Tootfinder

edit_wikiquote: Wikiquote edits (2010)
A bipartite user-page network extracted from Wikiquotes. A user connects to a page if that user edited that page. Edits (edges) are timestamped. Edge weights represent counts of the number of edits.
This network has 8005 nodes and 71775 edges.
Tags: Informational, Web graph, Multigraph, Timestamps

Yes, this is epic!
https://midtsveen.codeberg.page
You will love this one!

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[3/3]:
- StoryEnsemble: Enabling Dynamic Exploration & Iteration in the Design Process with AI and Forward...
Sangho Suh, Michael Lai, Kevin Pu, Steven P. Dow, Tovi Grossman

Ensemble of Pre-Trained Models for Long-Tailed Trajectory Prediction
Divya Thuremella, Yi Yang, Simon Wanna, Lars Kunze, Daniele De Martini
https://arxiv.org/abs/2509.13914 http…

Ensemble of Pre-Trained Models for Long-Tailed Trajectory Prediction
This work explores the application of ensemble modeling to the multidimensional regression problem of trajectory prediction for vehicles in urban environments. As newer and bigger state-of-the-art prediction models for autonomous driving continue to emerge, an important open challenge is the problem of how to combine the strengths of these big models without the need for costly re-training. We show how, perhaps surprisingly, combining state-of-the-art deep learning models out-of-the-box (withou…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/4]:
- Evaluating the Robustness of Open-Source Vision-Language Models to Domain Shift in Object Captioning
Federico Tavella, Amber Drinkwater, Angelo Cangelosi

Replaced article(s) found for cs.LO. https://arxiv.org/list/cs.LO/new
[1/1]:
- Separation and Definability in Fragments of Two-Variable First-Order Logic with Counting
Louwe Kuijer, Tony Tan, Frank Wolter, Michael Zakharyaschev

StylOch at PAN: Gradient-Boosted Trees with Frequency-Based Stylometric Features
Jeremi K. Ochab, Mateusz Matias, Tymoteusz Boba, Tomasz Walkowiak
https://arxiv.org/abs/2507.12064

StylOch at PAN: Gradient-Boosted Trees with Frequency-Based Stylometric Features
This submission to the binary AI detection task is based on a modular stylometric pipeline, where: public spaCy models are used for text preprocessing (including tokenisation, named entity recognition, dependency parsing, part-of-speech tagging, and morphology annotation) and extracting several thousand features (frequencies of n-grams of the above linguistic annotations); light-gradient boosting machines are used as the classifier. We collect a large corpus of more than 500 000 machine-generat…

Replaced article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[2/2]:
- DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge
Zhang, Liu, Qi, Wang, Yu, Zhang, Dong, He, Wang, Zhang, Yi, Zeng, Jin

Collaborative P4-SDN DDoS Detection and Mitigation with Early-Exit Neural Networks
Ouassim Karrakchou, Alaa Zniber, Anass Sebbar, Mounir Ghogho
https://arxiv.org/abs/2509.12291 …

Collaborative P4-SDN DDoS Detection and Mitigation with Early-Exit Neural Networks
Distributed Denial of Service (DDoS) attacks pose a persistent threat to network security, requiring timely and scalable mitigation strategies. In this paper, we propose a novel collaborative architecture that integrates a P4-programmable data plane with an SDN control plane to enable real-time DDoS detection and response. At the core of our approach is a split early-exit neural network that performs partial inference in the data plane using a quantized Convolutional Neural Network (CNN), while…

Mitigating the phase-mismatch effect in non-resonant four-wave mixing enabled by optimal control
Ning Jia, Hamid R. Hamedi, Jing Qian
https://arxiv.org/abs/2509.12827 https://…

Mitigating the phase-mismatch effect in non-resonant four-wave mixing enabled by optimal control
Phase-mismatch in nonlinear optical processes can severely limit the propagation and conversion efficiency of light fields. Here, we present an efficient optimal-control strategy to mitigate the detrimental effects of phase-mismatch in an electromagnetically induced transparency medium via non-resonant four-wave mixing (FWM). By applying a set of fixed, linearly modulated coupling fields that induce a dark eigenmode, we globally optimize a single coupling detuning to minimize the spontaneous em…

Crosslisted article(s) found for cs.SE. https://arxiv.org/list/cs.SE/new
[1/1]:
- Tabularis Formatus: Predictive Formatting for Tables
Mukul Singh, Jos\'e Cambronero, Sumit Gulwani, Vu Le, Gust Verbruggen

Comparing the Spatial Correlation of Binary Black Hole Mergers to Large-Scale Structure through the Illustris Simulation
Shaniya Jarrett, Kelly Holley-Bockelmann, Robert J. Scherrer
https://arxiv.org/abs/2507.11813

Comparing the Spatial Correlation of Binary Black Hole Mergers to Large-Scale Structure through the Illustris Simulation
Gravitational waves (GWs) have provided a new lens through which to view the universe beyond traditional electromagnetic methods. The upcoming space-based gravitational wave mission, Laser Interferometer Space Antenna (LISA), will give us the first glimpse of the sky in mHz gravitational waves, a waveband that contains a rich variety of sources including massive binary black hole (MBBH) mergers. In this work, we investigate the spatial distribution of MBBH mergers versus the galaxy distribution…

Babiš a Fiala: v rozpočtové politice velký rozdíl nevidíme, říkají Klimeš a Kalíškovš | FILTR Specišl – Page Not Found - https://pagenotfound.cz/clanek/babis-a-fiala-v-rozpoctove-politice-velky-rozdil-nevidime-rikaji-klimes-a-kalis…

Babiš a Fiala: v rozpočtové politice velký rozdíl nevidíme, říkají Klimeš a Kalíšková | FILTR Speciál – Page Not Found
Další díl FILTR Speciál se zaměřuje na ekonomiku České republiky a na to, co nás čeká v příštích letech. Hosty jsou Klára Kalíšková (ekonomka z VŠE a IDEA při CERGE-EI) a David Klimeš (ekonomický novinář, pořad Chyby systému). V rozhovoru s Apolenou Rychlíkovou se probírá nejen rozpočtová politika vlád Andreje Babiše a Petra Fialy, ale také dopady konsolidačního balíčku na domácnosti, genderové nerovnosti na trhu práce a zásadní výzvy, které přiná…

[2025-09-18 Thu (UTC), 16 new articles found for cs.HC Human-Computer Interaction]
toXiv_bot_toot

Intergenerational Support for Deepfake Scams Targeting Older Adults
Karina LaRubbio, Alyssa Lanter, Seihyun Lee, Mahima Ramesh, Diana Freed
https://arxiv.org/abs/2508.11579 http…

Intergenerational Support for Deepfake Scams Targeting Older Adults
AI-enhanced scams now employ deepfake technology to produce convincing audio and visual impersonations of trusted family members, often grandchildren, in real time. These attacks fabricate urgent scenarios, such as legal or medical emergencies, to socially engineer older adults into transferring money. The realism of these AI-generated impersonations undermines traditional cues used to detect fraud, making them a powerful tool for financial exploitation. In this study, we explore older adults' …

Replaced article(s) found for astro-ph.GA. https://arxiv.org/list/astro-ph.GA/new
[1/1]:
- The $R$-Process Alliance: Enrichment of $r$-process Elements in a Simulated Milky Way-like Galaxy
Yutaka Hirai, et al.

