Tootfinder

You Are What You Train: Effects of Data Composition on Training Context-aware Machine Translation Models
Pawe{\l} M\k{a}ka, Yusuf Can Semerci, Jan Scholtes, Gerasimos Spanakis
https://arxiv.org/abs/2509.14031

You Are What You Train: Effects of Data Composition on Training Context-aware Machine Translation Models
Achieving human-level translations requires leveraging context to ensure coherence and handle complex phenomena like pronoun disambiguation. Sparsity of contextually rich examples in the standard training data has been hypothesized as the reason for the difficulty of context utilization. In this work, we systematically validate this claim in both single- and multilingual settings by constructing training datasets with a controlled proportions of contextually relevant examples. We demonstrate a …

Are Prompts All You Need? Evaluating Prompt-Based Large Language Models (LLM)s for Software Requirements Classification
Manal Binkhonain, Reem Alfayaz
https://arxiv.org/abs/2509.13868

Are Prompts All You Need? Evaluating Prompt-Based Large Language Models (LLM)s for Software Requirements Classification
Requirements classification assigns natural language requirements to predefined classes, such as functional and non functional. Accurate classification reduces risk and improves software quality. Most existing models rely on supervised learning, which needs large labeled data that are costly, slow to create, and domain dependent; they also generalize poorly and often require retraining for each task. This study tests whether prompt based large language models can reduce data needs. We benchmark…

Invitation Is All You Need! Promptware Attacks Against LLM-Powered Assistants in Production Are Practical and Dangerous
Ben Nassi, Stav Cohen, Or Yair
https://arxiv.org/abs/2508.12175

Invitation Is All You Need! Promptware Attacks Against LLM-Powered Assistants in Production Are Practical and Dangerous
The growing integration of LLMs into applications has introduced new security risks, notably known as Promptware - maliciously engineered prompts designed to manipulate LLMs to compromise the CIA triad of these applications. While prior research warned about a potential shift in the threat landscape for LLM-powered applications, the risk posed by Promptware is frequently perceived as low. In this paper, we investigate the risk Promptware poses to users of Gemini-powered assistants (web applicat…

Not Even Nice Work If You Can Get It; A Longitudinal Study of Uber's Algorithmic Pay and Pricing
Reuben Binns, Jake Stein, Siddhartha Datta, Max Van Kleek, Nigel Shadbolt
https://arxiv.org/abs/2506.15278

Not Even Nice Work If You Can Get It; A Longitudinal Study of Uber's Algorithmic Pay and Pricing
Ride-sharing platforms like Uber market themselves as enabling `flexibility' for their workforce, meaning that drivers are expected to anticipate when and where the algorithm will allocate them jobs, and how well remunerated those jobs will be. In this work we describe our process of participatory action research with drivers and trade union organisers, culminating in a participatory audit of Uber's algorithmic pay and work allocation, before and after the introduction of dynamic pricing. Throu…

Spacetime duality between sequential and measurement-feedback circuits
Tsung-Cheng Lu, Sarang Gopalakrishnan, Yizhi You
https://arxiv.org/abs/2507.12523 ht…

Spacetime duality between sequential and measurement-feedback circuits
Two prevalent approaches for preparing long-range entangled quantum states are (i) linear-depth sequential unitary (SU) circuits, which apply local unitary gates sequentially, and (ii) constant-depth measurement-feedback (MF) circuits, which employ mid-circuit measurements and conditional feedback based on measurement outcomes. Here, we establish that a broad class of SU and MF circuits are dual to each other under a spacetime rotation. We investigate this spacetime duality in the preparation o…

"What do you expect? You're part of the internet": Analyzing Celebrities' Experiences as Usees of Deepfake Technology
John Twomey, Sarah Foley, Sarah Robinson, Michael Quayle, Matthew Peter Aylett, Conor Linehan, Gillian Murphy
https://arxiv.org/abs/2507.13065

"What do you expect? You're part of the internet": Analyzing Celebrities' Experiences as Usees of Deepfake Technology
Deepfake technology is often used to create non-consensual synthetic intimate imagery (NSII), mainly of celebrity women. Through Critical Discursive Psychological analysis we ask; i) how celebrities construct being targeted by deepfakes and ii) how they navigate infrastructural and social obstacles when seeking recourse. In this paper, we adopt Baumers concept of Usees (stakeholders who are non-consenting, unaware and directly targeted by technology), to understand public statements made by eig…

RadarQA: Multi-modal Quality Analysis of Weather Radar Forecasts
Xuming He, Zhiyuan You, Junchao Gong, Couhua Liu, Xiaoyu Yue, Peiqin Zhuang, Wenlong Zhang, Lei Bai
https://arxiv.org/abs/2508.12291

RadarQA: Multi-modal Quality Analysis of Weather Radar Forecasts
Quality analysis of weather forecasts is an essential topic in meteorology. Although traditional score-based evaluation metrics can quantify certain forecast errors, they are still far from meteorological experts in terms of descriptive capability, interpretability, and understanding of dynamic evolution. With the rapid development of Multi-modal Large Language Models (MLLMs), these models become potential tools to overcome the above challenges. In this work, we introduce an MLLM-based weather …

From LLMs to MLLMs to Agents: A Survey of Emerging Paradigms in Jailbreak Attacks and Defenses within LLM Ecosystem
Yanxu Mao, Tiehan Cui, Peipei Liu, Datao You, Hongsong Zhu
https://arxiv.org/abs/2506.15170

From LLMs to MLLMs to Agents: A Survey of Emerging Paradigms in Jailbreak Attacks and Defenses within LLM Ecosystem
Large language models (LLMs) are rapidly evolving from single-modal systems to multimodal LLMs and intelligent agents, significantly expanding their capabilities while introducing increasingly severe security risks. This paper presents a systematic survey of the growing complexity of jailbreak attacks and corresponding defense mechanisms within the expanding LLM ecosystem. We first trace the developmental trajectory from LLMs to MLLMs and Agents, highlighting the core security challenges emergi…

Near-field optical mode engineering-enabled freeform nonlocal metasurfaces
Zhongjun Jiang, Tianxiang Dai, Shuwei Guo, Soyaib Sohag, Yixuan Shao, Chenkai Mao, Andrea Al\`u, Jonathan A. Fan, You Zhou
https://arxiv.org/abs/2506.15495

Near-field optical mode engineering-enabled freeform nonlocal metasurfaces
Nanophotonic technologies inherently rely on tailoring light-matter interactions through the excitation and interference of deeply confined optical resonances. However, existing concepts in optical mode engineering remain heuristic and are challenging to extend towards complex and multi-functional resonant phenomena. Here, we introduce an inverse design framework that optimizes near-field distributions, ideally suited to tailor Mie-type modes within dielectric nanophotonic structures, and we de…

Using KL-Divergence to Focus Frequency Information in Low-Light Image Enhancement
Yan Xingyang, Huang Xiaohong, Zhang Zhao, You Tian, Xu Ziheng
https://arxiv.org/abs/2509.13083 …

Using KL-Divergence to Focus Frequency Information in Low-Light Image Enhancement
In the Fourier domain, luminance information is primarily encoded in the amplitude spectrum, while spatial structures are captured in the phase components. The traditional Fourier Frequency information fitting employs pixel-wise loss functions, which tend to focus excessively on local information and may lead to global information loss. In this paper, we present LLFDisc, a U-shaped deep enhancement network that integrates cross-attention and gating mechanisms tailored for frequency-aware enhanc…

Robust and efficient estimation of global quantum properties under realistic noise
Qingyue Zhang, Dayue Qin, Zhou You, Feng Xu, Jens Eisert, You Zhou
https://arxiv.org/abs/2507.13237

