Tootfinder

🦿 China's humanoid robots generate more soccer excitement than their human counterparts
https://techxplore.com/news/2025-06-china-humanoid-robots-generate-soccer.html

China's humanoid robots generate more soccer excitement than their human counterparts
While China's men's soccer team hasn't generated much excitement in recent years, humanoid robot teams have won over fans in Beijing based more on the AI technology involved than any athletic prowess shown.

from my link log —
A catalog of ways that compilers generate SSA.
https://bernsteinbear.com/blog/ssa/
saved 2025-02-11 https://

All incompatible sets of measurements can generate Buscemi nonlocality
Andr\'es F. Ducuara, Patryk Lipka-Bartosik, Cristian E. Susa, Paul Skrzypczyk
https://arxiv.org/abs/2508.14421

All incompatible sets of measurements can generate Buscemi nonlocality
The presence of Bell-nonlocality in the correlations arising from measuring spatially-separated systems guarantees that the sets of measurements used are necessarily incompatible. Not all sets of incompatible measurements can however lead to Bell-nonlocality, as there exist incompatible sets of measurements which can only produce local correlations. In this work we prove that all sets of incompatible measurements are nevertheless able to generate nonlocality in an extended Bell scenario where q…

How do Language Models Generate Slang: A Systematic Comparison between Human and Machine-Generated Slang Usages
Siyang Wu, Zhewei Sun
https://arxiv.org/abs/2509.15518 https://…

How do Language Models Generate Slang: A Systematic Comparison between Human and Machine-Generated Slang Usages
Slang is a commonly used type of informal language that poses a daunting challenge to NLP systems. Recent advances in large language models (LLMs), however, have made the problem more approachable. While LLM agents are becoming more widely applied to intermediary tasks such as slang detection and slang interpretation, their generalizability and reliability are heavily dependent on whether these models have captured structural knowledge about slang that align well with human attested slang usage…

#ScribesAndMakers 22
Show us something you've created. Tell us the story behind it.
https://cs.wellesley.edu/~pmwh/labyrinfinite/
I was thinking on a dog walk about how most games with procedurally generated content like Minecraft get pretty repetitive at some point if you zoom out far enough, and large-scale structures that have structural constraints like rivers are very hard to generate piecemeal. So I wanted to come up with an algorithm that could generate globally-consistent structures piece-by-piece, with consistency even if pieces were generated out-of-order, while maintaining only a fixed amount of context no matter how far from the origin you went. This demo is *almost* that, except the amount of context scales logarithmically with the distance-from-origin, which I find a very acceptable compromise. In the demo, there's a single infinitely-long path that eventually touches every cell of the infinite 2D grid (okay, computer limitations mean it's not really infinite, but mathematically it could be). You can get different path structures from different random seeds, although the generation trick does constrain things a lot relative to the set of all possible such paths (notice that in each 5x5 region it touches every cell before leaving; that's not in general necessary).

Adobe updates its Firefly video model and adds a beta Generate Sound Effects tool that can create custom sounds from text prompts and the user's voice (Sabrina Ortiz/ZDNET)
https://www.zdnet.com/article/adobe-firefly-ca…

Adobe Firefly can now generate AI sound effects for videos - and I'm seriously impressed
In addition to video sound effects, Text to Avatar is launching in beta. It allows you to turn scripts into avatar-led videos.

Can LLMs Generate User Stories and Assess Their Quality?
Giovanni Quattrocchi, Liliana Pasquale, Paola Spoletini, Luciano Baresi
https://arxiv.org/abs/2507.15157

Can LLMs Generate User Stories and Assess Their Quality?
Requirements elicitation is still one of the most challenging activities of the requirements engineering process due to the difficulty requirements analysts face in understanding and translating complex needs into concrete requirements. In addition, specifying high-quality requirements is crucial, as it can directly impact the quality of the software to be developed. Although automated tools allow for assessing the syntactic quality of requirements, evaluating semantic metrics (e.g., language c…

Reason #2608 I do not trust “AI” to generate captions or transcripts:
“Complete silence is always hallucinated as 'ترجمة نانسي قنقر' in Arabic which translates as 'Translation by Nancy Qunqar'”
More examples in replies.
#a11y #accessibility

AI experiment, no judgment, but try to get it to generate a file with 1000 random and unique words, with each word being on a separate line.

Tip: When you use DuckDuckGo's web browser you can generate a free email address that forwards to an email address of your choice but also removes trackers along the way. Handy for subscriptions and "giveaways".

For each email you get a report on what trackers were removed.

https://duckduckgo.com/email

#UX has never been able to shake the perception that our work is about the outputs. Managers now look at #AI tools that can generate outputs and ask — this is just as good, right?
They don't have the context to understand that it's NOT just as good. But many designers have *deliberately* avoided…

Only outputs can be automated
By emphasizing design's role as a producer of visual outputs, our leaders have eroded the entire justification for UX as a practice.

Google begins rolling out Asset Studio in beta, letting advertisers generate and edit images, videos, and voiceovers directly in the Google Ads interface (Anu Adegbola/Search Engine Land)
https://searchengineland.com/google-ads-asset-studio-beta-461165

Multi-Source Peak Age of Information Optimization in Mobile Edge Computing Systems
Jianhang Zhu, Jie Gong
https://arxiv.org/abs/2508.14328 https://arxiv.or…

Multi-Source Peak Age of Information Optimization in Mobile Edge Computing Systems
Age of Information (AoI) is emerging as a novel metric for measuring information freshness in real-time monitoring systems. For computation-intensive status data, the information is not revealed until being processed. We consider a status update problem in a multi-source single-server system where the sources are scheduled to generate and transmit status data which are received and processed at the edge server. Generate-at-will sources with both random transmission time and process time are con…

A curse on all those who generate JSON data with subfielded keys.
...I'm looking at you Dell PERC, with all your "Drive /c0/e32/s9 - Detailed Information" nonsense. - I KNOW what the device attachment is (I GAVE you that on the command line), what I want is CONSISTENT key names I can use JQ to filter on (This key should be "Drive Detailed Information" - or something even LESS of a mouthful).

Driven shear flow in biological magneto-active fluids
Malo Marmol, C\'ecile Cottin-Bizonne, Andrejs Cebers, Damien Faivre, Christophe Ybert
https://arxiv.org/abs/2507.13851

Driven shear flow in biological magneto-active fluids
Active fluids made of powered suspended particles have unique abilities to self-generate flow and density structures. How such dynamics can be triggered and leveraged by external cues is a key question of both biological and applied relevance. Here we use magnetotactic bacteria to explore how chemotaxis and magnetotaxis -- leading, respectively, to positional and orientational responses -- combine to generate global scale flows. Such steady regime can be quantitatively captured by a magneto-act…

“Developers on teams with high AI adoption complete 21% more tasks and merge 98% more pull requests, but PR review time increases 91%, revealing a critical bottleneck: human approval.“
Maybe it’s confirmation bias, but I can see that. You generate more, maybe harder to comprehend, code that still has to be double checked by people who weren’t involved in the process. That slows you down unless you ignore understanding by, you guessed it, moving fast and breaking things.

The AI Productivity Paradox Research Report | Faros AI
Key findings from the AI Productivity Paradox Report 2025. Research reveals AI coding assistants increase developer output, but not company productivity. Uncover strategies and enablers for a measurable return on investment.