Challenges for describing unitary evolution in nontrivial geometries: pictures and representations
Steven B. Giddings, Julie Perkins
https://arxiv.org/abs/2507.13351

Challenges for describing unitary evolution in nontrivial geometries: pictures and representations
Description of evolution between spatial slices in a general spacetime suffers from a significant difficulty: the states on the slices, in a given basis, are not related by a unitary transformation. This problem, which occurs in spacetime dimensions above two, is directly related to the infinite number of inequivalent representations of the canonical commutators, and in particular will arise for interacting theories in time-dependent spacetimes. We connect different facets of this issue, and di…

A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images
Yifan Gao, Shaohao Rui, Haoyang Su, Jinyi Xiang, Lianming Wu, Xiaosong Wang
https://arxiv.org/abs/2507.11886

A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images
Accurate segmentation of myocardial lesions from multi-sequence cardiac magnetic resonance imaging is essential for cardiac disease diagnosis and treatment planning. However, achieving optimal feature correspondence is challenging due to intensity variations across modalities and spatial misalignment caused by inconsistent slice acquisition protocols. We propose CAA-Seg, a composite alignment-aware framework that addresses these challenges through a two-stage approach. First, we introduce a sel…

Replaced article(s) found for math.OC. https://arxiv.org/list/math.OC/new
[1/1]:
- Stochastic Primal-Dual Q-Learning
Donghwan Lee, Niao He

edit_wiktionary: Wiktionary edits (2010)
Three bipartite user-page networks extracted from Wiktionary, for French, German, and English. A user connects to a page if that user edited that page. Edits (edges) are timestamped. Edge weights represent counts of the number of edits.
This network has 195 nodes and 204 edges.
Tags: Informational, Web graph, Multigraph, Timestamps

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/3]:
- Uncertainty-Aware Adaptation of Large Language Models for Protein-Protein Interaction Analysis
Jantre, Wang, Park, Chopra, Jeon, Qian, Urban, Yoon

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/4]:
- ALL-PET: A Low-resource and Low-shot PET Foundation Model in Projection Domain
Bin Huang, Kang Chen, Bingxuan Li, Huafeng Liu, Qiegen Liu

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

Replaced article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[1/2]:
- Human Demonstrations are Generalizable Knowledge for Robots
Cui, Zhou, Peng, Hu, Lu, Li, Chen, Wang, Yue

Global Mean-Amplitude Enhanced Spiking Neural Network Coherent Ising Machine
Yan Chen Jiang, Lu Ma, Chuan Wang, Tie Jun Wang
https://arxiv.org/abs/2509.13917 https://

Global Mean-Amplitude Enhanced Spiking Neural Network Coherent Ising Machine
The coherent Ising machine (CIM) is a quantum-inspired computing platform that leverages optical parametric oscillation dynamics to solve combinatorial optimization problems by searching for the ground state of an Ising Hamiltonian. Conventional CIM implementations face challenges in handling non-uniform coupling strengths and maintaining amplitude stability during computation. In this paper, a new global mean-amplitude feedback-enhanced spiking neural network CIM (GFSNN-CIM) is introduced with…

Replaced article(s) found for cs.CR. https://arxiv.org/list/cs.CR/new
[1/1]:
- Universal Session Protocol: Mitigating Unauthenticated Remote Code Execution
Jonathon Anderson

REST in Pieces: RESTful Design Rule Violations in Student-Built Web Apps
Sergio Di Meglio, Valeria Pontillo, Luigi Libero Lucio Starace
https://arxiv.org/abs/2507.11689

REST in Pieces: RESTful Design Rule Violations in Student-Built Web Apps
In Computer Science Bachelor's programs, software quality is often underemphasized due to limited time and a focus on foundational skills, leaving many students unprepared for industry expectations. To better understand the typical quality of student code and inform both education and hiring practices, we analyze 40 full-stack web applications developed in a third-year Web Technologies course. Using an automated static analysis pipeline, we assess adherence to REST API design rules. Results rev…

Replaced article(s) found for hep-th. https://arxiv.org/list/hep-th/new
[2/2]:
- Nuclear Cold QCD: Review and Future Strategy
F. Arleo, et al.

Stochastic Weakly Convex Optimization Under Heavy-Tailed Noises
Tianxi Zhu, Yi Xu, Xiangyang Ji
https://arxiv.org/abs/2507.13283 https://

Stochastic Weakly Convex Optimization Under Heavy-Tailed Noises
An increasing number of studies have focused on stochastic first-order methods (SFOMs) under heavy-tailed gradient noises, which have been observed in the training of practical deep learning models. In this paper, we focus on two types of gradient noises: one is sub-Weibull noise, and the other is noise under the assumption that it has a bounded $p$-th central moment ($p$-BCM) with $p\in (1, 2]$. The latter is more challenging due to the occurrence of infinite variance when $p\in (1, 2)$. Under…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/3]:
- Sophisticated Learning: A novel algorithm for active learning during model-based planning
Hodson, Bassett, van Hoof, Rosman, Solms, Shock, Smith

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/4]:
- RL-MoE: An Image-Based Privacy Preserving Approach In Intelligent Transportation System
Abdolazim Rezaei, Mehdi Sookhak, Mahboobeh Haghparast

Robust Topology and the Hausdorff-Smyth Monad on Metric Spaces over Continuous Quantales
Francesco Dagnino, Amin Farjudian Eugenio Moggi
https://arxiv.org/abs/2508.11623 https:/…

Robust Topology and the Hausdorff-Smyth Monad on Metric Spaces over Continuous Quantales
We define a (preorder-enriched) category $\mathsf{Met}$ of quantale-valued metric spaces and uniformly continuous maps, with the essential requirement that the quantales are continuous. For each object $(X,d,Q)$ in this category, where $X$ is the carrier set, $Q$ is a continuous quantale, and $d: X \times X \to Q$ is the metric, we consider a topology $τ_d$ on $X$, which generalizes the open ball topology, and a topology $τ_{d,R}$ on the powerset $\mathsf{P}(X)$, called the robust topology, w…

Latent Policy Steering with Embodiment-Agnostic Pretrained World Models
Yiqi Wang, Mrinal Verghese, Jeff Schneider
https://arxiv.org/abs/2507.13340 https:/…

Latent Policy Steering with Embodiment-Agnostic Pretrained World Models
Learning visuomotor policies via imitation has proven effective across a wide range of robotic domains. However, the performance of these policies is heavily dependent on the number of training demonstrations, which requires expensive data collection in the real world. In this work, we aim to reduce data collection efforts when learning visuomotor robot policies by leveraging existing or cost-effective data from a wide range of embodiments, such as public robot datasets and the datasets of huma…

What are we talking about when we discuss the Born-Oppenheimer approximation?
Olimpia Lombardi, Sebastian Fortin, Juan Camilo Martinez Gonzalez, Hernan Lucas Accorinti
https://arxiv.org/abs/2507.12223

