Tootfinder

Complex harmonic maps and rank 2 higher Teichm\"uller theory
Christian El Emam, Nathaniel Sagman
https://arxiv.org/abs/2506.11746 https://

Complex harmonic maps and rank 2 higher Teichmüller theory
We initiate and develop the theory of complex harmonic maps to holomorphic Riemannian symmetric spaces, which we make use of to study complex analytic aspects of higher Teichmüller theory, with a focus on rank $2$ Hitchin components. Complex harmonic maps lead to various generalizations of objects from the theory of Higgs bundles; for instance, the Hitchin fibration, cyclic Higgs bundles, and the affine Toda equations. Beyond such generalizations, we also find a relation between complex harm…

There’s gonna be an open space on Python and complex applications tomorrow at 10am in room 223 224:
“Is Python suitable for writing maintanable (complex) software?”
I’ll try to be there & as far as I am concerned, Betteridge's Law of Headlines doesn’t apply! #EuroPython

Opus: A Prompt Intention Framework for Complex Workflow Generation
Th\'eo Fagnoni, Mahsun Altin, Chia En Chung, Phillip Kingston, Alan Tuning, Dana O. Mohamed, In\`es Adnani
https://arxiv.org/abs/2507.11288

Opus: A Prompt Intention Framework for Complex Workflow Generation
This paper introduces the Opus Prompt Intention Framework, designed to improve complex Workflow Generation with instruction-tuned Large Language Models (LLMs). We propose an intermediate Intention Capture layer between user queries and Workflow Generation, implementing the Opus Workflow Intention Framework, which consists of extracting Workflow Signals from user queries, interpreting them into structured Workflow Intention objects, and generating Workflows based on these Intentions. Our results…

This is how ruthless the Israeli military complex is
The sign talks of"protecting the state of Israel", remaining silent to the fact that this offensive "protection" actually costs hundreds of thousands of lives in Israel and the region.
Context:
https://www.

Theory of superlensing with complex frequency illuminations
Philippe Lalanne, Tong Wu
https://arxiv.org/abs/2508.10742 https://arxiv.org/pdf/2508.10742

Theory of superlensing with complex frequency illuminations
Recent experiments have demonstrated that the resolution of superlensing slabs can be significantly enhanced with complex frequency illuminations. In this study, we introduce a novel theoretical framework for analyzing superlensing effects with flat lenses. The framework yields a simple and transparent expression for the steady-state transfer function of lossy superlensing slabs and offers clear criteria for selecting complex frequencies that maximize resolution. It also clarifies the condition…

Question for y'all #SystemicGames¹ folks who like multiplayer/#coop games:
How do you logistically manage actually having a co-op, multiplayer playthrough of a complex video-game in your life?
I've always struggled, hard, with satisfying my needs here; not only is it so hard to find people who 1. like complex games, 2. like multiplayer/social gaming, and 3. have time/s…

A statistical mechanical view of complex economies under the green transition
Filippo Marcuccini, Giacomo Livan
https://arxiv.org/abs/2506.11739 https://…

A statistical mechanical view of complex economies under the green transition
We propose a stylized model of a complex economy to explore the economic tradeoffs imposed by the so called "green transition" -- the shift towards more sustainable production paradigms -- using tools from the Statistical Mechanics of disordered systems. Namely, we promote the parameters of a standard input-output economic model to random variables, in order to characterize the typical statistical properties of its equilibria. A central feature of our work is the explicit inclusion of a waste v…

Learning to Move in Rhythm: Task-Conditioned Motion Policies with Orbital Stability Guarantees
Maximilian St\"olzle, T. Konstantin Rusch, Zach J. Patterson, Rodrigo P\'erez-Dattari, Francesco Stella, Josie Hughes, Cosimo Della Santina, Daniela Rus
https://arxiv.org/abs/2507.10602

Learning to Move in Rhythm: Task-Conditioned Motion Policies with Orbital Stability Guarantees
Learning from demonstration provides a sample-efficient approach to acquiring complex behaviors, enabling robots to move robustly, compliantly, and with fluidity. In this context, Dynamic Motion Primitives offer built - in stability and robustness to disturbances but often struggle to capture complex periodic behaviors. Moreover, they are limited in their ability to interpolate between different tasks. These shortcomings substantially narrow their applicability, excluding a wide class of practi…

Heute vor 67 Jahren: Am 16. Juni 1958 führte ein #Unfall im Y-12 National Security Complex dazu, dass hochangereichertes Uran eine 20-minütige #Kernspaltung auslöste. Acht Arbeiter erlitten starke Strahlenbelastungen. Die meisten erkrankten später an Krebs.

Detecting AI Assistance in Abstract Complex Tasks
Tyler King, Nikolos Gurney, John H. Miller, Volkan Ustun
https://arxiv.org/abs/2507.10761 https://…

Detecting AI Assistance in Abstract Complex Tasks
Detecting assistance from artificial intelligence is increasingly important as they become ubiquitous across complex tasks such as text generation, medical diagnosis, and autonomous driving. Aid detection is challenging for humans, especially when looking at abstract task data. Artificial neural networks excel at classification thanks to their ability to quickly learn from and process large amounts of data -- assuming appropriate preprocessing. We posit detecting help from AI as a classificatio…

Using Continual Learning for Real-Time Detection of Vulnerable Road Users in Complex Traffic Scenarios
Faryal Aurooj Nasir, Salman Liaquat, Nor Muzlifah Mahyuddin
https://arxiv.org/abs/2507.11046

Using Continual Learning for Real-Time Detection of Vulnerable Road Users in Complex Traffic Scenarios
Pedestrians and bicyclists are among the vulnerable road users (VRUs) that are inherently exposed to intricate traffic scenarios, which puts them at increased risk of sustaining injuries or facing fatal outcomes. This study presents an intelligent adaptive system that uses the YOLOv8-Dynamic (YOLOv8-D) algorithm that detects vulnerable road users and adapts in real time to prevent accidents before they occur. We select YOLOv8x as the detector by comparing it with other state-of-the-art object d…

On Complex Dimensions and Heat Content of Self-Similar Fractals
William E. Hoffer, Michel L. Lapidus
https://arxiv.org/abs/2508.09512 https://arxiv.org/pdf…

On Complex Dimensions and Heat Content of Self-Similar Fractals
Complex fractal dimensions, defined as poles of appropriate fractal zeta functions, describe the geometric oscillations in fractal sets. In this work, we show that the same possible complex dimensions in the geometric setting also govern the asymptotics of the heat content on self-similar fractals. We consider the Dirichlet problem for the heat equation on bounded open regions whose boundaries are self-similar fractals. The class of self-similar domains we consider allow for non-disjoint over…

The military is a lot more complex than most folks know. Keep that in mind. Don't give up and assume they're all just born to kill, or that if they are deployed against Americans they'll shoot. Some may, some may not, treat them like people and some of them might act like people. That's the take away here.
Since this toot is more popular than the parade itself, I'm gonna mute to save my inbox.
I also want to apologize to the admins here for the extra traffic. :P

Modeling Complex Life Systems: Bayesian Inference for Weibull Failure Times Using Adaptive MCMC
Tobias Oketch, Mohammad Sepehrifar
https://arxiv.org/abs/2506.11949

Modeling Complex Life Systems: Bayesian Inference for Weibull Failure Times Using Adaptive MCMC
This research develops a Bayesian framework for analyzing failure times using the Weibull distribution, addressing challenges in prior selection due to the lack of conjugate priors and multi-dimensional sufficient statistics. We propose an adaptive semi-parametric MCMC algorithm for lifetime data analysis, employing a hierarchical Bayesian model and the No-U-Turn Sampler (NUTS) in STAN. Twenty-four combinations of prior distributions are evaluated, with a noninformative LogNormal hyper-prior en…

Nuclear modification factor within a dynamical approach to the complex entropic index
R. Baptista, L. G. Rocha, J. M. C. Pareja, T. Bhattacharyya, A. Deppman, E. Megias, M. Rybczynski, G. Wilk, Z. Wlodarczyk
https://arxiv.org/abs/2507.11451