Source Wavefront Generation for a Non-Interferometric Reconfigurable Null Test using a Photonic Lantern
Nikolas Romer, Jannick P. Rolland
https://arxiv.org/abs/2507.15171

Source Wavefront Generation for a Non-Interferometric Reconfigurable Null Test using a Photonic Lantern
A method is presented to use a fiber-optic device known as a photonic lantern to generate a reconfigurable custom wavefront for a null test of spherical, aspheric, and freeform optical surfaces. By modulating input intensity and phases at single mode fiber input ports, the wavefront of the output light field from the multimode end can be controlled to generate a custom nulling phase function. Generation of a desired wavefront is demonstrated by simulating a nineteen-port non-mode-selective phot…

Rethinking Molecule Synthesizability with Chain-of-Reaction
Seul Lee, Karsten Kreis, Srimukh Prasad Veccham, Meng Liu, Danny Reidenbach, Saee Paliwal, Weili Nie, Arash Vahdat
https://arxiv.org/abs/2509.16084

Rethinking Molecule Synthesizability with Chain-of-Reaction
A well-known pitfall of molecular generative models is that they are not guaranteed to generate synthesizable molecules. There have been considerable attempts to address this problem, but given the exponentially large combinatorial space of synthesizable molecules, existing methods have shown limited coverage of the space and poor molecular optimization performance. To tackle these problems, we introduce ReaSyn, a generative framework for synthesizable projection where the model explores the ne…

MANZANO: A Simple and Scalable Unified Multimodal Model with a Hybrid Vision Tokenizer
Yanghao Li, Rui Qian, Bowen Pan, Haotian Zhang, Haoshuo Huang, Bowen Zhang, Jialing Tong, Haoxuan You, Xianzhi Du, Zhe Gan, Hyunjik Kim, Chao Jia, Zhenbang Wang, Yinfei Yang, Mingfei Gao, Zi-Yi Dou, Wenze Hu, Chang Gao, Dongxu Li, Philipp Dufter, Zirui Wang, Guoli Yin, Zhengdong Zhang, Chen Chen, Yang Zhao, Ruoming Pang, Zhifeng Chen

MANZANO: A Simple and Scalable Unified Multimodal Model with a Hybrid Vision Tokenizer
Unified multimodal Large Language Models (LLMs) that can both understand and generate visual content hold immense potential. However, existing open-source models often suffer from a performance trade-off between these capabilities. We present Manzano, a simple and scalable unified framework that substantially reduces this tension by coupling a hybrid image tokenizer with a well-curated training recipe. A single shared vision encoder feeds two lightweight adapters that produce continuous embeddi…

aiXiv: A Next-Generation Open Access Ecosystem for Scientific Discovery Generated by AI Scientists
Pengsong Zhang, Xiang Hu, Guowei Huang, Yang Qi, Heng Zhang, Xiuxu Li, Jiaxing Song, Jiabin Luo, Yijiang Li, Shuo Yin, Chengxiao Dai, Eric Hanchen Jiang, Xiaoyan Zhou, Zhenfei Yin, Boqin Yuan, Jing Dong, Guinan Su, Guanren Qiao, Haiming Tang, Anghong Du, Lili Pan, Zhenzhong Lan, Xinyu Liu

aiXiv: A Next-Generation Open Access Ecosystem for Scientific Discovery Generated by AI Scientists
Recent advances in large language models (LLMs) have enabled AI agents to autonomously generate scientific proposals, conduct experiments, author papers, and perform peer reviews. Yet this flood of AI-generated research content collides with a fragmented and largely closed publication ecosystem. Traditional journals and conferences rely on human peer review, making them difficult to scale and often reluctant to accept AI-generated research content; existing preprint servers (e.g. arXiv) lack ri…

Close is Good Enough: Component-Based Synthesis Modulo Logical Similarity
Ashish Mishra, Suresh Jagannathan
https://arxiv.org/abs/2508.14614 https://arxiv.…

Close is Good Enough: Component-Based Synthesis Modulo Logical Similarity
Component-based synthesis (CBS) aims to generate loop-free programs from a set of libraries whose methods are annotated with specifications and whose output must satisfy a set of logical constraints, expressed as a query. The effectiveness of a CBS algorithm critically depends on the severity of the constraints imposed by the query. The more exact these constraints are, the sparser the space of feasible solutions. This maxim also applies when we enrich the expressiveness of the specifications a…

Alibaba releases Qwen3-Omni, a family of open-source AI models that can process text, audio, image, and video inputs and generate both text and speech outputs (Carl Franzen/Venturebeat)
https://venturebeat.com/ai/chinas-alibaba-challenges-u…

A multi-strategy improved snake optimizer for three-dimensional UAV path planning and engineering problems
Genliang Li, Yaxin Cui, Jinyu Su
https://arxiv.org/abs/2507.14043

A multi-strategy improved snake optimizer for three-dimensional UAV path planning and engineering problems
Metaheuristic algorithms have gained widespread application across various fields owing to their ability to generate diverse solutions. One such algorithm is the Snake Optimizer (SO), a progressive optimization approach. However, SO suffers from the issues of slow convergence speed and susceptibility to local optima. In light of these shortcomings, we propose a novel Multi-strategy Improved Snake Optimizer (MISO). Firstly, we propose a new adaptive random disturbance strategy based on sine func…

GIFT: Gradient-aware Immunization of diffusion models against malicious Fine-Tuning with safe concepts retention
Amro Abdalla, Ismail Shaheen, Dan DeGenaro, Rupayan Mallick, Bogdan Raita, Sarah Adel Bargal
https://arxiv.org/abs/2507.13598

GIFT: Gradient-aware Immunization of diffusion models against malicious Fine-Tuning with safe concepts retention
We present \textbf{GIFT}: a \textbf{G}radient-aware \textbf{I}mmunization technique to defend diffusion models against malicious \textbf{F}ine-\textbf{T}uning while preserving their ability to generate safe content. Existing safety mechanisms like safety checkers are easily bypassed, and concept erasure methods fail under adversarial fine-tuning. GIFT addresses this by framing immunization as a bi-level optimization problem: the upper-level objective degrades the model's ability to represent ha…

Auto-grader Feedback Utilization and Its Impacts: An Observational Study Across Five Community Colleges
Adam Zhang, Heather Burte, Jaromir Savelka, Christopher Bogart, Majd Sakr
https://arxiv.org/abs/2507.14235

Auto-grader Feedback Utilization and Its Impacts: An Observational Study Across Five Community Colleges
Automated grading systems, or auto-graders, have become ubiquitous in programming education, and the way they generate feedback has become increasingly automated as well. However, there is insufficient evidence regarding auto-grader feedback's effectiveness in improving student learning outcomes, in a way that differentiates students who utilized the feedback and students who did not. In this study, we fill this critical gap. Specifically, we analyze students' interactions with auto-graders in …

Joe Budden Network CEO Ian Schwartzman says the podcast network is on pace to generate $20M in 2025, after Joe Budden accidentally showed his Patreon earnings (Jessica Testa/New York Times)
https://www.nytimes.com/2025/07/22/business/media/joe-budden-podca…

How Does Podcast Star Joe Budden Make $20 Million a Year?
Joe Budden, a former rapper who now oversees a robust podcast network, gave a rare look into the finances of the industry.

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning
Eugene Armah, Linda Amoako Bannning
https://arxiv.org/abs/2507.14597

Towards a Proactive Autoscaling Framework for Data Stream Processing at the Edge using GRU and Transfer Learning
Processing data at high speeds is becoming increasingly critical as digital economies generate enormous data. The current paradigms for timely data processing are edge computing and data stream processing (DSP). Edge computing places resources closer to where data is generated, while stream processing analyzes the unbounded high-speed data in motion. However, edge stream processing faces rapid workload fluctuations, complicating resource provisioning. Inadequate resource allocation leads to bot…

Sampling String Vacua Using Generative Models
Moritz Walden, Magdalena Larfors
https://arxiv.org/abs/2509.16029 https://arxiv.org/pdf/2509.16029

Sampling String Vacua Using Generative Models
We apply generative models to a key problem in the string compactification program, namely construction of type IIB string vacua. To this end, we make use of a Bayesian Flow Network, a generative model capable of handling discrete data, to generate flux vectors that give rise to type IIB vacua. Furthermore, we sample flux vacua that have certain desirable properties by employing a Transformer as a conditional generative model. Both models demonstrate good performance in finding flux vacua and t…

Combinatorial and Gaussian Foundations of Rational Nth Root Approximations: Theorems and Conjectures
Isaac Wolford
https://arxiv.org/abs/2508.14095 https://