What are we talking about when we discuss the Born-Oppenheimer approximation?
Nick Huggett, James Ladyman, and Karim Thebault (HLT) have presented a comprehensive article examining the Born-Oppenheimer Approximation (BOA). Their central objective is to challenge our position on the matter-namely, that the BOA incorporates a classical assumption incompatible with the Heisenberg Uncertainty Principle. In contrast, HLT contend that the BOA involves no such classical assumption and, as a result, supports the view that chemistry can be reduced to physics. The purpose of this …

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[5/5]:
- Handwritten Text Recognition of Historical Manuscripts Using Transformer-Based Models
Erez Meoded

Replaced article(s) found for hep-th. https://arxiv.org/list/hep-th/new
[1/2]:
- The two-sphere partition function in two-dimensional quantum gravity
Dionysios Anninos, Teresa Bautista, Beatrix M\"uhlmann

Weighted First Order Model Counting for Two-variable Logic with Axioms on Two Relations
Qipeng Kuang, V\'aclav K\r{u}la, Ond\v{r}ej Ku\v{z}elka, Yuanhong Wang, Yuyi Wang
https://arxiv.org/abs/2508.11515

Weighted First Order Model Counting for Two-variable Logic with Axioms on Two Relations
The Weighted First-Order Model Counting Problem (WFOMC) asks to compute the weighted sum of models of a given first-order logic sentence over a given domain. The boundary between fragments for which WFOMC can be computed in polynomial time relative to the domain size lies between the two-variable fragment ($\text{FO}^2$) and the three-variable fragment ($\text{FO}^3$). It is known that WFOMC for \FOthree{} is $\mathsf{\#P_1}$-hard while polynomial-time algorithms exist for computing WFOMC for $…

BSDE Approach for $\alpha$-Potential Stochastic Differential Games
Xin Guo, Xun Li, Liangquan Zhang
https://arxiv.org/abs/2507.13256 https://

BSDE Approach for $α$-Potential Stochastic Differential Games
In this paper, we examine a class of $α$-potential stochastic differential games with random coefficients via the backward stochastic differential equations (BSDEs) approach. Specifically, we show that the first and second order linear derivatives of the objective function for each player can be expressed through the corresponding first and second-order adjoint equations, which leads to rigorous estimates for $α$. We illustrate the dependence of $α$ on game characteristics through detailed a…

Evaluating Reinforcement Learning Algorithms for Navigation in Simulated Robotic Quadrupeds: A Comparative Study Inspired by Guide Dog Behaviour
Emma M. A. Harrison
https://arxiv.org/abs/2507.13277

Evaluating Reinforcement Learning Algorithms for Navigation in Simulated Robotic Quadrupeds: A Comparative Study Inspired by Guide Dog Behaviour
Robots are increasingly integrated across industries, particularly in healthcare. However, many valuable applications for quadrupedal robots remain overlooked. This research explores the effectiveness of three reinforcement learning algorithms in training a simulated quadruped robot for autonomous navigation and obstacle avoidance. The goal is to develop a robotic guide dog simulation capable of path following and obstacle avoidance, with long-term potential for real-world assistance to guide d…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[4/5]:
- SGSimEval: A Comprehensive Multifaceted and Similarity-Enhanced Benchmark for Automatic Survey Ge...
Beichen Guo, Zhiyuan Wen, Yu Yang, Peng Gao, Ruosong Yang, Jiaxing Shen

Interpolation in Classical Propositional Logic
Patrick Koopmann, Christoph Wernhard, Frank Wolter
https://arxiv.org/abs/2508.11449 https://arxiv.org/pdf/25…

Interpolation in Classical Propositional Logic
We introduce Craig interpolation and related notions such as uniform interpolation, Beth definability, and theory decomposition in classical propositional logic. We present four approaches to computing interpolants: via quantifier elimination, from formulas in disjunctive normal form, and by extraction from resolution or tableau refutations. We close with a discussion of the size of interpolants and links to circuit complexity.

Distributed Nash Equilibrium Seeking in Non-Monotone Games over the Simplex
Tatiana Tatarenko, S. Rasoul Etesami
https://arxiv.org/abs/2507.13244 https://

Distributed Nash Equilibrium Seeking in Non-Monotone Games over the Simplex
In this work, we present a novel characterization of approximate Nash equilibria in a class of convex games over the simplex. To achieve this, we regularize the utility functions using the Shannon entropy term, connect the solutions to the regularized game with the set of Nash equilibria, and formulate a multi-objective optimization problem to solve the regularized game. Based on the obtained properties of the stationary points in this optimization problem, we formulate two distributed heuristi…

Signal Temporal Logic Compliant Co-design of Planning and Control
Manas Sashank Juvvi, Tushar Dilip Kurne, Vaishnavi J, Shishir Kolathaya, Pushpak Jagtap
https://arxiv.org/abs/2507.13225

Signal Temporal Logic Compliant Co-design of Planning and Control
This work presents a novel co-design strategy that integrates trajectory planning and control to handle STL-based tasks in autonomous robots. The method consists of two phases: $(i)$ learning spatio-temporal motion primitives to encapsulate the inherent robot-specific constraints and $(ii)$ constructing an STL-compliant motion plan from these primitives. Initially, we employ reinforcement learning to construct a library of control policies that perform trajectories described by the motion primi…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[3/5]:
- Quantum-Boosted High-Fidelity Deep Learning
Wang, Chen, Xuan, Liu, Gao, Zhu, Hou, Yuan, Cheng, Yi, Wei, Ma, Xu, Wen, Li

Encoding and Reasoning About Arrays in Set Theory
Maximiliano Cristi\'a, Gianfranco Rossi
https://arxiv.org/abs/2508.11447 https://arxiv.org/pdf/2508.1…

Encoding and Reasoning About Arrays in Set Theory
We encode arrays as functions which, in turn, are encoded as sets of ordered pairs. The set cardinality of each of these functions coincides with the length of the array it is representing. Then we define a fragment of set theory that is used to give the specifications of a non-trivial class of programs with arrays. In this way, array reasoning becomes set reasoning. Furthermore, a decision procedure for this fragment is also provided and implemented as part of the {log} (read 'setlog') tool. {…

Few-shot transfer of tool-use skills using human demonstrations with proximity and tactile sensing
Marina Y. Aoyama, Sethu Vijayakumar, Tetsuya Narita
https://arxiv.org/abs/2507.13200

Few-shot transfer of tool-use skills using human demonstrations with proximity and tactile sensing
Tools extend the manipulation abilities of robots, much like they do for humans. Despite human expertise in tool manipulation, teaching robots these skills faces challenges. The complexity arises from the interplay of two simultaneous points of contact: one between the robot and the tool, and another between the tool and the environment. Tactile and proximity sensors play a crucial role in identifying these complex contacts. However, learning tool manipulation using these sensors remains challe…

Optimal regularity up to the boundary for Plateau-quasi-minimizers
Eve Machefert
https://arxiv.org/abs/2507.13189 https://arxiv.org/p…

Optimal regularity up to the boundary for Plateau-quasi-minimizers
We study the regularity of quasi-minimal sets (in the sense of David and Semmes) with a boundary condition, which can be interpreted as quasi-minimizers of Plateau's problem in co-dimension one. For these Plateau-quasi-minimizers, we establish the optimal regularity, which is a characterization by bi-John domains with Ahlfors regular boundaries. This requires to investigate the Ahlfors regularity and also the uniform rectifiability of those sets, up to the boundary.

