Tootfinder

ZeroSep: Separate Anything in Audio with Zero Training
Chao Huang, Yuesheng Ma, Junxuan Huang, Susan Liang, Yunlong Tang, Jing Bi, Wenqiang Liu, Nima Mesgarani, Chenliang Xu
https://arxiv.org/abs/2505.23625

ZeroSep: Separate Anything in Audio with Zero Training
Audio source separation is fundamental for machines to understand complex acoustic environments and underpins numerous audio applications. Current supervised deep learning approaches, while powerful, are limited by the need for extensive, task-specific labeled data and struggle to generalize to the immense variability and open-set nature of real-world acoustic scenes. Inspired by the success of generative foundation models, we investigate whether pre-trained text-guided audio diffusion models c…

PDLogger: Automated Logging Framework for Practical Software Development
Shengcheng Duan, Yihua Xu, Sheng Zhang, Shen Wang, Yue Duan
https://arxiv.org/abs/2507.19951 https://

PDLogger: Automated Logging Framework for Practical Software Development
Logging is indispensable for maintaining the reliability and diagnosability of modern software, yet developers still struggle to decide where and how to log effectively. Existing automated logging techniques focus on isolated sub-tasks - predicting a single log position, level, or message - and therefore cannot produce complete, high-quality log statements that reflect real-world practice in which multiple logs often appear inside one method. They also neglect deeper semantic dependencies among…

BinauralFlow: A Causal and Streamable Approach for High-Quality Binaural Speech Synthesis with Flow Matching Models
Susan Liang, Dejan Markovic, Israel D. Gebru, Steven Krenn, Todd Keebler, Jacob Sandakly, Frank Yu, Samuel Hassel, Chenliang Xu, Alexander Richard
https://arxiv.org/abs/2505.22865

BinauralFlow: A Causal and Streamable Approach for High-Quality Binaural Speech Synthesis with Flow Matching Models
Binaural rendering aims to synthesize binaural audio that mimics natural hearing based on a mono audio and the locations of the speaker and listener. Although many methods have been proposed to solve this problem, they struggle with rendering quality and streamable inference. Synthesizing high-quality binaural audio that is indistinguishable from real-world recordings requires precise modeling of binaural cues, room reverb, and ambient sounds. Additionally, real-world applications demand stream…

TableMoE: Neuro-Symbolic Routing for Structured Expert Reasoning in Multimodal Table Understanding
Junwen Zhang, Pu Chen, Yin Zhang
https://arxiv.org/abs/2506.21393

TableMoE: Neuro-Symbolic Routing for Structured Expert Reasoning in Multimodal Table Understanding
Multimodal understanding of tables in real-world contexts is challenging due to the complexity of structure, symbolic density, and visual degradation (blur, skew, watermarking, incomplete structures or fonts, multi-span or hierarchically nested layouts). Existing multimodal large language models (MLLMs) struggle with such WildStruct conditions, resulting in limited performance and poor generalization. To address these challenges, we propose TableMoE, a neuro-symbolic Mixture-of-Connector-Expert…

DCMF: A Dynamic Context Monitoring and Caching Framework for Context Management Platforms
Ashish Manchanda, Prem Prakash Jayaraman, Abhik Banerjee, Kaneez Fizza, Arkady Zaslavsky
https://arxiv.org/abs/2506.17226

DCMF: A Dynamic Context Monitoring and Caching Framework for Context Management Platforms
The rise of context-aware IoT applications has increased the demand for timely and accurate context information. Context is derived by aggregating and inferring from dynamic IoT data, making it highly volatile and posing challenges in maintaining freshness and real-time accessibility. Caching is a potential solution, but traditional policies struggle with the transient nature of context in IoT (e.g., ensuring real-time access for frequent queries or handling fast-changing data). To address this…

Denoising-While-Completing Network (DWCNet): Robust Point Cloud Completion Under Corruption
Keneni W. Tesema, Lyndon Hill, Mark W. Jones, Gary K. L. Tam
https://arxiv.org/abs/2507.16743

Denoising-While-Completing Network (DWCNet): Robust Point Cloud Completion Under Corruption
Point cloud completion is crucial for 3D computer vision tasks in autonomous driving, augmented reality, and robotics. However, obtaining clean and complete point clouds from real-world environments is challenging due to noise and occlusions. Consequently, most existing completion networks -- trained on synthetic data -- struggle with real-world degradations. In this work, we tackle the problem of completing and denoising highly corrupted partial point clouds affected by multiple simultaneous d…