Combinatorial and Gaussian Foundations of Rational Nth Root Approximations: Theorems and Conjectures
We present an approach (the biroot method) for nth root approximation that yields closed-form rational functions with coefficients derived from binomial structures, Gaussian functions, or qualifying DAG structures. The method emerges from an analysis of Newton's method applied to root extraction, revealing that successive iterations generate coefficients following rows of Pascal's triangle in an alternating numerator-denominator pattern. After further exploration of these patterns, we formulate…

Expansive Natural Neural Gradient Flows for Energy Minimization
Wolfgang Dahmen, Wuchen Li, Yuankai Teng, Zhu Wang
https://arxiv.org/abs/2507.13475 https:/…

Expansive Natural Neural Gradient Flows for Energy Minimization
This paper develops expansive gradient dynamics in deep neural network-induced mapping spaces. Specifically, we generate tools and concepts for minimizing a class of energy functionals in an abstract Hilbert space setting covering a wide scope of applications such as PDEs-based inverse problems and supervised learning. The approach hinges on a Hilbert space metric in the full diffeomorphism mapping space, which could be viewed as a generalized Wasserstein-2 metric. We then study a projection gr…

Fed-PISA: Federated Voice Cloning via Personalized Identity-Style Adaptation
Qi Wang, Shituo Ma, Guoxin Yu, Hanyang Peng, Yue Yu
https://arxiv.org/abs/2509.16010 https://…

Fed-PISA: Federated Voice Cloning via Personalized Identity-Style Adaptation
Voice cloning for Text-to-Speech (TTS) aims to generate expressive and personalized speech from text using limited data from a target speaker. Federated Learning (FL) offers a collaborative and privacy-preserving framework for this task, but existing approaches suffer from high communication costs and tend to suppress stylistic heterogeneity, resulting in insufficient personalization. To address these issues, we propose Fed-PISA, which stands for Federated Personalized Identity-Style Adaptation…

SERVAL: Surprisingly Effective Zero-Shot Visual Document Retrieval Powered by Large Vision and Language Models
Thong Nguyen, Yibin Lei, Jia-Huei Ju, Andrew Yates
https://arxiv.org/abs/2509.15432

SERVAL: Surprisingly Effective Zero-Shot Visual Document Retrieval Powered by Large Vision and Language Models
Visual Document Retrieval (VDR) typically operates as text-to-image retrieval using specialized bi-encoders trained to directly embed document images. We revisit a zero-shot generate-and-encode pipeline: a vision-language model first produces a detailed textual description of each document image, which is then embedded by a standard text encoder. On the ViDoRe-v2 benchmark, the method reaches 63.4% nDCG@5, surpassing the strongest specialised multi-vector visual document encoder. It also scales…

Latent Space Synergy: Text-Guided Data Augmentation for Direct Diffusion Biomedical Segmentation
Muhammad Aqeel, Maham Nazir, Zanxi Ruan, Francesco Setti
https://arxiv.org/abs/2507.15361

Latent Space Synergy: Text-Guided Data Augmentation for Direct Diffusion Biomedical Segmentation
Medical image segmentation suffers from data scarcity, particularly in polyp detection where annotation requires specialized expertise. We present SynDiff, a framework combining text-guided synthetic data generation with efficient diffusion-based segmentation. Our approach employs latent diffusion models to generate clinically realistic synthetic polyps through text-conditioned inpainting, augmenting limited training data with semantically diverse samples. Unlike traditional diffusion methods r…

Constructive Degenerations and the Algebraicity of Limiting Hodge
Badre Mounda
https://arxiv.org/abs/2507.15012 https://arxiv.org/pdf…

Constructive Degenerations and the Algebraicity of Limiting Hodge
We propose a novel constructive framework for approaching the Hodge Conjecture via explicit degenerations. Building on limiting mixed Hodge structures (LMHS), we formulate a criterion under which a rational class of type (p, p) on a smooth projective variety becomes algebraic in the limit of a semi-stable degeneration. We provide examples where vanishing cycles and monodromy explicitly generate new algebraic classes, and propose a general principle: every rational (p, p) class arises as the lim…

Scalable FPGA Framework for Real-Time Denoising in High-Throughput Imaging: A DRAM-Optimized Pipeline using High-Level Synthesis
Weichien Liao
https://arxiv.org/abs/2508.14917 h…

Scalable FPGA Framework for Real-Time Denoising in High-Throughput Imaging: A DRAM-Optimized Pipeline using High-Level Synthesis
High-throughput imaging workflows, such as Parallel Rapid Imaging with Spectroscopic Mapping (PRISM), generate data at rates that exceed conventional real-time processing capabilities. We present a scalable FPGA-based preprocessing pipeline for real-time denoising, implemented via High-Level Synthesis (HLS) and optimized for DRAM-backed buffering. Our architecture performs frame subtraction and averaging directly on streamed image data, minimizing latency through burst-mode AXI4 interfaces. The…

Some great new examples of using Copilot within Excel.

✅ Excel AI: Microsoft brings new 'COPILOT' function directly into spreadsheet cells – GeekWire
https://www.geekwire.com/2025/excel-formula-meets…

Excel + AI: Microsoft brings new ‘COPILOT’ function directly into spreadsheet cells
Microsoft adds a new Copilot function to Excel, letting users generate, analyze, and summarize data directly in spreadsheet cells with AI.

Language Generation in the Limit: Noise, Loss, and Feedback
Yannan Bai, Debmalya Panigrahi, Ian Zhang
https://arxiv.org/abs/2507.15319 https://

Language Generation in the Limit: Noise, Loss, and Feedback
Kleinberg and Mullainathan (2024) recently proposed a formal framework called language generation in the limit and showed that given a sequence of example strings from an unknown target language drawn from any countable collection, an algorithm can correctly generate unseen strings from the target language within finite time. This notion was further refined by Li, Raman, and Tewari (2024), who defined stricter categories of non-uniform and uniform generation. They showed that a finite union of …

A CARLA-based Simulation of Electrically Driven Forklifts
David Claus, Christiane Thielemann, Hans-Georg Stark
https://arxiv.org/abs/2509.15909 https://arx…

A CARLA-based Simulation of Electrically Driven Forklifts
This paper presents the simulation of the operation of an electric forklift fleet within an intralogistics scenario. For this purpose, the open source simulation tool CARLA is used; according to our knowledge this is a novel approach in the context of logistics simulation. First, CARLA is used to generate and visualize a realistic 3D outdoor warehouse scenario, incorporating a number of randomly moving forklifts. In a next step, intralogistics transport tasks, such as pick-and-place, are simula…

Learning Null Geodesics for Gravitational Lensing Rendering in General Relativity
Mingyuan Sun, Zheng Fang, Jiaxu Wang, Kunyi Zhang, Qiang Zhang, Renjing Xu
https://arxiv.org/abs/2507.15775

Learning Null Geodesics for Gravitational Lensing Rendering in General Relativity
We present GravLensX, an innovative method for rendering black holes with gravitational lensing effects using neural networks. The methodology involves training neural networks to fit the spacetime around black holes and then employing these trained models to generate the path of light rays affected by gravitational lensing. This enables efficient and scalable simulations of black holes with optically thin accretion disks, significantly decreasing the time required for rendering compared to tra…

Fast approximate Bayesian inference of HIV indicators using PCA adaptive Gauss-Hermite quadrature
Adam Howes, Alex Stringer, Seth R. Flaxman, Jeffrey W. Imai-Eaton
https://arxiv.org/abs/2508.15665

Fast approximate Bayesian inference of HIV indicators using PCA adaptive Gauss-Hermite quadrature
Naomi is a spatial evidence synthesis model used to produce district-level HIV epidemic indicators in sub-Saharan Africa. Multiple outcomes of policy interest, including HIV prevalence, HIV incidence, and antiretroviral therapy treatment coverage are jointly modelled using both household survey data and routinely reported health system data. The model is provided as a tool for countries to input their data to and generate estimates with during a yearly process supported by UNAIDS. Previously, i…

The Supersymmetric Standard Model, combined with a special Exotic Invariant, yields a new kind of SUSY mass splitting (E6)
John A. Dixon
https://arxiv.org/abs/2507.14381

