimum spanning tree (MST) we are given an $n$-node graph $G = (V,E)$ with edge weights $w : E \to \mathbb{Z}_{\geq 0}$ and edge lengths $l: E \to \mathbb{Z}_{\geq 0}$ along with a root node $r \in V$ and a length-constraint $h \in \mathbb{Z}_{\geq 0}$. Our goal is to output a spanning tree of minimum weight according to $w$ in which every node is at distance at most $h$ from $r$ according to $l$. We give a polynomial-time algorithm for planar graphs which, for any cons…

Robust Closed-Form Control for MIMO Nonlinear Systems under Conflicting Time-Varying Hard and Soft Constraints
Farhad Mehdifar, Charalampos P. Bechlioulis, Dimos V. Dimarogonas
https://arxiv.org/abs/2510.11393

Robust Closed-Form Control for MIMO Nonlinear Systems under Conflicting Time-Varying Hard and Soft Constraints
This paper introduces a novel robust closed-form control law to handle time-varying hard and soft constraints in uncertain high-relative-degree nonlinear MIMO systems. These constraints represent spatiotemporal specifications in mechanical systems' operational space, with hard constraints ensuring safety-critical requirements and soft constraints encoding performance or task objectives. Initially, all constraints are consolidated into two separate scalar time-varying hard and soft constraint fu…

Spectrum of pure $R^2$ gravity: full Hamiltonian analysis
Will Barker, Dra\v{z}en Glavan
https://arxiv.org/abs/2510.08201 https://arxiv.org/pdf/2510.08201

Spectrum of pure $R^2$ gravity: full Hamiltonian analysis
We perform a full Hamiltonian constraint analysis of pure Ricci-scalar-squared ($R^2$) gravity to clarify recent controversies regarding its particle spectrum. While it is well established that the full theory consistently propagates three degrees of freedom, we confirm that its linearised spectrum around Minkowski spacetime is empty. moreover, we show that this is not a feature unique to Minkowski spacetime, but a generic property of all traceless-Ricci spacetimes that have a vanishing Ricci s…

Style Over Story: A Process-Oriented Study of Authorial Creativity in Large Language Models
Donghoon Jung, Jiwoo Choi, Songeun Chae, Seohyon Jung
https://arxiv.org/abs/2510.02025

Style Over Story: A Process-Oriented Study of Authorial Creativity in Large Language Models
Evaluations of large language models (LLMs)' creativity have focused primarily on the quality of their outputs rather than the processes that shape them. This study takes a process-oriented approach, drawing on narratology to examine LLMs as computational authors. We introduce constraint-based decision-making as a lens for authorial creativity. Using controlled prompting to assign authorial personas, we analyze the creative preferences of the models. Our findings show that LLMs consistently emp…

Existence of ghost-eliminating constraints in multivielbein theory
J. Flinckman, S. F. Hassan
https://arxiv.org/abs/2510.03014 https://arxiv.org/pdf/2510.0…

Existence of ghost-eliminating constraints in multivielbein theory
We perform a Hamiltonian constraint analysis of the multivielbein theory proposed in arXiv:1804.09723. The analysis shows that the secondary constraints have the correct form to constrain the dynamical variables, thereby eliminating the problematic ghost modes that generally plague theories of interacting spin-2 fields. Under mild restrictions on the solution space, we also identify the tertiary constraints and show that these eliminate the canonical momenta associated with the ghost modes. Our…

Crosslisted article(s) found for math.LO. https://arxiv.org/list/math.LO/new
[1/1]:
- Janus-faces of temporal constraint languages: a dichotomy of expressivity
Johanna Brunar, Michael Pinsker, Moritz Sch\"obi

Private Learning of Littlestone Classes, Revisited
Xin Lyu
https://arxiv.org/abs/2510.00076 https://arxiv.org/pdf/2510.00076…

Private Learning of Littlestone Classes, Revisited
We consider online and PAC learning of Littlestone classes subject to the constraint of approximate differential privacy. Our main result is a private learner to online-learn a Littlestone class with a mistake bound of $\tilde{O}(d^{9.5}\cdot \log(T))$ in the realizable case, where $d$ denotes the Littlestone dimension and $T$ the time horizon. This is a doubly-exponential improvement over the state-of-the-art [GL'21] and comes polynomially close to the lower bound for this task. The advancem…

Robustness of Covariance Estimators with Application in Activity Detection
Hendrik Bernd Zarucha, Peter Jung, Giuseppe Caire
https://arxiv.org/abs/2510.07044 https://