Nuclear modification factor within a dynamical approach to the complex entropic index
This work introduces a novel approach to the nuclear deformation factor $R_{AA}$, grounded in the dynamical effects of the Quark-Gluon Plasma on parton momentum. The approach uses the Blast-Wave method combined with Tsallis Statistics, within the Copper-Frye freeze-out framework and, by profiting from appropriate simplifications, it gives analytical expressions that describe the observed $R_{AA}$ for two sets of independent measurements at $\sqrt{s}=2.76$ TeV and $\sqrt{s}=5.02$ TeV. A nonlinea…

Complex three-dimensional rearing environments amplify compensatory plasticity following early blindness https://www.biorxiv.org/content/10.1101/2025.05.02.651847v1 "underscoring the role of experience in directing compensatory plasticity following early sensory loss.&q…

Complex three-dimensional rearing environments amplify compensatory plasticity following early blindness
The neocortex has a remarkable capacity to alter its functional organization and connectivity in response to sensory loss, particularly if this loss occurs early in life. A key question is whether this cross-modal reorganization is driven by sensory deprivation itself or through enhanced use of the spared senses. We investigated how different rearing environments shape neural responses in primary somatosensory cortex (S1) of short-tailed opossums ( Monodelphis domestica) , following elimination…

As usual we watched the "Stoertebekker" stage play at Ralswiek on Rügen Island. The stage setups and wardrobes were as awesome as always. They really try to improve on that every year and come up with clever mechanisms to transform the building for the various acts of the play. The script this year left room for desire ;) Nevertheless a happing worth attending
htt…

Fourth- and Higher-order Interface Tracking of Three or More Materials with Arbitrarily Complex Topology and Geometry
Yan Tan, Yixiao Qian, Zhiqi Li, Qinghai Zhang
https://arxiv.org/abs/2506.11897

Fourth- and Higher-order Interface Tracking of Three or More Materials with Arbitrarily Complex Topology and Geometry
For interface tracking of an arbitrary number of materials in two dimensions, we propose a multiphase cubic MARS method that (a) accurately and efficiently represents the topology and geometry of the interface via graphs, cycles, and cubic splines, (b) maintains a $(r,h)$-regularity condition of the interface so that the distance between any pair of adjacent markers is within a user-specified range that may vary according to the local curvature, (c) applies to multiple materials with arbitraril…

I recently discovered gojq at work. I was looking to see if there was a pure Golang implementation of jq as a library to use from within Go code. Then I compared performance to native jq to make sure that it wouldn't be too much slower than the original when embedded in Golang.
It ran my complex jq script in ⇔ the time over the same data.
That was a nice free speedup!
Now my ~/bin/jq is a symlink to

Flagifying the Dowker Complex
Marius Huber, Patrick Schnider
https://arxiv.org/abs/2508.08025 https://arxiv.org/pdf/2508.08025

Flagifying the Dowker Complex
The Dowker complex $\mathrm{D}_{R}(X,Y)$ is a simplicial complex capturing the topological interplay between two finite sets $X$ and $Y$ under some relation $R\subseteq X\times Y$. While its definition is asymmetric, the famous Dowker duality states that $\mathrm{D}_{R}(X,Y)$ and $\mathrm{D}_{R}(Y,X)$ have homotopy equivalent geometric realizations. We introduce the Dowker-Rips complex $\mathrm{DR}_{R}(X,Y)$, defined as the flagification of the Dowker complex or, equivalently, as the maximal si…

Normal Holonomy of Complex Hyperbolic Submanifolds
Santiago Casta\~neda Montoya, Carlos E. Olmos
https://arxiv.org/abs/2506.11323 https://

Normal Holonomy of Complex Hyperbolic Submanifolds
We prove that the restricted normal holonomy group of a Kähler submanifold of the complex hyperbolic space $\mathbb{C}H^{n}$ is always transitive, provided the index of relative nullity is zero. This contrasts with the case of $\mathbb{C}P^{n}$, where a Berger type result was proved by Console, Di Scala, and the second author. The proof is based on lifting the submanifold to the pseudo-Riemannian space $\mathbb{C}^{n,1}$ and developing new tools to handle the difficulties arising from possible…

🔧 Request Types
Vibe Requests: Conversational interactions, coding questions, documentation
Spec Requests: Structured development workflow executions
Clear metering system replacing complex token billing
⚡ Welcome Bonus Details
🎁 14-Day Trial
100 spec requests 100 vibe requests
Clock starts on first Kiro usage
Available regardless of chosen tier
Unused requests roll over when upgrading
📋 Important Requirements
🔄 IDE Update Required…

Complex Ionization and Velocity Structures in GX 340 0 X-ray Binary Revealed by XRISM
Priyanka Chakraborty, Randall Smith, Lia Corrales, Elisa Costantini, Maria Diaz Trigo, Adam Foster, Caroline Kilbourne, Renee Ludlam, Takao Nakagawa, Frederick S. Porter, Ioanna Psaradaki, Hiromitsu Takahashi, Tahir Yaqoob, Sascha Zeegers
https://arxiv.or…

Complex Ionization and Velocity Structures in GX 340+0 X-ray Binary Revealed by XRISM
We present the first high-resolution XRISM spectrum of the neutron star low-mass X-ray binary GX 340+0, revealing unprecedented detail in its emission and absorption features. The spectrum reveals a rich and complex Fe XXV He$α$ line profile and a P-Cygni profile from Ca XX. We use the state-of-the-art spectral synthesis code Cloudy to model the emission and absorption features in detail. Our analysis reveals multi-ionization and multi-velocity structures, where the combination of broad ($\sim…

Conditional Information Bottleneck for Multimodal Fusion: Overcoming Shortcut Learning in Sarcasm Detection
Yihua Wang, Qi Jia, Cong Xu, Feiyu Chen, Yuhan Liu, Haotian Zhang, Liang Jin, Lu Liu, Zhichun Wang
https://arxiv.org/abs/2508.10644

Conditional Information Bottleneck for Multimodal Fusion: Overcoming Shortcut Learning in Sarcasm Detection
Multimodal sarcasm detection is a complex task that requires distinguishing subtle complementary signals across modalities while filtering out irrelevant information. Many advanced methods rely on learning shortcuts from datasets rather than extracting intended sarcasm-related features. However, our experiments show that shortcut learning impairs the model's generalization in real-world scenarios. Furthermore, we reveal the weaknesses of current modality fusion strategies for multimodal sarcasm…

Axis-level Symmetry Detection with Group-Equivariant Representation
Wongyun Yu, Ahyun Seo, Minsu Cho
https://arxiv.org/abs/2508.10740 https://arxiv.org/pdf…

Axis-level Symmetry Detection with Group-Equivariant Representation
Symmetry is a fundamental concept that has been extensively studied, yet detecting it in complex scenes remains a significant challenge in computer vision. Recent heatmap-based approaches can localize potential regions of symmetry axes but often lack precision in identifying individual axes. In this work, we propose a novel framework for axis-level detection of the two most common symmetry types-reflection and rotation-by representing them as explicit geometric primitives, i.e. lines and points…

Urban Living 🌝
都市生活 🌝
📷 Nikon FE
🎞️Lucky SHD 400
buy me ☕️ ?/请我喝杯☕️？
#filmphotography

Yiding Cao Geld mit PayPal.Me senden
Folgen Sie dem Link PayPal.Me/dingsterdingster und geben Sie den Betrag ein. Es ist bequem und sicher. Sie haben noch kein PayPal-Konto? Kein Problem.

