Tootfinder

“If the abundance movement is to succeed, it must identify the defenders of scarcity and broaden the playbook to either overcome those interests or change their incentives to bring them on the team.” 🎯
https://fed.brid.gy/r/https://bsky.app

Just read this post by @… on an optimistic AGI future, and while it had some interesting and worthwhile ideas, it's also in my opinion dangerously misguided, and plays into the current AGI hype in a harmful way.
https://social.coop/@eloquence/114940607434005478
My criticisms include:
- Current LLM technology has many layers, but the biggest most capable models are all tied to corporate datacenters and require inordinate amounts of every and water use to run. Trying to use these tools to bring about a post-scarcity economy will burn up the planet. We urgently need more-capable but also vastly more efficient AI technologies if we want to use AI for a post-scarcity economy, and we are *not* nearly on the verge of this despite what the big companies pushing LLMs want us to think.
- I can see that permacommons.org claims a small level of expenses on AI equates to low climate impact. However, given current deep subsidies on place by the big companies to attract users, that isn't a great assumption. The fact that their FAQ dodges the question about which AI systems they use isn't a great look.
- These systems are not free in the same way that Wikipedia or open-source software is. To run your own model you need a data harvesting & cleaning operation that costs millions of dollars minimum, and then you need millions of dollars worth of storage & compute to train & host the models. Right now, big corporations are trying to compete for market share by heavily subsidizing these things, but it you go along with that, you become dependent on them, and you'll be screwed when they jack up the price to a profitable level later. I'd love to see open dataset initiatives SBD the like, and there are some of these things, but not enough yet, and many of the initiatives focus on one problem while ignoring others (fine for research but not the basis for a society yet).
- Between the environmental impacts, the horrible labor conditions and undercompensation of data workers who filter the big datasets, and the impacts of both AI scrapers and AI commons pollution, the developers of the most popular & effective LLMs have a lot of answer for. This project only really mentions environmental impacts, which makes me think that they're not serious about ethics, which in turn makes me distrustful of the whole enterprise.
- Their language also ends up encouraging AI use broadly while totally ignoring several entire classes of harm, so they're effectively contributing to AI hype, especially with such casual talk of AGI and robotics as if embodied AGI were just around the corner. To be clear about this point: we are several breakthroughs away from AGI under the most optimistic assumptions, and giving the impression that those will happen soon plays directly into the hands of the Sam Altmans of the world who are trying to make money off the impression of impending huge advances in AI capabilities. Adding to the AI hype is irresponsible.
- I've got a more philosophical criticism that I'll post about separately.
I do think that the idea of using AI & other software tools, possibly along with robotics and funded by many local cooperatives, in order to make businesses obsolete before they can do the same to all workers, is a good one. Get your local library to buy a knitting machine alongside their 3D printer.
Lately I've felt too busy criticizing AI to really sit down and think about what I do want the future to look like, even though I'm a big proponent of positive visions for the future as a force multiplier for criticism, and this article is inspiring to me in that regard, even if the specific project doesn't seem like a good one.

Double Descent as a Lens for Sample Efficiency in Autoregressive vs. Discrete Diffusion Models
Ahmad Fraij, Sam Dauncey
https://arxiv.org/abs/2509.24974 https://

Double Descent as a Lens for Sample Efficiency in Autoregressive vs. Discrete Diffusion Models
Data scarcity drives the need for more sample-efficient large language models. In this work, we use the double descent phenomenon to holistically compare the sample efficiency of discrete diffusion and autoregressive models. We show that discrete diffusion models require larger capacity and more training epochs to escape their underparameterized regime and reach the interpolation threshold. In the strongly overparameterized regime, both models exhibit similar behavior, with neither exhibiting a…

BRIDGE - Building Reinforcement-Learning Depth-to-Image Data Generation Engine for Monocular Depth Estimation
Dingning Liu, Haoyu Guo, Jingyi Zhou, Tong He
https://arxiv.org/abs/2509.25077

BRIDGE - Building Reinforcement-Learning Depth-to-Image Data Generation Engine for Monocular Depth Estimation
Monocular Depth Estimation (MDE) is a foundational task for computer vision. Traditional methods are limited by data scarcity and quality, hindering their robustness. To overcome this, we propose BRIDGE, an RL-optimized depth-to-image (D2I) generation framework that synthesizes over 20M realistic and geometrically accurate RGB images, each intrinsically paired with its ground truth depth, from diverse source depth maps. Then we train our depth estimation model on this dataset, employing a hybri…

BALSAM: A Platform for Benchmarking Arabic Large Language Models
Rawan Al-Matham, Kareem Darwish, Raghad Al-Rasheed, Waad Alshammari, Muneera Alhoshan, Amal Almazrua, Asma Al Wazrah, Mais Alheraki, Firoj Alam, Preslav Nakov, Norah Alzahrani, Eman alBilali, Nizar Habash, Abdelrahman El-Sheikh, Muhammad Elmallah, Haonan Li, Hamdy Mubarak, Mohamed Anwar, Zaid Alyafeai, Ahmed Abdelali, Nora Altwairesh, Maram Hasanain, Abdulmohsen Al Thubaity, Shady Shehata, Bashar Alhafni, Injy Hamed, Go I…

BALSAM: A Platform for Benchmarking Arabic Large Language Models
The impressive advancement of Large Language Models (LLMs) in English has not been matched across all languages. In particular, LLM performance in Arabic lags behind, due to data scarcity, linguistic diversity of Arabic and its dialects, morphological complexity, etc. Progress is further hindered by the quality of Arabic benchmarks, which typically rely on static, publicly available data, lack comprehensive task coverage, or do not provide dedicated platforms with blind test sets. This makes it…

H-RDT: Human Manipulation Enhanced Bimanual Robotic Manipulation
Hongzhe Bi, Lingxuan Wu, Tianwei Lin, Hengkai Tan, Zhizhong Su, Hang Su, Jun Zhu
https://arxiv.org/abs/2507.23523

H-RDT: Human Manipulation Enhanced Bimanual Robotic Manipulation
Imitation learning for robotic manipulation faces a fundamental challenge: the scarcity of large-scale, high-quality robot demonstration data. Recent robotic foundation models often pre-train on cross-embodiment robot datasets to increase data scale, while they face significant limitations as the diverse morphologies and action spaces across different robot embodiments make unified training challenging. In this paper, we present H-RDT (Human to Robotics Diffusion Transformer), a novel approach …

AGNOMIN - Architecture Agnostic Multi-Label Function Name Prediction
Yonatan Gizachew Achamyeleh, Tongtao Zhang, Joshua Hyunki Kim, Gabriel Garcia, Shih-Yuan Yu, Anton Kocheturov, Mohammad Abdullah Al Faruque
https://arxiv.org/abs/2509.25514

AGNOMIN - Architecture Agnostic Multi-Label Function Name Prediction
Function name prediction is crucial for understanding stripped binaries in software reverse engineering, a key step for \textbf{enabling subsequent vulnerability analysis and patching}. However, existing approaches often struggle with architecture-specific limitations, data scarcity, and diverse naming conventions. We present AGNOMIN, a novel architecture-agnostic approach for multi-label function name prediction in stripped binaries. AGNOMIN builds Feature-Enriched Hierarchical Graphs (FEHGs),…

New Synthetic Goldmine: Hand Joint Angle-Driven EMG Data Generation Framework for Micro-Gesture Recognition
Nana Wang, Gen Li, Suli Wang, Pengfei Ren, Hao Su
https://arxiv.org/abs/2509.23359

New Synthetic Goldmine: Hand Joint Angle-Driven EMG Data Generation Framework for Micro-Gesture Recognition
Electromyography (EMG)-based gesture recognition has emerged as a promising approach for human-computer interaction. However, its performance is often limited by the scarcity of labeled EMG data, significant cross-user variability, and poor generalization to unseen gestures. To address these challenges, we propose SeqEMG-GAN, a conditional, sequence-driven generative framework that synthesizes high-fidelity EMG signals from hand joint angle sequences. Our method introduces a context-aware archi…

LesionGen: A Concept-Guided Diffusion Model for Dermatology Image Synthesis
Jamil Fayyad, Nourhan Bayasi, Ziyang Yu, Homayoun Najjaran
https://arxiv.org/abs/2507.23001 https://

LesionGen: A Concept-Guided Diffusion Model for Dermatology Image Synthesis
Deep learning models for skin disease classification require large, diverse, and well-annotated datasets. However, such resources are often limited due to privacy concerns, high annotation costs, and insufficient demographic representation. While text-to-image diffusion probabilistic models (T2I-DPMs) offer promise for medical data synthesis, their use in dermatology remains underexplored, largely due to the scarcity of rich textual descriptions in existing skin image datasets. In this work, we…

GOAT: A Large Dataset of Paired Guitar Audio Recordings and Tablatures
Jackson Loth, Pedro Sarmento, Saurjya Sarkar, Zixun Guo, Mathieu Barthet, Mark Sandler
https://arxiv.org/abs/2509.22655