Robust and efficient estimation of global quantum properties under realistic noise
Measuring global quantum properties -- such as the fidelity to complex multipartite states -- is both an essential and experimentally challenging task. Classical shadow estimation offers favorable sample complexity, but typically relies on many-qubit circuits that are difficult to realize on current platforms. We propose the robust phase shadow scheme, a measurement framework based on random circuits with controlled-Z as the unique entangling gate type, tailored to architectures such as trapped…

3D Wavelet Latent Diffusion Model for Whole-Body MR-to-CT Modality Translation
Jiaxu Zheng, Meiman He, Xuhui Tang, Xiong Wang, Tuoyu Cao, Tianyi Zeng, Lichi Zhang, Chenyu You
https://arxiv.org/abs/2507.11557

3D Wavelet Latent Diffusion Model for Whole-Body MR-to-CT Modality Translation
Magnetic Resonance (MR) imaging plays an essential role in contemporary clinical diagnostics. It is increasingly integrated into advanced therapeutic workflows, such as hybrid Positron Emission Tomography/Magnetic Resonance (PET/MR) imaging and MR-only radiation therapy. These integrated approaches are critically dependent on accurate estimation of radiation attenuation, which is typically facilitated by synthesizing Computed Tomography (CT) images from MR scans to generate attenuation maps. Ho…

Stochastic restarting with multiple restart conditions
Johannes Aspman, Daniel Mastropietro, Jakub Marecek
https://arxiv.org/abs/2509.11809 https://arxiv.o…

Stochastic restarting with multiple restart conditions
We consider the mean first passage time (MFPT) for a diffusive particle in a potential landscape with the extra condition that the particle is reset to its original position with some rate r. We study non-smooth and non-convex potentials and focus on the case where the restart rate depends on the space coordinate. There, we show that it is beneficial to restart at a lower rate once you are closer to your intended target.

Satellites are closer than you think: A near field MIMO approach for Ground stations
Rohith Reddy Vennam, Luke Wilson, Ish Kumar Jain, Dinesh Bharadia
https://arxiv.org/abs/2508.09374

Satellites are closer than you think: A near field MIMO approach for Ground stations
The rapid growth of low Earth orbit (LEO) satellite constellations has revolutionized broadband access, earth observation, and direct-to-device connectivity. However, the expansion of ground station infrastructure has not kept pace, creating a critical bottleneck in satellite-to-ground backhaul capacity. Traditional parabolic dish antennas, though effective for geostationary (GEO) satellites, are ill-suited for dense, fastmoving LEO networks due to mechanical steering delays and their inability…

“The users who choose Cursor are hardcore vibe addicts. They are tech incompetents who somehow BSed their way into a developer job. They cannot code without a vibe coding bot.”
I see no lie.
https://pivot-to-ai.com/2025/07/09/cursor-t…

Cursor tries setting less money on fire — AI vibe coders outraged
Anysphere is the startup that produces Cursor, your sort-of dependable vibe coding buddy. You tell Cursor what you’d like and it spits out a complete function! This leads to some spectacular vibe c…

Flink App: the only way to stop us forcing ads down your throat is by going outside the app to the system settings and blocking us from there.
#Flink

Real-time vacuum-state quantum random number generator on a chip
Guan-Ru Qiao, Bing Bai, Zi-Xuan Weng, Han-Shen Chen, Wei Zheng, Zhi-Yuan Zheng, You-Qi Nie, Jun Zhang, Jian-Wei Pan
https://arxiv.org/abs/2509.13105

Real-time vacuum-state quantum random number generator on a chip
Quantum random number generators (QRNGs) produce true random numbers, which are guaranteed by the fundamental principles of quantum physics. Miniaturization of QRNGs is crucial for a wide range of communication and cryptography applications. Here, we first report a fully functional QRNG chip based on vacuum-state fluctuations, with dimensions of 16.6 mm x 7.8 mm. The quantum entropy source, which is achieved via hybrid photonic integration with a SiO2 waveguide, generates raw quantum random num…

Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations
Marco De Nadai, Andreas Damianou, Mounia Lalmas
https://arxiv.org/abs/2508.09789 htt…

Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations
Existing video recommender systems rely primarily on user-defined metadata or on low-level visual and acoustic signals extracted by specialised encoders. These low-level features describe what appears on the screen but miss deeper semantics such as intent, humour, and world knowledge that make clips resonate with viewers. For example, is a 30-second clip simply a singer on a rooftop, or an ironic parody filmed amid the fairy chimneys of Cappadocia, Turkey? Such distinctions are critical to pers…

Modeling Code: Is Text All You Need?
Daniel Nichols, Konstantinos Parasyris, Harshitha Menon, Brian R. Bartoldson, Giorgis Georgakoudis, Tal Ben-Nun, Abhinav Bhatele
https://arxiv.org/abs/2507.11467

Modeling Code: Is Text All You Need?
Code LLMs have become extremely popular recently for modeling source code across a variety of tasks, such as generation, translation, and summarization. However, transformer-based models are limited in their capabilities to reason through structured, analytical properties of code, such as control and data flow. Previous work has explored the modeling of these properties with structured data and graph neural networks. However, these approaches lack the generative capabilities and scale of modern…

Functional Groups are All you Need for Chemically Interpretable Molecular Property Prediction
Roshan Balaji, Joe Bobby, Nirav Pravinbhai Bhatt
https://arxiv.org/abs/2509.09619 h…

Functional Groups are All you Need for Chemically Interpretable Molecular Property Prediction
Molecular property prediction using deep learning (DL) models has accelerated drug and materials discovery, but the resulting DL models often lack interpretability, hindering their adoption by chemists. This work proposes developing molecule representations using the concept of Functional Groups (FG) in chemistry. We introduce the Functional Group Representation (FGR) framework, a novel approach to encoding molecules based on their fundamental chemical substructures. Our method integrates two t…

You Are Not Alone: Designing Body Doubling for ADHD in Virtual Reality
Zinat Ara, Imtiaz Bin Rahim, Puqi Zhou, Liuchuan Yu, Behzad Esmaeili, Lap-Fai Yu, Sungsoo Ray Hong
https://arxiv.org/abs/2509.12153

You Are Not Alone: Designing Body Doubling for ADHD in Virtual Reality
Adults with Attention Deficit Hyperactivity Disorder (ADHD) experience challenges sustaining attention in the workplace. Body doubling, the concept of working alongside another person, has been proposed as a productivity aid for ADHD and other neurodivergent populations (NDs). However, prior work found no conclusive effectiveness and noted NDs' discomfort with social presence. This work investigates body doubling as an ADHD centered productivity strategy in construction tasks. In Study 1, we ex…

Disentangling Boltzmann brains, the time-asymmetry of memory, the H theorem, and the second law
David Wolpert, Carlo Rovelli, Jordan Scharnhorst
https://arxiv.org/abs/2507.10959

Disentangling Boltzmann brains, the time-asymmetry of memory, the H theorem, and the second law
Are you, with your perceptions, memories and observational data, a Boltzmann brain, i.e., a statistical fluctuation out of the thermal equilibrium of the universe? Arguments are given in the literature for and against taking this hypothesis seriously. Complicating these analyses have been the many subtle - and very often implicit - entanglements between related arguments that have been given for the past hypothesis, the second law, and even Bayesian inference of the reliability of experimental …

Accretion is All You Need: Black Hole Spin Alignment in Merger GW231123 Indicates Accretion Pathway
Imre Bartos, Zoltan Haiman
https://arxiv.org/abs/2508.08558 https://

Accretion is All You Need: Black Hole Spin Alignment in Merger GW231123 Indicates Accretion Pathway
GW231123 represents the most massive binary-black-hole merger detected to date, lying firmly within, or even above, the pair-instability mass gap. The component spins are both exceptionally high ($a_1 = 0.90^{+0.10}_{-0.19}$, $a_2 = 0.80^{+0.20}_{-0.51}$), which is difficult to explain with repeated mergers. Here we show that the black hole spin vectors are closely aligned with each other while significantly tilted relative to the binary's orbital angular momentum, pointing to a common accretio…