OdysseyBench: Evaluating LLM Agents on Long-Horizon Complex Office Application Workflows
Weixuan Wang, Dongge Han, Daniel Madrigal Diaz, Jin Xu, Victor R\"uhle, Saravan Rajmohan
https://arxiv.org/abs/2508.09124

OdysseyBench: Evaluating LLM Agents on Long-Horizon Complex Office Application Workflows
Autonomous agents powered by large language models (LLMs) are increasingly deployed in real-world applications requiring complex, long-horizon workflows. However, existing benchmarks predominantly focus on atomic tasks that are self-contained and independent, failing to capture the long-term contextual dependencies and multi-interaction coordination required in realistic scenarios. To address this gap, we introduce OdysseyBench, a comprehensive benchmark for evaluating LLM agents on long-horizo…

Suppressing excitations in the nonlinear Landau-Zener model
Sebastian Deffner, Steve Campbell
https://arxiv.org/abs/2506.11759 https://

Suppressing excitations in the nonlinear Landau-Zener model
Many complex quantum systems can be described by effectively nonlinear dynamics. While such dynamics have many appealing characteristics, they also make the analysis significantly more involved. This is due to the fact that only a few analytical treatments exist, and that the language of quantum mechanics is built for linear operators. For instance, the very formulation of the quantum adiabatic theorem requires the underlying dynamics to be linear. In this work we show that in a generalized Lan…

You're not in this place because you're a terrible person, you're in this place because of a complex mixture of circumstances that led to a complex mixture of circumstances. https://www.overcomecompulsivehoarding.co.uk/podcast-ep-190-self-com…

Automata Models for Effective Bug Description
Tom Yaacov, Gera Weiss, Gal Amram, Avi Hayoun
https://arxiv.org/abs/2507.11146 https://…

Automata Models for Effective Bug Description
Debugging complex systems is a crucial yet time-consuming task. This paper presents the use of automata learning and testing techniques to obtain concise and informative bug descriptions. We introduce the concepts of Failure Explanations (FE), Eventual Failure Explanations (EFE), and Early Detection (ED) to provide meaningful summaries of failing behavior patterns. By factoring out irrelevant information and focusing on essential test patterns, our approach aims to enhance bug detection and und…

NetBox Labs, whose open-source tools help manage networks and are used by CoreWeave, ARM, Cisco, JP Morgan, and Riot Games, raised a $35M Series B (Marcus Schuler/implicator.ai)
https://www.implicator.ai/netbox-labs-secures-35-milli…

NetBox Labs Raises $35M Series B as AI Drives Network Demand
NetBox Labs raised $35M as companies scramble to manage complex networks. The AI boom creates massive infrastructure demand, and legacy tools can't keep up. The open-source platform now serves Fortune 500 firms.

@… web components are substantially more technically complex than react as they involve changes to the web platform (though that complexity is abstracted from developers) 😅 (we can belabor it into place)

Global in-time rough large data solution to complex-valued semilinear damped evolution equations
Wenhui Chen, Michael Reissig
https://arxiv.org/abs/2507.08272

Global in-time rough large data solution to complex-valued semilinear damped evolution equations
We study the semilinear Cauchy problem for complex-valued damped evolution equations \begin{align*} \partial_t^2u+(-Δ)^σu+(-Δ)^δ\partial_tu=u^p,\ \ u(0,x)=u_0(x),\ \partial_tu(0,x)=u_1(x), \end{align*} with $δ\in[0,σ]$, $σ\in\mathbb{R}_+$ and $p\in\mathbb{N}_+\backslash\{1\}$, where the initial data belong to the rough space $E^α_s$ endowed with the norm \begin{align*} \|f\|_{E^α_s}=\big\|\langleξ\rangle^s\,2^{α|ξ|}\widehat{f}(ξ)\big\|_{L^2}\ \ \mb…

Multiverse Privacy Theory for Contextual Risks in Complex User-AI Interactions
Ece Gumusel
https://arxiv.org/abs/2506.10042 https://a…

Multiverse Privacy Theory for Contextual Risks in Complex User-AI Interactions
In an era of increasing interaction with artificial intelligence (AI), users face evolving privacy decisions shaped by complex, uncertain factors. This paper introduces Multiverse Privacy Theory, a novel framework in which each privacy decision spawns a parallel universe, representing a distinct potential outcome based on user choices over time. By simulating these universes, this theory provides a foundation for understanding privacy through the lens of contextual integrity, evolving preferenc…

Where that understanding goes wrong is that humans have perception and experience and common sense. And yes, people are foolish and fallible — but ultimately humans are the adaptable element of complex systems[1], and when we design processes involving humans, we always, always lean on that adaptability.
If we say “walk out the door,” humans generally will not just walk face first into the wall just because the door is behind it.
Code will — unless you tell it not to.
[1] https://how.complexsystems.fail/#12
3/

To the genocide-loving German from mastodon.social who reported @… for being antisemitic:
Fuck you, you genocide-loving piece of shit, opposing genocide isn’t antisemitism. What you’re doing – conflating being anti-genocide with being antisemitic – is the greatest antisemitism as you’re de facto stating that all Jews are pro-genocide.

Canonical Bayesian Linear System Identification
Andrey Bryutkin, Matthew E. Levine, I\~nigo Urteaga, Youssef Marzouk
https://arxiv.org/abs/2507.11535 https…

Canonical Bayesian Linear System Identification
Standard Bayesian approaches for linear time-invariant (LTI) system identification are hindered by parameter non-identifiability; the resulting complex, multi-modal posteriors make inference inefficient and impractical. We solve this problem by embedding canonical forms of LTI systems within the Bayesian framework. We rigorously establish that inference in these minimal parameterizations fully captures all invariant system dynamics (e.g., transfer functions, eigenvalues, predictive distribution…

Mental Effort Estimation in Motion Exploration and Concept Generation Design Tasks using Inter-Band Relative Power Difference of EEG
G. Kalyan Ramana, Sumit Yempalle, Prasad S. Onkar
https://arxiv.org/abs/2508.10353

Mental Effort Estimation in Motion Exploration and Concept Generation Design Tasks using Inter-Band Relative Power Difference of EEG
Conceptual design is a cognitively complex task, especially in the engineering design of products having relative motion between components. Designers prefer sketching as a medium for conceptual design and use gestures and annotations to represent such relative motion. Literature suggests that static representations of motion in sketches may not achieve the intended functionality when realised, because it primarily depends on the designers' mental capabilities for motion simulation. Thus, it is…

Normative Moral Pluralism for AI: A Framework for Deliberation in Complex Moral Contexts
David-Doron Yaacov
https://arxiv.org/abs/2508.08333 https://arxiv.…

Normative Moral Pluralism for AI: A Framework for Deliberation in Complex Moral Contexts
The conceptual framework proposed in this paper centers on the development of a deliberative moral reasoning system - one designed to process complex moral situations by generating, filtering, and weighing normative arguments drawn from diverse ethical perspectives. While the framework is rooted in Machine Ethics, it also makes a substantive contribution to Value Alignment by outlining a system architecture that links structured moral reasoning to action under time constraints. Grounded in norm…

Is Your Model Risk ALARP? Evaluating Prospective Safety-Critical Applications of Complex Models
Domenic Di Francesco, Alan Forrest, Fiona McGarry, Nicholas Hall, Adam Sobey
https://arxiv.org/abs/2507.10817

Is Your Model Risk ALARP? Evaluating Prospective Safety-Critical Applications of Complex Models
The increasing availability of advanced computational modelling offers new opportunities to improve safety, efficacy, and emissions reductions. Application of complex models to support engineering decisions has been slow in comparison to other sectors, reflecting the higher consequence of unsafe applications. Adopting a complex model introduces a \emph{model risk}, namely the expected consequence of incorrect or otherwise unhelpful outputs. This should be weighed against the prospective benefit…

Momentum expansions in finite-density perturbative calculations
Mika Nurmela, Juuso \"Osterman
https://arxiv.org/abs/2508.10623 https://arxiv.org/pdf/…

Momentum expansions in finite-density perturbative calculations
Complex-valued Feynman integrals in the imaginary time formalism and zero-temperature limit suffer from particular types of infrared divergences that can not be regulated by integration dimension alone. Related problems leading to integration order dependent results are even further pronounced in the presence of additional scales such as external momenta. This plays a noticeable role in systems featuring fermionic degrees of freedom such as cold Quantum Chromodynamics, where loop integrals are …

In 2024, Vandenberg Space Force Base had 51 launches,
but proposed changes would increase that number to 100 launches per year at both the Space Launch Complex (SLC)-6 and SLC-4,
including both Falcon 9 and Falcon Heavy launches. 
The Falcon Heavy, which according to the EIS, has not launched out of Vandenberg Space Force Base in the past, would launch a maximum of five times per year from the SLC-6.
Because of the Falcon Heavy’s two side boosters landing separately, …

Replaced article(s) found for math.CV. https://arxiv.org/list/math.CV/new
[1/1]:
An upper bound for the GSV-index of a foliation
https://

FLsim: A Modular and Library-Agnostic Simulation Framework for Federated Learning
Arnab Mukherjee, Raju Halder, Joydeep Chandra
https://arxiv.org/abs/2507.11430