Robustness of Covariance Estimators with Application in Activity Detection
The first part of this work considers a general class of covariance estimators. Each estimator of that class is generated by a real-valued function $g$ and a set of model covariance matrices $H$. If $\bf{W}$ is a potentially perturbed observation of a searched covariance matrix, then the estimator is the minimizer of the sum of $g$ applied to each eigenvalue of $\bf{W}^\frac{1}{2}\bf{Z}^{-1}\bf{W}^\frac{1}{2}$ under the constraint that $\bf{Z}$ is from $H$. It is shown that under mild condition…

A Decoy-like Protocol for Quantum Key Distribution: Enhancing the Performance with Imperfect Single Photon Sources
Chanaprom Cholsuk, Furkan A\u{g}larc{\i}, Daniel K. L. Oi, Serkan Ate\c{s}, Tobias Vogl
https://arxiv.org/abs/2510.09454

A Decoy-like Protocol for Quantum Key Distribution: Enhancing the Performance with Imperfect Single Photon Sources
Quantum key distribution (QKD) relies on single photon sources (SPSs), e.g. from solid-state systems, as flying qubits, where security strongly requires sub-Poissonian photon statistics with low second-order correlation values (\$g^{(2)}(0)\$). However, achieving such low \$g^{(2)}(0)\$ remains experimentally challenging. We therefore propose a decoy-like QKD protocol that relaxes this constraint while maintaining security. This enables the use of many SPSs with \$g^{(2)}(0) > \$0.1, routinely …

EC3R-SLAM: Efficient and Consistent Monocular Dense SLAM with Feed-Forward 3D Reconstruction
Lingxiang Hu, Naima Ait Oufroukh, Fabien Bonardi, Raymond Ghandour
https://arxiv.org/abs/2510.02080

EC3R-SLAM: Efficient and Consistent Monocular Dense SLAM with Feed-Forward 3D Reconstruction
The application of monocular dense Simultaneous Localization and Mapping (SLAM) is often hindered by high latency, large GPU memory consumption, and reliance on camera calibration. To relax this constraint, we propose EC3R-SLAM, a novel calibration-free monocular dense SLAM framework that jointly achieves high localization and mapping accuracy, low latency, and low GPU memory consumption. This enables the framework to achieve efficiency through the coupling of a tracking module, which maintains…

Replaced article(s) found for cs.PL. https://arxiv.org/list/cs.PL/new
[1/1]:
- Ranking Functions for Linear-Constraint Loops
Amir M. Ben-Amram, Samir Genaim
https:…

A Computer-Assisted Proof of the Optimal Density Bound for Pinwheel Covering
Akitoshi Kawamura, Yusuke Kobayashi
https://arxiv.org/abs/2510.06533 https://a…

A Computer-Assisted Proof of the Optimal Density Bound for Pinwheel Covering
In the covering version of the pinwheel scheduling problem, a daily task must be assigned to agents under the constraint that agent $i$ can perform the task at most once in any $a_i$-day interval. In this paper, we determine the optimal constant $α^* = 1.264\ldots {}$ such that every instance with $\sum_{i} \frac{1}{a_i} \ge α^*$ is schedulable. This resolves an open problem posed by Soejima and Kawamura (2020). Our proof combines Kawamura's (2024) techniques for the packing version with new …

A semi-Lagrangian method for solving state constraint Mean Field Games in Macroeconomics
Fabio Camilli, Qing Tang, Yong-shen Zhou
https://arxiv.org/abs/2510.00768 https://

A semi-Lagrangian method for solving state constraint Mean Field Games in Macroeconomics
We study continuous-time heterogeneous agent models cast as Mean Field Games, in the Aiyagari-Bewley-Huggett framework. The model couples a Hamilton-Jacobi-Bellman equation for individual optimization with a Fokker-Planck-Kolmogorov equation for the wealth distribution. We establish a comparison principle for constrained viscosity solutions of the HJB equation and propose a semi-Lagrangian (SL) scheme for its numerical solution, proving convergence via the Barles-Souganidis method. A policy ite…

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

Replaced article(s) found for cs.LO. https://arxiv.org/list/cs.LO/new
[1/1]:
- Janus-faces of temporal constraint languages: a dichotomy of expressivity
Johanna Brunar, Michael Pinsker, Moritz Sch\"obi

On Boolean PCSPs with Polynomial Threshold Polymorphisms
Katzper Michno
https://arxiv.org/abs/2509.26248 https://arxiv.org/pdf/2509.26248

