Tootfinder

YouTube will remove its Trending page and Trending Now list in favor of category-specific charts, says it better aligns with the way content is discovered now (Aisha Malik/TechCrunch)
https://techcrunch.com/2025/07/10/youtube-is…

YouTube is getting rid of its Trending page and Trending Now list | TechCrunch
YouTube is replacing these Trending lists with category-specific charts, the company announced on Thursday.

YouTube will remove its Trending page and Trending Now list in favor of category-specific charts, says it better aligns with the way content is discovered now (Aisha Malik/TechCrunch)
https://techcrunch.com/2025/07/10/youtube-is-getting-ri…

YouTube is getting rid of its Trending page and Trending Now list | TechCrunch
YouTube is replacing these Trending lists with category-specific charts, the company announced on Thursday.

📰 Einem US-amerikanischen Konzern Geld aus der Tasche zu leiern, um damit Hosting-Rechnungen für Gratis-Angebote zu bezahlen, kann man machen. Aber schön ist es nicht. Ich würde meine Amazon-Partnerlinks gern loswerden.
Bitte unterstütze meine Arbeit mit einer Steady-Mitgliedschaft: https://steady.page/klimacrew

KlimaCrew
Ich informiere über Zukunftsthemen, um Menschen vor Risiken zu warnen und Fortschritte sichtbar zu machen.

[2025-07-11 Fri (UTC), 26 new articles found for cs.AI Artificial Intelligence]
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[2025-07-11 Fri (UTC), no new articles found for cs.LO Logic in Computer Science]
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[2025-07-11 Fri (UTC), 3 new articles found for math.CT Category Theory]
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[2025-07-11 Fri (UTC), 1 new article found for cs.PL Programming Languages]
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これ読むといいんじゃないかな。うえのさんの。
--
二つのクリスマス. ——何かを「待ちのぞむ」ということ | by ueno | Medium
https://oryzivora.page/二つのクリスマス-750d0b08d274?gi=705df5f6d027

[2025-07-11 Fri (UTC), 23 new articles found for cs.RO Robotics]
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[2025-07-11 Fri (UTC), 18 new articles found for cs.CR Cryptography and Security]
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[2025-07-11 Fri (UTC), 6 new articles found for cs.SE Software Engineering]
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Performance and Practical Considerations of Large and Small Language Models in Clinical Decision Support in Rheumatology
Sabine Felde, R\"udiger Buchkremer, Gamal Chehab, Christian Thielscher, J\"org HW Distler, Matthias Schneider, Jutta G. Richter
https://arxiv.org/abs/2507.07983

Performance and Practical Considerations of Large and Small Language Models in Clinical Decision Support in Rheumatology
Large language models (LLMs) show promise for supporting clinical decision-making in complex fields such as rheumatology. Our evaluation shows that smaller language models (SLMs), combined with retrieval-augmented generation (RAG), achieve higher diagnostic and therapeutic performance than larger models, while requiring substantially less energy and enabling cost-efficient, local deployment. These features are attractive for resource-limited healthcare. However, expert oversight remains essenti…

[2025-07-11 Fri (UTC), 6 new articles found for stat.ML Machine Learning]
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There will come a day when my dad passes away, and eventually, a day when we all do. That thought makes me sad, but I realize there’s no use worrying about the future. Instead, I want to focus on living fully in the present moment.
Life is truly worth livin, and If you want to learn more about the challenges I have faced, including struggles with mental health and identity, you can read my personal story here:

[2025-07-11 Fri (UTC), 6 new articles found for cs.HC Human-Computer Interaction]
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[2025-07-11 Fri (UTC), 5 new articles found for math.LO Logic]
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[2025-07-11 Fri (UTC), 11 new articles found for math.CO Combinatorics]
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Replaced article(s) found for quant-ph. https://arxiv.org/list/quant-ph/new
[2/2]:
- Enhancing photon-axion conversion probability with squeezed coherent states
Taiki Ikeda, Sugumi Kanno, Jiro Soda

[2025-07-11 Fri (UTC), 7 new articles found for cs.CY Computers and Society]
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[2025-07-11 Fri (UTC), 4 new articles found for math.NT Number Theory]
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[2025-07-11 Fri (UTC), 11 new articles found for cs.DC Distributed, Parallel, and Cluster Computing]
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I think the National Space Operations Centre should have a public dashboard page, like the UKHSA dashboard, instead of monthly reports.
https://space.blog.gov.uk/2025/07/11/uk-space-safety-centre-marks-one-year-milestone/

[2025-07-11 Fri (UTC), 11 new articles found for math.AG Algebraic Geometry]
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[2025-07-11 Fri (UTC), 5 new articles found for eess.IV Image and Video Processing]
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[2025-07-11 Fri (UTC), 2 new articles found for math.AT Algebraic Topology]
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Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/4]:
- Cross-Problem Parameter Transfer in Quantum Approximate Optimization Algorithm: A Machine Learnin...
Kien X. Nguyen, Bao Bach, Ilya Safro

diese woche zu meiner verärgerung gelernt: es gibt verlage, die geld für subscribe2open-journals einsammeln und dann mit "#OpenAccess-APC = 0 €" werben, aber dennoch satte page charges und andere zusatzgebühren erheben, sodass z.b. ein 20-seiten-artikel rund 3.500 € kostet. #doubledipping

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[4/4]:
- Attention-Enhanced Deep Learning Ensemble for Breast Density Classification in Mammography
Peyman Sharifian, Xiaotong Hong, Alireza Karimian, Mehdi Amini, Hossein Arabi

[2025-07-11 Fri (UTC), 5 new articles found for cs.IR Information Retrieval]
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[2025-07-11 Fri (UTC), 4 new articles found for cs.IT Information Theory]
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[2025-07-11 Fri (UTC), 13 new articles found for math.OC Optimization and Control]
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Application of LLMs to Multi-Robot Path Planning and Task Allocation
Ashish Kumar
https://arxiv.org/abs/2507.07302 https://arxiv.org/…