FLsim: A Modular and Library-Agnostic Simulation Framework for Federated Learning
Federated Learning (FL) has undergone significant development since its inception in 2016, advancing from basic algorithms to complex methodologies tailored to address diverse challenges and use cases. However, research and benchmarking of novel FL techniques against a plethora of established state-of-the-art solutions remain challenging. To streamline this process, we introduce FLsim, a comprehensive FL simulation framework designed to meet the diverse requirements of FL workflows in the liter…

Predictive Position Control for Movable Antenna Arrays in UAV Communications: A Spatio-Temporal Transformer-LSTM Framework
Kan Yu, Kaixuan Li, Xiaowu Liu, Qixun Zhang, Zhiyong Feng
https://arxiv.org/abs/2508.10720

Predictive Position Control for Movable Antenna Arrays in UAV Communications: A Spatio-Temporal Transformer-LSTM Framework
In complex urban environments, dynamic obstacles and multipath effects lead to significant link attenuation and pervasive coverage blind spots. Conventional approaches based on large-scale fixed antenna arrays and UAV trajectory optimization struggle to balance energy efficiency, real-time adaptation, and spatial flexibility. The movable antenna (MA) technology has emerged as a promising solution, offering enhanced spatial flexibility and reduced energy consumption to overcome the bottlenecks o…

Oscillator Algebra in Complex Position-Dependent Mass Systems
M. I. Estrada-Delgado, Z. Blanco-Garcia
https://arxiv.org/abs/2508.09260 https://arxiv.org/pd…

Oscillator Algebra in Complex Position-Dependent Mass Systems
This work introduces non-Hermitian position-dependent mass Hamiltonians characterized by complex ladder operators and real, equidistant spectra. By imposing the Heisenberg-Weyl algebraic structure as a constraint, we derive the corresponding potentials, ladder operators, and eigenfunctions. The method provides a systematic procedure for constructing exactly solvable models for arbitrary mass profiles. Specific cases are illustrated for quadratic, cosenoidal, and exponential mass functions.

EvoCurr: Self-evolving Curriculum with Behavior Code Generation for Complex Decision-making
Yang Cheng, Zilai Wang, Weiyu Ma, Wenhui Zhu, Yue Deng, Jian Zhao
https://arxiv.org/abs/2508.09586

EvoCurr: Self-evolving Curriculum with Behavior Code Generation for Complex Decision-making
Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse domains, including programming, planning, and decision-making. However, their performance often degrades when faced with highly complex problem instances that require deep reasoning over long horizons. In such cases, direct problem-solving approaches can lead to inefficiency or failure due to the lack of structured intermediate guidance. To address this, we propose a novel self-evolve framework, EvoCurr, in wh…

Technical Report: Facilitating the Adoption of Causal Inference Methods Through LLM-Empowered Co-Pilot
Jeroen Berrevoets, Julianna Piskorz, Robert Davis, Harry Amad, Jim Weatherall, Mihaela van der Schaar
https://arxiv.org/abs/2508.10581

Technical Report: Facilitating the Adoption of Causal Inference Methods Through LLM-Empowered Co-Pilot
Estimating treatment effects (TE) from observational data is a critical yet complex task in many fields, from healthcare and economics to public policy. While recent advances in machine learning and causal inference have produced powerful estimation techniques, their adoption remains limited due to the need for deep expertise in causal assumptions, adjustment strategies, and model selection. In this paper, we introduce CATE-B, an open-source co-pilot system that uses large language models (LLMs…

A Mixed-Primitive-based Gaussian Splatting Method for Surface Reconstruction
Haoxuan Qu, Yujun Cai, Hossein Rahmani, Ajay Kumar, Junsong Yuan, Jun Liu
https://arxiv.org/abs/2507.11321

A Mixed-Primitive-based Gaussian Splatting Method for Surface Reconstruction
Recently, Gaussian Splatting (GS) has received a lot of attention in surface reconstruction. However, while 3D objects can be of complex and diverse shapes in the real world, existing GS-based methods only limitedly use a single type of splatting primitive (Gaussian ellipse or Gaussian ellipsoid) to represent object surfaces during their reconstruction. In this paper, we highlight that this can be insufficient for object surfaces to be represented in high quality. Thus, we propose a novel frame…

Atiyah Classes in the Context of Generalized Complex Geometry
Dadi Ni
https://arxiv.org/abs/2508.10723 https://arxiv.org/pdf/2508.10723

Atiyah Classes in the Context of Generalized Complex Geometry
In analogy to the classical holomorphic setting, Lang, Jia and Liu introduced the notion of the Atiyah class for a generalized holomorphic vector bundle using three different approaches: leveraging $\rm{\check{C}}$ech cohomology, employing the first jet short exact sequence, and adopting the perspective of Lie algebroid pairs. The purpose of this note is to establish the equivalence among these diverse definitions of the Atiyah class.

Improving Generative Cross-lingual Aspect-Based Sentiment Analysis with Constrained Decoding
Jakub \v{S}m\'id, Pavel P\v{r}ib\'a\v{n}, Pavel Kr\'al
https://arxiv.org/abs/2508.10369

Improving Generative Cross-lingual Aspect-Based Sentiment Analysis with Constrained Decoding
While aspect-based sentiment analysis (ABSA) has made substantial progress, challenges remain for low-resource languages, which are often overlooked in favour of English. Current cross-lingual ABSA approaches focus on limited, less complex tasks and often rely on external translation tools. This paper introduces a novel approach using constrained decoding with sequence-to-sequence models, eliminating the need for unreliable translation tools and improving cross-lingual performance by 5\% on ave…

Global in-time existence of solutions for the complex-valued Jordan-Moore-Gibson-Thompson equations of Westervelt-type under different conditions on initial data
Wenhui Chen
https://arxiv.org/abs/2507.08273

Global in-time existence of solutions for the complex-valued Jordan-Moore-Gibson-Thompson equations of Westervelt-type under different conditions on initial data
We are interested in the global in-time existence of solutions for the complex-valued Jordan-Moore-Gibson-Thompson (JMGT) equations of Westervelt-type, namely, \begin{align*} τ\partial_t^3ψ+\partial_t^2ψ+\mathcal{A}ψ+(δ+τ)\mathcal{A}\partial_tψ=(1+\tfrac{B}{2A})\partial_t[(\partial_tψ)^2] \end{align*} in the whole space $\mathbb{R}^n$, with $τ,δ,\frac{B}{A}\in\mathbb{R}_+$ and the fractional Laplacian $\mathcal{A}:=(-Δ)^σ$ equipping $σ\in\mathbb{R}_+$. Our aims are twofold. For one…

Diffusion-Based Imaginative Coordination for Bimanual Manipulation
Huilin Xu, Jian Ding, Jiakun Xu, Ruixiang Wang, Jun Chen, Jinjie Mai, Yanwei Fu, Bernard Ghanem, Feng Xu, Mohamed Elhoseiny
https://arxiv.org/abs/2507.11296

Diffusion-Based Imaginative Coordination for Bimanual Manipulation
Bimanual manipulation is crucial in robotics, enabling complex tasks in industrial automation and household services. However, it poses significant challenges due to the high-dimensional action space and intricate coordination requirements. While video prediction has been recently studied for representation learning and control, leveraging its ability to capture rich dynamic and behavioral information, its potential for enhancing bimanual coordination remains underexplored. To bridge this gap, …

Toward Realistic Evaluations of Just-In-Time Vulnerability Prediction
Duong Nguyen, Thanh Le-Cong, Triet Huynh Minh Le, M. Ali Babar, Quyet-Thang Huynh
https://arxiv.org/abs/2507.10729

Toward Realistic Evaluations of Just-In-Time Vulnerability Prediction
Modern software systems are increasingly complex, presenting significant challenges in quality assurance. Just-in-time vulnerability prediction (JIT-VP) is a proactive approach to identifying vulnerable commits and providing early warnings about potential security risks. However, we observe that current JIT-VP evaluations rely on an idealized setting, where the evaluation datasets are artificially balanced, consisting exclusively of vulnerability-introducing and vulnerability-fixing commits. …