On Boolean PCSPs with Polynomial Threshold Polymorphisms
In pursuit of a deeper understanding of Boolean Promise Constraint Satisfaction Problems (PCSPs), we identify a class of problems with restricted structural complexity, which could serve as a promising candidate for complete characterization. Specifically, we investigate the class of PCSPs whose polymorphisms are Polynomial Threshold Functions (PTFs) of bounded degree. We obtain two complexity characterization results: (1) with a hardness condition introduced in [ACMTCT'21], we establish a comp…

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/5]:
- Linguistic Patterns in Pandemic-Related Content: A Comparative Analysis of COVID-19, Constraint, ...
Mkululi Sikosana, Sean Maudsley-Barton, Oluwaseun Ajao

On Stable Cutsets in General and Minimum Degree Constrained Graphs
Mats Vroon, Hans L. Bodlaender
https://arxiv.org/abs/2510.09432 https://arxiv.org/pdf/25…

On Stable Cutsets in General and Minimum Degree Constrained Graphs
A stable cutset is a set of vertices $S$ of a connected graph, that is pairwise non-adjacent and when deleting $S$, the graph becomes disconnected. Determining the existence of a stable cutset in a graph is known to be NP-complete. In this paper, we introduce a new exact algorithm for Stable Cutset. By branching on graph configurations and using the $O^*(1.3645)$ algorithm for the (3,2)-Constraint Satisfaction Problem presented by Beigel and Eppstein, we achieve an improved running time of $O^*…

Improved Search-to-Decision Reduction for Random Local Functions
Kel Zin Tan, Prashant Nalini Vasudevan
https://arxiv.org/abs/2510.02944 https://arxiv.org/…

Improved Search-to-Decision Reduction for Random Local Functions
A random local function defined by a $d$-ary predicate $P$ is one where each output bit is computed by applying $P$ to $d$ randomly chosen bits of its input. These represent natural distributions of instances for constraint satisfaction problems. They were put forward by Goldreich as candidates for low-complexity one-way functions, and have subsequently been widely studied also as potential pseudo-random generators. We present a new search-to-decision reduction for random local functions defi…

CPU- and GPU-Based Parallelization of the Robust Reference Governor
Hamid R. Ossareh, William Shayne, Samuel Chevalier
https://arxiv.org/abs/2510.08288 https://

CPU- and GPU-Based Parallelization of the Robust Reference Governor
Constraint management is a central challenge in modern control systems. A solution is the Reference Governor (RG), which is an add-on strategy to pre-stabilized feedback control systems to enforce state and input constraints by shaping the reference command. While robust formulations of RG exist for linear systems, their extension to nonlinear systems is often computationally intractable. This paper develops a scenario-based robust RG formulation for nonlinear systems and investigates its paral…

Particle momentum spectra, correlations, and maximum entropy principle in high-multiplicity collision events
S. V. Akkelin
https://arxiv.org/abs/2510.01822 https://

Particle momentum spectra, correlations, and maximum entropy principle in high-multiplicity collision events
In this paper, we utilize the maximum entropy prescription to determine a quantum state of a small collision system at the kinetic freeze-out. We derive expressions for multiplicity-selected particle momentum spectra and correlation functions by applying a fixed particle number constraint to this state. The results of our analysis may be useful for interpreting the multiplicity dependence of the particle momentum spectra and correlations in high-multiplicity $pp$ collision events at a fixed LHC…

Burgers equation with a twist: A study on rotational-form equations
Adam Larios
https://arxiv.org/abs/2510.02761 https://arxiv.org/pdf/2510.02761

Burgers equation with a twist: A study on rotational-form equations
A new three-dimensional (3D) equation is proposed, which is formed like Burgers' equation by starting with the 3D incompressible Navier-Stokes equations (NSE) and eliminating the pressure and the divergence-free constraint, but instead the Bernoulli pressure is eliminated, leaving only the rotational form of the nonlinearity. This results in a globally well-posed 3D equation which has exactly the same energy balance as the 3D NSE. Moreover, we show in simulations that the system seems to exhibi…

Localized structures in two-field systems: exact solutions in the presence of Lorentz symmetry breaking and explicit connection with geometric constraints
G. H. Bandeira, D. Bazeia, G. S. Santiago, Ya. Shnir
https://arxiv.org/abs/2510.07012

Localized structures in two-field systems: exact solutions in the presence of Lorentz symmetry breaking and explicit connection with geometric constraints
We investigate a class of models described by two real scalar fields in two-dimensional spacetime. The study focuses mainly on the presence of exact static solutions which satisfy the first-order formalism, in models constructed to engender Lorentz symmetry violation. We start by exploring a direct connection between Lorentz breaking and geometric constraint, as experimentally examined in the case of domain walls in geometrically constrained magnetic materials. By means of a specific choice of …