Application of LLMs to Multi-Robot Path Planning and Task Allocation
Efficient exploration is a well known problem in deep reinforcement learning and this problem is exacerbated in multi-agent reinforcement learning due the intrinsic complexities of such algorithms. There are several approaches to efficiently explore an environment to learn to solve tasks by multi-agent operating in that environment, of which, the idea of expert exploration is investigated in this work. More specifically, this work investigates the application of large-language models as expert …

Cauchy density
Adri\'an Do\~na Mateo
https://arxiv.org/abs/2507.07869 https://arxiv.org/pdf/2507.07869

Cauchy density
In the paper where he defined the Cauchy completion of a $\mathscr{V}$-category, Lawvere also defined a condition on a $\mathscr{V}$-functor which made it analogous to a map of metric spaces whose image is topologically dense in its codomain. We call this condition Cauchy density. In this note, we focus on the fully faithful Cauchy dense $\mathscr{V}$-functors, and show that the Cauchy completion of $\mathscr{A}$ is the largest $\mathscr{V}$-category that admits a fully faithful Cauchy dense $\…

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[1/3]:
- A Theory of Response Sampling in LLMs: Part Descriptive and Part Prescriptive
Sarath Sivaprasad, Pramod Kaushik, Sahar Abdelnabi, Mario Fritz

Replaced article(s) found for quant-ph. https://arxiv.org/list/quant-ph/new
[1/2]:
- Heisenberg Scaling Quantum Microscopy: Experiment and Theory
Zhe He, Yide Zhang, Xin Tong, Lei Li, Lihong V. Wang

Self-Wearing Adaptive Garments via Soft Robotic Unfurling
Nam Gyun Kim, William E. Heap, Yimeng Qin, Elvy B. Yao, Jee-Hwan Ryu, Allison M. Okamura
https://arxiv.org/abs/2507.07221

Self-Wearing Adaptive Garments via Soft Robotic Unfurling
Robotic dressing assistance has the potential to improve the quality of life for individuals with limited mobility. Existing solutions predominantly rely on rigid robotic manipulators, which have challenges in handling deformable garments and ensuring safe physical interaction with the human body. Prior robotic dressing methods require excessive operation times, complex control strategies, and constrained user postures, limiting their practicality and adaptability. This paper proposes a novel s…

Disa: Accurate Learning-based Static Disassembly with Attentions
Peicheng Wang, Monika Santra, Mingyu Liu, Cong Sun, Dongrui Zeng, Gang Tan
https://arxiv.org/abs/2507.07246

Disa: Accurate Learning-based Static Disassembly with Attentions
For reverse engineering related security domains, such as vulnerability detection, malware analysis, and binary hardening, disassembly is crucial yet challenging. The fundamental challenge of disassembly is to identify instruction and function boundaries. Classic approaches rely on file-format assumptions and architecture-specific heuristics to guess the boundaries, resulting in incomplete and incorrect disassembly, especially when the binary is obfuscated. Recent advancements of disassembly ha…

Dirty Data in the Newsroom: Comparing Data Preparation in Journalism and Data Science
Stephen Kasica, Charles Berret, Tamara Munzner
https://arxiv.org/abs/2507.07238

Dirty Data in the Newsroom: Comparing Data Preparation in Journalism and Data Science
The work involved in gathering, wrangling, cleaning, and otherwise preparing data for analysis is often the most time consuming and tedious aspect of data work. Although many studies describe data preparation within the context of data science workflows, there has been little research on data preparation in data journalism. We address this gap with a hybrid form of thematic analysis that combines deductive codes derived from existing accounts of data science workflows and inductive codes arisin…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/4]:
- Description of the Training Process of Neural Networks via Ergodic Theorem : Ghost nodes
Eun-Ji Park, Sangwon Yun

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/4]:
- Adaptation of Multi-modal Representation Models for Multi-task Surgical Computer Vision
Soham Walimbe, Britty Baby, Vinkle Srivastav, Nicolas Padoy

Open Source Planning & Control System with Language Agents for Autonomous Scientific Discovery
Licong Xu, Milind Sarkar, Anto I. Lonappan, \'I\~nigo Zubeldia, Pablo Villanueva-Domingo, Santiago Casas, Christian Fidler, Chetana Amancharla, Ujjwal Tiwari, Adrian Bayer, Chadi Ait Ekiou, Miles Cranmer, Adrian Dimitrov, James Fergusson, Kahaan Gandhi, Sven Krippendorf, Andrew Laverick, Julien Lesgourgues, Antony Lewis, Thomas Meier, Blake Sherwin, Kristen Surrao, Francisco Villaescu…

Open Source Planning & Control System with Language Agents for Autonomous Scientific Discovery
We present a multi-agent system for automation of scientific research tasks, cmbagent. The system is formed by about 30 Large Language Model (LLM) agents and implements a Planning & Control strategy to orchestrate the agentic workflow, with no human-in-the-loop at any point. Each agent specializes in a different task (performing retrieval on scientific papers and codebases, writing code, interpreting results, critiquing the output of other agents) and the system is able to execute code locally.…

On the squares functor and the Gaitsgory-Rozenblyum conjectures
F\'elix Loubaton, Jaco Ruit
https://arxiv.org/abs/2507.07807 https://

On the squares functor and the Gaitsgory-Rozenblyum conjectures
In the seminal work of Gaitsgory and Rozenblyum on derived algebraic geometry, eight conjectures regarding the theory of $(\infty,2)$-categories are stated. This paper aims to clarify the status of these claims, and to provide a proof for the last remaining open one. Along the way, we demonstrate the universal property of the so-called squares functor, a construction that plays an important role in the $(\infty,2)$-categorical foundations of Gaitsgory-Rozenblyum.