QB Ground State Energy Estimation Benchmark
Nicole Bellonzi, Joshua T. Cantin, Mohammad Reza Jangrouei, Alexander Kunitsa, Jason Necaise, Nam Nguyen, John Penuel, Maxwell D. Radin, Jhonathan Romero Fontalvo, Rashmi Sundareswara, Linjun Wang, Thomas Watts, Yanbing Zhou, Michael C. Garrett, Adam Holmes, Artur F. Izmaylov, Matthew Otten
https://

QB Ground State Energy Estimation Benchmark
Ground State Energy Estimation (GSEE) is a central problem in quantum chemistry and condensed matter physics, demanding efficient algorithms to solve complex electronic structure calculations. This work introduces a structured benchmarking framework for evaluating the performance of both classical and quantum solvers on diverse GSEE problem instances. We assess three prominent methods -- Semistochastic Heat-Bath Configuration Interaction (SHCI), Density Matrix Renormalization Group (DMRG), and …

A Novel Study on Intelligent Methods and Explainable AI for Dynamic Malware Analysis
Richa Dasila, Vatsala Upadhyay, Samo Bobek, Abhishek Vaish
https://arxiv.org/abs/2508.10652 …

A Novel Study on Intelligent Methods and Explainable AI for Dynamic Malware Analysis
Deep learning models are one of the security strategies, trained on extensive datasets, and play a critical role in detecting and responding to these threats by recognizing complex patterns in malicious code. However, the opaque nature of these models-often described as "black boxes"-makes their decision-making processes difficult to understand, even for their creators. This research addresses these challenges by integrating Explainable AI (XAI) techniques to enhance the interpretability and tr…

Conic Formulations of Transport Metrics for Unbalanced Measure Networks and Hypernetworks
Mary Chriselda Antony Oliver, Emmanuel Hartman, Tom Needham
https://arxiv.org/abs/2508.10888

Conic Formulations of Transport Metrics for Unbalanced Measure Networks and Hypernetworks
The Gromov-Wasserstein (GW) variant of optimal transport, designed to compare probability densities defined over distinct metric spaces, has emerged as an important tool for the analysis of data with complex structure, such as ensembles of point clouds or networks. To overcome certain limitations, such as the restriction to comparisons of measures of equal mass and sensitivity to outliers, several unbalanced or partial transport relaxations of the GW distance have been introduced in the recent …

"I Want My Chart to Be Just for Me": Community-Engaged Design to Support Outpatient Healthcare for Resettled Communities
Zhanming Chen, Juan F. Maestre, May Hang, Alisha Ghaju, Ji Youn Shin
https://arxiv.org/abs/2508.10757

"I Want My Chart to Be Just for Me": Community-Engaged Design to Support Outpatient Healthcare for Resettled Communities
Individuals resettled in a new environment often face challenges in accessing adequate healthcare services, particularly within the complex processes of outpatient clinic care. Cultural differences, language barriers, and low socioeconomic status contribute to these difficulties. While previous studies have identified barriers and proposed technology-mediated solutions for resettled populations, many focus on addressing deficits rather than building on the strengths these communities already po…

Wheeler-DeWitt wavefunctions for 5d BKL dynamics, automorphic L-functions and complex primon gases
Marine De Clerck, Sean A. Hartnoll, Ming Yang
https://arxiv.org/abs/2507.08788

Wheeler-DeWitt wavefunctions for 5d BKL dynamics, automorphic L-functions and complex primon gases
The near-singularity BKL dynamics of five dimensional gravity and supergravity (and also an extended four-dimensional supergravity) is known to be given by the billiard problem of a particle within a fundamental domain of the Bianchi groups $PSL(2,{\mathcal O}) \subset PSL(2,\mathbb{C})$, acting on $\mathbb{H}_3$. Here ${\mathcal O}$ are the Gaussian or Eisenstein integers, which define a square or triangular lattice in $\mathbb{C}$. The Wheeler-DeWitt wavefunctions near the singularity are, co…

Replaced article(s) found for math.CV. https://arxiv.org/list/math.CV/new
[1/1]:
- Positivity and universal Pl\"ucker coordinates for spaces of quasi-exponentials
Steven N. Karp, Evgeny Mukhin, Vitaly Tarasov

Designing Effective LLM-Assisted Interfaces for Curriculum Development
Abdolali Faraji, Mohammadreza Tavakoli, Mohammad Moein, Mohammadreza Molavi, G\'abor Kismih\'ok
https://arxiv.org/abs/2506.11767

Designing Effective LLM-Assisted Interfaces for Curriculum Development
Large Language Models (LLMs) have the potential to transform the way a dynamic curriculum can be delivered. However, educators face significant challenges in interacting with these models, particularly due to complex prompt engineering and usability issues, which increase workload. Additionally, inaccuracies in LLM outputs can raise issues around output quality and ethical concerns in educational content delivery. Addressing these issues requires careful oversight, best achieved through coopera…

FCA2: Frame Compression-Aware Autoencoder for Modular and Fast Compressed Video Super-Resolution
Zhaoyang Wang, Jie Li, Wen Lu, Lihuo He, Maoguo Gong, Xinbo Gao
https://arxiv.org/abs/2506.11545

FCA2: Frame Compression-Aware Autoencoder for Modular and Fast Compressed Video Super-Resolution
State-of-the-art (SOTA) compressed video super-resolution (CVSR) models face persistent challenges, including prolonged inference time, complex training pipelines, and reliance on auxiliary information. As video frame rates continue to increase, the diminishing inter-frame differences further expose the limitations of traditional frame-to-frame information exploitation methods, which are inadequate for addressing current video super-resolution (VSR) demands. To overcome these challenges, we pro…

Koba-Nielsen local zeta functions, convex subsets, and generalized Selberg-Mehta-Macdonald and Dotsenko-Fateev-like integrals
Willem Veys, W. A. Z\'u\~niga-Galindo
https://arxiv.org/abs/2507.08120

Koba-Nielsen local zeta functions, convex subsets, and generalized Selberg-Mehta-Macdonald and Dotsenko-Fateev-like integrals
The Koba-Nielsen local zeta functions are integrals depending on several complex parameters, used to regularize the Koba-Nielsen string amplitudes. These integrals are convergent and admit meromorphic continuations in the complex parameters. In the original case, the integration is carried out on the n-dimensional Euclidean space. In this work, the integration is over a variety of (bounded or unbounded) convex subsets; the resulting integrals also admit meromorphic continuations in the complex …

Beyond conventional vision: RGB-event fusion for robust object detection in dynamic traffic scenarios
Zhanwen Liu, Yujing Sun, Yang Wang, Nan Yang, Shengbo Eben Li, Xiangmo Zhao
https://arxiv.org/abs/2508.10704

Beyond conventional vision: RGB-event fusion for robust object detection in dynamic traffic scenarios
The dynamic range limitation of conventional RGB cameras reduces global contrast and causes loss of high-frequency details such as textures and edges in complex traffic environments (e.g., nighttime driving, tunnels), hindering discriminative feature extraction and degrading frame-based object detection. To address this, we integrate a bio-inspired event camera with an RGB camera to provide high dynamic range information and propose a motion cue fusion network (MCFNet), which achieves optimal s…

Variance Reduced Policy Gradient Method for Multi-Objective Reinforcement Learning
Davide Guidobene, Lorenzo Benedetti, Diego Arapovic
https://arxiv.org/abs/2508.10608 https://

Variance Reduced Policy Gradient Method for Multi-Objective Reinforcement Learning
Multi-Objective Reinforcement Learning (MORL) is a generalization of traditional Reinforcement Learning (RL) that aims to optimize multiple, often conflicting objectives simultaneously rather than focusing on a single reward. This approach is crucial in complex decision-making scenarios where agents must balance trade-offs between various goals, such as maximizing performance while minimizing costs. We consider the problem of MORL where the objectives are combined using a non-linear scalarizati…

SMART-Merge Planner: A Safe Merging and Real-Time Motion Planner for Autonomous Highway On-Ramp Merging
Toktam Mohammadnejad, Jovin D'sa, Behdad Chalaki, Hossein Nourkhiz Mahjoub, Ehsan Moradi-Pari
https://arxiv.org/abs/2507.10968