Optimizing the Variant Calling Pipeline Execution on Human Genomes Using GPU-Enabled Machines
Ajay Kumar, Praveen Rao, Peter Sanders
https://arxiv.org/abs/2509.09058 https://

Optimizing the Variant Calling Pipeline Execution on Human Genomes Using GPU-Enabled Machines
Variant calling is the first step in analyzing a human genome and aims to detect variants in an individual's genome compared to a reference genome. Due to the computationally-intensive nature of variant calling, genomic data are increasingly processed in cloud environments as large amounts of compute and storage resources can be acquired with the pay-as-you-go pricing model. In this paper, we address the problem of efficiently executing a variant calling pipeline for a workload of human genomes…

Relative mirror symmetry, theta functions and the Gamma conjecture
Fenglong You
https://arxiv.org/abs/2508.06750 https://arxiv.org/pdf/2508.06750

Relative mirror symmetry, theta functions and the Gamma conjecture
Let $X$ be a Fano variety, and $D\subset X$ be an snc anticanonical divisor. We study relative mirror symmetry for the log Calabi--Yau pair $(X,D)$. (1) We prove a relative mirror theorem for snc pairs without assuming the divisors are nef. (2) We study theta functions associated with the pair $(X,D)$. (3) We introduce functions on the mirror that are obtained from the higher-degree part of the big relative quantum cohomology. As an application, we use these new ingredients in relative mirror s…

Spectral Bottleneck in Deep Neural Networks: Noise is All You Need
Hemanth Chandravamsi, Dhanush V. Shenoy, Itay Zinn, Shimon Pisnoy, Steven H. Frankel
https://arxiv.org/abs/2509.09719

Spectral Bottleneck in Deep Neural Networks: Noise is All You Need
Deep neural networks are known to exhibit a spectral learning bias, wherein low-frequency components are learned early in training, while high-frequency modes emerge more gradually in later epochs. However, when the target signal lacks low-frequency components and is dominated by broadband high frequencies, training suffers from a 'spectral bottleneck', and the model fails to reconstruct the entire signal, including the frequency components that lie within the network's representational capacit…

Here is a poll about #GenAI since that's all we're talking about at the moment:
Do you believe that you can detect AI-generated text?
If so, what are your tips to detect it? I found this article which has a few suggestions :

5 Easy Ways To Tell If Written Content Came From Generative AI
Spread of generative AI content online since ChatGPT 3.5 launched in November 2022 is up 8,362%. Here's how to tell if content was written by an AI bot or not.

Are you In or Out (of gallery)? Wisdom from the Same-Identity Crowd
Aman Bhatta, Maria Dhakal, Michael C. King, Kevin W. Bowyer
https://arxiv.org/abs/2508.06357 https://

Are you In or Out (of gallery)? Wisdom from the Same-Identity Crowd
A central problem in one-to-many facial identification is that the person in the probe image may or may not have enrolled image(s) in the gallery; that is, may be In-gallery or Out-of-gallery. Past approaches to detect when a rank-one result is Out-of-gallery have mostly focused on finding a suitable threshold on the similarity score. We take a new approach, using the additional enrolled images of the identity with the rank-one result to predict if the rank-one result is In-gallery / Out-of-gal…

Not All Preferences are What You Need for Post-Training: Selective Alignment Strategy for Preference Optimization
Zhijin Dong
https://arxiv.org/abs/2507.07725

Not All Preferences are What You Need for Post-Training: Selective Alignment Strategy for Preference Optimization
Post-training alignment of large language models (LLMs) is a critical challenge, as not all tokens contribute equally to model performance. This paper introduces a selective alignment strategy that prioritizes high-impact tokens within preference pairs, leveraging token-level log-probability differences between the current policy and a reference model. By focusing on these informative tokens, our approach reduces computational overhead and enhances alignment fidelity. We further explore the rol…

You May Use the Same Channel Knowledge Map for Environment-Aware NLoS Sensing and Communication
Di Wu, Zhuoyin Dai, Yong Zeng
https://arxiv.org/abs/2507.03589

You May Use the Same Channel Knowledge Map for Environment-Aware NLoS Sensing and Communication
As one of the key usage scenarios for the sixth generation (6G) wireless networks, integrated sensing and communication (ISAC) provides an efficient framework to achieve simultaneous wireless sensing and communication. However, traditional wireless sensing techniques mainly rely on the line-of-sight (LoS) assumptions, i.e., the sensing targets are directly visible to both the sensing transmitter and receiver. This hinders ISAC systems to be applied in complex environments such as the urban low-…

Quantum noncommutative ABJM field theory: four- and six-point functions
Carmelo P. Martin, Josip Trampetic, Jiangyang You
https://arxiv.org/abs/2507.04823 …

Quantum noncommutative ABJM field theory: four- and six-point functions
Following our previous paper Quantum noncommutative ABJM theory: first steps, JHEP {\bf 1804} (2018) 070), in this article we investigate one-loop 1PI four-, and six-point functions by using the component formalism in the Landau gauge and show that they are UV finite and have well-defined $(θ^{μν}\rightarrow 0)$ limit. Those results also hold for all one-loop functions which are UV finite by power counting. In summary, taking into account results from previous paper, JHEP {\bf 1804} (2018) 0…

You Share Beliefs, I Adapt: Progressive Heterogeneous Collaborative Perception
Hao Si, Ehsan Javanmardi, Manabu Tsukada
https://arxiv.org/abs/2509.09310 https://

You Share Beliefs, I Adapt: Progressive Heterogeneous Collaborative Perception
Collaborative perception enables vehicles to overcome individual perception limitations by sharing information, allowing them to see further and through occlusions. In real-world scenarios, models on different vehicles are often heterogeneous due to manufacturer variations. Existing methods for heterogeneous collaborative perception address this challenge by fine-tuning adapters or the entire network to bridge the domain gap. However, these methods are impractical in real-world applications, as…

What Were You Thinking? An LLM-Driven Large-Scale Study of Refactoring Motivations in Open-Source Projects
Mikel Robredo, Matteo Esposito, Fabio Palomba, Rafael Pe\~naloza, Valentina Lenarduzzi
https://arxiv.org/abs/2509.07763

What Were You Thinking? An LLM-Driven Large-Scale Study of Refactoring Motivations in Open-Source Projects
Context. Code refactoring improves software quality without changing external behavior. Despite its advantages, its benefits are hindered by the considerable cost of time, resources, and continuous effort it demands. Aim. Understanding why developers refactor, and which metrics capture these motivations, may support wider and more effective use of refactoring in practice. Method. We performed a large-scale empirical study to analyze developers refactoring activity, leveraging Large Language Mod…

$X(3872)$ and hidden charmed tetraquarks
You-You Lin, Ji-Ying Wang, Ailin Zhang
https://arxiv.org/abs/2506.23760 https://arxiv.org/pd…

$X(3872)$ and hidden charmed tetraquarks
In a constituent quark model, a hidden charmed tetraquark is assumed consisting of a $cq$ diquark and an $\bar c\bar q$ antidiquark or vice versa. The Semay-Silvestre-Brac potentials are employed to calculate the masses of $cq$ (q=u, d) diquarks. The mass of the $cq$ diquark or $\bar c\bar q$ antidiquark with spin-$0$ is predicted with $\sim 2175$ MeV, and the spin-$1$ one is predicted with $\sim 2220$ MeV. The masses of hidden charmed tetraquarks from $1S$ to $2P$ excitations are systemically …

A Functional Data Model and Query Language is All You Need
Jens Dittrich
https://arxiv.org/abs/2507.20671 https://arxiv.org/pdf/2507.20671