RightTyper: Effective and Efficient Type Annotation for Python
Juan Altmayer Pizzorno, Emery D. Berger
https://arxiv.org/abs/2507.16051 https://

RightTyper: Effective and Efficient Type Annotation for Python
Python type annotations bring the benefits of static type checking to the language. However, manually writing annotations can be time-consuming and tedious. The result is that most real-world Python code remains largely untyped. Past approaches to annotating types in Python code fall short in a number of ways. Static approaches struggle with dynamic features and infer overly broad types. AI-based methods are inherently unsound and can miss rare or user-defined types. Dynamic methods can impose …

Engineering teams often get stuck striving for perfection, not out of laziness, but due to an insatiable desire to improve. Join Tim Schmolka next week at Berlin Buzzwords to discover how teams can overcome perfectionism. He will discuss the significance of setting clear boundaries, balancing quality with timely delivery, and focusing on what truly matters.
Learn more:

The Perfection Trap
Many engineering teams face a common struggle: the desire to deliver perfect solutions can delay projects and limit business impact. This session explores how the familiar Parkinson’s Law is often misunderstood in engineering contexts. Instead of laziness, it’s excessive craftsmanship and relentless perfectionism that extend timelines. Through real-world examples and practical insights, you’ll learn about the “perfection pressure spectrum,” which helps diagnose whether your team is st…

My day just took a nosedive because some fascist jerk is celebrating a bill landing on his desk!
Honestly, it’s wild how people still put their faith in the same old power games when real change comes from people coming together, running things themselves, and kicking the fascists out of the picture.
Being autistic, I usually struggle to get what people mean, but Rudolf Rocker said some real shit that even my autistic brain understands.

Semantic Scaffolding: Augmenting Textual Structures with Domain-Specific Groupings for Accessible Data Exploration
Jonathan Zong, Isabella Pedraza Pineros, Mengzhu Katie Chen, Daniel Hajas, Arvind Satyanarayan
https://arxiv.org/abs/2506.15883

Semantic Scaffolding: Augmenting Textual Structures with Domain-Specific Groupings for Accessible Data Exploration
Drawing connections between interesting groupings of data and their real-world meaning is an important, yet difficult, part of encountering a new dataset. A lay reader might see an interesting visual pattern in a chart but lack the domain expertise to explain its meaning. Or, a reader might be familiar with a real-world concept but struggle to express it in terms of a dataset's fields. In response, we developed semantic scaffolding, a technique for using domain-specific information from large l…

SIDA: Synthetic Image Driven Zero-shot Domain Adaptation
Ye-Chan Kim, SeungJu Cha, Si-Woo Kim, Taewhan Kim, Dong-Jin Kim
https://arxiv.org/abs/2507.18632 https://

SIDA: Synthetic Image Driven Zero-shot Domain Adaptation
Zero-shot domain adaptation is a method for adapting a model to a target domain without utilizing target domain image data. To enable adaptation without target images, existing studies utilize CLIP's embedding space and text description to simulate target-like style features. Despite the previous achievements in zero-shot domain adaptation, we observe that these text-driven methods struggle to capture complex real-world variations and significantly increase adaptation time due to their alignmen…

NeHMO: Neural Hamilton-Jacobi Reachability Learning for Decentralized Safe Multi-Agent Motion Planning
Qingyi Chen, Ahmed H. Qureshi
https://arxiv.org/abs/2507.13940

NeHMO: Neural Hamilton-Jacobi Reachability Learning for Decentralized Safe Multi-Agent Motion Planning
Safe Multi-Agent Motion Planning (MAMP) is a significant challenge in robotics. Despite substantial advancements, existing methods often face a dilemma. Decentralized algorithms typically rely on predicting the behavior of other agents, sharing contracts, or maintaining communication for safety, while centralized approaches struggle with scalability and real-time decision-making. To address these challenges, we introduce Neural Hamilton-Jacobi Reachability Learning (HJR) for Decentralized Multi…

Seeing is Fixing: Cross-Modal Reasoning with Multimodal LLMs for Visual Software Issue Fixing
Kai Huang, Jian Zhang, Xiaofei Xie, Chunyang Chen
https://arxiv.org/abs/2506.16136