The Supersymmetric Standard Model, combined with a special Exotic Invariant, yields a new kind of SUSY mass splitting (E6)
Exotic Invariants, with Lorentz invariance, have been found in the BRS cohomology of SUSY in 3+1 dimensions. Until recently, it was generally accepted that no such objects could exist. It is shown here that the Supersymmetric Standard Model (``the SSM'') can be coupled to a special Exotic Invariant to form a new model, which we call the Exotic Model (``the XM'') . This Exotic Model continues to generate the usual Vacuum Expectation Value (``VEV'') that breaks gauge symmetry from SU(3) X SU(2) X…

CosmoGen: a cosmological model generator
D. Castel\~ao, I. Tereno
https://arxiv.org/abs/2509.15453 https://arxiv.org/pdf/2509.15453

CosmoGen: a cosmological model generator
The standard $Λ$CDM paradigm of the physical Universe suffers from well-known conceptual problems and is challenged by observational data. Alternative models exist in the literature, both phenomenological and physically motivated, many of them suffering from similar or new problems. We propose a method to mechanically generate alternative models in a procedure informed by data and tuned to mitigate specific problems. We implement a computational framework, dubbed CosmoGen, that integrates evol…

All-Electric Heavy-Duty Robotic Manipulator: Actuator Configuration Optimization and Sensorless Control
Mohammad Bahari, Amir Hossein Barjini, Pauli Mustalahti, Jouni Mattila
https://arxiv.org/abs/2509.15778

All-Electric Heavy-Duty Robotic Manipulator: Actuator Configuration Optimization and Sensorless Control
This paper presents a unified framework that integrates modeling, optimization, and sensorless control of an all-electric heavy-duty robotic manipulator (HDRM) driven by electromechanical linear actuators (EMLAs). An EMLA model is formulated to capture motor electromechanics and direction-dependent transmission efficiencies, while a mathematical model of the HDRM, incorporating both kinematics and dynamics, is established to generate joint-space motion profiles for prescribed TCP trajectories. …

Training thermodynamic computers by gradient descent
Stephen Whitelam
https://arxiv.org/abs/2509.15324 https://arxiv.org/pdf/2509.15324

Training thermodynamic computers by gradient descent
We show how to adjust the parameters of a thermodynamic computer by gradient descent in order to perform a desired computation at a specified observation time. Within a digital simulation of a thermodynamic computer, training proceeds by maximizing the probability with which the computer would generate an idealized dynamical trajectory. The idealized trajectory is designed to reproduce the activations of a neural network trained to perform the desired computation. This teacher-student scheme re…

PDEformer-2: A Versatile Foundation Model for Two-Dimensional Partial Differential Equations
Zhanhong Ye, Zining Liu, Bingyang Wu, Hongjie Jiang, Leheng Chen, Minyan Zhang, Xiang Huang, Qinghe Meng. Jingyuan Zou, Hongsheng Liu, Bin Dong
https://arxiv.org/abs/2507.15409

PDEformer-2: A Versatile Foundation Model for Two-Dimensional Partial Differential Equations
Partial differential equations (PDEs) play a central role in describing many physical phenomena. Various scientific and engineering applications demand a versatile and differentiable PDE solver that can quickly generate solutions with adequate accuracy, and limitations of the traditional solvers and specialized neural operators motivate the development of foundation models for solving PDEs. This paper introduces PDEformer-2, a versatile foundation model for two-dimensional PDEs. Based on our pr…

DHEvo: Data-Algorithm Based Heuristic Evolution for Generalizable MILP Solving
Zhihao Zhang, Siyuan Li, Chenxi Li, Feifan Liu, Mengjing Chen, Kai Li, Tao Zhong, Bo An, Peng Liu
https://arxiv.org/abs/2507.15615

DHEvo: Data-Algorithm Based Heuristic Evolution for Generalizable MILP Solving
Primal heuristics play a critical role in improving the efficiency of mixed integer programming (MILP) solvers. As large language models (LLMs) have demonstrated superior code generation abilities, recent MILP works are devoted to leveraging the evolutionary computation approaches with LLMs to generate effective primal heuristics. Although the generated heuristics have achieved better solving performance than the hand-crafted ones with little adaptability, the advantage of current LLM-based met…

Interface on demand: Towards AI native Control interfaces for 6G
Abhishek Dandekar, Prashiddha D. Thapa, Ashrafur Rahman, Julius Schulz-Zander
https://arxiv.org/abs/2508.15595 h…

Interface on demand: Towards AI native Control interfaces for 6G
Traditional standardized network interfaces face significant limitations, including vendor-specific incompatibilities, rigid design assumptions, and lack of adaptability for new functionalities. We propose a multi-agent framework leveraging large language models (LLMs) to generate control interfaces on demand between network functions (NFs). This includes a matching agent, which aligns required control functionalities with NF capabilities, and a code-generation agent, which generates the necess…

I don't know how #MacOS can be so popular with #developers given how actively hostile it is to being able to generate core dumps or attach #debuggers. I guess most people are using it to shell t…

Using AI for User Representation: An Analysis of 83 Persona Prompts
Joni Salminen, Danial Amin, Bernard Jansen
https://arxiv.org/abs/2508.13047 https://arx…

Using AI for User Representation: An Analysis of 83 Persona Prompts
We analyzed 83 persona prompts from 27 research articles that used large language models (LLMs) to generate user personas. Findings show that the prompts predominantly generate single personas. Several prompts express a desire for short or concise persona descriptions, which deviates from the tradition of creating rich, informative, and rounded persona profiles. Text is the most common format for generated persona attributes, followed by numbers. Text and numbers are often generated together, a…

"How Switzerland’s Solar Train Tracks Could Reshape Renewable Energy"
#Switzerland #Trains #SolarPower #Energy

How Switzerland’s Solar Train Tracks Could Reshape Renewable Energy | Happy Eco News
Switzerland is testing solar train tracks to generate clean energy using unused rail space. These removable panels could power 300,000 homes, create jobs, and reduce land use. Countries like South Korea and Japan are watching closely. If successful, the system could offer a new way to meet global energy demands.Switzerland is testing solar train tracks to generate clean energy using unused rail space. These removable panels could power 300,000 homes, create jobs, and reduce land use. Countries …

StructInbet: Integrating Explicit Structural Guidance into Inbetween Frame Generation
Zhenglin Pan, Haoran Xie
https://arxiv.org/abs/2507.13377 https://

StructInbet: Integrating Explicit Structural Guidance into Inbetween Frame Generation
In this paper, we propose StructInbet, an inbetweening system designed to generate controllable transitions over explicit structural guidance. StructInbet introduces two key contributions. First, we propose explicit structural guidance to the inbetweening problem to reduce the ambiguity inherent in pixel trajectories. Second, we adopt a temporal attention mechanism that incorporates visual identity from both the preceding and succeeding keyframes, ensuring consistency in character appearance.

Propagation of Chaos for Singular Interactions via Regular Drivers
Qian Qi
https://arxiv.org/abs/2507.14981 https://arxiv.org/pdf/250…

Propagation of Chaos for Singular Interactions via Regular Drivers
We introduce a framework to prove propagation of chaos for interacting particle systems with singular, density-dependent interactions, a classical challenge in mean-field theory. Our approach is to define the dynamics implicitly via a regular driver function. This regular driver is engineered to generate a singular effective interaction, yet its underlying regularity provides the necessary analytical control. Our main result establishes propagation of chaos under a key dissipation condition. Th…

SO close to 10x programmer! #agiAnytimeSoon https://infosec.exchange/@adamshostack/115050433640095929

On the application of a hypergeometric identity to generate generalized hypergeometric reduction formulas
J. L. Gonz\'alez-Santander
https://arxiv.org/abs/2508.13897 https:/…

On the application of a hypergeometric identity to generate generalized hypergeometric reduction formulas
We systematically exploit a new generalized hypergeometric identity to obtain new hypergeometric summation formulas. As a consistency test, alternative proofs for some special cases are also provided. As a byproduct new summation formulas involving finite sums involving the psi function, or a recursive formula for the Bateman's G function are derived. Finally, all the results have been numerically checked with MATHEMATICA.

