Tootfinder

[2025-07-14 Mon (UTC), 2 new articles found for cs.LO Logic in Computer Science]
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Replaced article(s) found for econ.EM. https://arxiv.org/list/econ.EM/new
[1/1]:
- Nonparametric Treatment Effect Identification in School Choice
Jiafeng Chen
https://arxiv.org/abs/2112.03872
- Potential weights and implicit causal designs in linear regression
Jiafeng Chen
https://arxiv.org/abs/2407.21119 https://mastoxiv.page/@arXiv_econEM_bot/112885535340833628
- Reinterpreting demand estimation
Jiafeng Chen
https://arxiv.org/abs/2503.23524 https://mastoxiv.page/@arXiv_econEM_bot/114262944644769755
- A step towards the integration of machine learning and classic model-based survey methods
Tomasz \.Z\k{a}d{\l}o, Adam Chwila
https://arxiv.org/abs/2402.07521 https://mastoxiv.page/@arXiv_statME_bot/111924442633277025
- Galerkin-ARIMA: A Two-Stage Polynomial Regression Framework for Fast Rolling One-Step-Ahead Forec...
Haojie Liu, Zihan Lin
https://arxiv.org/abs/2507.07469 https://mastoxiv.page/@arXiv_statML_bot/114833708254543213
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[2025-07-14 Mon (UTC), no new articles found for math.CT Category Theory]
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[2025-07-14 Mon (UTC), 2 new articles found for cs.PL Programming Languages]
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[2025-07-14 Mon (UTC), 12 new articles found for cs.RO Robotics]
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[2025-07-14 Mon (UTC), 10 new articles found for cs.SE Software Engineering]
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heise | JavaScript: Einen Discord-Bot für die eigene Gaming-Community programmieren
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[2025-07-14 Mon (UTC), 6 new articles found for stat.ML Machine Learning]
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[2025-07-14 Mon (UTC), 11 new articles found for cs.HC Human-Computer Interaction]
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[2025-07-14 Mon (UTC), 4 new articles found for cs.CY Computers and Society]
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[2025-07-14 Mon (UTC), 3 new articles found for math.LO Logic]
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[2025-07-14 Mon (UTC), 9 new articles found for math.CO Combinatorics]
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[2025-05-15 Thu (UTC), no new articles found for q-fin.TR Trading and Market Microstructure]
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Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[5/5]:
- Feasibility Study of CNNs and MLPs for Radiation Heat Transfer in 2-D Furnaces with Spectrally Pa...
Axel TahmasebiMoradi, Vincent Ren, Benjamin Le-Creurer, Chetra Mang, Mouadh Yagoubi

[2025-05-15 Thu (UTC), 1 new article found for cs.OS Operating Systems]
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[2025-07-14 Mon (UTC), 4 new articles found for cs.DC Distributed, Parallel, and Cluster Computing]
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Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/3]:
- Objectomaly: Objectness-Aware Refinement for OoD Segmentation with Structural Consistency and Bou...
Jeonghoon Song, Sunghun Kim, Jaegyun Im, Byeongjoon Noh

[2025-07-14 Mon (UTC), 11 new articles found for math.NT Number Theory]
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Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[4/4]:
- KeyRe-ID: Keypoint-Guided Person Re-Identification using Part-Aware Representation in Videos
Jinseong Kim, Junghoon Song, Gyeongseon Baek, Byeongjoon Noh

[2025-07-14 Mon (UTC), 9 new articles found for math.AG Algebraic Geometry]
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[2025-07-14 Mon (UTC), 9 new articles found for hep-th High Energy Physics - Theory]
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[2025-07-14 Mon (UTC), 6 new articles found for eess.IV Image and Video Processing]
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Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[2/2]:
- Flippi: End To End GenAI Assistant for E-Commerce
Rajasekar, Tangarajan, Nainani, Batwal, Dandin, Trivedi, Ersoy

[2025-07-14 Mon (UTC), 2 new articles found for math.AT Algebraic Topology]
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[2025-07-14 Mon (UTC), 7 new articles found for cs.IT Information Theory]
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[2025-07-14 Mon (UTC), 6 new articles found for cs.IR Information Retrieval]
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[2025-07-14 Mon (UTC), no new articles found for cs.FL Formal Languages and Automata Theory]
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[2025-07-14 Mon (UTC), 8 new articles found for math.OC Optimization and Control]
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[2025-07-14 Mon (UTC), 4 new articles found for math-ph Mathematical Physics]
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[2025-05-15 Thu (UTC), no new articles found for cs.MS Mathematical Software]
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[2025-07-14 Mon (UTC), 2 new articles found for cs.AR Hardware Architecture]
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[2025-05-15 Thu (UTC), no new articles found for math.HO History and Overview]
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[2025-05-14 Wed (UTC), no new articles found for q-fin.TR Trading and Market Microstructure]
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Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/5]:
- Data-driven system identification using quadratic embeddings of nonlinear dynamics
Stefan Klus, Joel-Pascal Ntwali N'konzi

[2025-05-14 Wed (UTC), no new articles found for cs.OS Operating Systems]
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Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/3]:
- InsViE-1M: Effective Instruction-based Video Editing with Elaborate Dataset Construction
Yuhui Wu, Liyi Chen, Ruibin Li, Shihao Wang, Chenxi Xie, Lei Zhang

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[3/4]:
- Boundary-Guided Trajectory Prediction for Road Aware and Physically Feasible Autonomous Driving
Ahmed Abouelazm, Mianzhi Liu, Christian Hubschneider, Yin Wu, Daniel Slieter, J. Marius Z\"ollner

Replaced article(s) found for cs.LO. https://arxiv.org/list/cs.LO/new
[1/1]:
- Tractable and Intractable Entailment Problems in Separation Logic with Inductively Defined Predic...
Mnacho Echenim, Nicolas Peltier