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/5]:
- Deep Learning-Based Automated Segmentation of Uterine Myomas
Tausifa Jan Saleem, Mohammad Yaqub

Automating the Derivation of Unification Algorithms: A Case Study in Deductive Program Synthesis
Richard Waldinger
https://arxiv.org/abs/2508.11136 https://

Automating the Derivation of Unification Algorithms: A Case Study in Deductive Program Synthesis
The unification algorithm has long been a target for program synthesis research, but a fully automatic derivation remains a research goal. In deductive program synthesis, computer programming is phrased as a task in theorem proving; a declarative specification is expressed in logical form and presented to an automatic theorem prover, and a program meeting the specification is extracted from the proof. The correctness of the program is supported by the proof, which also provides an explanation o…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/5]:
- A weighted U statistic for association analysis considering genetic heterogeneity
Changshuai Wei, Robert C. Elston, Qing Lu

Aligning Humans and Robots via Reinforcement Learning from Implicit Human Feedback
Suzie Kim, Hye-Bin Shin, Seong-Whan Lee
https://arxiv.org/abs/2507.13171

Aligning Humans and Robots via Reinforcement Learning from Implicit Human Feedback
Conventional reinforcement learning (RL) ap proaches often struggle to learn effective policies under sparse reward conditions, necessitating the manual design of complex, task-specific reward functions. To address this limitation, rein forcement learning from human feedback (RLHF) has emerged as a promising strategy that complements hand-crafted rewards with human-derived evaluation signals. However, most existing RLHF methods depend on explicit feedback mechanisms such as button presses or pr…

Characterizing NC1 with Typed Monoids
Anuj Dawar, Aidan T. Evans
https://arxiv.org/abs/2508.11019 https://arxiv.org/pdf/2508.11019

Characterizing NC1 with Typed Monoids
Krebs et al. (2007) gave a characterization of the complexity class TC0 as the class of languages recognized by a certain class of typed monoids. The notion of typed monoid was introduced to extend methods of algebraic automata theory to infinite monoids and hence characterize classes beyond the regular languages. We advance this line of work beyond TC0 by giving a characterization of NC1. This is obtained by first showing that NC1 can be defined as the languages expressible in an extension of …

A meaningful optimal control problem in quantum and classical physics
Arnaud Lazarus (DALEMBERT), Emmanuel Tr\'elat (LJLL)
https://arxiv.org/abs/2507.13125

A meaningful optimal control problem in quantum and classical physics
In this paper we study and solve an optimal control problem motivated by applications in quantum and classical physics. Although apparently simple, this optimal control problem is not easy to solve and we resort to various elaborated methods of optimal control theory. We finally show its relationships to two problems in physics: the computation of the ground state for 1D Schr{ö}dinger operators with a finite potential well, and the optimal dynamical Kapitza stabilization problem.

Inspire or Predict? Exploring New Paradigms in Assisting Classical Planners with Large Language Models
Wenkai Yu, Jianhang Tang, Yang Zhang, Shanjiang Tang, Kebing Jin, Hankz Hankui Zhuo
https://arxiv.org/abs/2508.11524

Inspire or Predict? Exploring New Paradigms in Assisting Classical Planners with Large Language Models
Addressing large-scale planning problems has become one of the central challenges in the planning community, deriving from the state-space explosion caused by growing objects and actions. Recently, researchers have explored the effectiveness of leveraging Large Language Models (LLMs) to generate helpful actions and states to prune the search space. However, prior works have largely overlooked integrating LLMs with domain-specific knowledge to ensure valid plans. In this paper, we propose a nove…

GraspGen: A Diffusion-based Framework for 6-DOF Grasping with On-Generator Training
Adithyavairavan Murali, Balakumar Sundaralingam, Yu-Wei Chao, Wentao Yuan, Jun Yamada, Mark Carlson, Fabio Ramos, Stan Birchfield, Dieter Fox, Clemens Eppner
https://arxiv.org/abs/2507.13097

GraspGen: A Diffusion-based Framework for 6-DOF Grasping with On-Generator Training
Grasping is a fundamental robot skill, yet despite significant research advancements, learning-based 6-DOF grasping approaches are still not turnkey and struggle to generalize across different embodiments and in-the-wild settings. We build upon the recent success on modeling the object-centric grasp generation process as an iterative diffusion process. Our proposed framework, GraspGen, consists of a DiffusionTransformer architecture that enhances grasp generation, paired with an efficient discr…

[2025-08-18 Mon (UTC), 6 new articles found for cs.LO Logic in Computer Science]
toXiv_bot_toot

Unsupervised Ground Metric Learning
Janis Auffenberg, Jonas Bresch, Oleh Melnyk, Gabriele Steidl
https://arxiv.org/abs/2507.13094 https://

Unsupervised Ground Metric Learning
Data classification without access to labeled samples remains a challenging problem. It usually depends on an appropriately chosen distance between features, a topic addressed in metric learning. Recently, Huizing, Cantini and Peyré proposed to simultaneously learn optimal transport (OT) cost matrices between samples and features of the dataset. This leads to the task of finding positive eigenvectors of a certain nonlinear function that maps cost matrices to OT distances. Having this basic ide…

Landmark-Assisted Monte Carlo Planning
David H. Chan, Mark Roberts, Dana S. Nau
https://arxiv.org/abs/2508.11493 https://arxiv.org/pdf/2508.11493

Landmark-Assisted Monte Carlo Planning
Landmarks$\unicode{x2013}$conditions that must be satisfied at some point in every solution plan$\unicode{x2013}$have contributed to major advancements in classical planning, but they have seldom been used in stochastic domains. We formalize probabilistic landmarks and adapt the UCT algorithm to leverage them as subgoals to decompose MDPs; core to the adaptation is balancing between greedy landmark achievement and final goal achievement. Our results in benchmark domains show that well-chosen la…

ZipMPC: Compressed Context-Dependent MPC Cost via Imitation Learning
Rahel Rickenbach, Alan A. Lahoud, Erik Schaffernicht, Melanie N. Zeilinger, Johannes A. Stork
https://arxiv.org/abs/2507.13088

ZipMPC: Compressed Context-Dependent MPC Cost via Imitation Learning
The computational burden of model predictive control (MPC) limits its application on real-time systems, such as robots, and often requires the use of short prediction horizons. This not only affects the control performance, but also increases the difficulty of designing MPC cost functions that reflect the desired long-term objective. This paper proposes ZipMPC, a method that imitates a long-horizon MPC behaviour by learning a compressed and context-dependent cost function for a short-horizon MP…