A Functional Data Model and Query Language is All You Need
We propose the vision of a functional data model (FDM) and an associated functional query language (FQL). Our proposal has far-reaching consequences: we show a path to come up with a modern QL that solves (almost if not) all problems of SQL (NULL-values, impedance mismatch, SQL injection, missing querying capabilities for updates, etc.). FDM and FQL are much more expressive than the relational model and SQL. In addition, in contrast to SQL, FQL integrates smoothly into existing programming lang…

Replaced article(s) found for econ.EM. https://arxiv.org/list/econ.EM/new
[1/1]:
- Nickell Bias in Panel Local Projection: Financial Crises Are Worse Than You Think
Ziwei Mei, Liugang Sheng, Zhentao Shi

Everything You Need to Know About CS Education: Open Results from a Survey of More Than 18,000 Participants
Katsiaryna Dzialets, Aleksandra Makeeva, Ilya Vlasov, Anna Potriasaeva, Aleksei Rostovskii, Yaroslav Golubev, Anastasiia Birillo
https://arxiv.org/abs/2508.05286

Everything You Need to Know About CS Education: Open Results from a Survey of More Than 18,000 Participants
Computer science education is a dynamic field with many aspects that influence the learner's path. While these aspects are usually studied in depth separately, it is also important to carry out broader large-scale studies that touch on many topics, because they allow us to put different results into each other's perspective. Past large-scale surveys have provided valuable insights, however, the emergence of new trends (e.g., AI), new learning formats (e.g., in-IDE learning), and the increasing …

I can't imagine OpenAI is going to Trumpify GPT. I'm pretty sure they've spent more on instruction tuning data than they have on model training.
Even if they wanted to throw all that out and start again, you'd need to build a coherent image of what the bot should do in any given situation. The MAGA worldview simply doesn't have the required coherence.

Tech elites are turning AI into ChatGPTrump
The administration's new AI plan gives Silicon Valley everything it wants — for a price.

A Little Clairvoyance Is All You Need
Anupam Gupta, Haim Kaplan, Alexander Lindermayr, Jens Schl\"oter, Sorrachai Yingchareonthawornchai
https://arxiv.org/abs/2508.17759 ht…

A Little Clairvoyance Is All You Need
We revisit the classical problem of minimizing the total flow time of jobs on a single machine in the online setting where jobs arrive over time. It has long been known that the Shortest Remaining Processing Time (SRPT) algorithm is optimal (i.e., $1$-competitive) when the job sizes are known up-front [Schrage, 1968]. But in the non-clairvoyant setting where job sizes are revealed only when the job finishes, no algorithm can be constant-competitive [Motwani, Phillips, and Torng, 1994]. We con…

Approximation theorems for classifying stacks over number fields
Ajneet Dhillon
https://arxiv.org/abs/2507.13900 https://arxiv.org/pd…

Approximation theorems for classifying stacks over number fields
Approximation theorems for algebraic stacks over a number field $k$ are studied in this article. For G a connected linear algebraic group over a number field we prove strong approximation with Brauer-Manin obstruction for the classifying stack $BG$. This result answers a very concrete question, given $G$-torsors $P_v$ over $k_v$, where $v$ ranges over a finite number of places, when can you approximate the $P_v$ by a $G$-torsor $P$ defined over $k$.

Efficient LLM Inference: Bandwidth, Compute, Synchronization, and Capacity are all you need
Michael Davies, Neal Crago, Karthikeyan Sankaralingam, Christos Kozyrakis
https://arxiv.org/abs/2507.14397

Efficient LLM Inference: Bandwidth, Compute, Synchronization, and Capacity are all you need
This paper presents a limit study of transformer-based large language model (LLM) inference, focusing on the fundamental performance bottlenecks imposed by memory bandwidth, memory capacity, and synchronization overhead in distributed inference systems. We develop a hardware-agnostic performance model that abstracts away implementation details, enabling the analysis of a wide range of current and near-future hardware technologies. Our analysis spans from current HBM3 memory technology used in A…

Approximate Synchronization of Memristive Hopfield Neural Networks
Yuncheng You
https://arxiv.org/abs/2506.23279 https://arxiv.org/pd…

Approximate Synchronization of Memristive Hopfield Neural Networks
Asymptotic synchronization is one of the essential differences between artificial neural networks and biologically inspired neural networks due to mismatches from dynamical update of weight parameters and heterogeneous activations. In this paper a new concept of approximate synchronization is proposed and investigated for Hopfield neural networks coupled with nonlinear memristors. It is proved that global solution dynamics are robustly dissipative and a sharp ultimate bound is acquired. Through…

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects
Shulan Ruan, Rongwei Wang, Xuchen Shen, Huijie Liu, Baihui Xiao, Jun Shi, Kun Zhang, Zhenya Huang, Yu Liu, Enhong Chen, You He
https://arxiv.org/abs/2506.19769

A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects
Multi-sensor fusion perception (MSFP) is a key technology for embodied AI, which can serve a variety of downstream tasks (e.g., 3D object detection and semantic segmentation) and application scenarios (e.g., autonomous driving and swarm robotics). Recently, impressive achievements on AI-based MSFP methods have been reviewed in relevant surveys. However, we observe that the existing surveys have some limitations after a rigorous and detailed investigation. For one thing, most surveys are oriente…

Phone physics and the Gateway Arch: Fun with friends and physics at the AAPT Winter Meeting in St. Louis
David Marasco, Bree Barnett Dreyfuss
https://arxiv.org/abs/2506.22746

Phone physics and the Gateway Arch: Fun with friends and physics at the AAPT Winter Meeting in St. Louis
As a famous landmark and feat of engineering, the Gateway Arch was a popular destination at the 2025 AAPT Winter Meeting in St. Louis. The visit to the observation deck of the Gateway Arch is unique, climbing the steps after exiting the small tram capsules and seeing a floor that continues to slope upward assures that you are in fact at the very top. Everyone in our group excitedly took pictures, pointing out local features like the Dred Scott Courthouse. There were many selfies at the pinnacle…

A Scalable and Quantum-Accurate Foundation Model for Biomolecular Force Field via Linearly Tensorized Quadrangle Attention
Qun Su, Kai Zhu, Qiaolin Gou, Jintu Zhang, Renling Hu, Yurong Li, Yongze Wang, Hui Zhang, Ziyi You, Linlong Jiang, Yu Kang, Jike Wang, Chang-Yu Hsieh, Tingjun Hou
https://arxiv.org/abs/2507.00884

A Scalable and Quantum-Accurate Foundation Model for Biomolecular Force Field via Linearly Tensorized Quadrangle Attention
Accurate atomistic biomolecular simulations are vital for disease mechanism understanding, drug discovery, and biomaterial design, but existing simulation methods exhibit significant limitations. Classical force fields are efficient but lack accuracy for transition states and fine conformational details critical in many chemical and biological processes. Quantum Mechanics (QM) methods are highly accurate but computationally infeasible for large-scale or long-time simulations. AI-based force fie…

Think before you fit: parameter identifiability, sensitivity and uncertainty in systems biology models
Simon P. Preston, Richard D. Wilkinson, Richard H. Clayton, Mike J. Chappell, Gary R. Mirams
https://arxiv.org/abs/2508.18853

Think before you fit: parameter identifiability, sensitivity and uncertainty in systems biology models
Reliable predictions from systems biology models require knowing whether parameters can be estimated from available data, and with what certainty. Identifiability analysis reveals whether parameters are learnable in principle (structural identifiability) and in practice (practical identifiability). We introduce the core ideas using linear models, highlighting how experimental design and output sensitivity shape identifiability. In nonlinear models, identifiability can vary with parameter values…