Waver: Wave Your Way to Lifelike Video Generation
Yifu Zhang, Hao Yang, Yuqi Zhang, Yifei Hu, Fengda Zhu, Chuang Lin, Xiaofeng Mei, Yi Jiang, Zehuan Yuan, Bingyue Peng
https://arxiv.org/abs/2508.15761 …

Waver: Wave Your Way to Lifelike Video Generation
We present Waver, a high-performance foundation model for unified image and video generation. Waver can directly generate videos with durations ranging from 5 to 10 seconds at a native resolution of 720p, which are subsequently upscaled to 1080p. The model simultaneously supports text-to-video (T2V), image-to-video (I2V), and text-to-image (T2I) generation within a single, integrated framework. We introduce a Hybrid Stream DiT architecture to enhance modality alignment and accelerate training c…

Dream 7B: Diffusion Large Language Models
Jiacheng Ye, Zhihui Xie, Lin Zheng, Jiahui Gao, Zirui Wu, Xin Jiang, Zhenguo Li, Lingpeng Kong
https://arxiv.org/abs/2508.15487 https:/…

Dream 7B: Diffusion Large Language Models
We introduce Dream 7B, the most powerful open diffusion large language model to date. Unlike autoregressive (AR) models that generate tokens sequentially, Dream 7B employs discrete diffusion modeling to refine sequences in parallel through iterative denoising. Our model consistently outperforms existing diffusion language models on general, mathematical, and coding tasks. Dream 7B demonstrates superior planning abilities and inference flexibility, including arbitrary-order generation, infilling…

CCrepairBench: A High-Fidelity Benchmark and Reinforcement Learning Framework for C Compilation Repair
Weixuan Sun, Jucai Zhai, Dengfeng Liu, Xin Zhang, Xiaojun Wu, Qiaobo Hao, AIMgroup, Yang Fang, Jiuyang Tang
https://arxiv.org/abs/2509.15690

CCrepairBench: A High-Fidelity Benchmark and Reinforcement Learning Framework for C++ Compilation Repair
The automated repair of C++ compilation errors presents a significant challenge, the resolution of which is critical for developer productivity. Progress in this domain is constrained by two primary factors: the scarcity of large-scale, high-fidelity datasets and the limitations of conventional supervised methods, which often fail to generate semantically correct patches.This paper addresses these gaps by introducing a comprehensive framework with three core contributions. First, we present CCr…

Tuning Random Generators: Property-Based Testing as Probabilistic Programming
Ryan Tjoa, Poorva Garg, Harrison Goldstein, Todd Millstein, Benjamin Pierce, Guy Van den Broeck
https://arxiv.org/abs/2508.14394

Tuning Random Generators: Property-Based Testing as Probabilistic Programming
Property-based testing validates software against an executable specification by evaluating it on randomly generated inputs. The standard way that PBT users generate test inputs is via generators that describe how to sample test inputs through random choices. To achieve a good distribution over test inputs, users must tune their generators, i.e., decide on the weights of these individual random choices. Unfortunately, it is very difficult to understand how to choose individual generator weights…

Enhancing Repository-Level Code Generation with Call Chain-Aware Multi-View Context
Yang Liu, Li Zhang, Fang Liu, Zhuohang Wang, Donglin Wei, Zhishuo Yang, Kechi Zhang, Jia Li, Lin Shi
https://arxiv.org/abs/2507.14791

Enhancing Repository-Level Code Generation with Call Chain-Aware Multi-View Context
Repository-level code generation aims to generate code within the context of a specified repository. Existing approaches typically employ retrieval-augmented generation (RAG) techniques to provide LLMs with relevant contextual information extracted from the repository. However, these approaches often struggle with effectively identifying truly relevant contexts that capture the rich semantics of the repository, and their contextual perspectives remains narrow. Moreover, most approaches fail to …

KGN-Pro: Keypoint-Based Grasp Prediction through Probabilistic 2D-3D Correspondence Learning
Bingran Chen, Baorun Li, Jian Yang, Yong Liu, Guangyao Zhai
https://arxiv.org/abs/2507.14820

KGN-Pro: Keypoint-Based Grasp Prediction through Probabilistic 2D-3D Correspondence Learning
High-level robotic manipulation tasks demand flexible 6-DoF grasp estimation to serve as a basic function. Previous approaches either directly generate grasps from point-cloud data, suffering from challenges with small objects and sensor noise, or infer 3D information from RGB images, which introduces expensive annotation requirements and discretization issues. Recent methods mitigate some challenges by retaining a 2D representation to estimate grasp keypoints and applying Perspective-n-Point (…

Efficient nonclassical state preparation via generalized parity measurement
Chen-yi Zhang, Jun Jing
https://arxiv.org/abs/2508.14750 https://arxiv.org/pdf/…

Efficient nonclassical state preparation via generalized parity measurement
Nonclassical states of bosonic modes, especially the large number states, are valuable resources for quantum information processing and quantum metrology. However, it is intricate to apply unitary protocols to generate a desired Fock state due to the uniform energy spectrum of bosonic system. We here propose a measurement-based protocol that leverages the resonant Jaynes-Cummings interaction of the bosonic mode with an ancillary two-level atom and sequential projective measurements on the atom.…

A Risk Assessment Framework for Digital Identification Systems
Allison Woodruff, Dirk Balfanz, Will Drewry, Mariana Raykova
https://arxiv.org/abs/2507.14755

A Risk Assessment Framework for Digital Identification Systems
We introduce a risk assessment framework for digital identification systems, as well as recommended best practices to enhance privacy, security, and other desirable properties in these systems. To generate these resources, we created a casebook of a wide range of digital identification systems, and we then applied expert analysis and critique to identify patterns. We piloted the framework on several reviews within our organization over a period of approximately one year, and found it to be robu…

Are Virtual DES Images a Valid Alternative to the Real Ones?
Ana C. Perre, Lu\'is A. Alexandre, Lu\'is C. Freire
https://arxiv.org/abs/2508.15594 https://

Are Virtual DES Images a Valid Alternative to the Real Ones?
Contrast-enhanced spectral mammography (CESM) is an imaging modality that provides two types of images, commonly known as low-energy (LE) and dual-energy subtracted (DES) images. In many domains, particularly in medicine, the emergence of image-to-image translation techniques has enabled the artificial generation of images using other images as input. Within CESM, applying such techniques to generate DES images from LE images could be highly beneficial, potentially reducing patient exposure to …

Adversarial Attacks against Neural Ranking Models via In-Context Learning
Amin Bigdeli, Negar Arabzadeh, Ebrahim Bagheri, Charles L. A. Clarke
https://arxiv.org/abs/2508.15283 h…

Adversarial Attacks against Neural Ranking Models via In-Context Learning
While neural ranking models (NRMs) have shown high effectiveness, they remain susceptible to adversarial manipulation. In this work, we introduce Few-Shot Adversarial Prompting (FSAP), a novel black-box attack framework that leverages the in-context learning capabilities of Large Language Models (LLMs) to generate high-ranking adversarial documents. Unlike previous approaches that rely on token-level perturbations or manual rewriting of existing documents, FSAP formulates adversarial attacks en…

Revisit Choice Network for Synthesis and Technology Mapping
Chen Chen, Jiaqi Yin, Cunxi Yu
https://arxiv.org/abs/2508.14068 https://arxiv.org/pdf/2508.1406…

Revisit Choice Network for Synthesis and Technology Mapping
Choice network construction is a critical technique for alleviating structural bias issues in Boolean optimization, equivalence checking, and technology mapping. Previous works on lossless synthesis utilize independent optimization to generate multiple snapshots, and use simulation and SAT solvers to identify functionally equivalent nodes. These nodes are then merged into a subject graph with choice nodes. However, such methods often neglect the quality of these choices, raising the question of…

CineScale: Free Lunch in High-Resolution Cinematic Visual Generation
Haonan Qiu, Ning Yu, Ziqi Huang, Paul Debevec, Ziwei Liu
https://arxiv.org/abs/2508.15774 https://