A Martingale approach to continuous Portfolio Optimization under CVaR like constraints
J\'er\^ome Lelong (LJK), V\'eronique Maume-Deschamps, William Thevenot
https://arxiv.org/abs/2509.26009

A Martingale approach to continuous Portfolio Optimization under CVaR like constraints
We study a continuous-time portfolio optimization problem under an explicit constraint on the Deviation Conditional Value-at-Risk (DCVaR), defined as the difference between the CVaR and the expected terminal wealth. While the mean-CVaR framework has been widely explored, its time-inconsistency complicates the use of dynamic programming. We follow the martingale approach in a complete market setting, as in Gao et al. [4], and extend it by retaining an explicit DCVaR constraint in the problem for…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/5]:
- Look-Ahead Reasoning on Learning Platforms
Haiqing Zhu, Tijana Zrnic, Celestine Mendler-D\"unner
https://arxiv.org/abs/2511.14745 https://mastoxiv.page/@arXiv_csLG_bot/115575981129228810
- Deep Gaussian Process Proximal Policy Optimization
Matthijs van der Lende, Juan Cardenas-Cartagena
https://arxiv.org/abs/2511.18214 https://mastoxiv.page/@arXiv_csLG_bot/115610315210502140
- Spectral Concentration at the Edge of Stability: Information Geometry of Kernel Associative Memory
Akira Tamamori
https://arxiv.org/abs/2511.23083 https://mastoxiv.page/@arXiv_csLG_bot/115644325602130493
- xGR: Efficient Generative Recommendation Serving at Scale
Sun, Liu, Zhang, Wu, Yang, Liang, Li, Ma, Liang, Ren, Zhang, Liu, Zhang, Qian, Yang
https://arxiv.org/abs/2512.11529 https://mastoxiv.page/@arXiv_csLG_bot/115723008170311172
- Credit Risk Estimation with Non-Financial Features: Evidence from a Synthetic Istanbul Dataset
Atalay Denknalbant, Emre Sezdi, Zeki Furkan Kutlu, Polat Goktas
https://arxiv.org/abs/2512.12783 https://mastoxiv.page/@arXiv_csLG_bot/115729287232895097
- The Semantic Illusion: Certified Limits of Embedding-Based Hallucination Detection in RAG Systems
Debu Sinha
https://arxiv.org/abs/2512.15068 https://mastoxiv.page/@arXiv_csLG_bot/115740048142898391
- Towards Reproducibility in Predictive Process Mining: SPICE -- A Deep Learning Library
Stritzel, H\"uhnerbein, Rauch, Zarate, Fleischmann, Buck, Lischka, Frey
https://arxiv.org/abs/2512.16715 https://mastoxiv.page/@arXiv_csLG_bot/115745910810427061
- Differentially private Bayesian tests
Abhisek Chakraborty, Saptati Datta
https://arxiv.org/abs/2401.15502 https://mastoxiv.page/@arXiv_statML_bot/111843467510507382
- SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning
Paul Mangold, Sergey Samsonov, Safwan Labbi, Ilya Levin, Reda Alami, Alexey Naumov, Eric Moulines
https://arxiv.org/abs/2402.04114
- Adjusting Model Size in Continual Gaussian Processes: How Big is Big Enough?
Guiomar Pescador-Barrios, Sarah Filippi, Mark van der Wilk
https://arxiv.org/abs/2408.07588 https://mastoxiv.page/@arXiv_statML_bot/112965266196097314
- Non-Perturbative Trivializing Flows for Lattice Gauge Theories
Mathis Gerdes, Pim de Haan, Roberto Bondesan, Miranda C. N. Cheng
https://arxiv.org/abs/2410.13161 https://mastoxiv.page/@arXiv_heplat_bot/113327593338897860
- Dynamic PET Image Prediction Using a Network Combining Reversible and Irreversible Modules
Sun, Zhang, Xia, Sun, Chen, Yang, Liu, Zhu, Liu
https://arxiv.org/abs/2410.22674 https://mastoxiv.page/@arXiv_eessIV_bot/113401026110345647
- Targeted Learning for Variable Importance
Xiaohan Wang, Yunzhe Zhou, Giles Hooker
https://arxiv.org/abs/2411.02221 https://mastoxiv.page/@arXiv_statML_bot/113429912435819479
- Refined Analysis of Federated Averaging and Federated Richardson-Romberg
Paul Mangold, Alain Durmus, Aymeric Dieuleveut, Sergey Samsonov, Eric Moulines
https://arxiv.org/abs/2412.01389 https://mastoxiv.page/@arXiv_statML_bot/113588027268311334
- Embedding-Driven Data Distillation for 360-Degree IQA With Residual-Aware Refinement
Abderrezzaq Sendjasni, Seif-Eddine Benkabou, Mohamed-Chaker Larabi
https://arxiv.org/abs/2412.12667 https://mastoxiv.page/@arXiv_csCV_bot/113672538318570349
- 3D Cell Oversegmentation Correction via Geo-Wasserstein Divergence
Peter Chen, Bryan Chang, Olivia A Creasey, Julie Beth Sneddon, Zev J Gartner, Yining Liu
https://arxiv.org/abs/2502.01890 https://mastoxiv.page/@arXiv_csCV_bot/113949981686723660
- DHP: Discrete Hierarchical Planning for Hierarchical Reinforcement Learning Agents
Shashank Sharma, Janina Hoffmann, Vinay Namboodiri
https://arxiv.org/abs/2502.01956 https://mastoxiv.page/@arXiv_csRO_bot/113949997485625086
- Foundation for unbiased cross-validation of spatio-temporal models for species distribution modeling
Diana Koldasbayeva, Alexey Zaytsev
https://arxiv.org/abs/2502.03480
- GraphCompNet: A Position-Aware Model for Predicting and Compensating Shape Deviations in 3D Printing
Juheon Lee (Rachel), Lei (Rachel), Chen, Juan Carlos Catana, Hui Wang, Jun Zeng
https://arxiv.org/abs/2502.09652 https://mastoxiv.page/@arXiv_csCV_bot/114017924551186136
- LookAhead Tuning: Safer Language Models via Partial Answer Previews
Liu, Wang, Luo, Yuan, Sun, Liang, Zhang, Zhou, Hooi, Deng
https://arxiv.org/abs/2503.19041 https://mastoxiv.page/@arXiv_csCL_bot/114227502448008352
- Constraint-based causal discovery with tiered background knowledge and latent variables in single...
Christine W. Bang, Vanessa Didelez
https://arxiv.org/abs/2503.21526 https://mastoxiv.page/@arXiv_statML_bot/114238919468512990
toXiv_bot_toot