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[1/2]:
- Answer Generation for Questions With Multiple Information Sources in E-Commerce
Anand A. Rajasekar, Nikesh Garera

Replaced article(s) found for math.CT. https://arxiv.org/list/math.CT/new
[1/1]:
- On the quotient of a groupoid by an action of a 2-group
Vladimir Drinfeld

Replaced article(s) found for cs.PL. https://arxiv.org/list/cs.PL/new
[1/1]:
- Denotational Semantics of Gradual Typing using Synthetic Guarded Domain Theory (Extended Version)
Eric Giovannini, Tingting Ding, Max S. New

[2025-05-14 Wed (UTC), no new articles found for cs.MS Mathematical Software]
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[2025-05-14 Wed (UTC), no new articles found for math.HO History and Overview]
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Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/5]:
- Answer Generation for Questions With Multiple Information Sources in E-Commerce
Anand A. Rajasekar, Nikesh Garera

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[1/3]:
- SDR-GAIN: A High Real-Time Occluded Pedestrian Pose Completion Method for Autonomous Driving
Honghao Fu, Yongli Gu, Yidong Yan, Yilang Shen, Yiwen Wu, Libo Sun

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/4]:
- Downscaling Extreme Precipitation with Wasserstein Regularized Diffusion
Yuhao Liu, James Doss-Gollin, Qiushi Dai, Ashok Veeraraghavan, Guha Balakrishnan

A Personalised Formal Verification Framework for Monitoring Activities of Daily Living of Older Adults Living Independently in Their Homes
Ricardo Contreras, Filip Smola, Nu\v{s}a Fari\v{c}, Jiawei Zheng, Jane Hillston, Jacques D. Fleuriot
https://arxiv.org/abs/2507.08701

A Personalised Formal Verification Framework for Monitoring Activities of Daily Living of Older Adults Living Independently in Their Homes
There is an imperative need to provide quality of life to a growing population of older adults living independently. Personalised solutions that focus on the person and take into consideration their preferences and context are key. In this work, we introduce a framework for representing and reasoning about the Activities of Daily Living of older adults living independently at home. The framework integrates data from sensors and contextual information that aggregates semi-structured interviews, …

KV Cache Steering for Inducing Reasoning in Small Language Models
Max Belitsky, Dawid J. Kopiczko, Michael Dorkenwald, M. Jehanzeb Mirza, Cees G. M. Snoek, Yuki M. Asano
https://arxiv.org/abs/2507.08799

KV Cache Steering for Inducing Reasoning in Small Language Models
We propose cache steering, a lightweight method for implicit steering of language models via a one-shot intervention applied directly to the key-value cache. To validate its effectiveness, we apply cache steering to induce chain-of-thought reasoning in small language models. Our approach leverages GPT-4o-generated reasoning traces to construct steering vectors that shift model behavior toward more explicit, multi-step reasoning without fine-tuning or prompt modifications. Experimental evaluatio…

Filter Equivariant Functions: A symmetric account of length-general extrapolation on lists
Owen Lewis, Neil Ghani, Andrew Dudzik, Christos Perivolaropoulos, Razvan Pascanu, Petar Veli\v{c}kovi\'c
https://arxiv.org/abs/2507.08796

Filter Equivariant Functions: A symmetric account of length-general extrapolation on lists
What should a function that extrapolates beyond known input/output examples look like? This is a tricky question to answer in general, as any function matching the outputs on those examples can in principle be a correct extrapolant. We argue that a "good" extrapolant should follow certain kinds of rules, and here we study a particularly appealing criterion for rule-following in list functions: that the function should behave predictably even when certain elements are removed. In functional prog…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/5]:
- On learning functions over biological sequence space: relating Gaussian process priors, regulariz...
Samantha Petti, Carlos Mart\'i-G\'omez, Justin B. Kinney, Juannan Zhou, David M. McCandlish

Lumos-1: On Autoregressive Video Generation from a Unified Model Perspective
Hangjie Yuan, Weihua Chen, Jun Cen, Hu Yu, Jingyun Liang, Shuning Chang, Zhihui Lin, Tao Feng, Pengwei Liu, Jiazheng Xing, Hao Luo, Jiasheng Tang, Fan Wang, Yi Yang
https://arxiv.org/abs/2507.08801

Lumos-1: On Autoregressive Video Generation from a Unified Model Perspective
Autoregressive large language models (LLMs) have unified a vast range of language tasks, inspiring preliminary efforts in autoregressive video generation. Existing autoregressive video generators either diverge from standard LLM architectures, depend on bulky external text encoders, or incur prohibitive latency due to next-token decoding. In this paper, we introduce Lumos-1, an autoregressive video generator that retains the LLM architecture with minimal architectural modifications. To inject s…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/4]:
- Interpreting systems as solving POMDPs: a step towards a formal understanding of agency
Martin Biehl, Nathaniel Virgo

Heterogeneous Dynamic Logic: Provability Modulo Program Theories
Samuel Teuber, Mattias Ulbrich, Andr\'e Platzer, Bernhard Beckert
https://arxiv.org/abs/2507.08581

Heterogeneous Dynamic Logic: Provability Modulo Program Theories
Formally specifying, let alone verifying, properties of systems involving multiple programming languages is inherently challenging. We introduce Heterogeneous Dynamic Logic (HDL), a framework for combining reasoning principles from distinct (dynamic) program logics in a modular and compositional way. HDL mirrors the architecture of satisfiability modulo theories (SMT): Individual dynamic logics, along with their calculi, are treated as dynamic theories that can be flexibly combined to reason ab…