CXLAimPod: CXL Memory is all you need in AI era
Yiwei Yang, Yusheng Zheng, Yiqi Chen, Zheng Liang, Kexin Chu, Zhe Zhou, Andi Quinn, Wei Zhang
https://arxiv.org/abs/2508.15980 ht…

CXLAimPod: CXL Memory is all you need in AI era
The proliferation of data-intensive applications, ranging from large language models to key-value stores, increasingly stresses memory systems with mixed read-write access patterns. Traditional half-duplex architectures such as DDR5 are ill-suited for such workloads, suffering bus turnaround penalties that reduce their effective bandwidth under mixed read-write patterns. Compute Express Link (CXL) offers a breakthrough with its full-duplex channels, yet this architectural potential remains unta…

Noninvertible symmetry and topological holography for modulated SPT in one dimension
Jintae Kim, Yizhi You, Jung Hoon Han
https://arxiv.org/abs/2507.02324 …

Noninvertible symmetry and topological holography for modulated SPT in one dimension
We examine noninvertible symmetry (NIS) in one-dimensional (1D) symmetry-protected topological (SPT) phases protected by dipolar and exponential-charge symmetries, which are two key examples of modulated SPT (MSPT). To set the stage, we first study NIS in the $\mathbb{Z}_N \times \mathbb{Z}_N$ cluster model, extending previous work on the $\mathbb{Z}_2 \times \mathbb{Z}_2$ case. For each symmetry type (charge, dipole, exponential), we explicitly construct the noninvertible Kramers-Wannier (KW) …

Higher-Order Neuromorphic Ising Machines -- Autoencoders and Fowler-Nordheim Annealers are all you need for Scalability
Faiek Ahsan, Saptarshi Maiti, Zihao Chen, Jakob Kaiser, Ankita Nandi, Madhuvanthi Srivatsav, Johannes Schemmel, Andreas G. Andreou, Jason Eshraghian, Chetan Singh Thakur, Shantanu Chakrabartty
https://arxiv.org…

Higher-Order Neuromorphic Ising Machines -- Autoencoders and Fowler-Nordheim Annealers are all you need for Scalability
We report a higher-order neuromorphic Ising machine that exhibits superior scalability compared to architectures based on quadratization, while also achieving state-of-the-art quality and reliability in solutions with competitive time-to-solution metrics. At the core of the proposed machine is an asynchronous autoencoder architecture that captures higher-order interactions by directly manipulating Ising clauses instead of Ising spins, thereby maintaining resource complexity independent of inter…

Daily fluctuations propagate damply through a black hole accretion disk
Han He, Yi Long, Bei You, Fu-Guo Xie, Zhen Yan, Andrzej A. Zdziarski, Sai-En Xu
https://arxiv.org/abs/2508.01384

Daily fluctuations propagate damply through a black hole accretion disk
Propagating fluctuations within accretion disks are known to induce multi-wavelength variability across diverse timescales. While these fluctuations have been widely invoked to explain rapid timing phenomena within the inner disk region in the frequency domain, observational signatures of outer-disk fluctuations propagating in the time domain remain sparse. Here, we present an analysis of observations by the Hard X-ray Modulation Telescope (HXMT) during the 2023 outburst of the newly discovered…

Dark sector search at BESIII
Zhijun Li, Zhengyun You
https://arxiv.org/abs/2507.22402 https://arxiv.org/pdf/2507.22402

Dark sector search at BESIII
The Standard Model has achieved significant success in particle physics; however, there are still some unresolved puzzles beyond it. This motivates the exploration of the dark sector. We present the recent dark sector search results from BESIII experiment, which include the search for axion-like particles in the $J/ψ$ data set, the search for muon-philic particles in muon radiation, the search for massless dark photons in $D^0\toωγ'$ and $D^0\toγγ'$, and the search for invisible decays of …

Not quite a piece of CHERI-cake: Are new digital security by design architectures usable?
Maysara Alhindi, Joseph Hallett
https://arxiv.org/abs/2506.23682 …

Not quite a piece of CHERI-cake: Are new digital security by design architectures usable?
A digital security-by-design computer architecture, like CHERI, lets you program without fear of buffer overflows or other memory safety errors, but CHERI also rewrites some of the assumptions about how C works and how fundamental types (such as pointers) are implemented in hardware. We conducted a usability study to examine how developers react to the changes required by CHERI when porting software to run on it. We find that developers struggle with CHERI's display of warnings and errors and a…

Your Model Is Unfair, Are You Even Aware? Inverse Relationship Between Comprehension and Trust in Explainability Visualizations of Biased ML Models
Zhanna Kaufman, Madeline Endres, Cindy Xiong Bearfield, Yuriy Brun
https://arxiv.org/abs/2508.00140

Your Model Is Unfair, Are You Even Aware? Inverse Relationship Between Comprehension and Trust in Explainability Visualizations of Biased ML Models
Systems relying on ML have become ubiquitous, but so has biased behavior within them. Research shows that bias significantly affects stakeholders' trust in systems and how they use them. Further, stakeholders of different backgrounds view and trust the same systems differently. Thus, how ML models' behavior is explained plays a key role in comprehension and trust. We survey explainability visualizations, creating a taxonomy of design characteristics. We conduct user studies to evaluate five sta…

Can Quantum Physics Help Fight Climate Change? A Thematic Analysis of Press Release Narratives
Maxwell Bernstein, Joanna Krajewski, Rachel Fisher
https://arxiv.org/abs/2507.13600 …

Can Quantum Physics Help Fight Climate Change? A Thematic Analysis of Press Release Narratives
The climate crisis demands innovative solutions and quantum technologies are considered by some a potential solution (Berger et al., 2021; Calvin et al., 2023). The United Nations declared 2025 the International Year of Quantum Science and Technology (United Nations, 2024), focusing on applications for quantum technologies. Quantum technologies, leveraging quantum mechanics, offer unprecedented computational power (Ajagekar & You, 2022) and sensitivity (Schleich et al., 2016) that could revolut…

Towards Operational Data Analytics Chatbots -- Virtual Knowledge Graph is All You Need
Junaid Ahmed Khan, Hiari Pizzini Cavagna, Andrea Proia, Andrea Bartolini
https://arxiv.org/abs/2506.22267

Towards Operational Data Analytics Chatbots -- Virtual Knowledge Graph is All You Need
With generative artificial intelligence challenging computational scientific computing, data centers are experiencing unprecedented growth in both scale and volume. As a result, computing efficiency has become more critical than ever. Operational Data Analytics (ODA) relies on the collection of data center telemetry to improve efficiency, but so far has been focusing on real-time telemetry data visualization and post-mortem analysis. However, with NoSQL databases now serving as the default stor…

BetaWeb: Towards a Blockchain-enabled Trustworthy Agentic Web
Zihan Guo, Yuanjian Zhou, Chenyi Wang, Linlin You, Minjie Bian, Weinan Zhang
https://arxiv.org/abs/2508.13787 https…

BetaWeb: Towards a Blockchain-enabled Trustworthy Agentic Web
The rapid development of large language models (LLMs) has significantly propelled the development of artificial intelligence (AI) agents, which are increasingly evolving into diverse autonomous entities, advancing the LLM-based multi-agent systems (LaMAS). However, current agentic ecosystems remain fragmented and closed. Establishing an interconnected and scalable paradigm for Agentic AI has become a critical prerequisite. Although Agentic Web proposes an open architecture to break the ecosyste…

IRanker: Towards Ranking Foundation Model
Tao Feng, Zhigang Hua, Zijie Lei, Yan Xie, Shuang Yang, Bo Long, Jiaxuan You
https://arxiv.org/abs/2506.21638 htt…