Correlations and quantum circuits with dynamical causal order
Rapha\"el Mothe, Alastair A. Abbott, Cyril Branciard
https://arxiv.org/abs/2507.07992 ht…

Correlations and quantum circuits with dynamical causal order
Requiring that the causal structure between different parties is well-defined imposes constraints on the correlations they can establish, which define so-called causal correlations. Some of these are known to have a "dynamical" causal order in the sense that their causal structure is not fixed a priori but is instead established on the fly, with for instance the causal order between future parties depending on some choice of action of parties in the past. Here we identify a new way that the cau…

g2o vs. Ceres: Optimizing Scan Matching in Cartographer SLAM
Quanjie Qiu, MengCheng Lau
https://arxiv.org/abs/2507.07142 https://arxi…

g2o vs. Ceres: Optimizing Scan Matching in Cartographer SLAM
This article presents a comparative analysis of g2o and Ceres solvers in enhancing scan matching performance within the Cartographer framework. Cartographer, a widely-used library for Simultaneous Localization and Mapping (SLAM), relies on optimization algorithms to refine pose estimates and improve map accuracy. The research aims to evaluate the performance, efficiency, and accuracy of the g2o solver in comparison to the Ceres solver, which is the default in Cartographer. In our experiments co…

Automated Attack Testflow Extraction from Cyber Threat Report using BERT for Contextual Analysis
Faissal Ahmadou, Sepehr Ghaffarzadegan, Boubakr Nour, Makan Pourzandi, Mourad Debbabi, Chadi Assi
https://arxiv.org/abs/2507.07244

Automated Attack Testflow Extraction from Cyber Threat Report using BERT for Contextual Analysis
In the ever-evolving landscape of cybersecurity, the rapid identification and mitigation of Advanced Persistent Threats (APTs) is crucial. Security practitioners rely on detailed threat reports to understand the tactics, techniques, and procedures (TTPs) employed by attackers. However, manually extracting attack testflows from these reports requires elusive knowledge and is time-consuming and prone to errors. This paper proposes FLOWGUARDIAN, a novel solution leveraging language models (i.e., B…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/4]:
- Harmonic Loss Trains Interpretable AI Models
David D. Baek, Ziming Liu, Riya Tyagi, Max Tegmark

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/4]:
- Dance Like a Chicken: Low-Rank Stylization for Human Motion Diffusion
Haim Sawdayee, Chuan Guo, Guy Tevet, Bing Zhou, Jian Wang, Amit H. Bermano

Neurosymbolic Feature Extraction for Identifying Forced Labor in Supply Chains
Zili Wang, Frank Montabon, Kristin Yvonne Rozier
https://arxiv.org/abs/2507.07217

Neurosymbolic Feature Extraction for Identifying Forced Labor in Supply Chains
Supply chain networks are complex systems that are challenging to analyze; this problem is exacerbated when there are illicit activities involved in the supply chain, such as counterfeit parts, forced labor, or human trafficking. While machine learning (ML) can find patterns in complex systems like supply chains, traditional ML techniques require large training data sets. However, illicit supply chains are characterized by very sparse data, and the data that is available is often (purposely) co…

Strong converse rate for asymptotic hypothesis testing in type III
Nicholas Laracuente, Marius Junge
https://arxiv.org/abs/2507.07989 https://

Strong converse rate for asymptotic hypothesis testing in type III
We extend from the hyperfinite setting to general von Neumann algebras Mosonyi and Ogawa's (2015) and Mosonyi and Hiai's (2023) results showing the operational interpretation of sandwiched relative Rényi entropy in the strong converse of hypothesis testing. The specific task is to distinguish between two quantum states given many copies. We use a reduction method of Haagerup, Junge, and Xu (2010) to approximate relative entropy inequalities in an arbitrary von Neumann algebra by those in finit…

WatchWitch: Interoperability, Privacy, and Autonomy for the Apple Watch
Nils Rollshausen, Alexander Heinrich, Matthias Hollick, Jiska Classen
https://arxiv.org/abs/2507.07210

WatchWitch: Interoperability, Privacy, and Autonomy for the Apple Watch
Smartwatches such as the Apple Watch collect vast amounts of intimate health and fitness data as we wear them. Users have little choice regarding how this data is processed: The Apple Watch can only be used with Apple's iPhones, using their software and their cloud services. We are the first to publicly reverse-engineer the watch's wireless protocols, which led to discovering multiple security issues in Apple's proprietary implementation. With WatchWitch, our custom Android reimplementation, we…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/4]:
- An Algorithm for Learning Smaller Representations of Models With Scarce Data
Adrian de Wynter

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/4]:
- Boundary Learning by Using Weighted Propagation in Convolution Network
Wei Liu, Jiahao Chen, Chuni Liu, Xiaojuan Ban, Boyuan Ma, Hao Wang, Weihua Xue, Yu Guo

State-Inference-Based Prompting for Natural Language Trading with Game NPCs
Minkyung Kim, Junsik Kim, Hwidong Bae, Woongcheol Yang, Sangdon Park, Sohee Bae
https://arxiv.org/abs/2507.07203

State-Inference-Based Prompting for Natural Language Trading with Game NPCs
Large Language Models enable dynamic game interactions but struggle with rule-governed trading systems. Current implementations suffer from rule violations, such as item hallucinations and calculation errors, that erode player trust. Here, State-Inference-Based Prompting (SIBP) enables reliable trading through autonomous dialogue state inference and context-specific rule adherence. The approach decomposes trading into six states within a unified prompt framework, implementing context-aware item…

Sharp estimates of quantum covering problems via a novel trace inequality
Hao-Chung Cheng, Li Gao, Christoph Hirche, Hao-Wei Huang, Po-Chieh Liu
https://arxiv.org/abs/2507.07961

Sharp estimates of quantum covering problems via a novel trace inequality
In this paper, we prove a novel trace inequality involving two operators. As applications, we sharpen the one-shot achievability bound on the relative entropy error in a wealth of quantum covering-type problems, such as soft covering, privacy amplification, convex splitting, quantum information decoupling, and quantum channel simulation by removing some dimension-dependent factors. Moreover, the established one-shot bounds extend to infinite-dimensional separable Hilbert spaces as well. The pro…

BOOST: Out-of-Distribution-Informed Adaptive Sampling for Bias Mitigation in Stylistic Convolutional Neural Networks
Mridula Vijendran, Shuang Chen, Jingjing Deng, Hubert P. H. Shum
https://arxiv.org/abs/2507.07134

BOOST: Out-of-Distribution-Informed Adaptive Sampling for Bias Mitigation in Stylistic Convolutional Neural Networks
The pervasive issue of bias in AI presents a significant challenge to painting classification, and is getting more serious as these systems become increasingly integrated into tasks like art curation and restoration. Biases, often arising from imbalanced datasets where certain artistic styles dominate, compromise the fairness and accuracy of model predictions, i.e., classifiers are less accurate on rarely seen paintings. While prior research has made strides in improving classification performa…