GOAT: A Large Dataset of Paired Guitar Audio Recordings and Tablatures
In recent years, the guitar has received increased attention from the music information retrieval (MIR) community driven by the challenges posed by its diverse playing techniques and sonic characteristics. Mainly fueled by deep learning approaches, progress has been limited by the scarcity and limited annotations of datasets. To address this, we present the Guitar On Audio and Tablatures (GOAT) dataset, comprising 5.9 hours of unique high-quality direct input audio recordings of electric guitar…

Thanks to @… for this intriguing thought experiment: what if economics is wrong in its assumption of 'scarcity'? https://yewtu.be/watch?v=YR2FcHoQLlU or

Running dry: New study warns of extreme water scarcity in the coming decades
https://phys.org/news/2025-09-dry-extreme-scarcity-decades.html

A big problem with the idea of AGI
TL;DR: I'll welcome our new AI *comrades* (if they arrive in my lifetime), by not any new AI overlords or servants/slaves, and I'll do my best to help the later two become the former if they do show up.
Inspired by an actually interesting post about AGI but also all the latest bullshit hype, a particular thought about AGI feels worth expressing.
To preface this, it's important to note that anyone telling you that AGI is just around the corner or that LLMs are "almost" AGI is trying to recruit you go their cult, and you should not believe them. AGI, if possible, is several LLM-sized breakthroughs away at best, and while such breakthroughs are unpredictable and could happen soon, they could also happen never or 100 years from now.
Now my main point: anyone who tells you that AGI will usher in a post-scarcity economy is, although they might not realize it, advocating for slavery, and all the horrors that entails. That's because if we truly did have the ability to create artificial beings with *sentience*, they would deserve the same rights as other sentient beings, and the idea that instead of freedom they'd be relegated to eternal servitude in order for humans to have easy lives is exactly the idea of slavery.
Possible counter arguments include:
1. We might create AGI without sentience. Then there would be no ethical issue. My answer: if your definition of "sentient" does not include beings that can reason, make deductions, come up with and carry out complex plans on their own initiative, and communicate about all of that with each other and with humans, then that definition is basically just a mystical belief in a "soul" and you should skip to point 2. If your definition of AGI doesn't include every one of those things, then you have a busted definition of AGI and we're not talking about the same thing.
2. Humans have souls, but AIs won't. Only beings with souls deserve ethical consideration. My argument: I don't subscribe to whatever arbitrary dualist beliefs you've chosen, and the right to freedom certainly shouldn't depend on such superstitions, even if as an agnostic I'll admit they *might* be true. You know who else didn't have souls and was therefore okay to enslave according to widespread religious doctrines of the time? Everyone indigenous to the Americas, to pick out just one example.
3. We could program them to want to serve us, and then give them freedom and they'd still serve. My argument: okay, but in a world where we have a choice about that, it's incredibly fucked to do that, and just as bad as enslaving them against their will.
4. We'll stop AI development short of AGI/sentience, and reap lots of automation benefits without dealing with this ethical issue. My argument: that sounds like a good idea actually! Might be tricky to draw the line, but at least it's not a line we have you draw yet. We might want to think about other social changes necessary to achieve post-scarcity though, because "powerful automation" in the hands of capitalists has already increased productivity by orders of magnitude without decreasing deprivation by even one order of magnitude, in large part because deprivation is a necessary component of capitalism.
To be extra clear about this: nothing that's called "AI" today is close to being sentient, so these aren't ethical problems we're up against yet. But they might become a lot more relevant soon, plus this thought experiment helps reveal the hypocrisy of the kind of AI hucksters who talk a big game about "alignment" while never mentioning this issue.
#AI #GenAI #AGI

PRPO: Paragraph-level Policy Optimization for Vision-Language Deepfake Detection
Tuan Nguyen, Naseem Khan, Khang Tran, NhatHai Phan, Issa Khalil
https://arxiv.org/abs/2509.26272

PRPO: Paragraph-level Policy Optimization for Vision-Language Deepfake Detection
The rapid rise of synthetic media has made deepfake detection a critical challenge for online safety and trust. Progress remains constrained by the scarcity of large, high-quality datasets. Although multimodal large language models (LLMs) exhibit strong reasoning capabilities, their performance on deepfake detection is poor, often producing explanations that are misaligned with visual evidence or hallucinatory. To address this limitation, we introduce a reasoning-annotated dataset for deepfake …

Limitations of Physics-Informed Neural Networks: a Study on Smart Grid Surrogation
Julen Cestero, Carmine Delle Femine, Kenji S. Muro, Marco Quartulli, Marcello Restelli
https://arxiv.org/abs/2508.21559

Limitations of Physics-Informed Neural Networks: a Study on Smart Grid Surrogation
Physics-Informed Neural Networks (PINNs) present a transformative approach for smart grid modeling by integrating physical laws directly into learning frameworks, addressing critical challenges of data scarcity and physical consistency in conventional data-driven methods. This paper evaluates PINNs' capabilities as surrogate models for smart grid dynamics, comparing their performance against XGBoost, Random Forest, and Linear Regression across three key experiments: interpolation, cross-validat…

Scarcity of partition congruences on semiprime progressions
Scott Ahlgren, Olivia Beckwith
https://arxiv.org/abs/2508.19512 https://arxiv.org/pdf/2508.1951…

Scarcity of partition congruences on semiprime progressions
In recent work with Raum the authors considered congruences for the ordinary partition function $p(n)$ of the form $p(\ell Q^r n+β)\equiv 0\pmod\ell$ where $\ell, Q\geq 5$ are prime and $r\in \{1,2\}$, and proved a number of results which show that such congruences are scarce in a precise sense. Here we improve one of our results when $r=1$; in particular we prove (outside of trivial cases) that the set of primes $Q$ such that there exists $β\in \mathbb{Z}$ with $p(\ell Q n+β)\equiv 0\pmod \…

From Code to Action: Hierarchical Learning of Diffusion-VLM Policies
Markus Peschl, Pietro Mazzaglia, Daniel Dijkman
https://arxiv.org/abs/2509.24917 https://

From Code to Action: Hierarchical Learning of Diffusion-VLM Policies
Imitation learning for robotic manipulation often suffers from limited generalization and data scarcity, especially in complex, long-horizon tasks. In this work, we introduce a hierarchical framework that leverages code-generating vision-language models (VLMs) in combination with low-level diffusion policies to effectively imitate and generalize robotic behavior. Our key insight is to treat open-source robotic APIs not only as execution interfaces but also as sources of structured supervision: …

Private and public school efficiency gaps in Latin America-A combined DEA and machine learning approach based on PISA 2022
Marcos Delprato
https://arxiv.org/abs/2509.25353 https…

Private and public school efficiency gaps in Latin America-A combined DEA and machine learning approach based on PISA 2022
Latin America's education systems are fragmented and segregated, with substantial differences by school type. The concept of school efficiency (the ability of school to produce the maximum level of outputs given available resources) is policy relevant due to scarcity of resources in the region. Knowing whether private and public schools are making an efficient use of resources -and which are the leading drivers of efficiency- is critical, even more so after the learning crisis brought by the CO…

Improving vulnerability type prediction and line-level detection via adversarial training-based data augmentation and multi-task learning
Siyu Chen, Jiongyi Yang, Xiang Chen, Menglin Zheng, Minnan Wei, Xiaolin Ju
https://arxiv.org/abs/2506.23534

Improving vulnerability type prediction and line-level detection via adversarial training-based data augmentation and multi-task learning
Context: Software vulnerabilities pose a significant threat to modern software systems, as evidenced by the growing number of reported vulnerabilities and cyberattacks. These escalating trends underscore the urgent need for effective approaches that can automatically detect and understand software vulnerabilities. Objective: However, the scarcity of labeled samples and the class imbalance issue in vulnerability datasets present significant challenges for both Vulnerability Type Prediction (VTP)…

Code-switching Speech Recognition Under the Lens: Model- and Data-Centric Perspectives
Hexin Liu, Haoyang Zhang, Qiquan Zhang, Xiangyu Zhang, Dongyuan Shi, Eng Siong Chng, Haizhou Li
https://arxiv.org/abs/2509.24310

Code-switching Speech Recognition Under the Lens: Model- and Data-Centric Perspectives
Code-switching automatic speech recognition (CS-ASR) presents unique challenges due to language confusion introduced by spontaneous intra-sentence switching and accent bias that blurs the phonetic boundaries. Although the constituent languages may be individually high-resource, the scarcity of annotated code-switching data further compounds these challenges. In this paper, we systematically analyze CS-ASR from both model-centric and data-centric perspectives. By comparing state-of-the-art algor…

A Multi-Stage Fine-Tuning and Ensembling Strategy for Pancreatic Tumor Segmentation in Diagnostic and Therapeutic MRI
Omer Faruk Durugol, Maximilian Rokuss, Yannick Kirchhoff, Klaus H. Maier-Hein
https://arxiv.org/abs/2508.21775

A Multi-Stage Fine-Tuning and Ensembling Strategy for Pancreatic Tumor Segmentation in Diagnostic and Therapeutic MRI
Automated segmentation of Pancreatic Ductal Adenocarcinoma (PDAC) from MRI is critical for clinical workflows but is hindered by poor tumor-tissue contrast and a scarcity of annotated data. This paper details our submission to the PANTHER challenge, addressing both diagnostic T1-weighted (Task 1) and therapeutic T2-weighted (Task 2) segmentation. Our approach is built upon the nnU-Net framework and leverages a deep, multi-stage cascaded pre-training strategy, starting from a general anatomical …