IRanker: Towards Ranking Foundation Model
Ranking tasks are ubiquitous, encompassing applications such as recommendation systems, LLM routing, and item re-ranking. We propose to unify these tasks using a single ranking foundation model (FM), as it eliminates the need for designing different models for each specific ranking task. However, unlike general supervision tasks in LLMs, ranking tasks do not have clear labels for supervision, posing great challenges to developing a ranking FM. To overcome these challenges, we propose IRanker, a…

Transition of blue-core helicon discharge
L. Chang, S. J. Zhang, J. T. Wu, Y. W. Zhang, C. Wang, Y. Peng, S. S. Gao, C. J. Sun, Q. Wang, C. F. Sang, S. C. Thakur, S. Isayama, S. J. You
https://arxiv.org/abs/2508.19662

Transition of blue-core helicon discharge
This study explores the transitional characteristics of blue-core helicon discharge, which to our knowledge was not particularly focused on before. Parameters are measured on recently built advanced linear plasma device, i.e. Multiple Plasma Simulation Linear Device (MPS-LD) by various diagnostics including Langmuir probe, optical emission spectrometer, and standard high-speed camera. It is found that the jump direction of electron density (from low level to high level) is opposite to that of e…

Inside you are many wolves: Using cognitive models to interpret value trade-offs in LLMs
Sonia K. Murthy, Rosie Zhao, Jennifer Hu, Sham Kakade, Markus Wulfmeier, Peng Qian, Tomer Ullman
https://arxiv.org/abs/2506.20666

Inside you are many wolves: Using cognitive models to interpret value trade-offs in LLMs
Navigating everyday social situations often requires juggling conflicting goals, such as conveying a harsh truth, maintaining trust, all while still being mindful of another person's feelings. These value trade-offs are an integral part of human decision-making and language use, however, current tools for interpreting such dynamic and multi-faceted notions of values in LLMs are limited. In cognitive science, so-called "cognitive models" provide formal accounts of these trade-offs in humans, by …

Repair Ingredients Are All You Need: Improving Large Language Model-Based Program Repair via Repair Ingredients Search
Jiayi Zhang, Kai Huang, Jian Zhang, Yang Liu, Chunyang Chen
https://arxiv.org/abs/2506.23100

Repair Ingredients Are All You Need: Improving Large Language Model-Based Program Repair via Repair Ingredients Search
Automated Program Repair (APR) techniques aim to automatically fix buggy programs. Among these, Large Language Model-based (LLM-based) approaches have shown great promise. Recent advances demonstrate that directly leveraging LLMs can achieve leading results. However, these techniques remain suboptimal in generating contextually relevant and accurate patches, as they often overlook repair ingredients crucial for practical program repair. In this paper, we propose ReinFix, a novel framework that …

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/6]:
- EchoMimicV3: 1.3B Parameters are All You Need for Unified Multi-Modal and Multi-Task Human Animation
Rang Meng, Yan Wang, Weipeng Wu, Ruobing Zheng, Yuming Li, Chenguang Ma

Coherent Two-State Oscillations in False Vacuum Decay Regimes
Peiyun Ge, Xiao Wang, Yu-Xin Chao, Rong Lv, Li You
https://arxiv.org/abs/2509.04272 https://a…

Coherent Two-State Oscillations in False Vacuum Decay Regimes
Coherent two-state oscillations are observed in numerical simulations of one-dimensional transverse- and longitudinal-field Ising model within the false vacuum decay regimes. Starting from the false vacuum state with all spins up, in moderate-sized systems with a small transverse field, we find conventional decay dynamics at resonance conditions $ h\approx 2J/n$ can change into coherent oscillations between the false vacuum and a symmetric resonant state, manifested by a sub-leading eigenstate …

🇺🇦 Auf #radioeins läuft...
Andy Lewis & Paul Weller:
🎵 Are You Trying To Be Lonely
#NowPlaying #AndyLewis #PaulWeller
#radioeins gespielten Titel als #Spotify Playliste: https://open.spotify.com/playlist/3hdH98B6uyXilhcWxCA6nv
Schon gewusst❓
Wenn Du einen Beitrag des Bots ⭐ FAVORISIERST, erfährt das nur der Bot - nicht Deine FollowerInnen.
Wenn Du einen Song weitererempfehlen möchtest, dann 🔁 TEILE ihn❗

Far-Field vs. Near-Field Propagation Channels: Key Differences and Impact on 6G XL-MIMO Performance Evaluation
Zihang Ding, Jianhua Zhang, Changsheng You, Pan Tang, Hongbo Xing, Zhiqiang Yuan, Jie Meng, Guangyi Liu
https://arxiv.org/abs/2506.23495

Far-Field vs. Near-Field Propagation Channels: Key Differences and Impact on 6G XL-MIMO Performance Evaluation
Extremely large-scale multiple-input multiple-output (XL-MIMO) is regarded as a promising technology for next-generation communication systems. However, this will expand the near-field (NF) range, rendering more users more likely to be located in the NF region. In this paper, we aim to answer two questions: What are the new characteristics of the NF channel? Is it necessary to develop new transciver techniques to maintain system performance within the NF region? To this end, we first review cur…

Towards Perception-Informed Latent HRTF Representations
You Zhang, Andrew Francl, Ruohan Gao, Paul Calamia, Zhiyao Duan, Ishwarya Ananthabhotla
https://arxiv.org/abs/2507.02815

Towards Perception-Informed Latent HRTF Representations
Personalized head-related transfer functions (HRTFs) are essential for ensuring a realistic auditory experience over headphones, because they take into account individual anatomical differences that affect listening. Most machine learning approaches to HRTF personalization rely on a learned low-dimensional latent space to generate or select custom HRTFs for a listener. However, these latent representations are typically learned in a manner that optimizes for spectral reconstruction but not for …

Are You Listening to Me? Fine-Tuning Chatbots for Empathetic Dialogue
Paulo Ricardo Knob, Leonardo Scholler, Juliano Rigatti, Soraia Raupp Musse
https://arxiv.org/abs/2507.02537

Are You Listening to Me? Fine-Tuning Chatbots for Empathetic Dialogue
Conversational agents have made significant progress since ELIZA, expanding their role across various domains, including healthcare, education, and customer service. As these agents become increasingly integrated into daily human interactions, the need for emotional intelligence, particularly empathetic listening, becomes increasingly essential. In this study, we explore how Large Language Models (LLMs) respond when tasked with generating emotionally rich interactions. Starting from a small dat…

Multibaryon states in the framework of an equivparticle model
Hao-Song You, Xinmei Zhu, Cheng-Jun Xia, Jialun Ping, Ren-Xin Xu
https://arxiv.org/abs/2507.20348 https://

Multibaryon states in the framework of an equivparticle model
Within the framework of an equivparticle model employing mean-field approximation, we investigate systematically the mass spectra of color-singlet $N$-quark configurations with $N = 3, 6, 9, 12, 15$, and 18, which are assumed to be spherically symmetric with quarks occupying the 1s$_{1/2}$ state, i.e., compact multibaryon states. At a given quark number $N$, these states collectively form a single irreducible representation under SU(6) symmetry. Our analysis yields comprehensive mass formulae t…

Performance Analysis of Spatiotemporal 2-D Polar Codes for Massive MIMO with MMSE Receivers
Yaqi Li, Xiaohu You, Jiamin Li, Chen Ji, Bin Sheng
https://arxiv.org/abs/2507.19986 h…

Performance Analysis of Spatiotemporal 2-D Polar Codes for Massive MIMO with MMSE Receivers
With the evolution from 5G to 6G, ultra-reliable low-latency communication (URLLC) faces increasingly stringent performance requirements. Lower latency constraints demand shorter channel coding lengths, which can severely degrade decoding performance. The massive multiple-input multiple-output (MIMO) system is considered a crucial technology to address this challenge due to its abundant spatial degrees of freedom (DoF). While polar codes are theoretically capacity-achieving in the limit of infi…