Impact of Pretraining Word Co-occurrence on Compositional Generalization in Multimodal Models
Helen Qu, Sang Michael Xie
https://arxiv.org/abs/2507.08000 h…

Impact of Pretraining Word Co-occurrence on Compositional Generalization in Multimodal Models
CLIP and large multimodal models (LMMs) have better accuracy on examples involving concepts that are highly represented in the training data. However, the role of concept combinations in the training data on compositional generalization is largely unclear -- for instance, how does accuracy vary when a common object appears in an uncommon pairing with another object? In this paper, we investigate how word co-occurrence statistics in the pretraining dataset (a proxy for co-occurrence of visual co…

Quantum Wall States for Noise Mitigation and Eternal Purity Bounds
Miguel Casanova, Francesco Ticozzi
https://arxiv.org/abs/2507.07944 https://

Quantum Wall States for Noise Mitigation and Eternal Purity Bounds
The present work analyzes state-stabilization techniques for decoupling a subsystem from environmental interactions. The proposed framework uses analytical and numerical tools to find an approximate decoherence-free subspace (DFS) with enhanced passive noise isolation. Active state-stabilizing control on a subsystem mediating dominant environmental interactions, which we call wall subsystem, creates an effective quantum wall state. The proposed method controls only the wall subsystem, leaving t…

Autonomous Control Leveraging LLMs: An Agentic Framework for Next-Generation Industrial Automation
Javal Vyas, Mehmet Mercangoz
https://arxiv.org/abs/2507.07115

Autonomous Control Leveraging LLMs: An Agentic Framework for Next-Generation Industrial Automation
The increasing complexity of modern chemical processes, coupled with workforce shortages and intricate fault scenarios, demands novel automation paradigms that blend symbolic reasoning with adaptive control. In this work, we introduce a unified agentic framework that leverages large language models (LLMs) for both discrete fault-recovery planning and continuous process control within a single architecture. We adopt Finite State Machines (FSMs) as interpretable operating envelopes: an LLM-driven…

Traceable Evidence Enhanced Visual Grounded Reasoning: Evaluation and Methodology
Haochen Wang, Xiangtai Li, Zilong Huang, Anran Wang, Jiacong Wang, Tao Zhang, Jiani Zheng, Sule Bai, Zijian Kang, Jiashi Feng, Zhuochen Wang, Zhaoxiang Zhang
https://arxiv.org/abs/2507.07999

Traceable Evidence Enhanced Visual Grounded Reasoning: Evaluation and Methodology
Models like OpenAI-o3 pioneer visual grounded reasoning by dynamically referencing visual regions, just like human "thinking with images". However, no benchmark exists to evaluate these capabilities holistically. To bridge this gap, we propose TreeBench (Traceable Evidence Evaluation Benchmark), a diagnostic benchmark built on three principles: (1) focused visual perception of subtle targets in complex scenes, (2) traceable evidence via bounding box evaluation, and (3) second-order reasoning to…

Robust Lindbladian Estimation for Quantum Dynamics
Yinchen Liu, James R. Seddon, Tamara Kohler, Emilio Onorati, Toby S. Cubitt
https://arxiv.org/abs/2507.07912

Robust Lindbladian Estimation for Quantum Dynamics
We revisit the problem of fitting Lindbladian models to the outputs of quantum process tomography. A sequence of prior theoretical works approached the problem by considering whether there exists a Lindbladian generator close to a matrix logarithm of the tomographically estimated transfer matrix. This technique must take into account the non-uniqueness of the matrix logarithm, so that in general multiple branches of the logarithm must be checked. In contrast, all practical demonstrations of Lin…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[5/5]:
- What Has a Foundation Model Found? Using Inductive Bias to Probe for World Models
Keyon Vafa, Peter G. Chang, Ashesh Rambachan, Sendhil Mullainathan

MGVQ: Could VQ-VAE Beat VAE? A Generalizable Tokenizer with Multi-group Quantization
Mingkai Jia, Wei Yin, Xiaotao Hu, Jiaxin Guo, Xiaoyang Guo, Qian Zhang, Xiao-Xiao Long, Ping Tan
https://arxiv.org/abs/2507.07997

MGVQ: Could VQ-VAE Beat VAE? A Generalizable Tokenizer with Multi-group Quantization
Vector Quantized Variational Autoencoders (VQ-VAEs) are fundamental models that compress continuous visual data into discrete tokens. Existing methods have tried to improve the quantization strategy for better reconstruction quality, however, there still exists a large gap between VQ-VAEs and VAEs. To narrow this gap, we propose \NickName, a novel method to augment the representation capability of discrete codebooks, facilitating easier optimization for codebooks and minimizing information loss…

Testing time order and Leggett-Garg inequalities with noninvasive measurements on public quantum computers
Tomasz Rybotycki, Tomasz Bia{\l}ecki, Josep Batle, Bart{\l}omiej Zglinicki, Adam Szereszewski, Wolfgang Belzig, Adam Bednorz
https://arxiv.org/abs/2507.07904

Testing time order and Leggett-Garg inequalities with noninvasive measurements on public quantum computers
We present two protocols, with one and two two-qubit gates, to noninvasively measure two incompatible observables on a qubit. Both protocols are completed with a projective measurement. By gathering sufficiently large statistics, we have been able to violate Leggett-Garg inequality and time-order invariance. The detailed analysis of the data on 10 qubit sets from 5 devices available on IBM Quantum and one on IonQ reveals violations beyond 5 standard deviations in almost all cases. The noninvasi…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[4/5]:
- Description of the Training Process of Neural Networks via Ergodic Theorem : Ghost nodes
Eun-Ji Park, Sangwon Yun

Single-pass Adaptive Image Tokenization for Minimum Program Search
Shivam Duggal, Sanghyun Byun, William T. Freeman, Antonio Torralba, Phillip Isola
https://arxiv.org/abs/2507.07995