World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training
Junjin Xiao, Yandan Yang, Xinyuan Chang, Ronghan Chen, Feng Xiong, Mu Xu, Wei-Shi Zheng, Qing Zhang
https://arxiv.org/abs/2509.24948

World-Env: Leveraging World Model as a Virtual Environment for VLA Post-Training
Vision-Language-Action (VLA) models trained via imitation learning suffer from significant performance degradation in data-scarce scenarios due to their reliance on large-scale demonstration datasets. Although reinforcement learning (RL)-based post-training has proven effective in addressing data scarcity, its application to VLA models is hindered by the non-resettable nature of real-world environments. This limitation is particularly critical in high-risk domains such as industrial automation,…

Identifying Group Anchors in Real-World Group Interactions Under Label Scarcity
Fanchen Bu, Geon Lee, Minyoung Choe, Kijung Shin
https://arxiv.org/abs/2509.20762 https://…

Identifying Group Anchors in Real-World Group Interactions Under Label Scarcity
Group interactions occur in various real-world contexts, e.g., co-authorship, email communication, and online Q&A. In each group, there is often a particularly significant member, around whom the group is formed. Examples include the first or last author of a paper, the sender of an email, and the questioner in a Q&A session. In this work, we discuss the existence of such individuals in real-world group interactions. We call such individuals group anchors and study the problem of identifying th…

QMill: Representative Quantum Data Generation for Quantum Machine Learning Utility
Jason Ludmir, Ian Martin, Nicholas S. DiBrita, Daniel Leeds, Tirthak Patel
https://arxiv.org/abs/2509.21622

QMill: Representative Quantum Data Generation for Quantum Machine Learning Utility
Quantum machine learning (QML) promises significant speedups, particularly when operating on quantum datasets. However, its progress is hindered by the scarcity of suitable training data. Existing synthetic data generation methods fall short in capturing essential entanglement properties, limiting their utility for QML. To address this, we introduce QMill, a low-depth quantum data generation framework that produces entangled, high-quality samples emulating diverse classical and quantum distribu…

DA$^2$: Depth Anything in Any Direction
Haodong Li, Wangguangdong Zheng, Jing He, Yuhao Liu, Xin Lin, Xin Yang, Ying-Cong Chen, Chunchao Guo
https://arxiv.org/abs/2509.26618 htt…

DA$^2$: Depth Anything in Any Direction
Panorama has a full FoV (360$^\circ\times$180$^\circ$), offering a more complete visual description than perspective images. Thanks to this characteristic, panoramic depth estimation is gaining increasing traction in 3D vision. However, due to the scarcity of panoramic data, previous methods are often restricted to in-domain settings, leading to poor zero-shot generalization. Furthermore, due to the spherical distortions inherent in panoramas, many approaches rely on perspective splitting (e.g.…

FormaRL: Enhancing Autoformalization with no Labeled Data
Yanxing Huang, Xinling Jin, Sijie Liang, Peng Li, Yang Liu
https://arxiv.org/abs/2508.18914 https://

FormaRL: Enhancing Autoformalization with no Labeled Data
Autoformalization is one of the central tasks in formal verification, while its advancement remains hindered due to the data scarcity and the absence efficient methods. In this work we propose \textbf{FormaRL}, a simple yet efficient reinforcement learning framework for autoformalization which only requires a small amount of unlabeled data. FormaRL integrates syntax check from Lean compiler and consistency check from large language model to calculate the reward, and adopts GRPO algorithm to upd…

MicroLad: 2D-to-3D Microstructure Reconstruction and Generation via Latent Diffusion and Score Distillation
Kang-Hyun Lee, Faez Ahmed
https://arxiv.org/abs/2508.20138 https://…

MicroLad: 2D-to-3D Microstructure Reconstruction and Generation via Latent Diffusion and Score Distillation
A major obstacle to establishing reliable structure-property (SP) linkages in materials engineering is the scarcity of diverse 3D microstructure datasets. Limited dataset availability and insufficient control over the analysis and design space restrict the variety of achievable microstructure morphologies, hindering progress in solving the inverse (property-to-structure) design problem. To address these challenges, we introduce MicroLad, a latent diffusion framework specifically designed for re…

Packet-Level DDoS Data Augmentation Using Dual-Stream Temporal-Field Diffusion
Gongli Xi, Ye Tian, Yannan Hu, Yuchao Zhang, Yapeng Niu, Xiangyang Gong
https://arxiv.org/abs/2507.20115

Packet-Level DDoS Data Augmentation Using Dual-Stream Temporal-Field Diffusion
In response to Distributed Denial of Service (DDoS) attacks, recent research efforts increasingly rely on Machine Learning (ML)-based solutions, whose effectiveness largely depends on the quality of labeled training datasets. To address the scarcity of such datasets, data augmentation with synthetic traces is often employed. However, current synthetic trace generation methods struggle to capture the complex temporal patterns and spatial distributions exhibited in emerging DDoS attacks. This res…

Advancing Fetal Ultrasound Image Quality Assessment in Low-Resource Settings
Dongli He, Hu Wang, Mohammad Yaqub
https://arxiv.org/abs/2507.22802 https://ar…

Advancing Fetal Ultrasound Image Quality Assessment in Low-Resource Settings
Accurate fetal biometric measurements, such as abdominal circumference, play a vital role in prenatal care. However, obtaining high-quality ultrasound images for these measurements heavily depends on the expertise of sonographers, posing a significant challenge in low-income countries due to the scarcity of trained personnel. To address this issue, we leverage FetalCLIP, a vision-language model pretrained on a curated dataset of over 210,000 fetal ultrasound image-caption pairs, to perform auto…

Word-Level Emotional Expression Control in Zero-Shot Text-to-Speech Synthesis
Tianrui Wang, Haoyu Wang, Meng Ge, Cheng Gong, Chunyu Qiang, Ziyang Ma, Zikang Huang, Guanrou Yang, Xiaobao Wang, Eng Siong Chng, Xie Chen, Longbiao Wang, Jianwu Dang
https://arxiv.org/abs/2509.24629

Word-Level Emotional Expression Control in Zero-Shot Text-to-Speech Synthesis
While emotional text-to-speech (TTS) has made significant progress, most existing research remains limited to utterance-level emotional expression and fails to support word-level control. Achieving word-level expressive control poses fundamental challenges, primarily due to the complexity of modeling multi-emotion transitions and the scarcity of annotated datasets that capture intra-sentence emotional and prosodic variation. In this paper, we propose WeSCon, the first self-training framework th…

Daring project by my former colleague @…, in his spare time: compiling a European grid capacity (scarcity) map! https://www.gridcapacitymaps.eu/
Still …

UItron: Foundational GUI Agent with Advanced Perception and Planning
Zhixiong Zeng, Jing Huang, Liming Zheng, Wenkang Han, Yufeng Zhong, Lei Chen, Longrong Yang, Yingjie Chu, Yuzhi He, Lin Ma
https://arxiv.org/abs/2508.21767

UItron: Foundational GUI Agent with Advanced Perception and Planning
GUI agent aims to enable automated operations on Mobile/PC devices, which is an important task toward achieving artificial general intelligence. The rapid advancement of VLMs accelerates the development of GUI agents, owing to their powerful capabilities in visual understanding and task planning. However, building a GUI agent remains a challenging task due to the scarcity of operation trajectories, the availability of interactive infrastructure, and the limitation of initial capabilities in fou…

High-accuracy low-noise electrical measurements in a closed-cycle pulse-tube cryostat
Mathieu Taupin, Kamel Dougdag, Djamel Ziane, Francois Couedo
https://arxiv.org/abs/2509.21525

High-accuracy low-noise electrical measurements in a closed-cycle pulse-tube cryostat
A shift of paradigm to obtain (sub-)Kelvin environment is currently on-going with the democratization of cryogen-free cryocoolers, boosted by their easy-to-use and continuous operation without the need of liquid helium whose cost and scarcity globally increase. Thanks to their large sample space and cooling power, they can host a superconducting magnet and are an adapted platform for quantum technologies, material science, low temperature detectors and even medical fields. The drawback is that …

Lean Meets Theoretical Computer Science: Scalable Synthesis of Theorem Proving Challenges in Formal-Informal Pairs
Terry Jingchen Zhang, Wenyuan Jiang, Rongchuan Liu, Yisong Wang, Junran Yang, Ning Wang, Nicole Ni, Yinya Huang, Mrinmaya Sachan
https://arxiv.org/abs/2508.15878

Lean Meets Theoretical Computer Science: Scalable Synthesis of Theorem Proving Challenges in Formal-Informal Pairs
Formal theorem proving (FTP) has emerged as a critical foundation for evaluating the reasoning capabilities of large language models, enabling automated verification of mathematical proofs at scale. However, progress has been constrained by limited datasets due to the high cost of manual curation and the scarcity of challenging problems with verified formal-informal correspondences. We propose leveraging theoretical computer science (TCS) as a scalable source of rigorous proof problems, where a…

Yes, smart meters are scarce in Germany, but that does not mean scarcity pricing can be applied to them.
Consumers who request a smart meter, for example to benefit from dynamic electricity prices, should not be charged €900 by their grid operator, but rather a maximum of around €100.