Clone What You Can't Steal: Black-Box LLM Replication via Logit Leakage and Distillation
Kanchon Gharami, Hansaka Aluvihare, Shafika Showkat Moni, Berker Pek\"oz
https://arxiv.org/abs/2509.00973

Clone What You Can't Steal: Black-Box LLM Replication via Logit Leakage and Distillation
Large Language Models (LLMs) are increasingly deployed in mission-critical systems, facilitating tasks such as satellite operations, command-and-control, military decision support, and cyber defense. Many of these systems are accessed through application programming interfaces (APIs). When such APIs lack robust access controls, they can expose full or top-k logits, creating a significant and often overlooked attack surface. Prior art has mainly focused on reconstructing the output projection la…

Transduction is All You Need for Structured Data Workflows
Alfio Gliozzo, Naweed Khan, Christodoulos Constantinides, Nandana Mihindukulasooriya, Nahuel Defosse, Junkyu Lee
https://arxiv.org/abs/2508.15610

Transduction is All You Need for Structured Data Workflows
This paper introduces Agentics, a modular framework for building agent-based systems capable of structured reasoning and compositional generalization over complex data. Designed with research and practical applications in mind, Agentics offers a novel perspective on working with data and AI workflows. In this framework, agents are abstracted from the logical flow and they are used internally to the data type to enable logical transduction among data. Agentics encourages AI developers to focus o…

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation
Ronan Docherty, Antonis Vamvakeros, Samuel J. Cooper
https://arxiv.org/abs/2508.21529

Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation
Feature foundation models - usually vision transformers - offer rich semantic descriptors of images, useful for downstream tasks such as (interactive) segmentation and object detection. For computational efficiency these descriptors are often patch-based, and so struggle to represent the fine features often present in micrographs; they also struggle with the large image sizes present in materials and biological image analysis. In this work, we train a convolutional neural network to upsample lo…

Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery
You Wu, Philip E. Bourne, Lei Xie
https://arxiv.org/abs/2507.19568 https://ar…

Programmable Virtual Humans Toward Human Physiologically-Based Drug Discovery
Artificial intelligence (AI) has sparked immense interest in drug discovery, but most current approaches only digitize existing high-throughput experiments. They remain constrained by conventional pipelines. As a result, they do not address the fundamental challenges of predicting drug effects in humans. Similarly, biomedical digital twins, largely grounded in real-world data and mechanistic models, are tailored for late-phase drug development and lack the resolution to model molecular interact…

Is It Really You? Exploring Biometric Verification Scenarios in Photorealistic Talking-Head Avatar Videos
Laura Pedrouzo-Rodriguez, Pedro Delgado-DeRobles, Luis F. Gomez, Ruben Tolosana, Ruben Vera-Rodriguez, Aythami Morales, Julian Fierrez
https://arxiv.org/abs/2508.00748

Is It Really You? Exploring Biometric Verification Scenarios in Photorealistic Talking-Head Avatar Videos
Photorealistic talking-head avatars are becoming increasingly common in virtual meetings, gaming, and social platforms. These avatars allow for more immersive communication, but they also introduce serious security risks. One emerging threat is impersonation: an attacker can steal a user's avatar-preserving their appearance and voice-making it nearly impossible to detect its fraudulent usage by sight or sound alone. In this paper, we explore the challenge of biometric verification in such avata…

When Life Gives You Samples: The Benefits of Scaling up Inference Compute for Multilingual LLMs
Ammar Khairi, Daniel D'souza, Ye Shen, Julia Kreutzer, Sara Hooker
https://arxiv.org/abs/2506.20544

When Life Gives You Samples: The Benefits of Scaling up Inference Compute for Multilingual LLMs
Recent advancements in large language models (LLMs) have shifted focus toward scaling inference-time compute, improving performance without retraining the model. A common approach is to sample multiple outputs in parallel, and select one of these as the final output. However, work to date has focused on English and a handful of domains such as math and code. In contrast, we are most interested in techniques that generalize across open-ended tasks, formally verifiable tasks, and across languages…

Transform Before You Query: A Privacy-Preserving Approach for Vector Retrieval with Embedding Space Alignment
Ruiqi He, Zekun Fei, Jiaqi Li, Xinyuan Zhu, Biao Yi, Siyi Lv, Weijie Liu, Zheli Liu
https://arxiv.org/abs/2507.18518

Transform Before You Query: A Privacy-Preserving Approach for Vector Retrieval with Embedding Space Alignment
Vector Database (VDB) can efficiently index and search high-dimensional vector embeddings from unstructured data, crucially enabling fast semantic similarity search essential for modern AI applications like generative AI and recommendation systems. Since current VDB service providers predominantly use proprietary black-box models, users are forced to expose raw query text to them via API in exchange for the vector retrieval services. Consequently, if query text involves confidential records fro…

If You Had to Pitch Your Ideal Software -- Evaluating Large Language Models to Support User Scenario Writing for User Experience Experts and Laypersons
Patrick Stadler, Christopher Lazik, Christopher Katins, Thomas Kosch
https://arxiv.org/abs/2506.23694

If You Had to Pitch Your Ideal Software -- Evaluating Large Language Models to Support User Scenario Writing for User Experience Experts and Laypersons
The process of requirements analysis requires an understanding of the end users of a system. Thus, expert stakeholders, such as User Experience (UX) designers, usually create various descriptions containing information about the users and their possible needs. In our paper, we investigate to what extent UX novices are able to write such descriptions into user scenarios. We conducted a user study with 60 participants consisting of 30 UX experts and 30 novices who were asked to write a user scena…

Realization of an untrusted intermediate relay architecture using a quantum dot single-photon source
Mi Zou, Yu-Ming He, Yizhi Huang, Jun-Yi Zhao, Bin-Chen Li, Yong-Peng Guo, Xing Ding, Mo-Chi Xu, Run-Ze Liu, Geng-Yan Zou, Zhen Ning, Xiang You, Hui Wang, Wen-Xin Pan, Hao-Tao Zhu, Ming-Yang Zheng, Xiu-Ping Xie, Dandan Qin, Xiao Jiang, Yong-Heng Huo, Qiang Zhang, Chao-Yang Lu, Xiongfeng Ma, Teng-Yun Chen, Jian-Wei Pan

Realization of an untrusted intermediate relay architecture using a quantum dot single-photon source
To fully exploit the potential of quantum technologies, quantum networks are needed to link different systems, significantly enhancing applications in computing, cryptography, and metrology. Central to these networks are quantum relays that can facilitate long-distance entanglement distribution and quantum communication. In this work, we present a modular and scalable quantum relay architecture using a high-quality single-photon source. The proposed network incorporates three untrusted intermed…

EEG-based Epileptic Prediction via a Two-stage Channel-aware Set Transformer Network
Ruifeng Zheng, Cong Chen, Shuang Wang, Yiming Liu, Lin You, Jindong Lu, Ruizhe Zhu, Guodao Zhang, Kejie Huang
https://arxiv.org/abs/2507.15364

EEG-based Epileptic Prediction via a Two-stage Channel-aware Set Transformer Network
Epilepsy is a chronic, noncommunicable brain disorder, and sudden seizure onsets can significantly impact patients' quality of life and health. However, wearable seizure-predicting devices are still limited, partly due to the bulky size of EEG-collecting devices. To relieve the problem, we proposed a novel two-stage channel-aware Set Transformer Network that could perform seizure prediction with fewer EEG channel sensors. We also tested a seizure-independent division method which could prevent …

Temporal evolution of quasi-periodic oscillations in an accreting black hole Swift J1727.8-1613: coevolution of the disk-corona during the state transition
Sai-En Xu, Bei You, Yi Long, Han He
https://arxiv.org/abs/2506.21131