Multilingual Multimodal Software Developer for Code Generation
Linzheng Chai, Jian Yang, Shukai Liu, Wei Zhang, Liran Wang, Ke Jin, Tao Sun, Congnan Liu, Chenchen Zhang, Hualei Zhu, Jiaheng Liu, Xianjie Wu, Ge Zhang, Tianyu Liu, Zhoujun Li
https://arxiv.org/abs/2507.08719

Multilingual Multimodal Software Developer for Code Generation
The rapid advancement of Large Language Models (LLMs) has significantly improved code generation, yet most models remain text-only, neglecting crucial visual aids like diagrams and flowcharts used in real-world software development. To bridge this gap, we introduce MM-Coder, a Multilingual Multimodal software developer. MM-Coder integrates visual design inputs-Unified Modeling Language (UML) diagrams and flowcharts (termed Visual Workflow)-with textual instructions to enhance code generation ac…

Dependent Multiplicities in Dependent Linear Type Theory
Maximilian Dor\'e
https://arxiv.org/abs/2507.08759 https://arxiv.org/pdf…

Dependent Multiplicities in Dependent Linear Type Theory
We present a novel dependent linear type theory in which the multiplicity of some variable - i.e., the number of times the variable can be used in a program - can depend on other variables. This allows us to give precise resource annotations to many higher-order functions that cannot be adequately typed in any other system. Inspired by the Dialectica translation, our typing discipline is obtained by embedding linear logic into dependent type theory and specifying how the embedded logic interact…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/5]:
- Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets
Xingang Peng, Shitong Luo, Jiaqi Guan, Qi Xie, Jian Peng, Jianzhu Ma

NeuralOS: Towards Simulating Operating Systems via Neural Generative Models
Luke Rivard, Sun Sun, Hongyu Guo, Wenhu Chen, Yuntian Deng
https://arxiv.org/abs/2507.08800

NeuralOS: Towards Simulating Operating Systems via Neural Generative Models
We introduce NeuralOS, a neural framework that simulates graphical user interfaces (GUIs) of operating systems by directly predicting screen frames in response to user inputs such as mouse movements, clicks, and keyboard events. NeuralOS combines a recurrent neural network (RNN), which tracks computer state, with a diffusion-based neural renderer that generates screen images. The model is trained on a large-scale dataset of Ubuntu XFCE recordings, which include both randomly generated interacti…

A simple formalization of alpha-equivalence
Kalmer Apinis, Danel Ahman
https://arxiv.org/abs/2507.10181 https://arxiv.org/pdf/2507.10…

A simple formalization of alpha-equivalence
While teaching untyped $λ$-calculus to undergraduate students, we were wondering why $α$-equivalence is not directly inductively defined. In this paper, we demonstrate that this is indeed feasible. Specifically, we provide a grounded, inductive definition for $α$-equivalence and show that it conforms to the specification provided in the literature. The work presented in this paper is fully formalized in the Rocq Prover.

KG-Attention: Knowledge Graph-Guided Attention at Test-Time via Bidirectional Information Aggregation
Songlin Zhai, Guilin Qi, Yuan Meng
https://arxiv.org/abs/2507.08704

KG-Attention: Knowledge Graph-Guided Attention at Test-Time via Bidirectional Information Aggregation
Knowledge graphs (KGs) play a critical role in enhancing large language models (LLMs) by introducing structured and grounded knowledge into the learning process. However, most existing KG-enhanced approaches rely on parameter-intensive fine-tuning, which risks catastrophic forgetting and degrades the pretrained model's generalization. Moreover, they exhibit limited adaptability to real-time knowledge updates due to their static integration frameworks. To address these issues, we introduce the f…

CLiFT: Compressive Light-Field Tokens for Compute-Efficient and Adaptive Neural Rendering
Zhengqing Wang, Yuefan Wu, Jiacheng Chen, Fuyang Zhang, Yasutaka Furukawa
https://arxiv.org/abs/2507.08776

CLiFT: Compressive Light-Field Tokens for Compute-Efficient and Adaptive Neural Rendering
This paper proposes a neural rendering approach that represents a scene as "compressed light-field tokens (CLiFTs)", retaining rich appearance and geometric information of a scene. CLiFT enables compute-efficient rendering by compressed tokens, while being capable of changing the number of tokens to represent a scene or render a novel view with one trained network. Concretely, given a set of images, multi-view encoder tokenizes the images with the camera poses. Latent-space K-means selects a re…

KELPS: A Framework for Verified Multi-Language Autoformalization via Semantic-Syntactic Alignment
Jiyao Zhang, Chengli Zhong, Hui Xu, Qige Li, Yi Zhou
https://arxiv.org/abs/2507.08665

KELPS: A Framework for Verified Multi-Language Autoformalization via Semantic-Syntactic Alignment
Modern large language models (LLMs) show promising progress in formalizing informal mathematics into machine-verifiable theorems. However, these methods still face bottlenecks due to the limited quantity and quality of multilingual parallel corpora. In this paper, we propose a novel neuro-symbolic framework KELPS (Knowledge-Equation based Logical Processing System) to address these problems. KELPS is an iterative framework for translating, synthesizing, and filtering informal data into multiple…

From One to More: Contextual Part Latents for 3D Generation
Shaocong Dong, Lihe Ding, Xiao Chen, Yaokun Li, Yuxin Wang, Yucheng Wang, Qi Wang, Jaehyeok Kim, Chenjian Gao, Zhanpeng Huang, Zibin Wang, Tianfan Xue, Dan Xu
https://arxiv.org/abs/2507.08772

From One to More: Contextual Part Latents for 3D Generation
Recent advances in 3D generation have transitioned from multi-view 2D rendering approaches to 3D-native latent diffusion frameworks that exploit geometric priors in ground truth data. Despite progress, three key limitations persist: (1) Single-latent representations fail to capture complex multi-part geometries, causing detail degradation; (2) Holistic latent coding neglects part independence and interrelationships critical for compositional design; (3) Global conditioning mechanisms lack fine-…