The NPA hierarchy does not always attain the commuting operator value
Marco Fanizza, Larissa Kroell, Arthur Mehta, Connor Paddock, Denis Rochette, William Slofstra, Yuming Zhao
https://arxiv.org/abs/2510.04943

The NPA hierarchy does not always attain the commuting operator value
We show that it is undecidable to determine whether the commuting operator value of a nonlocal game is strictly greater than 1/2. As a corollary, there is a boolean constraint system (BCS) game for which the value of the Navascués-Pironio-Acín (NPA) hierarchy does not attain the commuting operator value at any finite level. Our contribution involves establishing a computable mapping from Turing machines to BCS nonlocal games in which the halting property of the machine is encoded as a decisio…

Prepared mind, fast response: A temporal decoupling framework for adaptive knowledge orchestration in open-domain dialogue
Jinling Gan, Churong Liang, Runnan Li
https://arxiv.org/abs/2510.08175

Prepared mind, fast response: A temporal decoupling framework for adaptive knowledge orchestration in open-domain dialogue
The latency-quality tradeoff is a fundamental constraint in open-domain dialogue AI systems, since comprehensive knowledge access necessitates prohibitive response delays. Contemporary approaches offer two inadequate solutions: lightweight instruct models achieve sub-second latency but lack reasoning depth, while tool-augmented ReAct agents enhance factuality through external knowledge at the cost of synchronous execution that blocks interaction during re- trieval processes. PMFR is thus propos…

Neural Post-Einsteinian Test of General Relativity with the Third Gravitational-Wave Transient Catalog
Yiqi Xie, Gautham Narayan, Nicol\'as Yunes
https://arxiv.org/abs/2510.02515

Neural Post-Einsteinian Test of General Relativity with the Third Gravitational-Wave Transient Catalog
Gravitational waves (GWs) from compact binaries are excellent probes of gravity in the strong- and dynamical-field regime. We report a test of general relativity (GR) with the third GW Transient Catalog (GWTC-3) using the recently developed neural post-Einsteinian framework, both on individual events and at the population level through hierarchical modeling. We find no significant violation of GR and place a constraint that, for the first time, efficiently covers non-GR theories characterized b…

Progressively Sampled Equality-Constrained Optimization
Frank E. Curtis, Lingjun Guo, Daniel P. Robinson
https://arxiv.org/abs/2510.00417 https://arxiv.org…