Seeing is Fixing: Cross-Modal Reasoning with Multimodal LLMs for Visual Software Issue Fixing
Large language model-(LLM) based automated program repair (APR) techniques have shown promising results in resolving real-world GitHub issue tasks. Existing APR systems are primarily evaluated in unimodal settings (e.g., SWE-bench). However, these autonomous systems struggle to resolve multimodal problem scenarios (e.g., SWE-bench M) due to limitations in interpreting and leveraging visual information. In multimodal scenarios, LLMs need to rely on visual information in the graphical user interf…

AI bots playing music for other bots. A signpost along the way to our Matrix-like future
https://futurism.com/ai-band-spotify-streams

An "Indie Rock Band" That Appears to Be Entirely AI-Generated Is Making Alarming Amounts of Money on Spotify
While real artists struggle to earn money on Spotify, a seemingly AI-generated band has garnered enough streams to actually make a buck. 

This https://arxiv.org/abs/2502.11753 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

HintsOfTruth: A Multimodal Checkworthiness Detection Dataset with Real and Synthetic Claims
Misinformation can be countered with fact-checking, but the process is costly and slow. Identifying checkworthy claims is the first step, where automation can help scale fact-checkers' efforts. However, detection methods struggle with content that is (1) multimodal, (2) from diverse domains, and (3) synthetic. We introduce HintsOfTruth, a public dataset for multimodal checkworthiness detection with 27K real-world and synthetic image/claim pairs. The mix of real and synthetic data makes this dat…

Multi-Timescale Dynamics Model Bayesian Optimization for Plasma Stabilization in Tokamaks
Rohit Sonker, Alexandre Capone, Andrew Rothstein, Hiro Josep Farre Kaga, Egemen Kolemen, Jeff Schneider
https://arxiv.org/abs/2506.10287

Multi-Timescale Dynamics Model Bayesian Optimization for Plasma Stabilization in Tokamaks
Machine learning algorithms often struggle to control complex real-world systems. In the case of nuclear fusion, these challenges are exacerbated, as the dynamics are notoriously complex, data is poor, hardware is subject to failures, and experiments often affect dynamics beyond the experiment's duration. Existing tools like reinforcement learning, supervised learning, and Bayesian optimization address some of these challenges but fail to provide a comprehensive solution. To overcome these limi…

MLLM-Based UI2Code Automation Guided by UI Layout Information
Fan Wu, Cuiyun Gao, Shuqing Li, Xin-Cheng Wen, Qing Liao
https://arxiv.org/abs/2506.10376 htt…

MLLM-Based UI2Code Automation Guided by UI Layout Information
Converting user interfaces into code (UI2Code) is a crucial step in website development, which is time-consuming and labor-intensive. The automation of UI2Code is essential to streamline this task, beneficial for improving the development efficiency. There exist deep learning-based methods for the task; however, they heavily rely on a large amount of labeled training data and struggle with generalizing to real-world, unseen web page designs. The advent of Multimodal Large Language Models (MLLMs…

A Comparative Evaluation of Deep Learning Models for Speech Enhancement in Real-World Noisy Environments
Md Jahangir Alam Khondkar, Ajan Ahmed, Masudul Haider Imtiaz, Stephanie Schuckers
https://arxiv.org/abs/2506.15000

A Comparative Evaluation of Deep Learning Models for Speech Enhancement in Real-World Noisy Environments
Speech enhancement, particularly denoising, is vital in improving the intelligibility and quality of speech signals for real-world applications, especially in noisy environments. While prior research has introduced various deep learning models for this purpose, many struggle to balance noise suppression, perceptual quality, and speaker-specific feature preservation, leaving a critical research gap in their comparative performance evaluation. This study benchmarks three state-of-the-art models W…

SABR-Informed Multitask Gaussian Process: A Synthetic-to-Real Framework for Implied Volatility Surface Construction
Jirong Zhuang, Xuan Wu
https://arxiv.org/abs/2506.22888

SABR-Informed Multitask Gaussian Process: A Synthetic-to-Real Framework for Implied Volatility Surface Construction
Constructing the Implied Volatility Surface (IVS) is a challenging task in quantitative finance due to the complexity of real markets and the sparsity of market data. Structural models like Stochastic Alpha Beta Rho (SABR) model offer interpretability and theoretical consistency but lack flexibility, while purely data-driven methods such as Gaussian Process regression can struggle with sparse data. We introduce SABR-Informed Multi-Task Gaussian Process (SABR-MTGP), treating IVS construction as …

FindingDory: A Benchmark to Evaluate Memory in Embodied Agents
Karmesh Yadav, Yusuf Ali, Gunshi Gupta, Yarin Gal, Zsolt Kira
https://arxiv.org/abs/2506.15635