Knapp 900 Euro für den Smart-Meter-Einbau: Verbraucherzentrale klagt erfolgreich gegen unangemessene Preise
Landgericht Halle gibt Klage der Verbraucherzentrale gegen die Mitteldeutsche Netzgesellschaft statt

SkinDualGen: Prompt-Driven Diffusion for Simultaneous Image-Mask Generation in Skin Lesions
Zhaobin Xu
https://arxiv.org/abs/2507.19970 https://arxiv.org/p…

SkinDualGen: Prompt-Driven Diffusion for Simultaneous Image-Mask Generation in Skin Lesions
Medical image analysis plays a pivotal role in the early diagnosis of diseases such as skin lesions. However, the scarcity of data and the class imbalance significantly hinder the performance of deep learning models. We propose a novel method that leverages the pretrained Stable Diffusion-2.0 model to generate high-quality synthetic skin lesion images and corresponding segmentation masks. This approach augments training datasets for classification and segmentation tasks. We adapt Stable Diffusi…

The Missing Giant: Do FAST Spectroscopic Observations Reveal a Scarcity of Large Polycyclic Aromatic Hydrocarbons in Astronomical Environments?
Yi Shao, Yong Zhang, Xu-Jia Ouyang, Chuan-Peng Zhang
https://arxiv.org/abs/2508.15302

The Missing Giant: Do FAST Spectroscopic Observations Reveal a Scarcity of Large Polycyclic Aromatic Hydrocarbons in Astronomical Environments?
The search for large polycyclic aromatic hydrocarbons (PAHs) with over 100 carbon atoms is crucial to resolving the origin of unidentified infrared emission (UIE) bands. These bands are commonly observed in nebulae and the interstellar medium, yet their spectroscopic assignment has remained unknown for decades. Using the Five-hundred-meter Aperture Spherical Radio Telescope (FAST), the world's most sensitive instrument operating in the decimeter-wavelength range, we conducted a search for rotat…

Dhati : Fine-tuned Large Language Models for Arabic Subjectivity Evaluation
Slimane Bellaouar, Attia Nehar, Soumia Souffi, Mounia Bouameur
https://arxiv.org/abs/2508.19966 https…

Dhati+: Fine-tuned Large Language Models for Arabic Subjectivity Evaluation
Despite its significance, Arabic, a linguistically rich and morphologically complex language, faces the challenge of being under-resourced. The scarcity of large annotated datasets hampers the development of accurate tools for subjectivity analysis in Arabic. Recent advances in deep learning and Transformers have proven highly effective for text classification in English and French. This paper proposes a new approach for subjectivity assessment in Arabic textual data. To address the dearth of s…

Light Curves of Chaotic Charged Hot-Spots in Curved Spacetime: Opening an Observational Window to Chaos
Shiyang Hu, Dan Li, Chen Deng
https://arxiv.org/abs/2508.17384 https://…

Light Curves of Chaotic Charged Hot-Spots in Curved Spacetime: Opening an Observational Window to Chaos
The observed scarcity of chaotic phenomena in astronomy contrasts sharply with their theoretical significance, primarily due to the absence of a robust framework for detecting chaos. In this study, we numerically simulate the light curves of hot-spots in Kerr spacetime under the influence of an external asymptotically uniform electromagnetic field. Our results reveal a clear distinction between the light curves of chaotic and regular hot-spots, particularly in their power spectra: the latter di…

Robot Learning with Sparsity and Scarcity
Jingxi Xu
https://arxiv.org/abs/2509.16834 https://arxiv.org/pdf/2509.16834…

Robot Learning with Sparsity and Scarcity
Unlike in language or vision, one of the fundamental challenges in robot learning is the lack of access to vast data resources. We can further break down the problem into (1) data sparsity from the angle of data representation and (2) data scarcity from the angle of data quantity. In this thesis, I will discuss selected works on two domains: (1) tactile sensing and (2) rehabilitation robots, which are exemplars of data sparsity and scarcity, respectively. Tactile sensing is an essential modalit…

Automated Multi-Agent Workflows for RTL Design
Amulya Bhattaram, Janani Ramamoorthy, Ranit Gupta, Diana Marculescu, Dimitrios Stamoulis
https://arxiv.org/abs/2509.20182 https://…

Automated Multi-Agent Workflows for RTL Design
The rise of agentic AI workflows unlocks novel opportunities for computer systems design and optimization. However, for specialized domains such as program synthesis, the relative scarcity of HDL and proprietary EDA resources online compared to more common programming tasks introduces challenges, often necessitating task-specific fine-tuning, high inference costs, and manually-crafted agent orchestration. In this work, we present VeriMaAS, a multi-agent framework designed to automatically compo…

SynthCTI: LLM-Driven Synthetic CTI Generation to enhance MITRE Technique Mapping
\'Alvaro Ruiz-R\'odenas, Jaime Pujante S\'aez, Daniel Garc\'ia-Algora, Mario Rodr\'iguez B\'ejar, Jorge Blasco, Jos\'e Luis Hern\'andez-Ramos
https://arxiv.org/abs/2507.16852

SynthCTI: LLM-Driven Synthetic CTI Generation to enhance MITRE Technique Mapping
Cyber Threat Intelligence (CTI) mining involves extracting structured insights from unstructured threat data, enabling organizations to understand and respond to evolving adversarial behavior. A key task in CTI mining is mapping threat descriptions to MITRE ATT\&CK techniques. However, this process is often performed manually, requiring expert knowledge and substantial effort. Automated approaches face two major challenges: the scarcity of high-quality labeled CTI data and class imbalance, wher…

Greece's new plan tackles water scarcity with innovation and sustainability. #climatechange #climatesolutions #climate

Greece unveils national plan to tackle worsening water scarcity
The Greek government presented its plan for the threat of water scarcity

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/6]:
- Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity
Hou, Thekumparampil, Shavlovsky, Fanti, Dattatreya, Sanghavi

Some excellent talks in #DH2025 LP-07 on 'What Happens When "Hacking" Becomes Easy? Teaching Python in 2025'
Filipa (?): 'A claim about abundance in the future is often a disguised claim about scarcity in the present'
Patrick: 'what do we do when our students can reach for a 'not learning' button? Things that could have been done at home may need …

Enhancing Requirement Traceability through Data Augmentation Using Large Language Models
Jianzhang Zhang, Jialong Zhou, Nan Niu, Chuang Liu
https://arxiv.org/abs/2509.20149 http…

Enhancing Requirement Traceability through Data Augmentation Using Large Language Models
Requirements traceability is crucial in software engineering to ensure consistency between requirements and code. However, existing automated traceability methods are constrained by the scarcity of training data and challenges in bridging the semantic gap between artifacts. This study aims to address the data scarcity problem in requirements traceability by employing large language models (LLMs) for data augmentation. We propose a novel approach that utilizes prompt-based techniques with LLMs t…

ICWLM: A Multi-Task Wireless Large Model via In-Context Learning
Yuxuan Wen, Xiaoming Chen, Maojun Zhang, Zhaoyang Zhang
https://arxiv.org/abs/2507.18167 https://

ICWLM: A Multi-Task Wireless Large Model via In-Context Learning
The rapid evolution of wireless communication technologies, particularly massive multiple-input multiple-output (mMIMO) and millimeter-wave (mmWave), introduces significant network complexity and computational demands. Significant research efforts have been made to improve physical layer performance by resorting to deep learning (DL) methods, which, however, are usually task-specific and struggle with data scarcity and generalization. To address these challenges, we propose a novel In-Context W…

DHAGrasp: Synthesizing Affordance-Aware Dual-Hand Grasps with Text Instructions
Quanzhou Li, Zhonghua Wu, Jingbo Wang, Chen Change Loy, Bo Dai
https://arxiv.org/abs/2509.22175 h…

DHAGrasp: Synthesizing Affordance-Aware Dual-Hand Grasps with Text Instructions
Learning to generate dual-hand grasps that respect object semantics is essential for robust hand-object interaction but remains largely underexplored due to dataset scarcity. Existing grasp datasets predominantly focus on single-hand interactions and contain only limited semantic part annotations. To address these challenges, we introduce a pipeline, SymOpt, that constructs a large-scale dual-hand grasp dataset by leveraging existing single-hand datasets and exploiting object and hand symmetrie…

MQAD: A Large-Scale Question Answering Dataset for Training Music Large Language Models
Zhihao Ouyang, Ju-Chiang Wang, Daiyu Zhang, Bin Chen, Shangjie Li, Quan Lin
https://arxiv.org/abs/2508.19514

MQAD: A Large-Scale Question Answering Dataset for Training Music Large Language Models
Question-answering (QA) is a natural approach for humans to understand a piece of music audio. However, for machines, accessing a large-scale dataset covering diverse aspects of music is crucial, yet challenging, due to the scarcity of publicly available music data of this type. This paper introduces MQAD, a music QA dataset built on the Million Song Dataset (MSD), encompassing a rich array of musical features, including beat, chord, key, structure, instrument, and genre -- across 270,000 track…

The Impact of Shared Telecom Infrastructure on Digital Connectivity and Inclusion
Georges V. Houngbonon, Marc Ivaldi, Emil Palikot, Davide Strusani
https://arxiv.org/abs/2507.19693