Progressively Sampled Equality-Constrained Optimization
An algorithm is proposed, analyzed, and tested for solving continuous nonlinear-equality-constrained optimization problems where the constraints are defined by an expectation or an average over a large (finite) number of terms. The main idea of the algorithm is to solve a sequence of equality-constrained problems, each involving a finite sample of constraint-function terms, over which the sample set grows progressively. Under assumptions about the constraint functions and their first- and secon…

Reinforcement Learning from Probabilistic Forecasts for Safe Decision-Making via Conditional Value-at-Risk Planning
Michal Koren, Or Peretz, Tai Dinh, Philip S. Yu
https://arxiv.org/abs/2510.08226

Reinforcement Learning from Probabilistic Forecasts for Safe Decision-Making via Conditional Value-at-Risk Planning
Sequential decisions in volatile, high-stakes settings require more than maximizing expected return; they require principled uncertainty management. This paper presents the Uncertainty-Aware Markov Decision Process (UAMDP), a unified framework that couples Bayesian forecasting, posterior-sampling reinforcement learning, and planning under a conditional value-at-risk (CVaR) constraint. In a closed loop, the agent updates its beliefs over latent dynamics, samples plausible futures via Thompson sa…

Safe Landing on Small Celestial Bodies with Gravitational Uncertainty Using Disturbance Estimation and Control Barrier Functions
Felipe Arenas-Uribe, T. Michael Seigler, Jesse B. Hoagg
https://arxiv.org/abs/2510.05895

Safe Landing on Small Celestial Bodies with Gravitational Uncertainty Using Disturbance Estimation and Control Barrier Functions
Soft landing on small celestial bodies (SCBs) poses unique challenges, as uncertainties in gravitational models and poorly characterized, dynamic environments require a high level of autonomy. Existing control approaches lack formal guarantees for safety constraint satisfaction, necessary to ensure the safe execution of the maneuvers. This paper introduces a control that addresses this limitation by integrating trajectory tracking, disturbance estimation, and safety enforcement. An extended hig…

Optimal transport paths with capacity induced cost function
Qinglan Xia, Haotian Sun
https://arxiv.org/abs/2510.10557 https://arxiv.org/pdf/2510.10557

Optimal transport paths with capacity induced cost function
This article generalizes the study of ramified optimal transport with capacity constraint in transport multi-paths by generalizing the $\mathbf{M}_α$ cost to $\mathbf{M}_{α,c}$, which incorporates capacity constraints into the cost function. Equipped with $\mathbf{M}_{α,c}$ cost, we prove the existence of optimal transport path, $\mathbf{M}_{α,c}$ related inequalities, decomposition of any general transport paths, and occurrence of direct line segments in an optimal transport path.

Truth-Aware Decoding: A Program-Logic Approach to Factual Language Generation
Faruk Alpay, Hamdi Alakkad
https://arxiv.org/abs/2510.07331 https://arxiv.org…

Truth-Aware Decoding: A Program-Logic Approach to Factual Language Generation
This paper introduces Truth-Aware Decoding (TAD), a verification-oriented decoding scheme that aligns neural language generation with knowledge bases. Situated in the tradition of probabilistic program semantics for sequence models, TAD augments modern instruction-tuned systems with a lattice of semantic guards that operate at decode time. Our contributions are fourfold: (i) a constraint-based semantics that renders oracle filtering as a program-logic judgment, (ii) a proof that greedy selectio…

Quantifying the Accuracy-Interpretability Trade-Off in Concept-Based Sidechannel Models
David Debot, Giuseppe Marra
https://arxiv.org/abs/2510.05670 https://

Quantifying the Accuracy-Interpretability Trade-Off in Concept-Based Sidechannel Models
Concept Bottleneck Models (CBNMs) are deep learning models that provide interpretability by enforcing a bottleneck layer where predictions are based exclusively on human-understandable concepts. However, this constraint also restricts information flow and often results in reduced predictive accuracy. Concept Sidechannel Models (CSMs) address this limitation by introducing a sidechannel that bypasses the bottleneck and carry additional task-relevant information. While this improves accuracy, it …

Reinforce-Ada: An Adaptive Sampling Framework for Reinforce-Style LLM Training
Wei Xiong, Chenlu Ye, Baohao Liao, Hanze Dong, Xinxing Xu, Christof Monz, Jiang Bian, Nan Jiang, Tong Zhang
https://arxiv.org/abs/2510.04996

Reinforce-Ada: An Adaptive Sampling Framework for Reinforce-Style LLM Training
Reinforcement learning applied to large language models (LLMs) for reasoning tasks is often bottlenecked by unstable gradient estimates due to fixed and uniform sampling of responses across prompts. Prior work such as GVM-RAFT addresses this by dynamically allocating inference budget per prompt to minimize stochastic gradient variance under a budget constraint. Inspired by this insight, we propose Reinforce-Ada, an adaptive sampling framework for online RL post-training of LLMs that continuousl…