The Impact of Automatic Speech Transcription on Speaker Attribution
Cristina Aggazzotti, Matthew Wiesner, Elizabeth Allyn Smith, Nicholas Andrews
https://arxiv.org/abs/2507.08660 …

The Impact of Automatic Speech Transcription on Speaker Attribution
Speaker attribution from speech transcripts is the task of identifying a speaker from the transcript of their speech based on patterns in their language use. This task is especially useful when the audio is unavailable (e.g. deleted) or unreliable (e.g. anonymized speech). Prior work in this area has primarily focused on the feasibility of attributing speakers using transcripts produced by human annotators. However, in real-world settings, one often only has more errorful transcripts produced b…

A Hybrid Multi-Well Hopfield-CNN with Feature Extraction and K-Means for MNIST Classification
Ahmed Farooq
https://arxiv.org/abs/2507.08766 https://…

A Hybrid Multi-Well Hopfield-CNN with Feature Extraction and K-Means for MNIST Classification
This study presents a hybrid model for classifying handwritten digits in the MNIST dataset, combining convolutional neural networks (CNNs) with a multi-well Hopfield network. The approach employs a CNN to extract high-dimensional features from input images, which are then clustered into class-specific prototypes using k-means clustering. These prototypes serve as attractors in a multi-well energy landscape, where a Hopfield network performs classification by minimizing an energy function that b…

A comprehensive study of LLM-based argument classification: from LLAMA through GPT-4o to Deepseek-R1
Marcin Pietro\'n, Rafa{\l} Olszowski, Jakub Gomu{\l}ka, Filip Gampel, Andrzej Tomski
https://arxiv.org/abs/2507.08621

A comprehensive study of LLM-based argument classification: from LLAMA through GPT-4o to Deepseek-R1
Argument mining (AM) is an interdisciplinary research field that integrates insights from logic, philosophy, linguistics, rhetoric, law, psychology, and computer science. It involves the automatic identification and extraction of argumentative components, such as premises and claims, and the detection of relationships between them, such as support, attack, or neutrality. Recently, the field has advanced significantly, especially with the advent of large language models (LLMs), which have enhanc…

Compress Any Segment Anything Model (SAM)
Juntong Fan, Zhiwei Hao, Jianqiang Shen, Shang-Ling Jui, Yi Zhang, Jing-Xiao Liao, Feng-Lei Fan
https://arxiv.org/abs/2507.08765

Compress Any Segment Anything Model (SAM)
Due to the excellent performance in yielding high-quality, zero-shot segmentation, Segment Anything Model (SAM) and its variants have been widely applied in diverse scenarios such as healthcare and intelligent manufacturing. Therefore, effectively compressing SAMs has become an increasingly pressing practical need. In this study, we propose Birkhoff, a novel data-free compression algorithm for SAM and its variants. Unlike quantization, pruning, distillation, and other compression methods, Birkh…

DocPolarBERT: A Pre-trained Model for Document Understanding with Relative Polar Coordinate Encoding of Layout Structures
Benno Uthayasooriyar, Antoine Ly, Franck Vermet, Caio Corro
https://arxiv.org/abs/2507.08606

DocPolarBERT: A Pre-trained Model for Document Understanding with Relative Polar Coordinate Encoding of Layout Structures
We introduce DocPolarBERT, a layout-aware BERT model for document understanding that eliminates the need for absolute 2D positional embeddings. We extend self-attention to take into account text block positions in relative polar coordinate system rather than the Cartesian one. Despite being pre-trained on a dataset more than six times smaller than the widely used IIT-CDIP corpus, DocPolarBERT achieves state-of-the-art results. These results demonstrate that a carefully designed attention mechan…

Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry Detection
Rei Tamaru, Pei Li, Bin Ran
https://arxiv.org/abs/2507.08743 https…

Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry Detection
Digital Twins (DT) have the potential to transform traffic management and operations by creating dynamic, virtual representations of transportation systems that sense conditions, analyze operations, and support decision-making. A key component for DT of the transportation system is dynamic roadway geometry sensing. However, existing approaches often rely on static maps or costly sensors, limiting scalability and adaptability. Additionally, large-scale DTs that collect and analyze data from mult…

The AI Language Proficiency Monitor -- Tracking the Progress of LLMs on Multilingual Benchmarks
David Pomerenke, Jonas Nothnagel, Simon Ostermann
https://arxiv.org/abs/2507.08538 …

The AI Language Proficiency Monitor -- Tracking the Progress of LLMs on Multilingual Benchmarks
To ensure equitable access to the benefits of large language models (LLMs), it is essential to evaluate their capabilities across the world's languages. We introduce the AI Language Proficiency Monitor, a comprehensive multilingual benchmark that systematically assesses LLM performance across up to 200 languages, with a particular focus on low-resource languages. Our benchmark aggregates diverse tasks including translation, question answering, math, and reasoning, using datasets such as FLORES+…

HieraRS: A Hierarchical Segmentation Paradigm for Remote Sensing Enabling Multi-Granularity Interpretation and Cross-Domain Transfer
Tianlong Ai, Tianzhu Liu, Haochen Jiang, Yanfeng Gu
https://arxiv.org/abs/2507.08741

HieraRS: A Hierarchical Segmentation Paradigm for Remote Sensing Enabling Multi-Granularity Interpretation and Cross-Domain Transfer
Hierarchical land cover and land use (LCLU) classification aims to assign pixel-wise labels with multiple levels of semantic granularity to remote sensing (RS) imagery. However, existing deep learning-based methods face two major challenges: 1) They predominantly adopt a flat classification paradigm, which limits their ability to generate end-to-end multi-granularity hierarchical predictions aligned with tree-structured hierarchies used in practice. 2) Most cross-domain studies focus on perform…