Temporal evolution of quasi-periodic oscillations in an accreting black hole Swift J1727.8-1613: coevolution of the disk-corona during the state transition
Low-frequency quasi-periodic oscillations (QPOs) are commonly observed in black hole X-ray binaries, and their frequency has been found to correlate with various energy spectral properties. In this work, we present a detailed timing analysis of Swift J1727.8-1613, revealing a novel two-branch correlation between the QPO frequency and the observed disk emission, which differs from previous findings of a single correlation. Specifically, at QPO frequencies below 3 Hz, the QPO frequency is negativ…

DeOcc-1-to-3: 3D De-Occlusion from a Single Image via Self-Supervised Multi-View Diffusion
Yansong Qu, Shaohui Dai, Xinyang Li, Yuze Wang, You Shen, Liujuan Cao, Rongrong Ji
https://arxiv.org/abs/2506.21544

DeOcc-1-to-3: 3D De-Occlusion from a Single Image via Self-Supervised Multi-View Diffusion
Reconstructing 3D objects from a single image is a long-standing challenge, especially under real-world occlusions. While recent diffusion-based view synthesis models can generate consistent novel views from a single RGB image, they generally assume fully visible inputs and fail when parts of the object are occluded. This leads to inconsistent views and degraded 3D reconstruction quality. To overcome this limitation, we propose an end-to-end framework for occlusion-aware multi-view generation. …

Language Models Change Facts Based on the Way You Talk
Matthew Kearney, Reuben Binns, Yarin Gal
https://arxiv.org/abs/2507.14238 https://

Language Models Change Facts Based on the Way You Talk
Large language models (LLMs) are increasingly being used in user-facing applications, from providing medical consultations to job interview advice. Recent research suggests that these models are becoming increasingly proficient at inferring identity information about the author of a piece of text from linguistic patterns as subtle as the choice of a few words. However, little is known about how LLMs use this information in their decision-making in real-world applications. We perform the first c…

Pinching-Antenna-based Communications: Spectral Efficiency Analysis and Deployment Strategies
Mengyu Qian, Xidong Mu, Li You, Michail Matthaiou
https://arxiv.org/abs/2507.14831

Pinching-Antenna-based Communications: Spectral Efficiency Analysis and Deployment Strategies
A multiple-waveguide pinching-antenna (PA)-based multi-user communication system is investigated. With a given number of PAs, two deployment strategies are considered, namely the centralized PA deployment, where all PAs are switched between waveguides to serve users in a time-division manner to avail of beamforming gain, and the distributed PA deployment, where a single PA is deployed on each waveguide to simultaneously serve multiple users by leveraging the multiplexing gain. The spectral effi…

You Don't Know Until You Click:Automated GUI Testing for Production-Ready Software Evaluation
Yutong Bian, Xianhao Lin, Yupeng Xie, Tianyang Liu, Mingchen Zhuge, Siyuan Lu, Haoming Tang, Jinlin Wang, Jiayi Zhang, Jiaqi Chen, Xiangru Tang, Yongxin Ni, Sirui Hong, Chenglin Wu
https://arxiv.org/abs/2508.14104

You Don't Know Until You Click:Automated GUI Testing for Production-Ready Software Evaluation
Large Language Models (LLMs) and code agents in software development are rapidly evolving from generating isolated code snippets to producing full-fledged software applications with graphical interfaces, interactive logic, and dynamic behaviors. However, current benchmarks fall short in evaluating such production-ready software, as they often rely on static checks or binary pass/fail scripts, failing to capture the interactive behaviors and runtime dynamics that define real-world usability - qu…

You Don't Bring Me Flowers: Mitigating Unwanted Recommendations Through Conformal Risk Control
Giovanni De Toni, Erasmo Purificato, Emilia G\'omez, Bruno Lepri, Andrea Passerini, Cristian Consonni
https://arxiv.org/abs/2507.16829

You Don't Bring Me Flowers: Mitigating Unwanted Recommendations Through Conformal Risk Control
Recommenders are significantly shaping online information consumption. While effective at personalizing content, these systems increasingly face criticism for propagating irrelevant, unwanted, and even harmful recommendations. Such content degrades user satisfaction and contributes to significant societal issues, including misinformation, radicalization, and erosion of user trust. Although platforms offer mechanisms to mitigate exposure to undesired content, these mechanisms are often insuffici…

From BERT to LLMs: Comparing and Understanding Chinese Classifier Prediction in Language Models
ZiqiZhang, Jianfei Ma, Emmanuele Chersoni, Jieshun You, Zhaoxin Feng
https://arxiv.org/abs/2508.18253

From BERT to LLMs: Comparing and Understanding Chinese Classifier Prediction in Language Models
Classifiers are an important and defining feature of the Chinese language, and their correct prediction is key to numerous educational applications. Yet, whether the most popular Large Language Models (LLMs) possess proper knowledge the Chinese classifiers is an issue that has largely remain unexplored in the Natural Language Processing (NLP) literature. To address such a question, we employ various masking strategies to evaluate the LLMs' intrinsic ability, the contribution of different sent…

Promoting Online Safety by Simulating Unsafe Conversations with LLMs
Owen Hoffman, Kangze Peng, Zehua You, Sajid Kamal, Sukrit Venkatagiri
https://arxiv.org/abs/2507.22267 https…

Promoting Online Safety by Simulating Unsafe Conversations with LLMs
Generative AI, including large language models (LLMs) have the potential -- and already are being used -- to increase the speed, scale, and types of unsafe conversations online. LLMs lower the barrier for entry for bad actors to create unsafe conversations in particular because of their ability to generate persuasive and human-like text. In our current work, we explore ways to promote online safety by teaching people about unsafe conversations that can occur online with and without LLMs. We bui…

Approximate k-uniform states: definition, construction and applications
Kaiyi Guo, Fei Shi, You Zhou, Qi Zhao
https://arxiv.org/abs/2507.19018 https://arxi…

Approximate k-uniform states: definition, construction and applications
$k$-Uniform states are fundamental to quantum information and computing, with applications in multipartite entanglement and quantum error-correcting codes (QECCs). Prior work has primarily focused on constructing exact $k$-uniform states or proving their nonexistence. However, due to inevitable theoretical approximations and experimental imperfections, generating exact $k$-uniform states is neither feasible nor necessary in practice. In this work, we initiate the study of approximate $k$-unifor…

RoofSeg: An edge-aware transformer-based network for end-to-end roof plane segmentation
Siyuan You, Guozheng Xu, Pengwei Zhou, Qiwen Jin, Jian Yao, Li Li
https://arxiv.org/abs/2508.19003

RoofSeg: An edge-aware transformer-based network for end-to-end roof plane segmentation
Roof plane segmentation is one of the key procedures for reconstructing three-dimensional (3D) building models at levels of detail (LoD) 2 and 3 from airborne light detection and ranging (LiDAR) point clouds. The majority of current approaches for roof plane segmentation rely on the manually designed or learned features followed by some specifically designed geometric clustering strategies. Because the learned features are more powerful than the manually designed features, the deep learning-bas…

Demonstration of Efficient Predictive Surrogates for Large-scale Quantum Processors
Wei-You Liao, Yuxuan Du, Xinbiao Wang, Tian-Ci Tian, Yong Luo, Bo Du, Dacheng Tao, He-Liang Huang
https://arxiv.org/abs/2507.17470

Demonstration of Efficient Predictive Surrogates for Large-scale Quantum Processors
The ongoing development of quantum processors is driving breakthroughs in scientific discovery. Despite this progress, the formidable cost of fabricating large-scale quantum processors means they will remain rare for the foreseeable future, limiting their widespread application. To address this bottleneck, we introduce the concept of predictive surrogates, which are classical learning models designed to emulate the mean-value behavior of a given quantum processor with provably computational eff…