Off-Policy Reinforcement Learning with Anytime Safety Guarantees via Robust Safe Gradient Flow
Pol Mestres, Arnau Marzabal, Jorge Cort\'es
https://arxiv.org/abs/2510.01492 h…

Off-Policy Reinforcement Learning with Anytime Safety Guarantees via Robust Safe Gradient Flow
This paper considers the problem of solving constrained reinforcement learning (RL) problems with anytime guarantees, meaning that the algorithmic solution must yield a constraint-satisfying policy at every iteration of its evolution. Our design is based on a discretization of the Robust Safe Gradient Flow (RSGF), a continuous-time dynamics for anytime constrained optimization whose forward invariance and stability properties we formally characterize. The proposed strategy, termed RSGF-RL, is a…

Optimizing Fairness in Production Planning: A Human-Centric Approach to Machine and Workforce Allocation
Alexander Nasuta, Alessandro Cisi, Sylwia Olbrych, Gustavo Vieira, Rui Fernandes, Lucas Paletta, Marlene Mayr, Rishyank Chevuri, Robert Woitsch, Hans Aoyang Zhou, Anas Abdelrazeq, Robert H. Schmitt
https://arxiv.org/abs/2510.01094

Optimizing Fairness in Production Planning: A Human-Centric Approach to Machine and Workforce Allocation
This work presents a two-layer, human-centric production planning framework designed to optimize both operational efficiency and workforce fairness in industrial manufacturing. The first layer formulates the Order-Line allocation as a Constraint Programming (CP) problem, generating high-utilization production schedules that respect machine capacities, processing times, and due dates. The second layer models Worker-Line allocation as a Markov Decision Process (MDP), integrating human factors suc…

Reinforcement Learning with Action-Triggered Observations
Alexander Ryabchenko, Wenlong Mou
https://arxiv.org/abs/2510.02149 https://arxiv.org/pdf/2510.021…

Reinforcement Learning with Action-Triggered Observations
We study reinforcement learning problems where state observations are stochastically triggered by actions, a constraint common in many real-world applications. This framework is formulated as Action-Triggered Sporadically Traceable Markov Decision Processes (ATST-MDPs), where each action has a specified probability of triggering a state observation. We derive tailored Bellman optimality equations for this framework and introduce the action-sequence learning paradigm in which agents commit to ex…

Convergence analysis of inexact MBA method for constrained upper-$\mathcal{C}^2$ optimization problems
Ruyu Liu, Shaohua Pan
https://arxiv.org/abs/2511.09940 https://arxiv.org/pdf/2511.09940 https://arxiv.org/html/2511.09940
arXiv:2511.09940v1 Announce Type: new
Abstract: This paper concerns a class of constrained optimization problems in which, the objective and constraint functions are both upper-$\mathcal{C}^2$. For such nonconvex and nonsmooth optimization problems, we develop an inexact moving balls approximation (MBA) method by a workable inexactness criterion for the solving of subproblems. By leveraging a global error bound for the strongly convex program associated with parametric optimization problems, we establish the full convergence of the iterate sequence under the partial bounded multiplier property (BMP) and the Kurdyka-{\L}ojasiewicz (KL) property of the constructed potential function, and achieve the local convergence rate of the iterate and objective value sequences if the potential function satisfies the KL property of exponent $q\in[1/2,1)$. A verifiable condition is also provided to check whether the potential function satisfies the KL property of exponent $q\in[1/2,1)$ at the given critical point. To the best of our knowledge, this is the first implementable inexact MBA method with a full convergence certificate for the constrained nonconvex and nonsmooth optimization problem.
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Optimizing Fairness in Production Planning: A Human-Centric Approach to Machine and Workforce Allocation
Alexander Nasuta, Alessandro Cisi, Sylwia Olbrych, Gustavo Vieira, Rui Fernandes, Lucas Paletta, Marlene Mayr, Rishyank Chevuri, Robert Woitsch, Hans Aoyang Zhou, Anas Abdelrazeq, Robert H. Schmitt
https://arxiv.org/abs/2510.01094

Optimizing Fairness in Production Planning: A Human-Centric Approach to Machine and Workforce Allocation
This work presents a two-layer, human-centric production planning framework designed to optimize both operational efficiency and workforce fairness in industrial manufacturing. The first layer formulates the Order-Line allocation as a Constraint Programming (CP) problem, generating high-utilization production schedules that respect machine capacities, processing times, and due dates. The second layer models Worker-Line allocation as a Markov Decision Process (MDP), integrating human factors suc…