SMART-Merge Planner: A Safe Merging and Real-Time Motion Planner for Autonomous Highway On-Ramp Merging
Merging onto a highway is a complex driving task that requires identifying a safe gap, adjusting speed, often interactions to create a merging gap, and completing the merge maneuver within a limited time window while maintaining safety and driving comfort. In this paper, we introduce a Safe Merging and Real-Time Merge (SMART-Merge) planner, a lattice-based motion planner designed to facilitate safe and comfortable forced merging. By deliberately adapting cost terms to the unique challenges of f…

RefModel: Detecting Refactorings using Foundation Models
Pedro Sim\~oes, Rohit Gheyi, Rian Melo, Jonhnanthan Oliveira, M\'arcio Ribeiro, Wesley K. G. Assun\c{c}\~ao
https://arxiv.org/abs/2507.11346

RefModel: Detecting Refactorings using Foundation Models
Refactoring is a common software engineering practice that improves code quality without altering program behavior. Although tools like ReExtractor+, RefactoringMiner, and RefDiff have been developed to detect refactorings automatically, they rely on complex rule definitions and static analysis, making them difficult to extend and generalize to other programming languages. In this paper, we investigate the viability of using foundation models for refactoring detection, implemented in a tool nam…

System design and realisation towards optimising secure key bits in free space QKD
Pooja Chandravanshi, Jayanth Ramakrishnan, Tanya Sharma, Ayan Biswas, Ravindra P. Singh
https://arxiv.org/abs/2508.10458

System design and realisation towards optimising secure key bits in free space QKD
Quantum Key Distribution (QKD) is rapidly transitioning from cutting-edge laboratory research to real-world deployment in established communication networks. Although QKD promises future-proof security, practical challenges stil exist due to imperfections in physical devices. Many protocols offer strong security guarantees, but their implementation can be complex and difficult. To bridge this gap, we present a practical and systematic framework for implementing QKD, focused on the BB84 protocol…

Promoting Efficient Reasoning with Verifiable Stepwise Reward
Chuhuai Yue, Chengqi Dong, Yinan Gao, Hang He, Jiajun Chai, Guojun Yin, Wei Lin
https://arxiv.org/abs/2508.10293 ht…

Promoting Efficient Reasoning with Verifiable Stepwise Reward
Large reasoning models (LRMs) have recently achieved significant progress in complex reasoning tasks, aided by reinforcement learning with verifiable rewards. However, LRMs often suffer from overthinking, expending excessive computation on simple problems and reducing efficiency. Existing efficient reasoning methods typically require accurate task assessment to preset token budgets or select reasoning modes, which limits their flexibility and reliability. In this work, we revisit the essence of…

Replaced article(s) found for math.CV. https://arxiv.org/list/math.CV/new
[1/1]:
- Mirrors of conformal dynamics: Interplay between anti-rational maps, reflection groups, Schwarz r...
Mikhail Lyubich, Sabyasachi Mukherjee

Complex Logical Instruction Generation
Mian Zhang, Shujian Liu, Sixun Dong, Ming Yin, Yebowen Hu, Xun Wang, Steven Ma, Song Wang, Sathish Reddy Indurthi, Haoyun Deng, Zhiyu Zoey Chen, Kaiqiang Song
https://arxiv.org/abs/2508.09125

Complex Logical Instruction Generation
Instruction following has catalyzed the recent era of Large Language Models (LLMs) and is the foundational skill underpinning more advanced capabilities such as reasoning and agentic behaviors. As tasks grow more challenging, the logic structures embedded in natural language instructions becomes increasingly intricate. However, how well LLMs perform on such logic-rich instructions remains under-explored. We propose LogicIFGen and LogicIFEval. LogicIFGen is a scalable, automated framework for ge…

CATVis: Context-Aware Thought Visualization
Tariq Mehmood, Hamza Ahmad, Muhammad Haroon Shakeel, Murtaza Taj
https://arxiv.org/abs/2507.11522 https://

CATVis: Context-Aware Thought Visualization
EEG-based brain-computer interfaces (BCIs) have shown promise in various applications, such as motor imagery and cognitive state monitoring. However, decoding visual representations from EEG signals remains a significant challenge due to their complex and noisy nature. We thus propose a novel 5-stage framework for decoding visual representations from EEG signals: (1) an EEG encoder for concept classification, (2) cross-modal alignment of EEG and text embeddings in CLIP feature space, (3) captio…

FreeGAD: A Training-Free yet Effective Approach for Graph Anomaly Detection
Yunfeng Zhao, Yixin Liu, Shiyuan Li, Qingfeng Chen, Yu Zheng, Shirui Pan
https://arxiv.org/abs/2508.10594

FreeGAD: A Training-Free yet Effective Approach for Graph Anomaly Detection
Graph Anomaly Detection (GAD) aims to identify nodes that deviate from the majority within a graph, playing a crucial role in applications such as social networks and e-commerce. Despite the current advancements in deep learning-based GAD, existing approaches often suffer from high deployment costs and poor scalability due to their complex and resource-intensive training processes. Surprisingly, our empirical findings suggest that the training phase of deep GAD methods, commonly perceived as cr…

Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis
Yuan Gao, Mattia Piccinini, Yuchen Zhang, Dingrui Wang, Korbinian Moller, Roberto Brusnicki, Baha Zarrouki, Alessio Gambi, Jan Frederik Totz, Kai Storms, Steven Peters, Andrea Stocco, Bassam Alrifaee, Marco Pavone, Johannes Betz
https://

Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis
For autonomous vehicles, safe navigation in complex environments depends on handling a broad range of diverse and rare driving scenarios. Simulation- and scenario-based testing have emerged as key approaches to development and validation of autonomous driving systems. Traditional scenario generation relies on rule-based systems, knowledge-driven models, and data-driven synthesis, often producing limited diversity and unrealistic safety-critical cases. With the emergence of foundation models, wh…

Pruning Long Chain-of-Thought of Large Reasoning Models via Small-Scale Preference Optimization
Bin Hong, Jiayu Liu, Zhenya Huang, Kai Zhang, Mengdi Zhang
https://arxiv.org/abs/2508.10164

Pruning Long Chain-of-Thought of Large Reasoning Models via Small-Scale Preference Optimization
Recent advances in Large Reasoning Models (LRMs) have demonstrated strong performance on complex tasks through long Chain-of-Thought (CoT) reasoning. However, their lengthy outputs increase computational costs and may lead to overthinking, raising challenges in balancing reasoning effectiveness and efficiency. Current methods for efficient reasoning often compromise reasoning quality or require extensive resources. This paper investigates efficient methods to reduce the generation length of LRM…

Invocable APIs derived from NL2SQL datasets for LLM Tool-Calling Evaluation
Benjamin Elder, Anupama Murthi, Jungkoo Kang, Ankita Rajaram Naik, Kiran Kate, Kinjal Basu, Danish Contractor
https://arxiv.org/abs/2506.11266

Invocable APIs derived from NL2SQL datasets for LLM Tool-Calling Evaluation
Large language models (LLMs) are routinely deployed as agentic systems, with access to tools that interact with live environments to accomplish tasks. In enterprise deployments these systems need to interact with API collections that can be extremely large and complex, often backed by databases. In order to create datasets with such characteristics, we explore how existing NL2SQL (Natural Language to SQL query) datasets can be used to automatically create NL2API datasets. Specifically, this wor…

Gauge-invariant Wigner equation for electromagnetic fields: Strong and weak formulation
Clemens Etl, Mauro Ballicchia, Mihail Nedjalkov, Hans Kosina
https://arxiv.org/abs/2506.11816

Gauge-invariant Wigner equation for electromagnetic fields: Strong and weak formulation
Gauge-invariant Wigner theory describes the quantum-mechanical evolution of charged particles in the presence of an electromagnetic field in phase space, which is spanned by position and kinetic momentum. This approach is independent of the chosen potentials, as it depends only on the electric and magnetic field variables. Several approaches to derive a gauge-invariant Wigner evolution equation have been reported, which are generally complex. This work presents a new formulation for a single el…

HANS-Net: Hyperbolic Convolution and Adaptive Temporal Attention for Accurate and Generalizable Liver and Tumor Segmentation in CT Imaging
Arefin Ittesafun Abian, Ripon Kumar Debnath, Md. Abdur Rahman, Mohaimenul Azam Khan Raiaan, Md Rafiqul Islam, Asif Karim, Reem E. Mohamed, Sami Azam
https://arxiv.org/abs/2507.11325