Inclusion Arena: An Open Platform for Evaluating Large Foundation Models with Real-World Apps
Kangyu Wang, Hongliang He, Lin Liu, Ruiqi Liang, Zhenzhong Lan, Jianguo Li
https://arxiv.org/abs/2508.11452

Inclusion Arena: An Open Platform for Evaluating Large Foundation Models with Real-World Apps
Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) have ushered in a new era of AI capabilities, demonstrating near-human-level performance across diverse scenarios. While numerous benchmarks (e.g., MMLU) and leaderboards (e.g., Chatbot Arena) have been proposed to help evolve the development of LLMs and MLLMs, most rely on static datasets or crowdsourced general-domain prompts, often falling short of reflecting performance in real-world applications. To bridge this criti…

A Nonparallel Support Tensor Machine for Binary Classification based Large Margin Distribution and Iterative Optimization
Zhuolin Du, Yisheng Song
https://arxiv.org/abs/2507.13012

A Nonparallel Support Tensor Machine for Binary Classification based Large Margin Distribution and Iterative Optimization
Based on the tensor-based large margin distribution and the nonparallel support tensor machine, we establish a novel classifier for binary classification problem in this paper, termed the Large Margin Distribution based NonParallel Support Tensor Machine (LDM-NPSTM). The proposed classifier has the following advantages: First, it utilizes tensor data as training samples, which helps to comprehensively preserve the inherent structural information of high-dimensional data, thereby improving class…

Efficient Online Learning and Adaptive Planning for Robotic Information Gathering Based on Streaming Data
Sanjeev Ramkumar Sudha, Joel Jose, Erlend M. Coates
https://arxiv.org/abs/2507.13053

Efficient Online Learning and Adaptive Planning for Robotic Information Gathering Based on Streaming Data
Robotic information gathering (RIG) techniques refer to methods where mobile robots are used to acquire data about the physical environment with a suite of sensors. Informative planning is an important part of RIG where the goal is to find sequences of actions or paths that maximize efficiency or the quality of information collected. Many existing solutions solve this problem by assuming that the environment is known in advance. However, real environments could be unknown or time-varying, and a…

AIM-Bench: Evaluating Decision-making Biases of Agentic LLM as Inventory Manager
Xuhua Zhao, Yuxuan Xie, Caihua Chen, Yuxiang Sun
https://arxiv.org/abs/2508.11416 https://

AIM-Bench: Evaluating Decision-making Biases of Agentic LLM as Inventory Manager
Recent advances in mathematical reasoning and the long-term planning capabilities of large language models (LLMs) have precipitated the development of agents, which are being increasingly leveraged in business operations processes. Decision models to optimize inventory levels are one of the core elements of operations management. However, the capabilities of the LLM agent in making inventory decisions in uncertain contexts, as well as the decision-making biases (e.g. framing effect, etc.) of th…

Faster stochastic cubic regularized Newton methods with momentum
Yiming Yang, Chuan He, Xiao Wang, Zheng Peng
https://arxiv.org/abs/2507.13003 https://

Faster stochastic cubic regularized Newton methods with momentum
Cubic regularized Newton (CRN) methods have attracted signiffcant research interest because they offer stronger solution guarantees and lower iteration complexity. With the rise of the big-data era, there is growing interest in developing stochastic cubic regularized Newton (SCRN) methods that do not require exact gradient and Hessian evaluations. In this paper, we propose faster SCRN methods that incorporate gradient estimation with small, controlled errors and Hessian estimation with momentum…

What Can Robots Teach Us About Trust and Reliance? An interdisciplinary dialogue between Social Sciences and Social Robotics
Julien Wacquez (ETIS, CNRS), Elisabetta Zibetti (CHART), Joffrey Becker (ENSEA, ETIS), Lorenzo Aloe (ETIS, CHART), Fabio Amadio (LARSEN), Salvatore Anzalone (CHART), Lola Ca\~namero (ETIS, CY, CNRS, ENSEA), Serena Ivaldi (LARSEN, LORIA - AIS)

What Can Robots Teach Us About Trust and Reliance? An interdisciplinary dialogue between Social Sciences and Social Robotics
As robots find their way into more and more aspects of everyday life, questions around trust are becoming increasingly important. What does it mean to trust a robot? And how should we think about trust in relationships that involve both humans and non-human agents? While the field of Human-Robot Interaction (HRI) has made trust a central topic, the concept is often approached in fragmented ways. At the same time, established work in sociology, where trust has long been a key theme, is rarely br…

CRAFT-GUI: Curriculum-Reinforced Agent For GUI Tasks
Songqin Nong, Jingxuan Xu, Sheng Zhou, Jianfeng Chen, Xiaoxuan Tang, Tao Jiang, Wenhao Xu
https://arxiv.org/abs/2508.11360 h…

CRAFT-GUI: Curriculum-Reinforced Agent For GUI Tasks
As autonomous agents become adept at understanding and interacting with graphical user interface (GUI) environments, a new era of automated task execution is emerging. Recent studies have demonstrated that Reinforcement Learning (RL) can effectively enhance agents' performance in dynamic interactive GUI environments. However, these methods face two key limitations: (1) they overlook the significant variation in difficulty across different GUI tasks by treating the entire training data as a unif…

Rethinking the Embodied Gap in Vision-and-Language Navigation: A Holistic Study of Physical and Visual Disparities
Liuyi Wang, Xinyuan Xia, Hui Zhao, Hanqing Wang, Tai Wang, Yilun Chen, Chengju Liu, Qijun Chen, Jiangmiao Pang
https://arxiv.org/abs/2507.13019

Rethinking the Embodied Gap in Vision-and-Language Navigation: A Holistic Study of Physical and Visual Disparities
Recent Vision-and-Language Navigation (VLN) advancements are promising, but their idealized assumptions about robot movement and control fail to reflect physically embodied deployment challenges. To bridge this gap, we introduce VLN-PE, a physically realistic VLN platform supporting humanoid, quadruped, and wheeled robots. For the first time, we systematically evaluate several ego-centric VLN methods in physical robotic settings across different technical pipelines, including classification mod…

Geometric Stability Analysis for Differential Inclusions Governed by Maximally Monotone Operators
Hassan Saoud, Michel Th\'era, Minh N. Dao
https://arxiv.org/abs/2507.13000

Geometric Stability Analysis for Differential Inclusions Governed by Maximally Monotone Operators
This paper develops a geometric framework for the stability analysis of differential inclusions governed by maximally monotone operators. A key structural decomposition expresses the operator as the sum of a convexified limit mapping and a normal cone. However, the resulting dynamics are often difficult to analyze directly due to the absence of Lipschitz selections and boundedness. To overcome these challenges, we introduce a regularized system based on a fixed Lipschitz approximation of the co…

SAGE: Scale-Aware Gradual Evolution for Continual Knowledge Graph Embedding
Yifei Li, Lingling Zhang, Hang Yan, Tianzhe Zhao, Zihan Ma, Muye Huang, Jun Liu
https://arxiv.org/abs/2508.11347