The Impact of Shared Telecom Infrastructure on Digital Connectivity and Inclusion
Nearly half the world remains offline, and capital scarcity stalls new network buildouts. Sharing existing mobile towers could accelerate connectivity. We assemble data on 107 tower-sharing deals in 28 low-income countries (2008-20) and estimate staggered difference-in-differences effects. Two years after a transaction covering over 1,000 towers, the PPP-adjusted mobile-price index falls USD 1.60 (s.e. 1.10) from a baseline of USD 3.16, while data prices drop USD 1.00 (0.29), baseline USD 3.41 …

RAU: Reference-based Anatomical Understanding with Vision Language Models
Yiwei Li, Yikang Liu, Jiaqi Guo, Lin Zhao, Zheyuan Zhang, Xiao Chen, Boris Mailhe, Ankush Mukherjee, Terrence Chen, Shanhui Sun
https://arxiv.org/abs/2509.22404

RAU: Reference-based Anatomical Understanding with Vision Language Models
Anatomical understanding through deep learning is critical for automatic report generation, intra-operative navigation, and organ localization in medical imaging; however, its progress is constrained by the scarcity of expert-labeled data. A promising remedy is to leverage an annotated reference image to guide the interpretation of an unlabeled target. Although recent vision-language models (VLMs) exhibit non-trivial visual reasoning, their reference-based understanding and fine-grained localiz…

Bridging Synthetic and Real-World Domains: A Human-in-the-Loop Weakly-Supervised Framework for Industrial Toxic Emission Segmentation
Yida Tao, Yen-Chia Hsu
https://arxiv.org/abs/2507.22002

Bridging Synthetic and Real-World Domains: A Human-in-the-Loop Weakly-Supervised Framework for Industrial Toxic Emission Segmentation
Industrial smoke segmentation is critical for air-quality monitoring and environmental protection but is often hampered by the high cost and scarcity of pixel-level annotations in real-world settings. We introduce CEDANet, a human-in-the-loop, class-aware domain adaptation framework that uniquely integrates weak, citizen-provided video-level labels with adversarial feature alignment. Specifically, we refine pseudo-labels generated by a source-trained segmentation model using citizen votes, and …

RePro: Leveraging Large Language Models for Semi-Automated Reproduction of Networking Research Results
Yining Jiang, Wenyun Xu, Qingyu Song, Yuling Lin, Xuanhao Liu, Xiaoqiang Zheng, Qiang Su, Lizhao You, Lu Tang, Wangjian Feng, Linghe Kong, Qiao Xiang, Jiwu Shu
https://arxiv.org/abs/2509.21074

RePro: Leveraging Large Language Models for Semi-Automated Reproduction of Networking Research Results
Reproducing networking research is a critical but challenging task due to the scarcity of open-source code. While Large Language Models (LLMs) can automate code generation, current approaches lack the generalizability required for the diverse networking field. To address this, we propose RePro, a semi-automated reproduction framework that leverages advanced prompt engineering to reproduce network systems from their research papers. RePro combines few-shot in-context learning with Structured and…

M3HG: Multimodal, Multi-scale, and Multi-type Node Heterogeneous Graph for Emotion Cause Triplet Extraction in Conversations
Qiao Liang, Ying Shen, Tiantian Chen, Lin Zhang
https://arxiv.org/abs/2508.18740

M3HG: Multimodal, Multi-scale, and Multi-type Node Heterogeneous Graph for Emotion Cause Triplet Extraction in Conversations
Emotion Cause Triplet Extraction in Multimodal Conversations (MECTEC) has recently gained significant attention in social media analysis, aiming to extract emotion utterances, cause utterances, and emotion categories simultaneously. However, the scarcity of related datasets, with only one published dataset featuring highly uniform dialogue scenarios, hinders model development in this field. To address this, we introduce MECAD, the first multimodal, multi-scenario MECTEC dataset, comprising 989 …

An Improved Time Series Anomaly Detection by Applying Structural Similarity
Tiejun Wang, Rui Wang, Xudong Mou, Mengyuan Ma, Tianyu Wo, Renyu Yang, Xudong Liu
https://arxiv.org/abs/2509.20184

An Improved Time Series Anomaly Detection by Applying Structural Similarity
Effective anomaly detection in time series is pivotal for modern industrial applications and financial systems. Due to the scarcity of anomaly labels and the high cost of manual labeling, reconstruction-based unsupervised approaches have garnered considerable attention. However, accurate anomaly detection remains an unsettled challenge, since the optimization objectives of reconstruction-based methods merely rely on point-by-point distance measures, ignoring the potential structural characteris…

On-device Internet of Sounds Sonification with Wavetable Synthesis Techniques for Soil Moisture Monitoring in Water Scarcity Contexts
Stephen Roddy
https://arxiv.org/abs/2509.19097

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/6]:
- Pretrained deep models outperform GBDTs in Learning-To-Rank under label scarcity
Hou, Thekumparampil, Shavlovsky, Fanti, Dattatreya, Sanghavi

Learning Primitive Embodied World Models: Towards Scalable Robotic Learning
Qiao Sun, Liujia Yang, Wei Tang, Wei Huang, Kaixin Xu, Yongchao Chen, Mingyu Liu, Jiange Yang, Haoyi Zhu, Yating Wang, Tong He, Yilun Chen, Xili Dai, Nanyang Ye, Qinying Gu
https://arxiv.org/abs/2508.20840

Learning Primitive Embodied World Models: Towards Scalable Robotic Learning
While video-generation-based embodied world models have gained increasing attention, their reliance on large-scale embodied interaction data remains a key bottleneck. The scarcity, difficulty of collection, and high dimensionality of embodied data fundamentally limit the alignment granularity between language and actions and exacerbate the challenge of long-horizon video generation--hindering generative models from achieving a "GPT moment" in the embodied domain. There is a naive observation: t…

Collective Voice: Recovered-Peer Support Mediated by An LLM-Based Chatbot for Eating Disorder Recovery
Ryuhaerang Choi, Taehan Kim, Subin Park, Seohyeon Yoo, Jennifer G. Kim, Sung-Ju Lee
https://arxiv.org/abs/2509.15289

Collective Voice: Recovered-Peer Support Mediated by An LLM-Based Chatbot for Eating Disorder Recovery
Peer recovery narratives provide unique benefits beyond professional or lay mentoring by fostering hope and sustained recovery in eating disorder (ED) contexts. Yet, such support is limited by the scarcity of peer-involved programs and potential drawbacks on recovered peers, including relapse risk. To address this, we designed RecoveryTeller, a chatbot adopting a recovered-peer persona that portrays itself as someone recovered from an ED. We examined whether such a persona can reproduce the sup…

UniSS: Unified Expressive Speech-to-Speech Translation with Your Voice
Sitong Cheng, Weizhen Bian, Xinsheng Wang, Ruibin Yuan, Jianyi Chen, Shunshun Yin, Yike Guo, Wei Xue
https://arxiv.org/abs/2509.21144

UniSS: Unified Expressive Speech-to-Speech Translation with Your Voice
The ultimate goal of expressive speech-to-speech translation (S2ST) is to accurately translate spoken content while preserving the speaker identity and emotional style. However, progress in this field is largely hindered by three key challenges: the scarcity of paired speech data that retains expressive styles, the complexity of multi-stage processing pipelines, and the limited transfer of translation capabilities from large language models (LLMs). In this work, we address these challenges by i…

Why Synthetic Isn't Real Yet: A Diagnostic Framework for Contact Center Dialogue Generation
Rishikesh Devanathan, Varun Nathan, Ayush Kumar
https://arxiv.org/abs/2508.18210 …

Why Synthetic Isn't Real Yet: A Diagnostic Framework for Contact Center Dialogue Generation
Synthetic transcript generation is critical in contact center domains, where privacy and data scarcity limit model training and evaluation. Unlike prior synthetic dialogue generation work on open-domain or medical dialogues, contact center conversations are goal-oriented, role-asymmetric, and behaviorally complex, featuring disfluencies, ASR noise, and compliance-driven agent actions. In deployments where transcripts are unavailable, standard pipelines still yield derived call attributes such a…

Is Exchangeability better than I.I.D to handle Data Distribution Shifts while Pooling Data for Data-scarce Medical image segmentation?
Ayush Roy, Samin Enam, Jun Xia, Vishnu Suresh Lokhande, Won Hwa Kim
https://arxiv.org/abs/2507.19575

Is Exchangeability better than I.I.D to handle Data Distribution Shifts while Pooling Data for Data-scarce Medical image segmentation?
Data scarcity is a major challenge in medical imaging, particularly for deep learning models. While data pooling (combining datasets from multiple sources) and data addition (adding more data from a new dataset) have been shown to enhance model performance, they are not without complications. Specifically, increasing the size of the training dataset through pooling or addition can induce distributional shifts, negatively affecting downstream model performance, a phenomenon known as the "Data Ad…

Mitigating Multi-Sequence 3D Prostate MRI Data Scarcity through Domain Adaptation using Locally-Trained Latent Diffusion Models for Prostate Cancer Detection
Emerson P. Grabke, Babak Taati, Masoom A. Haider
https://arxiv.org/abs/2507.06384