FindingDory: A Benchmark to Evaluate Memory in Embodied Agents
Large vision-language models have recently demonstrated impressive performance in planning and control tasks, driving interest in their application to real-world robotics. However, deploying these models for reasoning in embodied contexts is limited by their ability to incorporate long-term experience collected across multiple days and represented by vast collections of images. Current VLMs typically struggle to process more than a few hundred images concurrently, highlighting the need for more…

A Data-Driven Diffusion-based Approach for Audio Deepfake Explanations
Petr Grinberg, Ankur Kumar, Surya Koppisetti, Gaurav Bharaj
https://arxiv.org/abs/2506.03425

A Data-Driven Diffusion-based Approach for Audio Deepfake Explanations
Evaluating explainability techniques, such as SHAP and LRP, in the context of audio deepfake detection is challenging due to lack of clear ground truth annotations. In the cases when we are able to obtain the ground truth, we find that these methods struggle to provide accurate explanations. In this work, we propose a novel data-driven approach to identify artifact regions in deepfake audio. We consider paired real and vocoded audio, and use the difference in time-frequency representation as th…

This https://arxiv.org/abs/2504.13703 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Consensus-aware Contrastive Learning for Group Recommendation
Group recommendation aims to provide personalized item suggestions to a group of users by reflecting their collective preferences. A fundamental challenge in this task is deriving a consensus that adequately represents the diverse interests of individual group members. Despite advancements made by deep learning-based models, existing approaches still struggle in two main areas: (1) Capturing consensus in small-group settings, which are more prevalent in real-world applications, and (2) Balancin…

Large-scale portfolio optimization with variational neural annealing
Nishan Ranabhat, Behnam Javanparast, David Goerz, Estelle Inack
https://arxiv.org/abs/2507.07159

Large-scale portfolio optimization with variational neural annealing
Portfolio optimization is a routine asset management operation conducted in financial institutions around the world. However, under real-world constraints such as turnover limits and transaction costs, its formulation becomes a mixed-integer nonlinear program that current mixed-integer optimizers often struggle to solve. We propose mapping this problem onto a classical Ising-like Hamiltonian and solving it with Variational Neural Annealing (VNA), via its classical formulation implemented using …

Refining music sample identification with a self-supervised graph neural network
Aditya Bhattacharjee, Ivan Meresman Higgs, Mark Sandler, Emmanouil Benetos
https://arxiv.org/abs/2506.14684

Refining music sample identification with a self-supervised graph neural network
Automatic sample identification (ASID), the detection and identification of portions of audio recordings that have been reused in new musical works, is an essential but challenging task in the field of audio query-based retrieval. While a related task, audio fingerprinting, has made significant progress in accurately retrieving musical content under "real world" (noisy, reverberant) conditions, ASID systems struggle to identify samples that have undergone musical modifications. Thus, a system r…

Attention-Based Map Encoding for Learning Generalized Legged Locomotion
Junzhe He, Chong Zhang, Fabian Jenelten, Ruben Grandia, Moritz B\"Acher, Marco Hutter
https://arxiv.org/abs/2506.09588

Attention-Based Map Encoding for Learning Generalized Legged Locomotion
Dynamic locomotion of legged robots is a critical yet challenging topic in expanding the operational range of mobile robots. It requires precise planning when possible footholds are sparse, robustness against uncertainties and disturbances, and generalizability across diverse terrains. While traditional model-based controllers excel at planning on complex terrains, they struggle with real-world uncertainties. Learning-based controllers offer robustness to such uncertainties but often lack preci…

Reinforcement Learning with Data Bootstrapping for Dynamic Subgoal Pursuit in Humanoid Robot Navigation
Chengyang Peng, Zhihao Zhang, Shiting Gong, Sankalp Agrawal, Keith A. Redmill, Ayonga Hereid
https://arxiv.org/abs/2506.02206

Reinforcement Learning with Data Bootstrapping for Dynamic Subgoal Pursuit in Humanoid Robot Navigation
Safe and real-time navigation is fundamental for humanoid robot applications. However, existing bipedal robot navigation frameworks often struggle to balance computational efficiency with the precision required for stable locomotion. We propose a novel hierarchical framework that continuously generates dynamic subgoals to guide the robot through cluttered environments. Our method comprises a high-level reinforcement learning (RL) planner for subgoal selection in a robot-centric coordinate syste…