PromotionGo at SemEval-2025 Task 11: A Feature-Centric Framework for Cross-Lingual Multi-Emotion Detection in Short Texts
Ziyi Huang, Xia Cui
https://arxiv.org/abs/2507.08499

PromotionGo at SemEval-2025 Task 11: A Feature-Centric Framework for Cross-Lingual Multi-Emotion Detection in Short Texts
This paper presents our system for SemEval 2025 Task 11: Bridging the Gap in Text-Based Emotion Detection (Track A), which focuses on multi-label emotion detection in short texts. We propose a feature-centric framework that dynamically adapts document representations and learning algorithms to optimize language-specific performance. Our study evaluates three key components: document representation, dimensionality reduction, and model training in 28 languages, highlighting five for detailed anal…

Ensemble of Weak Spectral Total Variation Learners: a PET-CT Case Study
Anna Rosenberg, John Kennedy, Zohar Keidar, Yehoshua Y. Zeevi, Guy Gilboa
https://arxiv.org/abs/2507.08735 …

Ensemble of Weak Spectral Total Variation Learners: a PET-CT Case Study
Solving computer vision problems through machine learning, one often encounters lack of sufficient training data. To mitigate this we propose the use of ensembles of weak learners based on spectral total-variation (STV) features (Gilboa 2014). The features are related to nonlinear eigenfunctions of the total-variation subgradient and can characterize well textures at various scales. It was shown (Burger et-al 2016) that, in the one-dimensional case, orthogonal features are generated, whereas in…

Semantic-Augmented Latent Topic Modeling with LLM-in-the-Loop
Mengze Hong, Chen Jason Zhang, Di Jiang
https://arxiv.org/abs/2507.08498 https://

Semantic-Augmented Latent Topic Modeling with LLM-in-the-Loop
Latent Dirichlet Allocation (LDA) is a prominent generative probabilistic model used for uncovering abstract topics within document collections. In this paper, we explore the effectiveness of augmenting topic models with Large Language Models (LLMs) through integration into two key phases: Initialization and Post-Correction. Since the LDA is highly dependent on the quality of its initialization, we conduct extensive experiments on the LLM-guided topic clustering for initializing the Gibbs sampl…

RoundaboutHD: High-Resolution Real-World Urban Environment Benchmark for Multi-Camera Vehicle Tracking
Yuqiang Lin, Sam Lockyer, Mingxuan Sui, Li Gan, Florian Stanek, Markus Zarbock, Wenbin Li, Adrian Evans, Nic Zhang
https://arxiv.org/abs/2507.08729

RoundaboutHD: High-Resolution Real-World Urban Environment Benchmark for Multi-Camera Vehicle Tracking
The multi-camera vehicle tracking (MCVT) framework holds significant potential for smart city applications, including anomaly detection, traffic density estimation, and suspect vehicle tracking. However, current publicly available datasets exhibit limitations, such as overly simplistic scenarios, low-resolution footage, and insufficiently diverse conditions, creating a considerable gap between academic research and real-world scenario. To fill this gap, we introduce RoundaboutHD, a comprehensiv…

LLaPa: A Vision-Language Model Framework for Counterfactual-Aware Procedural Planning
Shibo Sun, Xue Li, Donglin Di, Mingjie Wei, Lanshun Nie, Wei-Nan Zhang, Dechen Zhan, Yang Song, Lei Fan
https://arxiv.org/abs/2507.08496

LLaPa: A Vision-Language Model Framework for Counterfactual-Aware Procedural Planning
While large language models (LLMs) have advanced procedural planning for embodied AI systems through strong reasoning abilities, the integration of multimodal inputs and counterfactual reasoning remains underexplored. To tackle these challenges, we introduce LLaPa, a vision-language model framework designed for multimodal procedural planning. LLaPa generates executable action sequences from textual task descriptions and visual environmental images using vision-language models (VLMs). Furthermor…

Unreal is all you need: Multimodal ISAC Data Simulation with Only One Engine
Kongwu Huang, Shiyi Mu, Jun Jiang, Yuan Gao, Shugong Xu
https://arxiv.org/abs/2507.08716

Unreal is all you need: Multimodal ISAC Data Simulation with Only One Engine
Scaling laws have achieved success in LLM and foundation models. To explore their potential in ISAC research, we propose Great-X. This single-engine multimodal data twin platform reconstructs the ray-tracing computation of Sionna within Unreal Engine and is deeply integrated with autonomous driving tools. This enables efficient and synchronized simulation of multimodal data, including CSI, RGB, Radar, and LiDAR. Based on this platform, we construct an open-source, large-scale, low-altitude UAV …

A Third Paradigm for LLM Evaluation: Dialogue Game-Based Evaluation using clembench
David Schlangen, Sherzod Hakimov, Jonathan Jordan, Philipp Sadler
https://arxiv.org/abs/2507.08491

A Third Paradigm for LLM Evaluation: Dialogue Game-Based Evaluation using clembench
There are currently two main paradigms for evaluating large language models (LLMs), reference-based evaluation and preference-based evaluation. The first, carried over from the evaluation of machine learning models in general, relies on pre-defined task instances, for which reference task executions are available. The second, best exemplified by the LM-arena, relies on (often self-selected) users bringing their own intents to a site that routes these to several models in parallel, among whose r…

SGPMIL: Sparse Gaussian Process Multiple Instance Learning
Andreas Lolos, Stergios Christodoulidis, Maria Vakalopoulou, Jose Dolz, Aris Moustakas
https://arxiv.org/abs/2507.08711 …