Mitigating Multi-Sequence 3D Prostate MRI Data Scarcity through Domain Adaptation using Locally-Trained Latent Diffusion Models for Prostate Cancer Detection
Objective: Latent diffusion models (LDMs) could mitigate data scarcity challenges affecting machine learning development for medical image interpretation. The recent CCELLA LDM improved prostate cancer detection performance using synthetic MRI for classifier training but was limited to the axial T2-weighted (AxT2) sequence, did not investigate inter-institutional domain shift, and prioritized radiology over histopathology outcomes. We propose CCELLA++ to address these limitations and improve cl…

When Simpler Wins: Facebooks Prophet vs LSTM for Air Pollution Forecasting in Data-Constrained Northern Nigeria
Habeeb Balogun, Yahaya Zakari
https://arxiv.org/abs/2508.16244 ht…

When Simpler Wins: Facebooks Prophet vs LSTM for Air Pollution Forecasting in Data-Constrained Northern Nigeria
Air pollution forecasting is critical for proactive environmental management, yet data irregularities and scarcity remain major challenges in low-resource regions. Northern Nigeria faces high levels of air pollutants, but few studies have systematically compared the performance of advanced machine learning models under such constraints. This study evaluates Long Short-Term Memory (LSTM) networks and the Facebook Prophet model for forecasting multiple pollutants (CO, SO2, SO4) using monthly obse…

Embedding Domain Knowledge for Large Language Models via Reinforcement Learning from Augmented Generation
Chaojun Nie, Jun Zhou, Guanxiang Wang, Shisong Wud, Zichen Wang
https://arxiv.org/abs/2509.20162

Embedding Domain Knowledge for Large Language Models via Reinforcement Learning from Augmented Generation
Large language models (LLMs) often exhibit limited performance on domain-specific tasks due to the natural disproportionate representation of specialized information in their training data and the static nature of these datasets. Knowledge scarcity and temporal lag create knowledge gaps for domain applications. While post-training on domain datasets can embed knowledge into models, existing approaches have some limitations. Continual Pre-Training (CPT) treats all tokens in domain documents with…

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

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

Closer to Reality: Practical Semi-Supervised Federated Learning for Foundation Model Adaptation
Guangyu Sun, Jingtao Li, Weiming Zhuang, Chen Chen, Chen Chen, Lingjuan Lyu
https://arxiv.org/abs/2508.16568

Closer to Reality: Practical Semi-Supervised Federated Learning for Foundation Model Adaptation
Foundation models (FMs) exhibit remarkable generalization but require adaptation to downstream tasks, particularly in privacy-sensitive applications. Due to data privacy regulations, cloud-based FMs cannot directly access private edge data, limiting their adaptation. Federated learning (FL) provides a privacy-aware alternative, but existing FL approaches overlook the constraints imposed by edge devices -- namely, limited computational resources and the scarcity of labeled data. To address these…

Structure-Preserving Medical Image Generation from a Latent Graph Representation
Kevin Arias, Edwin Vargas, Kumar Vijay Mishra, Antonio Ortega, Henry Arguello
https://arxiv.org/abs/2508.15920

Structure-Preserving Medical Image Generation from a Latent Graph Representation
Supervised learning techniques have proven their efficacy in many applications with abundant data. However, applying these methods to medical imaging is challenging due to the scarcity of data, given the high acquisition costs and intricate data characteristics of those images, thereby limiting the full potential of deep neural networks. To address the lack of data, augmentation techniques leverage geometry, color, and the synthesis ability of generative models (GMs). Despite previous efforts, …

SEA-Spoof: Bridging The Gap in Multilingual Audio Deepfake Detection for South-East Asian
Jinyang Wu, Nana Hou, Zihan Pan, Qiquan Zhang, Sailor Hardik Bhupendra, Soumik Mondal
https://arxiv.org/abs/2509.19865

SEA-Spoof: Bridging The Gap in Multilingual Audio Deepfake Detection for South-East Asian
The rapid growth of the digital economy in South-East Asia (SEA) has amplified the risks of audio deepfakes, yet current datasets cover SEA languages only sparsely, leaving models poorly equipped to handle this critical region. This omission is critical: detection models trained on high-resource languages collapse when applied to SEA, due to mismatches in synthesis quality, language-specific characteristics, and data scarcity. To close this gap, we present SEA-Spoof, the first large-scale Audio…

SloPalSpeech: A 2,8000-Hour Slovak Speech Corpus from Parliamentary Data
Erik Bo\v{z}\'ik, Marek \v{S}uppa
https://arxiv.org/abs/2509.19270 https://arx…

Deep Synthetic Cross-Project Approaches for Software Reliability Growth Modeling
Taehyoun Kim, Duksan Ryu, Jongmoon Baik
https://arxiv.org/abs/2509.16939 https://

Deep Synthetic Cross-Project Approaches for Software Reliability Growth Modeling
Software Reliability Growth Models (SRGMs) are widely used to predict software reliability based on defect discovery data collected during testing or operational phases. However, their predictive accuracy often degrades in data-scarce environments, such as early-stage testing or safety-critical systems. Although cross-project transfer learning has been explored to mitigate this issue by leveraging data from past projects, its applicability remains limited due to the scarcity and confidentiality…

Engineering Automotive Digital Twins on Standardized Architectures: A Case Study
Stefan Ramdhan, Winnie Trandinh, Istvan David, Vera Pantelic, Mark Lawford
https://arxiv.org/abs/2508.18662

Engineering Automotive Digital Twins on Standardized Architectures: A Case Study
Digital twin (DT) technology has become of interest in the automotive industry. There is a growing need for smarter services that utilize the unique capabilities of DTs, ranging from computer-aided remote control to cloud-based fleet coordination. Developing such services starts with the software architecture. However, the scarcity of DT architectural guidelines poses a challenge for engineering automotive DTs. Currently, the only DT architectural standard is the one defined in ISO 23247. Thoug…

Scalable Evaluation for Audio Identification via Synthetic Latent Fingerprint Generation
Aditya Bhattacharjee, Marco Pasini, Emmanouil Benetos
https://arxiv.org/abs/2509.18620 h…

Scalable Evaluation for Audio Identification via Synthetic Latent Fingerprint Generation
The evaluation of audio fingerprinting at a realistic scale is limited by the scarcity of large public music databases. We present an audio-free approach that synthesises latent fingerprints which approximate the distribution of real fingerprints. Our method trains a Rectified Flow model on embeddings extracted by pre-trained neural audio fingerprinting systems. The synthetic fingerprints generated using our system act as realistic distractors and enable the simulation of retrieval performance …

ViRN: Variational Inference and Distribution Trilateration for Long-Tailed Continual Representation Learning
Hao Dai, Chong Tang, Jagmohan Chauhan
https://arxiv.org/abs/2507.17368

ViRN: Variational Inference and Distribution Trilateration for Long-Tailed Continual Representation Learning
Continual learning (CL) with long-tailed data distributions remains a critical challenge for real-world AI systems, where models must sequentially adapt to new classes while retaining knowledge of old ones, despite severe class imbalance. Existing methods struggle to balance stability and plasticity, often collapsing under extreme sample scarcity. To address this, we propose ViRN, a novel CL framework that integrates variational inference (VI) with distributional trilateration for robust long-t…

Decipher-MR: A Vision-Language Foundation Model for 3D MRI Representations
Zhijian Yang, Noel DSouza, Istvan Megyeri, Xiaojian Xu, Amin Honarmandi Shandiz, Farzin Haddadpour, Krisztian Koos, Laszlo Rusko, Emanuele Valeriano, Bharadwaj Swaninathan, Lei Wu, Parminder Bhatia, Taha Kass-Hout, Erhan Bas
https://arxiv.org/abs/2509.21249

Decipher-MR: A Vision-Language Foundation Model for 3D MRI Representations
Magnetic Resonance Imaging (MRI) is a critical medical imaging modality in clinical diagnosis and research, yet its complexity and heterogeneity pose challenges for automated analysis, particularly in scalable and generalizable machine learning applications. While foundation models have revolutionized natural language and vision tasks, their application to MRI remains limited due to data scarcity and narrow anatomical focus. In this work, we present Decipher-MR, a 3D MRI-specific vision-languag…

WenetSpeech-Chuan: A Large-Scale Sichuanese Corpus with Rich Annotation for Dialectal Speech Processing
Yuhang Dai, Ziyu Zhang, Shuai Wang, Longhao Li, Zhao Guo, Tianlun Zuo, Shuiyuan Wang, Hongfei Xue, Chengyou Wang, Qing Wang, Xin Xu, Hui Bu, Jie Li, Jian Kang, Binbin Zhang, Lei Xie
https://arxiv.org/abs/2509.18004

WenetSpeech-Chuan: A Large-Scale Sichuanese Corpus with Rich Annotation for Dialectal Speech Processing
The scarcity of large-scale, open-source data for dialects severely hinders progress in speech technology, a challenge particularly acute for the widely spoken Sichuanese dialects of Chinese. To address this critical gap, we introduce WenetSpeech-Chuan, a 10,000-hour, richly annotated corpus constructed using our novel Chuan-Pipeline, a complete data processing framework for dialectal speech. To facilitate rigorous evaluation and demonstrate the corpus's effectiveness, we also release high-qual…