Single-pass Adaptive Image Tokenization for Minimum Program Search
According to Algorithmic Information Theory (AIT) -- Intelligent representations compress data into the shortest possible program that can reconstruct its content, exhibiting low Kolmogorov Complexity (KC). In contrast, most visual representation learning systems use fixed-length representations for all inputs, ignoring variations in complexity or familiarity. Recent adaptive tokenization methods address this by allocating variable-length representations but typically require test-time search o…

Methods for Reducing Ancilla-Overhead in Block Encodings
Francisca Vasconcelos, Andr\'as Gily\'en
https://arxiv.org/abs/2507.07900 https://

Methods for Reducing Ancilla-Overhead in Block Encodings
Block encodings are a fundamental primitive in quantum algorithms, but can often have large ancilla overhead. In this work, we introduce novel techniques for reducing this overhead in two distinct ways. In Part I, we prove the existence of a "space-time tradeoff" by deriving an algorithm that, for any block encoding, approximately uncomputes all but one of its ancilla (freeing up those ancillae for reuse in later parts of a quantum algorithm). In Part II, we evaluate the minimum number of ancil…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[3/5]:
- Localized Concept Erasure for Text-to-Image Diffusion Models Using Training-Free Gated Low-Rank A...
Byung Hyun Lee, Sungjin Lim, Se Young Chun

Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection
Subhajit Maity, Ayan Kumar Bhunia, Subhadeep Koley, Pinaki Nath Chowdhury, Aneeshan Sain, Yi-Zhe Song
https://arxiv.org/abs/2507.07994

Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection
Keypoint detection, integral to modern machine perception, faces challenges in few-shot learning, particularly when source data from the same distribution as the query is unavailable. This gap is addressed by leveraging sketches, a popular form of human expression, providing a source-free alternative. However, challenges arise in mastering cross-modal embeddings and handling user-specific sketch styles. Our proposed framework overcomes these hurdles with a prototypical setup, combined with a gr…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/5]:
- Solving Probabilistic Verification Problems of Neural Networks using Branch and Bound
David Boetius, Stefan Leue, Tobias Sutter

Preparing spin-squeezed states in Rydberg atom arrays via quantum optimal control
Edison S. Carrera, Harold Erbin, Gr\'egoire Misguich
https://arxiv.org/abs/2507.07875

Preparing spin-squeezed states in Rydberg atom arrays via quantum optimal control
We present a quantum optimal control protocol to generate highly spin-squeezed states in Rydberg atom arrays coupled via Ising-type Van der Waals interactions. Using gradient-based optimization techniques we construct time-dependent pulse sequences that steer an initial product state toward highly entangled, spin-squeezed states with predefined magnetization and squeezing axes. We focus on the Wineland parameter $ξ_W^2$ to measure spin squeezing and our approach achieves near-optimal spin sque…

Multigranular Evaluation for Brain Visual Decoding
Weihao Xia, Cengiz Oztireli
https://arxiv.org/abs/2507.07993 https://arxiv.org/pdf…

Multigranular Evaluation for Brain Visual Decoding
Existing evaluation protocols for brain visual decoding predominantly rely on coarse metrics that obscure inter-model differences, lack neuroscientific foundation, and fail to capture fine-grained visual distinctions. To address these limitations, we introduce BASIC, a unified, multigranular evaluation framework that jointly quantifies structural fidelity, inferential alignment, and contextual coherence between decoded and ground truth images. For the structural level, we introduce a hierarchic…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/5]:
- Solving a Stackelberg Game on Transportation Networks in a Dynamic Crime Scenario: A Mixed Approa...
Sukanya Samanta, Kei Kimura, Makoto Yokoo

Violation of Bell Inequality with Unentangled Photons
Kai Wang, Zhaohua Hou, Kaiyi Qian, Leizhen Chen, Mario Krenn, Markus Aspelmeyer, Anton Zeilinger, Shining Zhu, Xiao-Song Ma
https://arxiv.org/abs/2507.07756

Violation of Bell Inequality with Unentangled Photons
Violation of local realism via Bell inequality - a profound and counterintuitive manifestation of quantum theory that conflicts with the prediction of local realism - is viewed to be intimately linked with quantum entanglement. Experimental demonstrations of such a phenomenon using quantum entangled states are among the landmark experiments of modern physics and paved the way for quantum technology. Here we report the violation of the Bell inequality that cannot be described by quantum entangle…

Multi-Granular Spatio-Temporal Token Merging for Training-Free Acceleration of Video LLMs
Jeongseok Hyun, Sukjun Hwang, Su Ho Han, Taeoh Kim, Inwoong Lee, Dongyoon Wee, Joon-Young Lee, Seon Joo Kim, Minho Shim
https://arxiv.org/abs/2507.07990

Multi-Granular Spatio-Temporal Token Merging for Training-Free Acceleration of Video LLMs
Video large language models (LLMs) achieve strong video understanding by leveraging a large number of spatio-temporal tokens, but suffer from quadratic computational scaling with token count. To address this, we propose a training-free spatio-temporal token merging method, named STTM. Our key insight is to exploit local spatial and temporal redundancy in video data which has been overlooked in prior work. STTM first transforms each frame into multi-granular spatial tokens using a coarse-to-fine…

Gate Freezing Method for Gradient-Free Variational Quantum Algorithms in Circuit Optimization
Joona Pankkonen, Lauri Ylinen, Matti Raasakka, Andrea Marchesin, Ilkka Tittonen
https://arxiv.org/abs/2507.07742

Gate Freezing Method for Gradient-Free Variational Quantum Algorithms in Circuit Optimization
Parameterized quantum circuits (PQCs) are pivotal components of variational quantum algorithms (VQAs), which represent a promising pathway to quantum advantage in noisy intermediate-scale quantum (NISQ) devices. PQCs enable flexible encoding of quantum information through tunable quantum gates and have been successfully applied across domains such as quantum chemistry, combinatorial optimization, and quantum machine learning. Despite their potential, PQC performance on NISQ hardware is hindered…

CLIP Won't Learn Object-Attribute Binding from Natural Data and Here is Why
Bijay Gurung, David T. Hoffmann, Thomas Brox
https://arxiv.org/abs/2507.07985