SGPMIL: Sparse Gaussian Process Multiple Instance Learning
Multiple Instance Learning (MIL) offers a natural solution for settings where only coarse, bag-level labels are available, without having access to instance-level annotations. This is usually the case in digital pathology, which consists of gigapixel sized images. While deterministic attention-based MIL approaches achieve strong bag-level performance, they often overlook the uncertainty inherent in instance relevance. In this paper, we address the lack of uncertainty quantification in instance-…

Enhancing Essay Cohesion Assessment: A Novel Item Response Theory Approach
Bruno Alexandre Rosa, Hil\'ario Oliveira, Luiz Rodrigues, Eduardo Araujo Oliveira, Rafael Ferreira Mello
https://arxiv.org/abs/2507.08487

Enhancing Essay Cohesion Assessment: A Novel Item Response Theory Approach
Essays are considered a valuable mechanism for evaluating learning outcomes in writing. Textual cohesion is an essential characteristic of a text, as it facilitates the establishment of meaning between its parts. Automatically scoring cohesion in essays presents a challenge in the field of educational artificial intelligence. The machine learning algorithms used to evaluate texts generally do not consider the individual characteristics of the instances that comprise the analysed corpus. In this…

L-CLIPScore: a Lightweight Embedding-based Captioning Metric for Evaluating and Training
Li Li, Yingzhe Peng, Xu Yang, Ruoxi Cheng, Haiyang Xu, Ming Yan, Fei Huang
https://arxiv.org/abs/2507.08710

L-CLIPScore: a Lightweight Embedding-based Captioning Metric for Evaluating and Training
We propose a novel embedding-based captioning metric termed as L-CLIPScore that can be used for efficiently evaluating caption quality and training captioning model. L-CLIPScore is calculated from a lightweight CLIP (L-CLIP), which is a dual-encoder architecture compressed and distilled from CLIP. To compress, we apply two powerful techniques which are weight multiplexing and matrix decomposition for reducing the parameters of encoders and word embedding matrix, respectively. To distill, we des…

ILT-Iterative LoRA Training through Focus-Feedback-Fix for Multilingual Speech Recognition
Qingliang Meng, Hao Wu, Wei Liang, Wei Xu, Qing Zhao
https://arxiv.org/abs/2507.08477

ILT-Iterative LoRA Training through Focus-Feedback-Fix for Multilingual Speech Recognition
The deep integration of large language models and automatic speech recognition systems has become a promising research direction with high practical value. To address the overfitting issue commonly observed in Low-Rank Adaptation (LoRA) during the supervised fine-tuning (SFT) stage, this work proposes an innovative training paradigm Iterative LoRA Training (ILT) in combination with an Iterative Pseudo Labeling strategy, effectively enhancing the theoretical upper bound of model performance. Bas…

An Efficient Approach for Muscle Segmentation and 3D Reconstruction Using Keypoint Tracking in MRI Scan
Mengyuan Liu, Jeongkyu Lee
https://arxiv.org/abs/2507.08690

An Efficient Approach for Muscle Segmentation and 3D Reconstruction Using Keypoint Tracking in MRI Scan
Magnetic resonance imaging (MRI) enables non-invasive, high-resolution analysis of muscle structures. However, automated segmentation remains limited by high computational costs, reliance on large training datasets, and reduced accuracy in segmenting smaller muscles. Convolutional neural network (CNN)-based methods, while powerful, often suffer from substantial computational overhead, limited generalizability, and poor interpretability across diverse populations. This study proposes a training-…

Using Large Language Models for Legal Decision-Making in Austrian Value-Added Tax Law: An Experimental Study
Marina Luketina, Andrea Benkel, Christoph G. Schuetz
https://arxiv.org/abs/2507.08468

Using Large Language Models for Legal Decision-Making in Austrian Value-Added Tax Law: An Experimental Study
This paper provides an experimental evaluation of the capability of large language models (LLMs) to assist in legal decision-making within the framework of Austrian and European Union value-added tax (VAT) law. In tax consulting practice, clients often describe cases in natural language, making LLMs a prime candidate for supporting automated decision-making and reducing the workload of tax professionals. Given the requirement for legally grounded and well-justified analyses, the propensity of L…

MoSAiC: Multi-Modal Multi-Label Supervision-Aware Contrastive Learning for Remote Sensing
Debashis Gupta, Aditi Golder, Rongkhun Zhu, Kangning Cui, Wei Tang, Fan Yang, Ovidiu Csillik, Sarra Alaqahtani, V. Paul Pauca
https://arxiv.org/abs/2507.08683

MoSAiC: Multi-Modal Multi-Label Supervision-Aware Contrastive Learning for Remote Sensing
Contrastive learning (CL) has emerged as a powerful paradigm for learning transferable representations without the reliance on large labeled datasets. Its ability to capture intrinsic similarities and differences among data samples has led to state-of-the-art results in computer vision tasks. These strengths make CL particularly well-suited for Earth System Observation (ESO), where diverse satellite modalities such as optical and SAR imagery offer naturally aligned views of the same geospatial …

Diagnosing Failures in Large Language Models' Answers: Integrating Error Attribution into Evaluation Framework
Zishan Xu, Shuyi Xie, Qingsong Lv, Shupei Xiao, Linlin Song, Sui Wenjuan, Fan Lin
https://arxiv.org/abs/2507.08459

Diagnosing Failures in Large Language Models' Answers: Integrating Error Attribution into Evaluation Framework
With the widespread application of Large Language Models (LLMs) in various tasks, the mainstream LLM platforms generate massive user-model interactions daily. In order to efficiently analyze the performance of models and diagnose failures in their answers, it is essential to develop an automated framework to systematically categorize and attribute errors. However, existing evaluation models lack error attribution capability. In this work, we establish a comprehensive Misattribution Framework wi…