MM-LMPC: Multi-Modal Learning Model Predictive Control via Bandit-Based Mode Selection
Wataru Hashimoto, Kazumune Hashimoto
https://arxiv.org/abs/2510.00410 https://

MM-LMPC: Multi-Modal Learning Model Predictive Control via Bandit-Based Mode Selection
Learning Model Predictive Control (LMPC) improves performance on iterative tasks by leveraging data from previous executions. At each iteration, LMPC constructs a sampled safe set from past trajectories and uses it as a terminal constraint, with a terminal cost given by the corresponding cost-to-go. While effective, LMPC heavily depends on the initial trajectories: states with high cost-to-go are rarely selected as terminal candidates in later iterations, leaving parts of the state space unexpl…

Riccati-ZORO: An efficient algorithm for heuristic online optimization of internal feedback laws in robust and stochastic model predictive control
Florian Messerer, Yunfan Gao, Jonathan Frey, Moritz Diehl
https://arxiv.org/abs/2511.10473 https://arxiv.org/pdf/2511.10473 https://arxiv.org/html/2511.10473
arXiv:2511.10473v1 Announce Type: new
Abstract: We present Riccati-ZORO, an algorithm for tube-based optimal control problems (OCP). Tube OCPs predict a tube of trajectories in order to capture predictive uncertainty. The tube induces a constraint tightening via additional backoff terms. This backoff can significantly affect the performance, and thus implicitly defines a cost of uncertainty. Optimizing the feedback law used to predict the tube can significantly reduce the backoffs, but its online computation is challenging.
Riccati-ZORO jointly optimizes the nominal trajectory and uncertainty tube based on a heuristic uncertainty cost design. The algorithm alternates between two subproblems: (i) a nominal OCP with fixed backoffs, (ii) an unconstrained tube OCP, which optimizes the feedback gains for a fixed nominal trajectory. For the tube optimization, we propose a cost function informed by the proximity of the nominal trajectory to constraints, prioritizing reduction of the corresponding backoffs. These ideas are developed in detail for ellipsoidal tubes under linear state feedback. In this case, the decomposition into the two subproblems yields a substantial reduction of the computational complexity with respect to the state dimension from $\mathcal{O}(n_x^6)$ to $\mathcal{O}(n_x^3)$, i.e., the complexity of a nominal OCP.
We investigate the algorithm in numerical experiments, and provide two open-source implementations: a prototyping version in CasADi and a high-performance implementation integrated into the acados OCP solver.
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Riemannian Consistency Model
Chaoran Cheng, Yusong Wang, Yuxin Chen, Xiangxin Zhou, Nanning Zheng, Ge Liu
https://arxiv.org/abs/2510.00983 https://arxiv.or…

Riemannian Consistency Model
Consistency models are a class of generative models that enable few-step generation for diffusion and flow matching models. While consistency models have achieved promising results on Euclidean domains like images, their applications to Riemannian manifolds remain challenging due to the curved geometry. In this work, we propose the Riemannian Consistency Model (RCM), which, for the first time, enables few-step consistency modeling while respecting the intrinsic manifold constraint imposed by th…

The Trajectory Bundle Method: Unifying Sequential-Convex Programming and Sampling-Based Trajectory Optimization
Kevin Tracy, John Z. Zhang, Jon Arrizabalaga, Stefan Schaal, Yuval Tassa, Tom Erez, Zachary Manchester
https://arxiv.org/abs/2509.26575

The Trajectory Bundle Method: Unifying Sequential-Convex Programming and Sampling-Based Trajectory Optimization
We present a unified framework for solving trajectory optimization problems in a derivative-free manner through the use of sequential convex programming. Traditionally, nonconvex optimization problems are solved by forming and solving a sequence of convex optimization problems, where the cost and constraint functions are approximated locally through Taylor series expansions. This presents a challenge for functions where differentiation is expensive or unavailable. In this work, we present a der…

A Block-Activated Decomposition Algorithm for Multi-Stage Stochastic Variational Inequalities
Minh N. B\`ui
https://arxiv.org/abs/2509.26198 https://arxiv.…

A Block-Activated Decomposition Algorithm for Multi-Stage Stochastic Variational Inequalities
We develop a block-activated decomposition algorithm for multi-stage stochastic variational inequalities with nonanticipativity constraints, which offers two computational novelties: (i) At each iteration, our method activates only a user-chosen block of scenarios. (ii) For each activated scenario, it employs the resolvent of the cost operator and the projector onto the constraint set separately. These features enhance computational tractability, in contrast with existing approaches, which ofte…