CLIP Won't Learn Object-Attribute Binding from Natural Data and Here is Why
Contrastive vision-language models like CLIP are used for a large variety of applications, such as zero-shot classification or as vision encoder for multi-modal models. Despite their popularity, their representations show major limitations. For instance, CLIP models learn bag-of-words representations and, as a consequence, fail to distinguish whether an image is of "a yellow submarine and a blue bus" or "a blue submarine and a yellow bus". Previous attempts to fix this issue added hard negative…

Kinetic Inductance Traveling Wave Parametric Amplifiers Near the Quantum Limit: Methodology and Characterization
L. Howe, A. Giachero, M. Vissers, P. Campana, J. Wheeler, J. Gao, J. Austermann, J. Hubmayr, A. Nucciotti, J. Ullom
https://arxiv.org/abs/2507.07706

Kinetic Inductance Traveling Wave Parametric Amplifiers Near the Quantum Limit: Methodology and Characterization
We present a detailed simulation and design framework for realizing traveling wave parametric amplifiers (TWPAs) using the nonlinear kinetic inductance of disordered superconductors -- in our case niobium-titanium-nitride (NbTiN). These kinetic inductance TWPAs (KITs) operate via three-wave mixing (3WM) to achieve high broadband gain and near-quantum-limited (nQL) noise. Representative fabricated devices -- realized using an inverted microstrip (IMS), dispersion-engineered, artificial transmiss…

Strong squeezing and perfect one-way EPR steering in electro-optomechanical system
Qing-Min Zeng, A-Peng Liu, Qi Guo
https://arxiv.org/abs/2507.07697 https…

Strong squeezing and perfect one-way EPR steering in electro-optomechanical system
We consider a three-mode electro-optomechanical system in which a mechanical oscillator is coupled to an optical cavity and a LC circuit through radiation pressure and capacitive coupling, respectively. By controlling the two-tone driving of the optical cavity and the microwave driving of the LC circuit, the strong squeezing of both the microwave field of the LC circuit and the mechanical mode will be obtained. Moreover, by further altering the driving power of the system, the perfect one-way E…

OST-Bench: Evaluating the Capabilities of MLLMs in Online Spatio-temporal Scene Understanding
JingLi Lin, Chenming Zhu, Runsen Xu, Xiaohan Mao, Xihui Liu, Tai Wang, Jiangmiao Pang
https://arxiv.org/abs/2507.07984

OST-Bench: Evaluating the Capabilities of MLLMs in Online Spatio-temporal Scene Understanding
Recent advances in multimodal large language models (MLLMs) have shown remarkable capabilities in integrating vision and language for complex reasoning. While most existing benchmarks evaluate models under offline settings with a fixed set of pre-recorded inputs, we introduce OST-Bench, a benchmark designed to evaluate Online Spatio-Temporal understanding from the perspective of an agent actively exploring a scene. The Online aspect emphasizes the need to process and reason over incrementally a…

Working with AI: Measuring the Occupational Implications of Generative AI
Kiran Tomlinson, Sonia Jaffe, Will Wang, Scott Counts, Siddharth Suri
https://arxiv.org/abs/2507.07935

Working with AI: Measuring the Occupational Implications of Generative AI
Given the rapid adoption of generative AI and its potential to impact a wide range of tasks, understanding the effects of AI on the economy is one of society's most important questions. In this work, we take a step toward that goal by analyzing the work activities people do with AI, how successfully and broadly those activities are done, and combine that with data on what occupations do those activities. We analyze a dataset of 200k anonymized and privacy-scrubbed conversations between users an…

Phase-Space Framework for Noisy Intermediate-Scale Quantum Optical Neural Networks
Stanis{\l}aw \'Swierczewski, Wouter Verstraelen, Piotr Deuar, Barbara Pi\k{e}tka, Timothy C. H. Liew, Micha{\l} Matuszewski, Andrzej Opala
https://arxiv.org/abs/2507.07684

Phase-Space Framework for Noisy Intermediate-Scale Quantum Optical Neural Networks
Quantum optical neural networks (QONNs) enable information processing beyond classical limits by exploiting the advantages of classical and quantum optics. However, simulation of large-scale bosonic lattices remains a significant challenge due to the exponential growth of the Hilbert space required to describe a quantum network accurately. Consequently, previous theoretical studies have been limited to small-scale systems, leaving the behaviour of multimode QONNs largely unexplored. This work p…

Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling
Haoyu Wu, Diankun Wu, Tianyu He, Junliang Guo, Yang Ye, Yueqi Duan, Jiang Bian
https://arxiv.org/abs/2507.07982

Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling
Videos inherently represent 2D projections of a dynamic 3D world. However, our analysis suggests that video diffusion models trained solely on raw video data often fail to capture meaningful geometric-aware structure in their learned representations. To bridge this gap between video diffusion models and the underlying 3D nature of the physical world, we propose Geometry Forcing, a simple yet effective method that encourages video diffusion models to internalize latent 3D representations. Our ke…

Meek Models Shall Inherit the Earth
Hans Gundlach, Jayson Lynch, Neil Thompson
https://arxiv.org/abs/2507.07931 https://arxiv.org/pdf…

Meek Models Shall Inherit the Earth
The past decade has seen incredible scaling of AI systems by a few companies, leading to inequality in AI model performance. This paper argues that, contrary to prevailing intuition, the diminishing returns to compute scaling will lead to a convergence of AI model capabilities. In other words, meek models (those with limited computation budget) shall inherit the earth, approaching the performance level of the best models overall. We develop a model illustrating that under a fixed-distribution n…

Effects of One-particle Reduced Density Matrix Optimization in Variational Quantum Eigensolvers
Amanda Marques de Lima, Erico Souza Teixeira, Eivson Darlivam Rodrigues de Aguiar Silva, Ricardo Luiz Longo
https://arxiv.org/abs/2507.07667

Effects of One-particle Reduced Density Matrix Optimization in Variational Quantum Eigensolvers
The variational quantum eigensolver (VQE) is a promising method for simulating molecular systems on near-term quantum computers. This approach employs energy estimation; however, other relevant molecular properties can be extracted from the one-particle reduced density matrix (1-RDM) generated by VQE. The accuracy of these properties strongly depends on the reliability and convergence of the 1-RDM, which is not guaranteed by energy-only optimization. Thus, we investigate the effect of optimizin…