CineScale: Free Lunch in High-Resolution Cinematic Visual Generation
Visual diffusion models achieve remarkable progress, yet they are typically trained at limited resolutions due to the lack of high-resolution data and constrained computation resources, hampering their ability to generate high-fidelity images or videos at higher resolutions. Recent efforts have explored tuning-free strategies to exhibit the untapped potential higher-resolution visual generation of pre-trained models. However, these methods are still prone to producing low-quality visual content…

Collaborative Rational Speech Act: Pragmatic Reasoning for Multi-Turn Dialog
Lautaro Estienne, Gabriel Ben Zenou, Nona Naderi, Jackie Cheung, Pablo Piantanida
https://arxiv.org/abs/2507.14063

Collaborative Rational Speech Act: Pragmatic Reasoning for Multi-Turn Dialog
As AI systems take on collaborative roles, they must reason about shared goals and beliefs-not just generate fluent language. The Rational Speech Act (RSA) framework offers a principled approach to pragmatic reasoning, but existing extensions face challenges in scaling to multi-turn, collaborative scenarios. In this paper, we introduce Collaborative Rational Speech Act (CRSA), an information-theoretic (IT) extension of RSA that models multi-turn dialog by optimizing a gain function adapted from…

Beyond Video-to-SFX: Video to Audio Synthesis with Environmentally Aware Speech
Xinlei Niu, Jianbo Ma, Dylan Harper-Harris, Xiangyu Zhang, Charles Patrick Martin, Jing Zhang
https://arxiv.org/abs/2509.15492

Beyond Video-to-SFX: Video to Audio Synthesis with Environmentally Aware Speech
The generation of realistic, context-aware audio is important in real-world applications such as video game development. While existing video-to-audio (V2A) methods mainly focus on Foley sound generation, they struggle to produce intelligible speech. Meanwhile, current environmental speech synthesis approaches remain text-driven and fail to temporally align with dynamic video content. In this paper, we propose Beyond Video-to-SFX (BVS), a method to generate synchronized audio with environmental…

RETAIL: Towards Real-world Travel Planning for Large Language Models
Bin Deng, Yizhe Feng, Zeming Liu, Qing Wei, Xiangrong Zhu, Shuai Chen, Yuanfang Guo, Yunhong Wang
https://arxiv.org/abs/2508.15335

RETAIL: Towards Real-world Travel Planning for Large Language Models
Although large language models have enhanced automated travel planning abilities, current systems remain misaligned with real-world scenarios. First, they assume users provide explicit queries, while in reality requirements are often implicit. Second, existing solutions ignore diverse environmental factors and user preferences, limiting the feasibility of plans. Third, systems can only generate plans with basic POI arrangements, failing to provide all-in-one plans with rich details. To mitigate…

Random Variate Generation with Formal Guarantees
Feras A. Saad, Wonyeol Lee
https://arxiv.org/abs/2507.13494 https://arxiv.org/pdf/25…

Random Variate Generation with Formal Guarantees
This article introduces a new approach to principled and practical random variate generation with formal guarantees. The key idea is to first specify the desired probability distribution in terms of a finite-precision numerical program that defines its cumulative distribution function (CDF), and then generate exact random variates according to this CDF. We present a universal and fully automated method to synthesize exact random variate generators given any numerical CDF implemented in any bina…

⚡ Coin-sized device uses nut waste and drops of water to generate green energy
#technology

Coin-sized device uses nut waste and drops of water to generate green energy
Researchers at the University of Waterloo have developed an inexpensive device that generates enough electricity to power a calculator using only waste walnut shells and drops of water. Their work is published in the journal Energy & Environmental Materials.

Complete Sampling of the $uv$ Plane with Realistic Radio Arrays: Introducing the RULES Algorithm, with Application to 21 cm Foreground Wedge Removal
Vincent MacKay, Zhilei Xu, Ruby Byrne, Jacqueline Hewitt
https://arxiv.org/abs/2509.15296

Complete Sampling of the $uv$ Plane with Realistic Radio Arrays: Introducing the RULES Algorithm, with Application to 21 cm Foreground Wedge Removal
We introduce the Radio-array $uv$ Layout Engineering Strategy (RULES), an algorithm for designing radio arrays that achieve complete coverage of the $uv$ plane, defined as, at minimum, regular sampling at half the observing wavelength ($λ$) along the $u$ and $v$ axes within a specified range of baseline lengths. Using RULES, we generate $uv$-complete layouts that cover the range $10λ\leq|(u,v)|\leq 100λ$ with fewer than 1000 antennas of diameter $5λ$, comparable to current and pla…

Modeling Elastic-Body Dynamics of Fish Swimming Using a Variational Framework
Zhiheng Chen, Wei Wang
https://arxiv.org/abs/2509.16145 https://arxiv.org/pdf…

Modeling Elastic-Body Dynamics of Fish Swimming Using a Variational Framework
Fish-inspired aquatic robots are gaining increasing attention in research communities due to their high swimming speeds and efficient propulsion enabled by flexible bodies that generate undulatory motions. To support the design optimizations and control of such systems, accurate, interpretable, and computationally tractable modeling of the underlying swimming dynamics is indispensable. In this letter, we present a full-body dynamics model for fish swimming, rigorously derived from Hamilton's pr…

The Proportional Fair Scheduler in Wavelength-Multiplexed Quantum Networks
Sanidhay Bhambay, Siddarth Koduru Joshi, Thirupathaiah Vasantam, Neil Walton
https://arxiv.org/abs/2507.13999

The Proportional Fair Scheduler in Wavelength-Multiplexed Quantum Networks
We address the problem of optimal pumping strategies in quantum networks. These networks enable secure communication by distributing entangled photon pairs to user (or node) pairs. Quantum Key Distribution (QKD) protocols, like BBM92, generate secret keys from entangled photons. While secure communication and error correction are essential for any quantum communication channel, resource contention, optimization, and fairness issues are critical for networks. In this article, we analyze the perf…

Numerical Discretization Methods for Seismic Response Analysis of SDOF Systems: A Unified Perspective
Farid Ghahari
https://arxiv.org/abs/2509.15474 https://

Numerical Discretization Methods for Seismic Response Analysis of SDOF Systems: A Unified Perspective
This paper reviews the most commonly used numerical methods for solving the differential equation governing the dynamic response of linear elastic Single-Degree-of-Freedom (SDOF) systems. For more than 80 years since its introduction, the response spectrum has remained the cornerstone of every seismic design code. The second-order differential equation that governs the dynamic response of a linear elastic SDOF system must be solved numerically to generate such response spectra. Although only on…

Phase-matched Deep Ultraviolet Chiral Bound States in the Continuum Metalens
Omar A. M. Abdelraouf
https://arxiv.org/abs/2509.15904 https://arxiv.org/pdf/2…

Phase-matched Deep Ultraviolet Chiral Bound States in the Continuum Metalens
Coherent light sources in deep ultra-violet (DUV) is essential for various applications such as biomedical imaging and biosensing. Nonlinear crystals (NLCs) generate DUV light, but limited by its bulky thickness, low transparency, and strict narrowband phase-matching conditions. Demonstrating compact, broadband, chiral, and focused DUV light integrated device is elusive. In this work, we present the first demonstration of chiral DUV third harmonic generation (THG) in a dielectric metasurface ma…

Brevity is the Soul of Wit: Condensing Code Changes to Improve Commit Message Generation
Hongyu Kuang, Ning Zhang, Hui Gao, Xin Zhou, Wesley K. G. Assun\c{c}\~ao, Xiaoxing Ma, Dong Shao, Guoping Rong, He Zhang
https://arxiv.org/abs/2509.15567

Brevity is the Soul of Wit: Condensing Code Changes to Improve Commit Message Generation
Commit messages are valuable resources for describing why code changes are committed to repositories in version control systems (e.g., Git). They effectively help developers understand code changes and better perform software maintenance tasks. Unfortunately, developers often neglect to write high-quality commit messages in practice. Therefore, a growing body of work is proposed to generate commit messages automatically. These works all demonstrated that how to organize and represent code chang…