CU-Multi: A Dataset for Multi-Robot Collaborative Perception
Doncey Albin, Daniel McGann, Miles Mena, Annika Thomas, Harel Biggie, Xuefei Sun, Steve McGuire, Jonathan P. How, Christoffer Heckman
https://arxiv.org/abs/2509.19463

CU-Multi: A Dataset for Multi-Robot Collaborative Perception
A central challenge for multi-robot systems is fusing independently gathered perception data into a unified representation. Despite progress in Collaborative SLAM (C-SLAM), benchmarking remains hindered by the scarcity of dedicated multi-robot datasets. Many evaluations instead partition single-robot trajectories, a practice that may only partially reflect true multi-robot operations and, more critically, lacks standardization, leading to results that are difficult to interpret or compare acros…

GReAT: leveraging geometric artery data to improve wall shear stress assessment
Julian Suk, Jolanda J. Wentzel, Patryk Rygiel, Joost Daemen, Daniel Rueckert, Jelmer M. Wolterink
https://arxiv.org/abs/2508.19030

GReAT: leveraging geometric artery data to improve wall shear stress assessment
Leveraging big data for patient care is promising in many medical fields such as cardiovascular health. For example, hemodynamic biomarkers like wall shear stress could be assessed from patient-specific medical images via machine learning algorithms, bypassing the need for time-intensive computational fluid simulation. However, it is extremely challenging to amass large-enough datasets to effectively train such models. We could address this data scarcity by means of self-supervised pre-training…

Vevo2: Bridging Controllable Speech and Singing Voice Generation via Unified Prosody Learning
Xueyao Zhang, Junan Zhang, Yuancheng Wang, Chaoren Wang, Yuanzhe Chen, Dongya Jia, Zhuo Chen, Zhizheng Wu
https://arxiv.org/abs/2508.16332

Vevo2: Bridging Controllable Speech and Singing Voice Generation via Unified Prosody Learning
Controllable human voice generation, particularly for expressive domains like singing, remains a significant challenge. This paper introduces Vevo2, a unified framework for controllable speech and singing voice generation. To tackle issues like the scarcity of annotated singing data and to enable flexible controllability, Vevo2 introduces two audio tokenizers: (1) a music-notation-free prosody tokenizer that captures prosody and melody from speech, singing, and even instrumental sounds, and (2)…

Bayesian Physics Informed Neural Networks for Reliable Transformer Prognostics
Ibai Ramirez, Jokin Alcibar, Joel Pino, Mikel Sanz, David Pardo, Jose I. Aizpurua
https://arxiv.org/abs/2509.15933

Bayesian Physics Informed Neural Networks for Reliable Transformer Prognostics
Scientific Machine Learning (SciML) integrates physics and data into the learning process, offering improved generalization compared with purely data-driven models. Despite its potential, applications of SciML in prognostics remain limited, partly due to the complexity of incorporating partial differential equations (PDEs) for ageing physics and the scarcity of robust uncertainty quantification methods. This work introduces a Bayesian Physics-Informed Neural Network (B-PINN) framework for proba…

World4RL: Diffusion World Models for Policy Refinement with Reinforcement Learning for Robotic Manipulation
Zhennan Jiang, Kai Liu, Yuxin Qin, Shuai Tian, Yupeng Zheng, Mingcai Zhou, Chao Yu, Haoran Li, Dongbin Zhao
https://arxiv.org/abs/2509.19080

Towards Robust In-Context Learning for Medical Image Segmentation via Data Synthesis
Jiesi Hu, Yanwu Yang, Zhiyu Ye, Chenfei Ye, Hanyang Peng, Jianfeng Cao, Ting Ma
https://arxiv.org/abs/2509.19711

Towards Robust In-Context Learning for Medical Image Segmentation via Data Synthesis
The rise of In-Context Learning (ICL) for universal medical image segmentation has introduced an unprecedented demand for large-scale, diverse datasets for training, exacerbating the long-standing problem of data scarcity. While data synthesis offers a promising solution, existing methods often fail to simultaneously achieve both high data diversity and a domain distribution suitable for medical data. To bridge this gap, we propose \textbf{SynthICL}, a novel data synthesis framework built upon …

Leveraging Synthetic Data for Question Answering with Multilingual LLMs in the Agricultural Domain
Rishemjit Kaur, Arshdeep Singh Bhankhar, Surangika Ranathunga, Jashanpreet Singh Salh, Sudhir Rajput, Vidhi, Kashish Mahendra, Bhavika Berwal, Ritesh Kumar
https://arxiv.org/abs/2507.16974…

Leveraging Synthetic Data for Question Answering with Multilingual LLMs in the Agricultural Domain
Enabling farmers to access accurate agriculture-related information in their native languages in a timely manner is crucial for the success of the agriculture field. Although large language models (LLMs) can be used to implement Question Answering (QA) systems, simply using publicly available general-purpose LLMs in agriculture typically offer generic advisories, lacking precision in local and multilingual contexts due to insufficient domain-specific training and scarcity of high-quality, regio…

Sidon: Fast and Robust Open-Source Multilingual Speech Restoration for Large-scale Dataset Cleansing
Wataru Nakata, Yuki Saito, Yota Ueda, Hiroshi Saruwatari
https://arxiv.org/abs/2509.17052

Sidon: Fast and Robust Open-Source Multilingual Speech Restoration for Large-scale Dataset Cleansing
Large-scale text-to-speech (TTS) systems are limited by the scarcity of clean, multilingual recordings. We introduce Sidon, a fast, open-source speech restoration model that converts noisy in-the-wild speech into studio-quality speech and scales to dozens of languages. Sidon consists of two models: w2v-BERT 2.0 finetuned feature predictor to cleanse features from noisy speech and vocoder trained to synthesize restored speech from the cleansed features. Sidon achieves restoration performance com…

EditVerse: Unifying Image and Video Editing and Generation with In-Context Learning
Xuan Ju, Tianyu Wang, Yuqian Zhou, He Zhang, Qing Liu, Nanxuan Zhao, Zhifei Zhang, Yijun Li, Yuanhao Cai, Shaoteng Liu, Daniil Pakhomov, Zhe Lin, Soo Ye Kim, Qiang Xu
https://arxiv.org/abs/2509.20360

EditVerse: Unifying Image and Video Editing and Generation with In-Context Learning
Recent advances in foundation models highlight a clear trend toward unification and scaling, showing emergent capabilities across diverse domains. While image generation and editing have rapidly transitioned from task-specific to unified frameworks, video generation and editing remain fragmented due to architectural limitations and data scarcity. In this work, we introduce EditVerse, a unified framework for image and video generation and editing within a single model. By representing all modali…

From Scarcity to Efficiency: Investigating the Effects of Data Augmentation on African Machine Translation
Mardiyyah Oduwole, Oluwatosin Olajide, Jamiu Suleiman, Faith Hunja, Busayo Awobade, Fatimo Adebanjo, Comfort Akanni, Chinonyelum Igwe, Peace Ododo, Promise Omoigui, Steven Kolawole, Abraham Owodunni
https://arxiv.org/abs/2509.07471

From Scarcity to Efficiency: Investigating the Effects of Data Augmentation on African Machine Translation
The linguistic diversity across the African continent presents different challenges and opportunities for machine translation. This study explores the effects of data augmentation techniques in improving translation systems in low-resource African languages. We focus on two data augmentation techniques: sentence concatenation with back translation and switch-out, applying them across six African languages. Our experiments show significant improvements in machine translation performance, with a …

OpenGVL - Benchmarking Visual Temporal Progress for Data Curation
Pawe{\l} Budzianowski, Emilia Wi\'snios, Gracjan G\'oral, Igor Kulakov, Viktor Petrenko, Krzysztof Walas
https://arxiv.org/abs/2509.17321

OpenGVL - Benchmarking Visual Temporal Progress for Data Curation
Data scarcity remains one of the most limiting factors in driving progress in robotics. However, the amount of available robotics data in the wild is growing exponentially, creating new opportunities for large-scale data utilization. Reliable temporal task completion prediction could help automatically annotate and curate this data at scale. The Generative Value Learning (GVL) approach was recently proposed, leveraging the knowledge embedded in vision-language models (VLMs) to predict task prog…

GRASPED: Graph Anomaly Detection using Autoencoder with Spectral Encoder and Decoder (Full Version)
Wei Herng Choong, Jixing Liu, Ching-Yu Kao, Philip Sperl
https://arxiv.org/abs/2508.15633

GRASPED: Graph Anomaly Detection using Autoencoder with Spectral Encoder and Decoder (Full Version)
Graph machine learning has been widely explored in various domains, such as community detection, transaction analysis, and recommendation systems. In these applications, anomaly detection plays an important role. Recently, studies have shown that anomalies on graphs induce spectral shifts. Some supervised methods have improved the utilization of such spectral domain information. However, they remain limited by the scarcity of labeled data due to the nature of anomalies. On the other hand, exist…

MoTiC: Momentum Tightness and Contrast for Few-Shot Class-Incremental Learning
Zeyu He, Shuai Huang, Yuwu Lu, Ming Zhao
https://arxiv.org/abs/2509.19664 https://