HANS-Net: Hyperbolic Convolution and Adaptive Temporal Attention for Accurate and Generalizable Liver and Tumor Segmentation in CT Imaging
Accurate liver and tumor segmentation on abdominal CT images is critical for reliable diagnosis and treatment planning, but remains challenging due to complex anatomical structures, variability in tumor appearance, and limited annotated data. To address these issues, we introduce Hyperbolic-convolutions Adaptive-temporal-attention with Neural-representation and Synaptic-plasticity Network (HANS-Net), a novel segmentation framework that synergistically combines hyperbolic convolutions for hierar…

Crosslisted article(s) found for math.CV. https://arxiv.org/list/math.CV/new
[1/1]:
- Existence and uniqueness of the conformally covariant geodesic metric on non-simple conformal loo...
Jason Miller, Yizheng Yuan

Energy-Based Models for Predicting Mutational Effects on Proteins
Patrick Soga, Zhenyu Lei, Yinhan He, Camille Bilodeau, Jundong Li
https://arxiv.org/abs/2508.10629 https://

Energy-Based Models for Predicting Mutational Effects on Proteins
Predicting changes in binding free energy ($ΔΔG$) is a vital task in protein engineering and protein-protein interaction (PPI) engineering for drug discovery. Previous works have observed a high correlation between $ΔΔG$ and entropy, using probabilities of biologically important objects such as side chain angles and residue identities to estimate $ΔΔG$. However, estimating the full conformational distribution of a protein complex is generally considered intractable. In this work, we propo…

SAIL: Faster-than-Demonstration Execution of Imitation Learning Policies
Nadun Ranawaka Arachchige, Zhenyang Chen, Wonsuhk Jung, Woo Chul Shin, Rohan Bansal, Pierre Barroso, Yu Hang He, Yingyang Celine Lin, Benjamin Joffe, Shreyas Kousik, Danfei Xu
https://arxiv.org/abs/2506.11948

SAIL: Faster-than-Demonstration Execution of Imitation Learning Policies
Offline Imitation Learning (IL) methods such as Behavior Cloning are effective at acquiring complex robotic manipulation skills. However, existing IL-trained policies are confined to executing the task at the same speed as shown in demonstration data. This limits the task throughput of a robotic system, a critical requirement for applications such as industrial automation. In this paper, we introduce and formalize the novel problem of enabling faster-than-demonstration execution of visuomotor p…

SEQ-GPT: LLM-assisted Spatial Query via Example
Ivan Khai Ze Lim, Ningyi Liao, Yiming Yang, Gerald Wei Yong Yip, Siqiang Luo
https://arxiv.org/abs/2508.10486 https://

SEQ-GPT: LLM-assisted Spatial Query via Example
Contemporary spatial services such as online maps predominantly rely on user queries for location searches. However, the user experience is limited when performing complex tasks, such as searching for a group of locations simultaneously. In this study, we examine the extended scenario known as Spatial Exemplar Query (SEQ), where multiple relevant locations are jointly searched based on user-specified examples. We introduce SEQ-GPT, a spatial query system powered by Large Language Models (LLMs) …

VGR: Visual Grounded Reasoning
Jiacong Wang, Zijiang Kang, Haochen Wang, Haiyong Jiang, Jiawen Li, Bohong Wu, Ya Wang, Jiao Ran, Xiao Liang, Chao Feng, Jun Xiao
https://arxiv.org/abs/2506.11991

VGR: Visual Grounded Reasoning
In the field of multimodal chain-of-thought (CoT) reasoning, existing approaches predominantly rely on reasoning on pure language space, which inherently suffers from language bias and is largely confined to math or science domains. This narrow focus limits their ability to handle complex visual reasoning tasks that demand comprehensive understanding of image details. To address these limitations, this paper introduces VGR, a novel reasoning multimodal large language model (MLLM) with enhanced …

Critical Reflections on Overcoming a Challenge for Bohmian Mechanics by H. Nikolic and the Experimental Findings of Sharoglazova et al
Miko{\l}aj Sienicki, Krzysztof Sienicki
https://arxiv.org/abs/2507.10989

Critical Reflections on Overcoming a Challenge for Bohmian Mechanics by H. Nikolic and the Experimental Findings of Sharoglazova et al
This paper offers a brief reflection on H. Nikolic's response to the experimental findings of Sharoglazova et al., which challenge Bohmian mechanics. While Nikolic's revision satisfies the continuity equation, it reintroduces assumptions he seeks to avoid and overlooks key empirical and nonlocal aspects of the system. These issues underscore unresolved tensions in applying Bohmian mechanics to complex, interacting regimes.

Next Edit Prediction: Learning to Predict Code Edits from Context and Interaction History
Ruofan Lu, Yintong Huo, Meng Zhang, Yichen Li, Michael R. Lyu
https://arxiv.org/abs/2508.10074

Next Edit Prediction: Learning to Predict Code Edits from Context and Interaction History
The rapid advancement of large language models (LLMs) has led to the widespread adoption of AI-powered coding assistants integrated into a development environment. On one hand, low-latency code completion offers completion suggestions but is fundamentally constrained to the cursor's current position. On the other hand, chat-based editing can perform complex modifications, yet forces developers to stop their work, describe the intent in natural language, which causes a context-switch away from t…

IBEX: Information-Bottleneck-EXplored Coarse-to-Fine Molecular Generation under Limited Data
Dong Xu, Zhangfan Yang, Jenna Xinyi Yao, Shuangbao Song, Zexuan Zhu, Junkai Ji
https://arxiv.org/abs/2508.10775

IBEX: Information-Bottleneck-EXplored Coarse-to-Fine Molecular Generation under Limited Data
Three-dimensional generative models increasingly drive structure-based drug discovery, yet it remains constrained by the scarce publicly available protein-ligand complexes. Under such data scarcity, almost all existing pipelines struggle to learn transferable geometric priors and consequently overfit to training-set biases. As such, we present IBEX, an Information-Bottleneck-EXplored coarse-to-fine pipeline to tackle the chronic shortage of protein-ligand complex data in structure-based drug de…

BEASST: Behavioral Entropic Gradient based Adaptive Source Seeking for Mobile Robots
Donipolo Ghimire, Aamodh Suresh, Carlos Nieto-Granda, Solmaz S. Kia
https://arxiv.org/abs/2508.10363

BEASST: Behavioral Entropic Gradient based Adaptive Source Seeking for Mobile Robots
This paper presents BEASST (Behavioral Entropic Gradient-based Adaptive Source Seeking for Mobile Robots), a novel framework for robotic source seeking in complex, unknown environments. Our approach enables mobile robots to efficiently balance exploration and exploitation by modeling normalized signal strength as a surrogate probability of source location. Building on Behavioral Entropy(BE) with Prelec's probability weighting function, we define an objective function that adapts robot behavior …

Improving Value-based Process Verifier via Low-Cost Variance Reduction
Zetian Sun, Dongfang Li, Baotian Hu, Min Zhang
https://arxiv.org/abs/2508.10539 https://

Improving Value-based Process Verifier via Low-Cost Variance Reduction
Large language models (LLMs) have achieved remarkable success in a wide range of tasks. However, their reasoning capabilities, particularly in complex domains like mathematics, remain a significant challenge. Value-based process verifiers, which estimate the probability of a partial reasoning chain leading to a correct solution, are a promising approach for improving reasoning. Nevertheless, their effectiveness is often hindered by estimation error in their training annotations, a consequence o…

HUG-VAS: A Hierarchical NURBS-Based Generative Model for Aortic Geometry Synthesis and Controllable Editing
Pan Du, Mingqi Xu, Xiaozhi Zhu, Jian-xun Wang
https://arxiv.org/abs/2507.11474

HUG-VAS: A Hierarchical NURBS-Based Generative Model for Aortic Geometry Synthesis and Controllable Editing
Accurate characterization of vascular geometry is essential for cardiovascular diagnosis and treatment planning. Traditional statistical shape modeling (SSM) methods rely on linear assumptions, limiting their expressivity and scalability to complex topologies such as multi-branch vascular structures. We introduce HUG-VAS, a Hierarchical NURBS Generative model for Vascular geometry Synthesis, which integrates NURBS surface parameterization with diffusion-based generative modeling to synthesize r…