A novel approach to generate distributions
Subhankar Dutta, Roberto Vila, Terezinha K. A. Ribeiro
https://arxiv.org/abs/2508.11861 https://arxiv.org/pdf/25…

A novel approach to generate distributions
A novel approach to adding two additional parameters to a family of distributions for better adaptability has been put forth. This approach yields a versatile class of distributions supported on the positive real line. We proceed to analyze its mathematical characteristics, such as critical points, modality, stochastic representation, identifiability, quantiles, moments, and truncated moments. We present a new regression model for unimodal continuous data based on a submodel of the newly propos…

Towards a 3D Transfer-based Black-box Attack via Critical Feature Guidance
Shuchao Pang, Zhenghan Chen, Shen Zhang, Liming Lu, Siyuan Liang, Anan Du, Yongbin Zhou
https://arxiv.org/abs/2508.15650

Towards a 3D Transfer-based Black-box Attack via Critical Feature Guidance
Deep neural networks for 3D point clouds have been demonstrated to be vulnerable to adversarial examples. Previous 3D adversarial attack methods often exploit certain information about the target models, such as model parameters or outputs, to generate adversarial point clouds. However, in realistic scenarios, it is challenging to obtain any information about the target models under conditions of absolute security. Therefore, we focus on transfer-based attacks, where generating adversarial poin…

Innocence in the Crossfire: Roles of Skip Connections in Jailbreaking Visual Language Models
Palash Nandi, Maithili Joshi, Tanmoy Chakraborty
https://arxiv.org/abs/2507.13761

Innocence in the Crossfire: Roles of Skip Connections in Jailbreaking Visual Language Models
Language models are highly sensitive to prompt formulations - small changes in input can drastically alter their output. This raises a critical question: To what extent can prompt sensitivity be exploited to generate inapt content? In this paper, we investigate how discrete components of prompt design influence the generation of inappropriate content in Visual Language Models (VLMs). Specifically, we analyze the impact of three key factors on successful jailbreaks: (a) the inclusion of detailed…

Planning with Minimal Disruption
Alberto Pozanco, Marianela Morales, Daniel Borrajo, Manuela Veloso
https://arxiv.org/abs/2508.15358 https://arxiv.org/pdf/…

Planning with Minimal Disruption
In many planning applications, we might be interested in finding plans that minimally modify the initial state to achieve the goals. We refer to this concept as plan disruption. In this paper, we formally introduce it, and define various planning-based compilations that aim to jointly optimize both the sum of action costs and plan disruption. Experimental results in different benchmarks show that the reformulated task can be effectively solved in practice to generate plans that balance both obj…

Rule-based Key-Point Extraction for MR-Guided Biomechanical Digital Twins of the Spine
Robert Graf, Tanja Lerchl, Kati Nispel, Hendrik M\"oller, Matan Atad, Julian McGinnis, Julius Maria Watrinet, Johannes Paetzold, Daniel Rueckert, Jan S. Kirschke
https://arxiv.org/abs/2508.14708

Rule-based Key-Point Extraction for MR-Guided Biomechanical Digital Twins of the Spine
Digital twins offer a powerful framework for subject-specific simulation and clinical decision support, yet their development often hinges on accurate, individualized anatomical modeling. In this work, we present a rule-based approach for subpixel-accurate key-point extraction from MRI, adapted from prior CT-based methods. Our approach incorporates robust image alignment and vertebra-specific orientation estimation to generate anatomically meaningful landmarks that serve as boundary conditions …

M-$LLM^3$REC: A Motivation-Aware User-Item Interaction Framework for Enhancing Recommendation Accuracy with LLMs
Lining Chen, Qingwen Zeng, Huaming Chen
https://arxiv.org/abs/2508.15262

M-$LLM^3$REC: A Motivation-Aware User-Item Interaction Framework for Enhancing Recommendation Accuracy with LLMs
Recommendation systems have been essential for both user experience and platform efficiency by alleviating information overload and supporting decision-making. Traditional methods, i.e., content-based filtering, collaborative filtering, and deep learning, have achieved impressive results in recommendation systems. However, the cold-start and sparse-data scenarios are still challenging to deal with. Existing solutions either generate pseudo-interaction sequence, which often introduces redundant …

Heterogeneous object manipulation on nonlinear soft surface through linear controller
Pratik Ingle, Kasper St{\o}y, Andres Fai\~na
https://arxiv.org/abs/2507.14967

Heterogeneous object manipulation on nonlinear soft surface through linear controller
Manipulation surfaces indirectly control and reposition objects by actively modifying their shape or properties rather than directly gripping objects. These surfaces, equipped with dense actuator arrays, generate dynamic deformations. However, a high-density actuator array introduces considerable complexity due to increased degrees of freedom (DOF), complicating control tasks. High DOF restrict the implementation and utilization of manipulation surfaces in real-world applications as the mainten…

Photon Blockade Mediated by Two-Photon Absorption in an Optical Parametric Amplifier
Weiyi An, Jie Zhu
https://arxiv.org/abs/2509.15696 https://arxiv.org/p…

Photon Blockade Mediated by Two-Photon Absorption in an Optical Parametric Amplifier
Photon blockade (PB) is a quantum effect in strongly nonlinear systems where a single photon prevents the system from being excited to a higher level, generating anti-bunched light fields. It enables the generation of single-photon sources for quantum information processing. Conventional photon blockade (CPB) leverages strong nonlinear interactions to generate an anharmonic energy spectrum. Unconventional photon blockade (UPB) utilizes destructive quantum interference between excitation pathway…

Mastodon, the nonprofit behind the social network, plans to offer paid hosting, moderation, and support services for organizations joining the fediverse (Sarah Perez/TechCrunch)
https://techcrunch.com/2025/09/19/mast

Mastodon has a new plan to make money: Hosting and support services for the open social web | TechCrunch
Mastodon launches hosting and support services to generate revenue and expand the open social web.

Static Analysis as a Feedback Loop: Enhancing LLM-Generated Code Beyond Correctness
Scott Blyth, Sherlock A. Licorish, Christoph Treude, Markus Wagner
https://arxiv.org/abs/2508.14419

Static Analysis as a Feedback Loop: Enhancing LLM-Generated Code Beyond Correctness
Large language models (LLMs) have demonstrated impressive capabilities in code generation, achieving high scores on benchmarks such as HumanEval and MBPP. However, these benchmarks primarily assess functional correctness and neglect broader dimensions of code quality, including security, reliability, readability, and maintainability. In this work, we systematically evaluate the ability of LLMs to generate high-quality code across multiple dimensions using the PythonSecurityEval benchmark. We in…

Towards PerSense : Advancing Training-Free Personalized Instance Segmentation in Dense Images
Muhammad Ibraheem Siddiqui, Muhammad Umer Sheikh, Hassan Abid, Kevin Henry, Muhammad Haris Khan
https://arxiv.org/abs/2508.14660

Towards PerSense++: Advancing Training-Free Personalized Instance Segmentation in Dense Images
Segmentation in dense visual scenes poses significant challenges due to occlusions, background clutter, and scale variations. To address this, we introduce PerSense, an end-to-end, training-free, and model-agnostic one-shot framework for Personalized instance Segmentation in dense images. PerSense employs a novel Instance Detection Module (IDM) that leverages density maps (DMs) to generate instance-level candidate point prompts, followed by a Point Prompt Selection Module (PPSM) that filters fa…

Once Upon a Time: Interactive Learning for Storytelling with Small Language Models
Jonas Mayer Martins, Ali Hamza Bashir, Muhammad Rehan Khalid, Lisa Beinborn
https://arxiv.org/abs/2509.15714

Once Upon a Time: Interactive Learning for Storytelling with Small Language Models
Children efficiently acquire language not just by listening, but by interacting with others in their social environment. Conversely, large language models are typically trained with next-word prediction on massive amounts of text. Motivated by this contrast, we investigate whether language models can be trained with less data by learning not only from next-word prediction but also from high-level, cognitively inspired feedback. We train a student model to generate stories, which a teacher model…