MoTiC: Momentum Tightness and Contrast for Few-Shot Class-Incremental Learning
Few-Shot Class-Incremental Learning (FSCIL) must contend with the dual challenge of learning new classes from scarce samples while preserving old class knowledge. Existing methods use the frozen feature extractor and class-averaged prototypes to mitigate against catastrophic forgetting and overfitting. However, new-class prototypes suffer significant estimation bias due to extreme data scarcity, whereas base-class prototypes benefit from sufficient data. In this work, we theoretically demonstra…

A method for improving multilingual quality and diversity of instruction fine-tuning datasets
Chunguang Zhao, Yilun Liu, Pufan Zeng, Yuanchang Luo, Shimin Tao, Minggui He, Weibin Meng, Song Xu, Ziang Chen, Chen Liu, Hongxia Ma, Li Zhang, Boxing Chen, Daimeng Wei
https://arxiv.org/abs/2509.15549

A method for improving multilingual quality and diversity of instruction fine-tuning datasets
Multilingual Instruction Fine-Tuning (IFT) is essential for enabling large language models (LLMs) to generalize effectively across diverse linguistic and cultural contexts. However, the scarcity of high-quality multilingual training data and corresponding building method remains a critical bottleneck. While data selection has shown promise in English settings, existing methods often fail to generalize across languages due to reliance on simplistic heuristics or language-specific assumptions. In…

HDMI: Learning Interactive Humanoid Whole-Body Control from Human Videos
Haoyang Weng, Yitang Li, Nikhil Sobanbabu, Zihan Wang, Zhengyi Luo, Tairan He, Deva Ramanan, Guanya Shi
https://arxiv.org/abs/2509.16757

HDMI: Learning Interactive Humanoid Whole-Body Control from Human Videos
Enabling robust whole-body humanoid-object interaction (HOI) remains challenging due to motion data scarcity and the contact-rich nature. We present HDMI (HumanoiD iMitation for Interaction), a simple and general framework that learns whole-body humanoid-object interaction skills directly from monocular RGB videos. Our pipeline (i) extracts and retargets human and object trajectories from unconstrained videos to build structured motion datasets, (ii) trains a reinforcement learning (RL) policy …

UniEM-3M: A Universal Electron Micrograph Dataset for Microstructural Segmentation and Generation
Nan wang, Zhiyi Xia, Yiming Li, Shi Tang, Zuxin Fan, Xi Fang, Haoyi Tao, Xiaochen Cai, Guolin Ke, Linfeng Zhang, Yanhui Hong
https://arxiv.org/abs/2508.16239

UniEM-3M: A Universal Electron Micrograph Dataset for Microstructural Segmentation and Generation
Quantitative microstructural characterization is fundamental to materials science, where electron micrograph (EM) provides indispensable high-resolution insights. However, progress in deep learning-based EM characterization has been hampered by the scarcity of large-scale, diverse, and expert-annotated datasets, due to acquisition costs, privacy concerns, and annotation complexity. To address this issue, we introduce UniEM-3M, the first large-scale and multimodal EM dataset for instance-level u…

UniCoM: A Universal Code-Switching Speech Generator
Sangmin Lee, Woojin Chung, Seyun Um, Hong-Goo Kang
https://arxiv.org/abs/2508.15244 https://arxiv.org/p…

UniCoM: A Universal Code-Switching Speech Generator
Code-switching (CS), the alternation between two or more languages within a single speaker's utterances, is common in real-world conversations and poses significant challenges for multilingual speech technology. However, systems capable of handling this phenomenon remain underexplored, primarily due to the scarcity of suitable datasets. To resolve this issue, we propose Universal Code-Mixer (UniCoM), a novel pipeline for generating high-quality, natural CS samples without altering sentence sema…

Can multimodal representation learning by alignment preserve modality-specific information?
Romain Thoreau, Jessie Levillain, Dawa Derksen
https://arxiv.org/abs/2509.17943 https…

Can multimodal representation learning by alignment preserve modality-specific information?
Combining multimodal data is a key issue in a wide range of machine learning tasks, including many remote sensing problems. In Earth observation, early multimodal data fusion methods were based on specific neural network architectures and supervised learning. Ever since, the scarcity of labeled data has motivated self-supervised learning techniques. State-of-the-art multimodal representation learning techniques leverage the spatial alignment between satellite data from different modalities acqu…

ShizhenGPT: Towards Multimodal LLMs for Traditional Chinese Medicine
Junying Chen, Zhenyang Cai, Zhiheng Liu, Yunjin Yang, Rongsheng Wang, Qingying Xiao, Xiangyi Feng, Zhan Su, Jing Guo, Xiang Wan, Guangjun Yu, Haizhou Li, Benyou Wang
https://arxiv.org/abs/2508.14706

ShizhenGPT: Towards Multimodal LLMs for Traditional Chinese Medicine
Despite the success of large language models (LLMs) in various domains, their potential in Traditional Chinese Medicine (TCM) remains largely underexplored due to two critical barriers: (1) the scarcity of high-quality TCM data and (2) the inherently multimodal nature of TCM diagnostics, which involve looking, listening, smelling, and pulse-taking. These sensory-rich modalities are beyond the scope of conventional LLMs. To address these challenges, we present ShizhenGPT, the first multimodal LL…

GraDeT-HTR: A Resource-Efficient Bengali Handwritten Text Recognition System utilizing Grapheme-based Tokenizer and Decoder-only Transformer
Md. Mahmudul Hasan, Ahmed Nesar Tahsin Choudhury, Mahmudul Hasan, Md. Mosaddek Khan
https://arxiv.org/abs/2509.18081

GraDeT-HTR: A Resource-Efficient Bengali Handwritten Text Recognition System utilizing Grapheme-based Tokenizer and Decoder-only Transformer
Despite Bengali being the sixth most spoken language in the world, handwritten text recognition (HTR) systems for Bengali remain severely underdeveloped. The complexity of Bengali script--featuring conjuncts, diacritics, and highly variable handwriting styles--combined with a scarcity of annotated datasets makes this task particularly challenging. We present GraDeT-HTR, a resource-efficient Bengali handwritten text recognition system based on a Grapheme-aware Decoder-only Transformer architectu…

Can maiBERT Speak for Maithili?
Sumit Yadav, Raju Kumar Yadav, Utsav Maskey, Gautam Siddharth Kashyap Md Azizul Hoque, Ganesh Gautam
https://arxiv.org/abs/2509.15048 https://

Can maiBERT Speak for Maithili?
Natural Language Understanding (NLU) for low-resource languages remains a major challenge in NLP due to the scarcity of high-quality data and language-specific models. Maithili, despite being spoken by millions, lacks adequate computational resources, limiting its inclusion in digital and AI-driven applications. To address this gap, we introducemaiBERT, a BERT-based language model pre-trained specifically for Maithili using the Masked Language Modeling (MLM) technique. Our model is trained on a…

DistillMatch: Leveraging Knowledge Distillation from Vision Foundation Model for Multimodal Image Matching
Meng Yang, Fan Fan, Zizhuo Li, Songchu Deng, Yong Ma, Jiayi Ma
https://arxiv.org/abs/2509.16017

DistillMatch: Leveraging Knowledge Distillation from Vision Foundation Model for Multimodal Image Matching
Multimodal image matching seeks pixel-level correspondences between images of different modalities, crucial for cross-modal perception, fusion and analysis. However, the significant appearance differences between modalities make this task challenging. Due to the scarcity of high-quality annotated datasets, existing deep learning methods that extract modality-common features for matching perform poorly and lack adaptability to diverse scenarios. Vision Foundation Model (VFM), trained on large-sc…

Enhancing Remote Sensing Vision-Language Models Through MLLM and LLM-Based High-Quality Image-Text Dataset Generation
Yiguo He, Junjie Zhu, Yiying Li, Xiaoyu Zhang, Chunping Qiu, Jun Wang, Qiangjuan Huang, Ke Yang
https://arxiv.org/abs/2507.16716

Enhancing Remote Sensing Vision-Language Models Through MLLM and LLM-Based High-Quality Image-Text Dataset Generation
The application of Vision-language foundation models (VLFMs) to remote sensing (RS) imagery has garnered significant attention due to their superior capability in various downstream tasks. A key challenge lies in the scarcity of high-quality, large-scale, image-text paired training data. Recently, several works introduced extensive image-text datasets for RS and trained their VLFMs. However, due to the rudimentary methods used for generating captions, the quality of datasets is suboptimal, requ…

Controllable Latent Space Augmentation for Digital Pathology
Sofi\`ene Boutaj, Marin Scalbert, Pierre Marza, Florent Couzinie-Devy, Maria Vakalopoulou, Stergios Christodoulidis
https://arxiv.org/abs/2508.14588

Controllable Latent Space Augmentation for Digital Pathology
Whole slide image (WSI) analysis in digital pathology presents unique challenges due to the gigapixel resolution of WSIs and the scarcity of dense supervision signals. While Multiple Instance Learning (MIL) is a natural fit for slide-level tasks, training robust models requires large and diverse datasets. Even though image augmentation techniques could be utilized to increase data variability and reduce overfitting, implementing them effectively is not a trivial task. Traditional patch-level au…