Leveraging GPT-4 for Vulnerability-Witnessing Unit Test Generation
G\'abor Antal, D\'enes B\'an, Martin Isztin, Rudolf Ferenc, P\'eter Heged\H{u}s
https://arxiv.org/abs/2506.11559

Leveraging GPT-4 for Vulnerability-Witnessing Unit Test Generation
In the life-cycle of software development, testing plays a crucial role in quality assurance. Proper testing not only increases code coverage and prevents regressions but it can also ensure that any potential vulnerabilities in the software are identified and effectively fixed. However, creating such tests is a complex, resource-consuming manual process. To help developers and security experts, this paper explores the automatic unit test generation capability of one of the most widely used larg…

We-Math 2.0: A Versatile MathBook System for Incentivizing Visual Mathematical Reasoning
Runqi Qiao, Qiuna Tan, Peiqing Yang, Yanzi Wang, Xiaowan Wang, Enhui Wan, Sitong Zhou, Guanting Dong, Yuchen Zeng, Yida Xu, Jie Wang, Chong Sun, Chen Li, Honggang Zhang
https://arxiv.org/abs/2508.10433

We-Math 2.0: A Versatile MathBook System for Incentivizing Visual Mathematical Reasoning
Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities across various tasks, but still struggle with complex mathematical reasoning. Existing research primarily focuses on dataset construction and method optimization, often overlooking two critical aspects: comprehensive knowledge-driven design and model-centric data space modeling. In this paper, we introduce We-Math 2.0, a unified system that integrates a structured mathematical knowledge system, model-centric data…

Auditory-Tactile Congruence for Synthesis of Adaptive Pain Expressions in RoboPatients
Saitarun Nadipineni, Chapa Sirithunge, Yue Xie, Fumiya Iida, Thilina Dulantha Lalitharatne
https://arxiv.org/abs/2506.11827

Auditory-Tactile Congruence for Synthesis of Adaptive Pain Expressions in RoboPatients
Misdiagnosis can lead to delayed treatments and harm. Robotic patients offer a controlled way to train and evaluate clinicians in rare, subtle, or complex cases, reducing diagnostic errors. We present RoboPatient, a medical robotic simulator aimed at multimodal pain synthesis based on haptic and auditory feedback during palpation-based training scenarios. The robopatient functions as an adaptive intermediary, capable of synthesizing plausible pain expressions vocal and facial in response to tac…

BrainLesion Suite: A Flexible and User-Friendly Framework for Modular Brain Lesion Image Analysis
Florian Kofler, Marcel Rosier, Mehdi Astaraki, Hendrik M\"oller, Ilhem Isra Mekki, Josef A. Buchner, Anton Schmick, Arianna Pfiffer, Eva Oswald, Lucas Zimmer, Ezequiel de la Rosa, Sarthak Pati, Julian Canisius, Arianna Piffer, Ujjwal Baid, Mahyar Valizadeh, Akis Linardos, Jan C. Peeken, Surprosanna Shit, Felix Steinbauer, Daniel Rueckert, Rolf Heckemann, Spyridon Bakas, Jan Kirschke, …

BrainLesion Suite: A Flexible and User-Friendly Framework for Modular Brain Lesion Image Analysis
BrainLesion Suite is a versatile toolkit for building modular brain lesion image analysis pipelines in Python. Following Pythonic principles, BrainLesion Suite is designed to provide a 'brainless' development experience, minimizing cognitive effort and streamlining the creation of complex workflows for clinical and scientific practice. At its core is an adaptable preprocessing module that performs co-registration, atlas registration, and optional skull-stripping and defacing on arbitrary multi-…

Taming Uncertainty via Automation: Observing, Analyzing, and Optimizing Agentic AI Systems
Dany Moshkovich, Sergey Zeltyn
https://arxiv.org/abs/2507.11277 …

Taming Uncertainty via Automation: Observing, Analyzing, and Optimizing Agentic AI Systems
Large Language Models (LLMs) are increasingly deployed within agentic systems-collections of interacting, LLM-powered agents that execute complex, adaptive workflows using memory, tools, and dynamic planning. While enabling powerful new capabilities, these systems also introduce unique forms of uncertainty stemming from probabilistic reasoning, evolving memory states, and fluid execution paths. Traditional software observability and operations practices fall short in addressing these challenges…

Human-Robot collaboration in surgery: Advances and challenges towards autonomous surgical assistants
Jacinto Colan, Ana Davila, Yutaro Yamada, Yasuhisa Hasegawa
https://arxiv.org/abs/2507.11460

Human-Robot collaboration in surgery: Advances and challenges towards autonomous surgical assistants
Human-robot collaboration in surgery represents a significant area of research, driven by the increasing capability of autonomous robotic systems to assist surgeons in complex procedures. This systematic review examines the advancements and persistent challenges in the development of autonomous surgical robotic assistants (ASARs), focusing specifically on scenarios where robots provide meaningful and active support to human surgeons. Adhering to the PRISMA guidelines, a comprehensive literature…

NavComposer: Composing Language Instructions for Navigation Trajectories through Action-Scene-Object Modularization
Zongtao He, Liuyi Wang, Lu Chen, Chengju Liu, Qijun Chen
https://arxiv.org/abs/2507.10894

NavComposer: Composing Language Instructions for Navigation Trajectories through Action-Scene-Object Modularization
Language-guided navigation is a cornerstone of embodied AI, enabling agents to interpret language instructions and navigate complex environments. However, expert-provided instructions are limited in quantity, while synthesized annotations often lack quality, making them insufficient for large-scale research. To address this, we propose NavComposer, a novel framework for automatically generating high-quality navigation instructions. NavComposer explicitly decomposes semantic entities such as act…

The SET Perceptual Factors Framework: Towards Assured Perception for Autonomous Systems
Troi Williams
https://arxiv.org/abs/2508.10798 https://arxiv.org/pd…

The SET Perceptual Factors Framework: Towards Assured Perception for Autonomous Systems
Future autonomous systems promise significant societal benefits, yet their deployment raises concerns about safety and trustworthiness. A key concern is assuring the reliability of robot perception, as perception seeds safe decision-making. Failures in perception are often due to complex yet common environmental factors and can lead to accidents that erode public trust. To address this concern, we introduce the SET (Self, Environment, and Target) Perceptual Factors Framework. We designed the fr…

Collaborative Trustworthiness for Good Decision Making in Autonomous Systems
Selma Saidi, Omar Laimona, Christoph Schmickler, Dirk Ziegenbein
https://arxiv.org/abs/2507.11135

Collaborative Trustworthiness for Good Decision Making in Autonomous Systems
Autonomous systems are becoming an integral part of many application domains, like in the mobility sector. However, ensuring their safe and correct behaviour in dynamic and complex environments remains a significant challenge, where systems should autonomously make decisions e.g., about manoeuvring. We propose in this paper a general collaborative approach for increasing the level of trustworthiness in the environment of operation and improve reliability and good decision making in autonomous s…

GenOM: Ontology Matching with Description Generation and Large Language Model
Yiping Song, Jiaoyan Chen, Renate A. Schmidt
https://arxiv.org/abs/2508.10703 https://

GenOM: Ontology Matching with Description Generation and Large Language Model
Ontology matching (OM) plays an essential role in enabling semantic interoperability and integration across heterogeneous knowledge sources, particularly in the biomedical domain which contains numerous complex concepts related to diseases and pharmaceuticals. This paper introduces GenOM, a large language model (LLM)-based ontology alignment framework, which enriches the semantic representations of ontology concepts via generating textual definitions, retrieves alignment candidates with an embe…

Palpation Alters Auditory Pain Expressions with Gender-Specific Variations in Robopatients
Chapa Sirithunge, Yue Xie, Saitarun Nadipineni, Fumiya Iida, Thilina Dulantha Lalitharatne
https://arxiv.org/abs/2506.11906

Palpation Alters Auditory Pain Expressions with Gender-Specific Variations in Robopatients
Diagnostic errors remain a major cause of preventable deaths, particularly in resource-limited regions. Medical training simulators, including robopatients, play a vital role in reducing these errors by mimicking real patients for procedural training such as palpation. However, generating multimodal feedback, especially auditory pain expressions, remains challenging due to the complex relationship between palpation behavior and sound. The high-dimensional nature of pain sounds makes exploration…