Martian World Models: Controllable Video Synthesis with Physically Accurate 3D Reconstructions
Longfei Li, Zhiwen Fan, Wenyan Cong, Xinhang Liu, Yuyang Yin, Matt Foutter, Panwang Pan, Chenyu You, Yue Wang, Zhangyang Wang, Yao Zhao, Marco Pavone, Yunchao Wei
https://arxiv.org/abs/2507.07978…

Martian World Models: Controllable Video Synthesis with Physically Accurate 3D Reconstructions
Synthesizing realistic Martian landscape videos is crucial for mission rehearsal and robotic simulation. However, this task poses unique challenges due to the scarcity of high-quality Martian data and the significant domain gap between Martian and terrestrial imagery. To address these challenges, we propose a holistic solution composed of two key components: 1) A data curation pipeline Multimodal Mars Synthesis (M3arsSynth), which reconstructs 3D Martian environments from real stereo navigation…

ProvideQ: A Quantum Optimization Toolbox
Domenik Eichhorn, Nick Poser, Maximilian Schweikart, Ina Schaefer
https://arxiv.org/abs/2507.07649 https://…

ProvideQ: A Quantum Optimization Toolbox
Hybrid solvers for combinatorial optimization problems combine the advantages of classical and quantum computing to overcome difficult computational challenges. Although their theoretical performance seems promising, their practical applicability is challenging due to the lack of a technological stack that can seamlessly integrate quantum solutions with existing classical optimization frameworks. We tackle this challenge by introducing the ProvideQ toolbox, a software tool that enables users to…

Scaling RL to Long Videos
Yukang Chen, Wei Huang, Baifeng Shi, Qinghao Hu, Hanrong Ye, Ligeng Zhu, Zhijian Liu, Pavlo Molchanov, Jan Kautz, Xiaojuan Qi, Sifei Liu, Hongxu Yin, Yao Lu, Song Han
https://arxiv.org/abs/2507.07966

Scaling RL to Long Videos
We introduce a full-stack framework that scales up reasoning in vision-language models (VLMs) to long videos, leveraging reinforcement learning. We address the unique challenges of long video reasoning by integrating three critical components: (1) a large-scale dataset, LongVideo-Reason, comprising 52K long video QA pairs with high-quality reasoning annotations across diverse domains such as sports, games, and vlogs; (2) a two-stage training pipeline that extends VLMs with chain-of-thought supe…

Tangent Space Excitation Ansatz for Quantum Circuits
Ji-Yao Chen, Bochen Huang, D. L. Zhou, Norbert Schuch, Chenfeng Cao, Muchun Yang
https://arxiv.org/abs/2507.07646

Tangent Space Excitation Ansatz for Quantum Circuits
Computing the excitation spectra of quantum many-body systems on noisy quantum devices is a promising avenue to demonstrate the practical utility of current quantum processors, especially as we move toward the ``megaquop'' regime. For this task, here we introduce a \textit{tangent-space excitation ansatz} for quantum circuits, motivated by the quasi-particle picture of many-body systems and the structural similarity between quantum circuits and classical tensor networks. Increasing the circuit …

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices
Sizhen Bian, Mengxi Liu, Vitor Fortes Rey, Daniel Geissler, Paul Lukowicz
https://arxiv.org/abs/2507.07949

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices
Human Activity Recognition (HAR) on resource-constrained wearable devices demands inference models that harmonize accuracy with computational efficiency. This paper introduces TinierHAR, an ultra-lightweight deep learning architecture that synergizes residual depthwise separable convolutions, gated recurrent units (GRUs), and temporal aggregation to achieve SOTA efficiency without compromising performance. Evaluated across 14 public HAR datasets, TinierHAR reduces Parameters by 2.7x (vs. TinyHA…

Online Quantum State Tomography via Stochastic Gradient Descent
Jian-Feng Cai, Yuling Jiao, Yinan Li, Xiliang Lu, Jerry Zhijian Yang, Juntao You
https://arxiv.org/abs/2507.07601

Online Quantum State Tomography via Stochastic Gradient Descent
We initiate the study of online quantum state tomography (QST), where the matrix representation of an unknown quantum state is reconstructed by sequentially performing a batch of measurements and updating the state estimate using only the measurement statistics from the current round. Motivated by recent advances in non-convex optimization algorithms for solving low-rank QST, we propose non-convex mini-batch stochastic gradient descent (SGD) algorithms to tackle online QST, which leverage the l…

Towards Continuous Home Cage Monitoring: An Evaluation of Tracking and Identification Strategies for Laboratory Mice
Juan Pablo Oberhauser, Daniel Grzenda
https://arxiv.org/abs/2507.07929

Towards Continuous Home Cage Monitoring: An Evaluation of Tracking and Identification Strategies for Laboratory Mice
Continuous, automated monitoring of laboratory mice enables more accurate data collection and improves animal welfare through real-time insights. Researchers can achieve a more dynamic and clinically relevant characterization of disease progression and therapeutic effects by integrating behavioral and physiological monitoring in the home cage. However, providing individual mouse metrics is difficult because of their housing density, similar appearances, high mobility, and frequent interactions.…

Quantum Executor: A Unified Interface for Quantum Computing
Giuseppe Bisicchia, Alessandro Bocci, Antonio Brogi
https://arxiv.org/abs/2507.07597 https://…

Quantum Executor: A Unified Interface for Quantum Computing
As quantum computing evolves from theoretical promise to practical deployment, the demand for robust, portable, and scalable tools for quantum software experimentation is growing. This paper introduces Quantum Executor, a backend-agnostic execution engine designed to orchestrate quantum experiments across heterogeneous platforms. Quantum Executor provides a declarative and modular interface that decouples experiment design from backend execution, enabling seamless interoperability and code reus…

ArteryX: Advancing Brain Artery Feature Extraction with Vessel-Fused Networks and a Robust Validation Framework
Abrar Faiyaz, Nhat Hoang, Giovanni Schifitto, Md Nasir Uddin
https://arxiv.org/abs/2507.07920