ByDeWay: Boost Your multimodal LLM with DEpth prompting in a Training-Free Way
Rajarshi Roy, Devleena Das, Ankesh Banerjee, Arjya Bhattacharjee, Kousik Dasgupta, Subarna Tripathi
https://arxiv.org/abs/2507.08679

ByDeWay: Boost Your multimodal LLM with DEpth prompting in a Training-Free Way
We introduce ByDeWay, a training-free framework designed to enhance the performance of Multimodal Large Language Models (MLLMs). ByDeWay uses a novel prompting strategy called Layered-Depth-Based Prompting (LDP), which improves spatial reasoning and grounding without modifying any model parameters. It segments the scene into closest, mid-range, and farthest layers using monocular depth estimation, then generates region-specific captions with a grounded vision-language model. These structured, d…

Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences
Selina Heller, Mohamed Ibrahim, David Antony Selby, Sebastian Vollmer
https://arxiv.org/abs/2507.08440

Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences
Decision conferences are structured, collaborative meetings that bring together experts from various fields to address complex issues and reach a consensus on recommendations for future actions or policies. These conferences often rely on facilitated discussions to ensure productive dialogue and collective agreement. Recently, Large Language Models (LLMs) have shown significant promise in simulating real-world scenarios, particularly through collaborative multi-agent systems that mimic group in…

Generalizable 7T T1-map Synthesis from 1.5T and 3T T1 MRI with an Efficient Transformer Model
Zach Eidex, Mojtaba Safari, Tonghe Wang, Vanessa Wildman, David S. Yu, Hui Mao, Erik Middlebrooks, Aparna Kesewala, Xiaofeng Yang
https://arxiv.org/abs/2507.08655

Generalizable 7T T1-map Synthesis from 1.5T and 3T T1 MRI with an Efficient Transformer Model
Purpose: Ultra-high-field 7T MRI offers improved resolution and contrast over standard clinical field strengths (1.5T, 3T). However, 7T scanners are costly, scarce, and introduce additional challenges such as susceptibility artifacts. We propose an efficient transformer-based model (7T-Restormer) to synthesize 7T-quality T1-maps from routine 1.5T or 3T T1-weighted (T1W) images. Methods: Our model was validated on 35 1.5T and 108 3T T1w MRI paired with corresponding 7T T1 maps of patients with c…

ChainEdit: Propagating Ripple Effects in LLM Knowledge Editing through Logical Rule-Guided Chains
Zilu Dong, Xiangqing Shen, Zinong Yang, Rui Xia
https://arxiv.org/abs/2507.08427 …

ChainEdit: Propagating Ripple Effects in LLM Knowledge Editing through Logical Rule-Guided Chains
Current knowledge editing methods for large language models (LLMs) struggle to maintain logical consistency when propagating ripple effects to associated facts. We propose ChainEdit, a framework that synergizes knowledge graph-derived logical rules with LLM logical reasoning capabilities to enable systematic chain updates. By automatically extracting logical patterns from structured knowledge bases and aligning them with LLMs' internal logics, ChainEdit dynamically generates and edits logically…

DatasetAgent: A Novel Multi-Agent System for Auto-Constructing Datasets from Real-World Images
Haoran Sun, Haoyu Bian, Shaoning Zeng, Yunbo Rao, Xu Xu, Lin Mei, Jianping Gou
https://arxiv.org/abs/2507.08648

DatasetAgent: A Novel Multi-Agent System for Auto-Constructing Datasets from Real-World Images
Common knowledge indicates that the process of constructing image datasets usually depends on the time-intensive and inefficient method of manual collection and annotation. Large models offer a solution via data generation. Nonetheless, real-world data are obviously more valuable comparing to artificially intelligence generated data, particularly in constructing image datasets. For this reason, we propose a novel method for auto-constructing datasets from real-world images by a multiagent colla…

OnlineBEV: Recurrent Temporal Fusion in Bird's Eye View Representations for Multi-Camera 3D Perception
Junho Koh, Youngwoo Lee, Jungho Kim, Dongyoung Lee, Jun Won Choi
https://arxiv.org/abs/2507.08644

OnlineBEV: Recurrent Temporal Fusion in Bird's Eye View Representations for Multi-Camera 3D Perception
Multi-view camera-based 3D perception can be conducted using bird's eye view (BEV) features obtained through perspective view-to-BEV transformations. Several studies have shown that the performance of these 3D perception methods can be further enhanced by combining sequential BEV features obtained from multiple camera frames. However, even after compensating for the ego-motion of an autonomous agent, the performance gain from temporal aggregation is limited when combining a large number of imag…

A Survey of Large Language Models in Discipline-specific Research: Challenges, Methods and Opportunities
Lu Xiang, Yang Zhao, Yaping Zhang, Chengqing Zong
https://arxiv.org/abs/2507.08425

A Survey of Large Language Models in Discipline-specific Research: Challenges, Methods and Opportunities
Large Language Models (LLMs) have demonstrated their transformative potential across numerous disciplinary studies, reshaping the existing research methodologies and fostering interdisciplinary collaboration. However, a systematic understanding of their integration into diverse disciplines remains underexplored. This survey paper provides a comprehensive overview of the application of LLMs in interdisciplinary studies, categorising research efforts from both a technical perspective and with reg…

The Curious Case of Factuality Finetuning: Models' Internal Beliefs Can Improve Factuality
Benjamin Newman, Abhilasha Ravichander, Jaehun Jung, Rui Xin, Hamish Ivison, Yegor Kuznetsov, Pang Wei Koh, Yejin Choi
https://arxiv.org/abs/2507.08371