SAGE: Scale-Aware Gradual Evolution for Continual Knowledge Graph Embedding
Traditional knowledge graph (KG) embedding methods aim to represent entities and relations in a low-dimensional space, primarily focusing on static graphs. However, real-world KGs are dynamically evolving with the constant addition of entities, relations and facts. To address such dynamic nature of KGs, several continual knowledge graph embedding (CKGE) methods have been developed to efficiently update KG embeddings to accommodate new facts while maintaining learned knowledge. As KGs grow at di…

Beyond Solving Math Quiz: Evaluating the Ability of Large Reasoning Models to Ask for Information
Youcheng Huang, Bowen Qin, Chen Huang, Duanyu Feng, Xi Yang, Wenqiang Lei
https://arxiv.org/abs/2508.11252

Beyond Solving Math Quiz: Evaluating the Ability of Large Reasoning Models to Ask for Information
Large Reasoning Models (LRMs) have demonstrated remarkable problem-solving abilities in mathematics, as evaluated by existing benchmarks exclusively on well-defined problems. However, such evaluation setup constitutes a critical gap, since a genuine intelligent agent should not only solve problems (as a math quiz solver), but also be able~to ask for information when the problems lack sufficient information, enabling proactivity in responding users' requests. To bridge such gap, we proposes a ne…

Robustness Requirement Coverage using a Situation Coverage Approach for Vision-based AI Systems
Sepeedeh Shahbeigi, Nawshin Mannan Proma, Victoria Hodge, Richard Hawkins, Boda Li, Valentina Donzella
https://arxiv.org/abs/2507.12986

Robustness Requirement Coverage using a Situation Coverage Approach for Vision-based AI Systems
AI-based robots and vehicles are expected to operate safely in complex and dynamic environments, even in the presence of component degradation. In such systems, perception relies on sensors such as cameras to capture environmental data, which is then processed by AI models to support decision-making. However, degradation in sensor performance directly impacts input data quality and can impair AI inference. Specifying safety requirements for all possible sensor degradation scenarios leads to unm…

Impact Analysis of Optimal EV Bi-directional Charging with Spatial-temporal Constraints
Xian-Long Lee, Adel N. Toosi, Peter Pudney, Ian McLeod, Muhammad Aamir Cheema, Hao Wang
https://arxiv.org/abs/2507.12877

Impact Analysis of Optimal EV Bi-directional Charging with Spatial-temporal Constraints
The growth in Electric Vehicle (EV) market share is expected to increase power demand on distribution networks. Uncoordinated residential EV charging, based on driving routines, creates peak demand at various zone substations depending on location and time. Leveraging smart charge scheduling and Vehicle-to-Grid (V2G) technologies offers opportunities to adjust charge schedules, allowing for load shifting and grid support, which can reduce both charging costs and grid stress. In this work, we de…

On Strong and Weak Admissibility in Non-Flat Assumption-Based Argumentation
Matti Berthold, Lydia Bl\"umel, Anna Rapberger
https://arxiv.org/abs/2508.11182 https://

On Strong and Weak Admissibility in Non-Flat Assumption-Based Argumentation
In this work, we broaden the investigation of admissibility notions in the context of assumption-based argumentation (ABA). More specifically, we study two prominent alternatives to the standard notion of admissibility from abstract argumentation, namely strong and weak admissibility, and introduce the respective preferred, complete and grounded semantics for general (sometimes called non-flat) ABA. To do so, we use abstract bipolar set-based argumentation frameworks (BSAFs) as formal playgroun…

Non-differentiable Reward Optimization for Diffusion-based Autonomous Motion Planning
Giwon Lee, Daehee Park, Jaewoo Jeong, Kuk-Jin Yoon
https://arxiv.org/abs/2507.12977

Non-differentiable Reward Optimization for Diffusion-based Autonomous Motion Planning
Safe and effective motion planning is crucial for autonomous robots. Diffusion models excel at capturing complex agent interactions, a fundamental aspect of decision-making in dynamic environments. Recent studies have successfully applied diffusion models to motion planning, demonstrating their competence in handling complex scenarios and accurately predicting multi-modal future trajectories. Despite their effectiveness, diffusion models have limitations in training objectives, as they approxim…

The complete edge relaxation for binary polynomial optimization
Alberto Del Pia, Aida Khajavirad
https://arxiv.org/abs/2507.12831 https://

The complete edge relaxation for binary polynomial optimization
We consider the multilinear polytope defined as the convex hull of the feasible region of a linearized binary polynomial optimization problem. We define a relaxation in an extended space for this polytope, which we refer to as the complete edge relaxation. The complete edge relaxation is stronger than several well-known relaxations of the multilinear polytope, including the standard linearization, the flower relaxation, and the intersection of all possible recursive McCormick relaxations. We pr…

Learn to optimize for automatic proton PBS treatment planning for H&N cancers
Qingqing Wang, Liqiang Xiao, Chang Chang
https://arxiv.org/abs/2508.11085 https://

Learn to optimize for automatic proton PBS treatment planning for H&N cancers
Proton PBS treatment planning for H&N cancers involves numerous conflicting objectives, requiring significant effort from human planners to balance and satisfy multiple clinical goals during planning. To achieve this, experience-demanding objective parameter adjustment and computationally expensive inverse optimization are performed iteratively. Extensive efforts have been made to automatically adjust objective parameters, but the most time-consuming component, i.e., inverse optimization, still…

MoCap2GT: A High-Precision Ground Truth Estimator for SLAM Benchmarking Based on Motion Capture and IMU Fusion
Zichao Shu, Shitao Bei, Jicheng Dai, Lijun Li, Zetao Chen
https://arxiv.org/abs/2507.12920

MoCap2GT: A High-Precision Ground Truth Estimator for SLAM Benchmarking Based on Motion Capture and IMU Fusion
Marker-based optical motion capture (MoCap) systems are widely used to provide ground truth (GT) trajectories for benchmarking SLAM algorithms. However, the accuracy of MoCap-based GT trajectories is mainly affected by two factors: spatiotemporal calibration errors between the MoCap system and the device under test (DUT), and inherent MoCap jitter. Consequently, existing benchmarks focus primarily on absolute translation error, as accurate assessment of rotation and inter-frame errors remains c…

From Individual to Multi-Agent Algorithmic Recourse: Minimizing the Welfare Gap via Capacitated Bipartite Matching
Zahra Khotanlou, Kate Larson, Amir-Hossein Karimi
https://arxiv.org/abs/2508.11070

From Individual to Multi-Agent Algorithmic Recourse: Minimizing the Welfare Gap via Capacitated Bipartite Matching
Decision makers are increasingly relying on machine learning in sensitive situations. In such settings, algorithmic recourse aims to provide individuals with actionable and minimally costly steps to reverse unfavorable AI-driven decisions. While existing research predominantly focuses on single-individual (i.e., seeker) and single-model (i.e., provider) scenarios, real-world applications often involve multiple interacting stakeholders. Optimizing outcomes for seekers under an individual welfare…

Tensor-Tensor Products, Group Representations, and Semidefinite Programming
Alex Dunbar, Elizabeth Newman
https://arxiv.org/abs/2507.12729 https://