Koopman Operator Based Time-Delay Embeddings and State History Augmented LQR for Periodic Hybrid Systems: Bouncing Pendulum and Bipedal Walking
Chun-Ming Yang, Pranav A. Bhounsule
https://arxiv.org/abs/2507.14455

Koopman Operator Based Time-Delay Embeddings and State History Augmented LQR for Periodic Hybrid Systems: Bouncing Pendulum and Bipedal Walking
Time-delay embedding is a technique that uses snapshots of state history over time to build a linear state space model of a nonlinear smooth system. We demonstrate that periodic non-smooth or hybrid system can also be modeled as a linear state space system using this approach as long as its behavior is consistent in modes and timings. We extended time-delay embeddings to generate a linear model of two periodic hybrid systems: the bouncing pendulum and the simplest walker with control inputs. Th…

Assessing the Quality and Security of AI-Generated Code: A Quantitative Analysis
Abbas Sabra, Olivier Schmitt, Joseph Tyler
https://arxiv.org/abs/2508.14727 https://

Assessing the Quality and Security of AI-Generated Code: A Quantitative Analysis
This study presents a quantitative evaluation of the code quality and security of five prominent Large Language Models (LLMs): Claude Sonnet 4, Claude 3.7 Sonnet, GPT-4o, Llama 3.2 90B, and OpenCoder 8B. While prior research has assessed the functional performance of LLM-generated code, this research tested LLM output from 4,442 Java coding assignments through comprehensive static analysis using SonarQube. The findings suggest that although LLMs can generate functional code, they also introduce…

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles
Jiangfan Liu, Yongkang Guo, Fangzhi Zhong, Tianyuan Zhang, Zonglei Jing, Siyuan Liang, Jiakai Wang, Mingchuan Zhang, Aishan Liu, Xianglong Liu
https://arxiv.org/abs/2508.14527

Adversarial Generation and Collaborative Evolution of Safety-Critical Scenarios for Autonomous Vehicles
The generation of safety-critical scenarios in simulation has become increasingly crucial for safety evaluation in autonomous vehicles prior to road deployment in society. However, current approaches largely rely on predefined threat patterns or rule-based strategies, which limit their ability to expose diverse and unforeseen failure modes. To overcome these, we propose ScenGE, a framework that can generate plentiful safety-critical scenarios by reasoning novel adversarial cases and then amplif…

Question-Answer Extraction from Scientific Articles Using Knowledge Graphs and Large Language Models
Hosein Azarbonyad, Zi Long Zhu, Georgios Cheirmpos, Zubair Afzal, Vikrant Yadav, Georgios Tsatsaronis
https://arxiv.org/abs/2507.13827

Question-Answer Extraction from Scientific Articles Using Knowledge Graphs and Large Language Models
When deciding to read an article or incorporate it into their research, scholars often seek to quickly identify and understand its main ideas. In this paper, we aim to extract these key concepts and contributions from scientific articles in the form of Question and Answer (QA) pairs. We propose two distinct approaches for generating QAs. The first approach involves selecting salient paragraphs, using a Large Language Model (LLM) to generate questions, ranking these questions by the likelihood o…

Post-hoc LLM-Supported Debugging of Distributed Processes
Dennis Schiese, Andreas Both
https://arxiv.org/abs/2508.14540 https://arxiv.org/pdf/2508.14540

Post-hoc LLM-Supported Debugging of Distributed Processes
In this paper, we address the problem of manual debugging, which nowadays remains resource-intensive and in some parts archaic. This problem is especially evident in increasingly complex and distributed software systems. Therefore, our objective of this work is to introduce an approach that can possibly be applied to any system, at both the macro- and micro-level, to ease this debugging process. This approach utilizes a system's process data, in conjunction with generative AI, to generate natur…

Understand Before You Generate: Self-Guided Training for Autoregressive Image Generation
Xiaoyu Yue, Zidong Wang, Yuqing Wang, Wenlong Zhang, Xihui Liu, Wanli Ouyang, Lei Bai, Luping Zhou
https://arxiv.org/abs/2509.15185

Understand Before You Generate: Self-Guided Training for Autoregressive Image Generation
Recent studies have demonstrated the importance of high-quality visual representations in image generation and have highlighted the limitations of generative models in image understanding. As a generative paradigm originally designed for natural language, autoregressive models face similar challenges. In this work, we present the first systematic investigation into the mechanisms of applying the next-token prediction paradigm to the visual domain. We identify three key properties that hinder th…

Improved Generalized Planning with LLMs through Strategy Refinement and Reflection
Katharina Stein, Nils Hodel, Daniel Fi\v{s}er, J\"org Hoffmann, Michael Katz, Alexander Koller
https://arxiv.org/abs/2508.13876

Improved Generalized Planning with LLMs through Strategy Refinement and Reflection
LLMs have recently been used to generate Python programs representing generalized plans in PDDL planning, i.e., plans that generalize across the tasks of a given PDDL domain. Previous work proposed a framework consisting of three steps: the LLM first generates a summary and then a strategy for the domain, both in natural language, and then implements that strategy as a Python program, that gets debugged on example planning tasks. In that work, only one strategy is generated and passed directly …

CineTrans: Learning to Generate Videos with Cinematic Transitions via Masked Diffusion Models
Xiaoxue Wu, Bingjie Gao, Yu Qiao, Yaohui Wang, Xinyuan Chen
https://arxiv.org/abs/2508.11484

CineTrans: Learning to Generate Videos with Cinematic Transitions via Masked Diffusion Models
Despite significant advances in video synthesis, research into multi-shot video generation remains in its infancy. Even with scaled-up models and massive datasets, the shot transition capabilities remain rudimentary and unstable, largely confining generated videos to single-shot sequences. In this work, we introduce CineTrans, a novel framework for generating coherent multi-shot videos with cinematic, film-style transitions. To facilitate insights into the film editing style, we construct a mul…

A Comparative Study of Decoding Strategies in Medical Text Generation
Oriana Presacan, Alireza Nik, Vajira Thambawita, Bogdan Ionescu, Michael Riegler
https://arxiv.org/abs/2508.13580

A Comparative Study of Decoding Strategies in Medical Text Generation
Large Language Models (LLMs) rely on various decoding strategies to generate text, and these choices can significantly affect output quality. In healthcare, where accuracy is critical, the impact of decoding strategies remains underexplored. We investigate this effect in five open-ended medical tasks, including translation, summarization, question answering, dialogue, and image captioning, evaluating 11 decoding strategies with medically specialized and general-purpose LLMs of different sizes. …

MGT-Prism: Enhancing Domain Generalization for Machine-Generated Text Detection via Spectral Alignment
Shengchao Liu, Xiaoming Liu, Chengzhengxu Li, Zhaohan Zhang, Guoxin Ma, Yu Lan, Shuai Xiao
https://arxiv.org/abs/2508.13768

MGT-Prism: Enhancing Domain Generalization for Machine-Generated Text Detection via Spectral Alignment
Large Language Models have shown growing ability to generate fluent and coherent texts that are highly similar to the writing style of humans. Current detectors for Machine-Generated Text (MGT) perform well when they are trained and tested in the same domain but generalize poorly to unseen domains, due to domain shift between data from different sources. In this work, we propose MGT-Prism, an MGT detection method from the perspective of the frequency domain for better domain generalization. Our…

Diversity-enhanced Collaborative Mamba for Semi-supervised Medical Image Segmentation
Shumeng Li, Jian Zhang, Lei Qi, Luping Zhou, Yinghuan Shi, Yang Gao
https://arxiv.org/abs/2508.13712

Diversity-enhanced Collaborative Mamba for Semi-supervised Medical Image Segmentation
Acquiring high-quality annotated data for medical image segmentation is tedious and costly. Semi-supervised segmentation techniques alleviate this burden by leveraging unlabeled data to generate pseudo labels. Recently, advanced state space models, represented by Mamba, have shown efficient handling of long-range dependencies. This drives us to explore their potential in semi-supervised medical image segmentation. In this paper, we propose a novel Diversity-enhanced Collaborative Mamba framewor…