The Curious Case of Factuality Finetuning: Models' Internal Beliefs Can Improve Factuality
Language models are prone to hallucination - generating text that is factually incorrect. Finetuning models on high-quality factual information can potentially reduce hallucination, but concerns remain; obtaining factual gold data can be expensive and training on correct but unfamiliar data may potentially lead to even more downstream hallucination. What data should practitioners finetune on to mitigate hallucinations in language models? In this work, we study the relationship between the factu…

Exploring Design of Multi-Agent LLM Dialogues for Research Ideation
Keisuke Ueda, Wataru Hirota, Takuto Asakura, Takahiro Omi, Kosuke Takahashi, Kosuke Arima, Tatsuya Ishigaki
https://arxiv.org/abs/2507.08350

Exploring Design of Multi-Agent LLM Dialogues for Research Ideation
Large language models (LLMs) are increasingly used to support creative tasks such as research idea generation. While recent work has shown that structured dialogues between LLMs can improve the novelty and feasibility of generated ideas, the optimal design of such interactions remains unclear. In this study, we conduct a comprehensive analysis of multi-agent LLM dialogues for scientific ideation. We compare different configurations of agent roles, number of agents, and dialogue depth to underst…

Beyond N-Grams: Rethinking Evaluation Metrics and Strategies for Multilingual Abstractive Summarization
Itai Mondshine, Tzuf Paz-Argaman, Reut Tsarfaty
https://arxiv.org/abs/2507.08342

Beyond N-Grams: Rethinking Evaluation Metrics and Strategies for Multilingual Abstractive Summarization
Automatic n-gram based metrics such as ROUGE are widely used for evaluating generative tasks such as summarization. While these metrics are considered indicative (even if imperfect) of human evaluation for English, their suitability for other languages remains unclear. To address this, we systematically assess evaluation metrics for generation both n-gram-based and neural based to evaluate their effectiveness across languages and tasks. Specifically, we design a large-scale evaluation suite acr…

What Factors Affect LLMs and RLLMs in Financial Question Answering?
Peng Wang, Xuesi Hu, Jiageng Wu, Yuntao Zou, Qiancheng Zhang, Dagang Li
https://arxiv.org/abs/2507.08339

What Factors Affect LLMs and RLLMs in Financial Question Answering?
Recently, the development of large language models (LLMs) and reasoning large language models (RLLMs) have gained considerable attention from many researchers. RLLMs enhance the reasoning capabilities of LLMs through Long Chain-of-Thought (Long CoT) processes, significantly improving the performance of LLMs in addressing complex problems. However, there are few works that systematically explore what methods can fully unlock the performance of LLMs and RLLMs within the financial domain. To inves…

Distillation versus Contrastive Learning: How to Train Your Rerankers
Zhichao Xu, Zhiqi Huang, Shengyao Zhuang, Ashim Gupta, Vivek Srikumar
https://arxiv.org/abs/2507.08336

Distillation versus Contrastive Learning: How to Train Your Rerankers
Training text rerankers is crucial for information retrieval. Two primary strategies are widely used: contrastive learning (optimizing directly on ground-truth labels) and knowledge distillation (transferring knowledge from a larger reranker). While both have been studied in the literature, a clear comparison of their effectiveness for training cross-encoder rerankers under practical conditions is needed. This paper empirically compares these strategies by training rerankers of different size…

MK2 at PBIG Competition: A Prompt Generation Solution
Yuzheng Xu, Tosho Hirasawa, Seiya Kawano, Shota Kato, Tadashi Kozuno
https://arxiv.org/abs/2507.08335

MK2 at PBIG Competition: A Prompt Generation Solution
The Patent-Based Idea Generation task asks systems to turn real patents into product ideas viable within three years. We propose MK2, a prompt-centric pipeline: Gemini 2.5 drafts and iteratively edits a prompt, grafting useful fragments from weaker outputs; GPT-4.1 then uses this prompt to create one idea per patent, and an Elo loop judged by Qwen3-8B selects the best prompt-all without extra training data. Across three domains, two evaluator types, and six criteria, MK2 topped the automatic le…

Dynamic Parameter Memory: Temporary LoRA-Enhanced LLM for Long-Sequence Emotion Recognition in Conversation
Jialong Mai, Xiaofen Xing, Yawei Li, Zhipeng Li, Jingyuan Xing, Xiangmin Xu
https://arxiv.org/abs/2507.09076

Dynamic Parameter Memory: Temporary LoRA-Enhanced LLM for Long-Sequence Emotion Recognition in Conversation
Recent research has focused on applying speech large language model (SLLM) to improve speech emotion recognition (SER). However, the inherently high frame rate in speech modality severely limits the signal processing and understanding capabilities of SLLM. For example, a SLLM with a 4K context window can only process 80 seconds of audio at 50Hz feature sampling rate before reaching its capacity limit. Input token compression methods used in SLLM overlook the continuity and inertia of emotions a…

OpenCodeReasoning-II: A Simple Test Time Scaling Approach via Self-Critique
Wasi Uddin Ahmad, Somshubra Majumdar, Aleksander Ficek, Sean Narenthiran, Mehrzad Samadi, Jocelyn Huang, Siddhartha Jain, Vahid Noroozi, Boris Ginsburg
https://arxiv.org/abs/2507.09075

OpenCodeReasoning-II: A Simple Test Time Scaling Approach via Self-Critique
Recent advancements in reasoning-based Large Language Models (LLMs), particularly their potential through test-time scaling, have created significant opportunities for distillation in code generation and critique. However, progress in both areas fundamentally depends on large-scale, high-quality datasets. In this work, we introduce OpenCodeReasoning-II, a dataset consists of 2.5M question-solution-critique triples (approx. 35K unique programming questions), making it nearly twice the size of th…