Tensor-Tensor Products, Group Representations, and Semidefinite Programming
The $\star_M$-family of tensor-tensor products is a framework which generalizes many properties from linear algebra to third order tensors. Here, we investigate positive semidefiniteness and semidefinite programming under the $\star_M$-product. Critical to our investigation is a connection between the choice of matrix M in the $\star_M$-product and the representation theory of an underlying group action. Using this framework, third order tensors equipped with the $\star_M$-product are a natural…

LaViPlan : Language-Guided Visual Path Planning with RLVR
Hayeon Oh
https://arxiv.org/abs/2507.12911 https://arxiv.org/pdf/2507.12911…

LaViPlan : Language-Guided Visual Path Planning with RLVR
Out-of-distribution (OOD) scenarios in autonomous driving refer to situations that deviate from the training domain, often leading to unexpected and potentially hazardous behavior from planners that lack prior exposure to such cases. Recently, Vision-Language Models (VLMs) have been introduced into autonomous driving research for their promising generalization capabilities in OOD settings. Early studies demonstrated that VLMs could recognize OOD scenarios and generate user-level decisions such …

Grounding Rule-Based Argumentation Using Datalog
Martin Diller, Sarah Alice Gaggl, Philipp Hanisch, Giuseppina Monterosso, Fritz Rauschenbach
https://arxiv.org/abs/2508.10976 ht…

Grounding Rule-Based Argumentation Using Datalog
ASPIC+ is one of the main general frameworks for rule-based argumentation for AI. Although first-order rules are commonly used in ASPIC+ examples, most existing approaches to reason over rule-based argumentation only support propositional rules. To enable reasoning over first-order instances, a preliminary grounding step is required. As groundings can lead to an exponential increase in the size of the input theories, intelligent procedures are needed. However, there is a lack of dedicated solut…

Fast Consensus over Almost Regular Directed Graphs
Susie Lu, Marco Gamarra, Ji Liu
https://arxiv.org/abs/2507.12726 https://arxiv.org…

Fast Consensus over Almost Regular Directed Graphs
This paper studies an open consensus network design problem: identifying the optimal simple directed graphs, given a fixed number of vertices and arcs, that maximize the second smallest real part of all Laplacian eigenvalues, referred to as algebraic connectivity. For sparse and dense graphs, the class of all optimal directed graphs that maximize algebraic connectivity is theoretically identified, leading to the fastest consensus. For general graphs, a computationally efficient sequence of almo…

DEMONSTRATE: Zero-shot Language to Robotic Control via Multi-task Demonstration Learning
Rahel Rickenbach, Bruce Lee, Ren\'e Zurbr\"ugg, Carmen Amo Alonso, Melanie N. Zeilinger
https://arxiv.org/abs/2507.12855

DEMONSTRATE: Zero-shot Language to Robotic Control via Multi-task Demonstration Learning
The integration of large language models (LLMs) with control systems has demonstrated significant potential in various settings, such as task completion with a robotic manipulator. A main reason for this success is the ability of LLMs to perform in-context learning, which, however, strongly relies on the design of task examples, closely related to the target tasks. Consequently, employing LLMs to formulate optimal control problems often requires task examples that contain explicit mathematical …

[2025-08-18 Mon (UTC), 11 new articles found for cs.AI Artificial Intelligence]
toXiv_bot_toot

Enter the Mind Palace: Reasoning and Planning for Long-term Active Embodied Question Answering
Muhammad Fadhil Ginting, Dong-Ki Kim, Xiangyun Meng, Andrzej Reinke, Bandi Jai Krishna, Navid Kayhani, Oriana Peltzer, David D. Fan, Amirreza Shaban, Sung-Kyun Kim, Mykel J. Kochenderfer, Ali-akbar Agha-mohammadi, Shayegan Omidshafiei
https://

Enter the Mind Palace: Reasoning and Planning for Long-term Active Embodied Question Answering
As robots become increasingly capable of operating over extended periods -- spanning days, weeks, and even months -- they are expected to accumulate knowledge of their environments and leverage this experience to assist humans more effectively. This paper studies the problem of Long-term Active Embodied Question Answering (LA-EQA), a new task in which a robot must both recall past experiences and actively explore its environment to answer complex, temporally-grounded questions. Unlike tradition…

On second-order weak sharp minima of general nonconvex set-constrained optimization problems
Xiaoxiao Ma, Wei Ouyang, Jane Ye, Binbin Zhang
https://arxiv.org/abs/2507.12682

On second-order weak sharp minima of general nonconvex set-constrained optimization problems
This paper explores local second-order weak sharp minima for a broad class of nonconvex optimization problems. We propose novel second-order optimality conditions formulated through the use of classical and lower generalized support functions. These results are based on asymptotic second-order tangent cones and outer second-order tangent sets. Specifically, our findings eliminate the necessity of assuming convexity in the constraint set and/or the outer second-order tangent set, or the nonempti…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[3/5]:
- TokenSkip: Controllable Chain-of-Thought Compression in LLMs
Heming Xia, Chak Tou Leong, Wenjie Wang, Yongqi Li, Wenjie Li

FFI-VTR: Lightweight and Robust Visual Teach and Repeat Navigation based on Feature Flow Indicator and Probabilistic Motion Planning
Jikai Wang, Yunqi Cheng, Zonghai Chen
https://arxiv.org/abs/2507.12800

FFI-VTR: Lightweight and Robust Visual Teach and Repeat Navigation based on Feature Flow Indicator and Probabilistic Motion Planning
Though visual and repeat navigation is a convenient solution for mobile robot self-navigation, achieving balance between efficiency and robustness in task environment still remains challenges. In this paper, we propose a novel visual and repeat robotic autonomous navigation method that requires no accurate localization and dense reconstruction modules, which makes our system featured by lightweight and robustness. Firstly, feature flow is introduced and we develop a qualitative mapping between …

On the factorization of matrices into products of positive definite ones
Mahmoud Abdelgalil, Tryphon T. Georgiou
https://arxiv.org/abs/2507.12560 https://

On the factorization of matrices into products of positive definite ones
The present work revisits and provides a new approach on a result by Charles Ballantine, on the factorization of a square matrix with positive determinant into a product of positive definite factors. {\em Ballantine-type} factorizations, that bound the number of positive definite factors, proved central in solving a basic, yet elusive control problem--the strong controllability of a linear system via control in the form of state feedback. Ballantine's result transcends control engineering, and …

osmAG-LLM: Zero-Shot Open-Vocabulary Object Navigation via Semantic Maps and Large Language Models Reasoning
Fujing Xie, S\"oren Schwertfeger, Hermann Blum
https://arxiv.org/abs/2507.12753

osmAG-LLM: Zero-Shot Open-Vocabulary Object Navigation via Semantic Maps and Large Language Models Reasoning
Recent open-vocabulary robot mapping methods enrich dense geometric maps with pre-trained visual-language features, achieving a high level of detail and guiding robots to find objects specified by open-vocabulary language queries. While the issue of scalability for such approaches has received some attention, another fundamental problem is that high-detail object mapping quickly becomes outdated, as objects get moved around a lot. In this work, we develop a mapping and navigation system for obj…