ArteryX: Advancing Brain Artery Feature Extraction with Vessel-Fused Networks and a Robust Validation Framework
Cerebrovascular pathology significantly contributes to cognitive decline and neurological disorders, underscoring the need for advanced tools to assess vascular integrity. Three-dimensional Time-of-Flight Magnetic Resonance Angiography (3D TOF MRA) is widely used to visualize cerebral vasculature, however, clinical evaluations generally focus on major arterial abnormalities, overlooking quantitative metrics critical for understanding subtle vascular changes. Existing methods for extracting stru…

CleanQRL: Lightweight Single-file Implementations of Quantum Reinforcement Learning Algorithms
Georg Kruse, Rodrigo Coelho, Andreas Rosskopf, Robert Wille, Jeanette Miriam Lorenz
https://arxiv.org/abs/2507.07593

CleanQRL: Lightweight Single-file Implementations of Quantum Reinforcement Learning Algorithms
At the interception between quantum computing and machine learning, Quantum Reinforcement Learning (QRL) has emerged as a promising research field. Due to its novelty, a standardized and comprehensive collection for QRL algorithms has not yet been established. Researchers rely on numerous software stacks for classical Reinforcement Learning (RL) as well as on various quantum computing frameworks for the implementation of the quantum subroutines of their QRL algorithms. Inspired by the CleanRL l…

Not Only Consistency: Enhance Test-Time Adaptation with Spatio-temporal Inconsistency for Remote Physiological Measurement
Xiao Yang, Yuxuan Fan, Can Liu, Houcheng Su, Weichen Guo, Jiyao Wang, Dengbo He
https://arxiv.org/abs/2507.07908

Not Only Consistency: Enhance Test-Time Adaptation with Spatio-temporal Inconsistency for Remote Physiological Measurement
Remote photoplethysmography (rPPG) has emerged as a promising non-invasive method for monitoring physiological signals using the camera. Although various domain adaptation and generalization methods were proposed to promote the adaptability of deep-based rPPG models in unseen deployment environments, considerations in aspects like privacy concerns and real-time adaptation restrict their application in real-world deployment. Thus, we aim to propose a novel fully Test-Time Adaptation (TTA) strate…

A Neural-Guided Variational Quantum Algorithm for Efficient Sign Structure Learning in Hybrid Architectures
Mengzhen Ren, Yu-Cheng Chen, Yangsen Ye, Min-Hsiu Hsieh, Alice Hu, Chang-Yu Hsieh
https://arxiv.org/abs/2507.07555

A Neural-Guided Variational Quantum Algorithm for Efficient Sign Structure Learning in Hybrid Architectures
Variational quantum algorithms hold great promise for unlocking the power of near-term quantum processors, yet high measurement costs, barren plateaus, and challenging optimization landscapes frequently hinder them. Here, we introduce sVQNHE, a neural-guided variational quantum algorithm that decouples amplitude and sign learning across classical and quantum modules, respectively. Our approach employs shallow quantum circuits composed of commuting diagonal gates to efficiently model quantum pha…

Hardware-Aware Feature Extraction Quantisation for Real-Time Visual Odometry on FPGA Platforms
Mateusz Wasala, Mateusz Smolarczyk, Michal Danilowicz, Tomasz Kryjak
https://arxiv.org/abs/2507.07903

Hardware-Aware Feature Extraction Quantisation for Real-Time Visual Odometry on FPGA Platforms
Accurate position estimation is essential for modern navigation systems deployed in autonomous platforms, including ground vehicles, marine vessels, and aerial drones. In this context, Visual Simultaneous Localisation and Mapping (VSLAM) - which includes Visual Odometry - relies heavily on the reliable extraction of salient feature points from the visual input data. In this work, we propose an embedded implementation of an unsupervised architecture capable of detecting and describing feature po…

Towards Nonlinear Quantum Thermodynamics
Gershon Kurizki, Nilakantha Meher, Avijit Misra, Durga Bhaktavatsala Rao Dasari, Tomas Opatrny
https://arxiv.org/abs/2507.07537

Towards Nonlinear Quantum Thermodynamics
We have recently put forth several schemes of unconventional, nonlinearly-enabled thermodynamic (TD) devices that can operate in either the classical or the quantum domain by transforming thermal-state input in multiple uncorrelated modes into non-gaussian state output in selected modes: a four-mode Kerr-nonlinear interferometer that acts as a heat engine; two coupled Kerr-nonlinear Mach-Zehnder interferometers that act as a phase microscope with unprecedented phase resolution; and a noise sens…

Simulating sparse SYK model with a randomized algorithm on a trapped-ion quantum computer
Etienne Granet, Yuta Kikuchi, Henrik Dreyer, Enrico Rinaldi
https://arxiv.org/abs/2507.07530

Simulating sparse SYK model with a randomized algorithm on a trapped-ion quantum computer
The Sachdev-Ye-Kitaev (SYK) model describes a strongly correlated quantum system that shows a strong signature of quantum chaos. Due to its chaotic nature, the simulation of real-time dynamics becomes quickly intractable by means of classical numerics, and thus, quantum simulation is deemed to be an attractive alternative. Nevertheless, quantum simulations of the SYK model on noisy quantum processors are severely limited by the complexity of its Hamiltonian. In this work, we simulate the real-t…

Conditions for Large-Sample Majorization of Pairs of Flat States in Terms of $\alpha$-$z$ Relative Entropies
Frits Verhagen, Marco Tomamichel, Erkka Haapasalo
https://arxiv.org/abs/2507.07520

Conditions for Large-Sample Majorization of Pairs of Flat States in Terms of $α$-$z$ Relative Entropies
In this work, we offer the first operational interpretation of the $α$-$z$ relative entropies, which were introduced by Jakšić {\it et al.} \cite{Jaksic2012} and Audenaert and Datta \cite{Audenaert_Datta_2015}, where the $α$ and $z$ parameters are truly independent from each other. Namely, we show that these relative entropies appear in the conditions for large-sample or catalytic relative majorization of pairs of flat states and certain generalizations of them. Additionally, the optimal ra…