Refining Motion for Peak Performance: Identifying Optimal Gait Parameters for Energy-Efficient Quadrupedal Bounding
Yasser G. Alqaham, Jing Cheng, Zhenyu Gan
https://arxiv.org/abs/2507.12751

Refining Motion for Peak Performance: Identifying Optimal Gait Parameters for Energy-Efficient Quadrupedal Bounding
Energy efficiency is a critical factor in the performance and autonomy of quadrupedal robots. While previous research has focused on mechanical design and actuation improvements, the impact of gait parameters on energetics has been less explored. In this paper, we hypothesize that gait parameters, specifically duty factor, phase shift, and stride duration, are key determinants of energy consumption in quadrupedal locomotion. To test this hypothesis, we modeled the Unitree A1 quadrupedal robot a…

ASC-SW: Atrous strip convolution network with sliding windows for visual-assisted map navigation
Cheng Liu, Fan Zhu, Yaoyu Zhuang Zhinan Chen Jiefeng Tang
https://arxiv.org/abs/2507.12744

ASC-SW: Atrous strip convolution network with sliding windows for visual-assisted map navigation
With the rapid development of lightweight visual neural network architectures, traditional high-performance vision models have undergone significant compression, greatly improving their computational efficiency and energy consumption ratio. This makes them feasible for deployment on resource-constrained edge computing devices. We propose a visual-assisted navigation framework called Atrous Strip Convolution-Sliding Window (ASC-SW), which leverages a depth camera and a lightweight visual neural …

Learning to Predict Mobile Robot Stability in Off-Road Environments
Nathaniel Rose, Arif Ahmed, Emanuel Gutierrez-Cornejo, Parikshit Maini
https://arxiv.org/abs/2507.12731

Learning to Predict Mobile Robot Stability in Off-Road Environments
Navigating in off-road environments for wheeled mobile robots is challenging due to dynamic and rugged terrain. Traditional physics-based stability metrics, such as Static Stability Margin (SSM) or Zero Moment Point (ZMP) require knowledge of contact forces, terrain geometry, and the robot's precise center-of-mass that are difficult to measure accurately in real-world field conditions. In this work, we propose a learning-based approach to estimate robot platform stability directly from proprioc…

MoistureMapper: An Autonomous Mobile Robot for High-Resolution Soil Moisture Mapping at Scale
Nathaniel Rose, Hannah Chuang, Manuel A Andrade-Rodriguez, Rishi Parashar, Dani Or, Parikshit Maini
https://arxiv.org/abs/2507.12716

MoistureMapper: An Autonomous Mobile Robot for High-Resolution Soil Moisture Mapping at Scale
Soil moisture is a quantity of interest in many application areas including agriculture and climate modeling. Existing methods are not suitable for scale applications due to large deployment costs in high-resolution sensing applications such as for variable irrigation. In this work, we design, build and field deploy an autonomous mobile robot, MoistureMapper, for soil moisture sensing. The robot is equipped with Time Domain Reflectometry (TDR) sensors and a direct push drill mechanism for deplo…

VLMgineer: Vision Language Models as Robotic Toolsmiths
George Jiayuan Gao, Tianyu Li, Junyao Shi, Yihan Li, Zizhe Zhang, Nadia Figueroa, Dinesh Jayaraman
https://arxiv.org/abs/2507.12644

VLMgineer: Vision Language Models as Robotic Toolsmiths
Tool design and use reflect the ability to understand and manipulate the physical world through creativity, planning, and foresight. As such, these capabilities are often regarded as measurable indicators of intelligence across biological species. While much of today's research on robotic intelligence focuses on generating better controllers, inventing smarter tools offers a complementary form of physical intelligence: shifting the onus of problem-solving onto the tool's design. Given the vast …

ReAL-AD: Towards Human-Like Reasoning in End-to-End Autonomous Driving
Yuhang Lu, Jiadong Tu, Yuexin Ma, Xinge Zhu
https://arxiv.org/abs/2507.12499 https:/…

ReAL-AD: Towards Human-Like Reasoning in End-to-End Autonomous Driving
End-to-end autonomous driving has emerged as a promising approach to unify perception, prediction, and planning within a single framework, reducing information loss and improving adaptability. However, existing methods often rely on fixed and sparse trajectory supervision, limiting their ability to capture the hierarchical reasoning process that human drivers naturally employ. To bridge this gap, we propose ReAL-AD, a Reasoning-Augmented Learning framework that structures decision-making in aut…

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making
Yucen Wang, Rui Yu, Shenghua Wan, Le Gan, De-Chuan Zhan
https://arxiv.org/abs/2507.12496

FOUNDER: Grounding Foundation Models in World Models for Open-Ended Embodied Decision Making
Foundation Models (FMs) and World Models (WMs) offer complementary strengths in task generalization at different levels. In this work, we propose FOUNDER, a framework that integrates the generalizable knowledge embedded in FMs with the dynamic modeling capabilities of WMs to enable open-ended task solving in embodied environments in a reward-free manner. We learn a mapping function that grounds FM representations in the WM state space, effectively inferring the agent's physical states in the wo…

Robot Policy Evaluation for Sim-to-Real Transfer: A Benchmarking Perspective
Xuning Yang, Clemens Eppner, Jonathan Tremblay, Dieter Fox, Stan Birchfield, Fabio Ramos
https://arxiv.org/abs/2508.11117

Robot Policy Evaluation for Sim-to-Real Transfer: A Benchmarking Perspective
Current vision-based robotics simulation benchmarks have significantly advanced robotic manipulation research. However, robotics is fundamentally a real-world problem, and evaluation for real-world applications has lagged behind in evaluating generalist policies. In this paper, we discuss challenges and desiderata in designing benchmarks for generalist robotic manipulation policies for the goal of sim-to-real policy transfer. We propose 1) utilizing high visual-fidelity simulation for improved …

Pre-Manipulation Alignment Prediction with Parallel Deep State-Space and Transformer Models
Motonari Kambara, Komei Sugiura
https://arxiv.org/abs/2509.13839 https://

Pre-Manipulation Alignment Prediction with Parallel Deep State-Space and Transformer Models
In this work, we address the problem of predicting the future success of open-vocabulary object manipulation tasks. Conventional approaches typically determine success or failure after the action has been carried out. However, they make it difficult to prevent potential hazards and rely on failures to trigger replanning, thereby reducing the efficiency of object manipulation sequences. To overcome these challenges, we propose a model, which predicts the alignment between a pre-manipulation egoc…

Track Any Motions under Any Disturbances
Zhikai Zhang, Jun Guo, Chao Chen, Jilong Wang, Chenghuai Lin, Yunrui Lian, Han Xue, Zhenrong Wang, Maoqi Liu, Huaping Liu, He Wang, Li Yi
https://arxiv.org/abs/2509.13833

Track Any Motions under Any Disturbances
A foundational humanoid motion tracker is expected to be able to track diverse, highly dynamic, and contact-rich motions. More importantly, it needs to operate stably in real-world scenarios against various dynamics disturbances, including terrains, external forces, and physical property changes for general practical use. To achieve this goal, we propose Any2Track (Track Any motions under Any disturbances), a two-stage RL framework to track various motions under multiple disturbances in the rea…