Tootfinder

In-domain SSL pre-training and streaming ASR
Jarod Duret, Salima Mdhaffar, Ga\"elle Laperri\`ere, Ryan Whetten, Audrey Galametz, Catherine Kobus, Marion-C\'ecile Martin, Jo Oleiwan, Yannick Est\`eve
https://arxiv.org/abs/2509.12101

In-domain SSL pre-training and streaming ASR
In this study, we investigate the benefits of domain-specific self-supervised pre-training for both offline and streaming ASR in Air Traffic Control (ATC) environments. We train BEST-RQ models on 4.5k hours of unlabeled ATC data, then fine-tune on a smaller supervised ATC set. To enable real-time processing, we propose using chunked attention and dynamic convolutions, ensuring low-latency inference. We compare these in-domain SSL models against state-of-the-art, general-purpose speech encoders …

Education and Outreach Across Seventeen Countries of the Pierre Auger Collaboration
Fabio Convenga (for the Pierre Auger Collaboration)
https://arxiv.org/abs/2509.11850 https://…

Education and Outreach Across Seventeen Countries of the Pierre Auger Collaboration
The Pierre Auger Observatory, dedicated to measuring ultra-high energy cosmic rays, has been promoting for more than two decades educational and scientific outreach activities to make its results known in an understandable language to diverse audiences. Among its most notable efforts, we can mention the creation of a visitor center at the Observatory site in Malargüe, Argentina, the production of brochures, posters, videos, talks, and special actions with the community in the site of the Obser…

Data-driven Methods of Extracting Text Structure and Information Transfer
Shinichi Honna, Taichi Murayama, Akira Matsui
https://arxiv.org/abs/2509.12999 https://

Data-driven Methods of Extracting Text Structure and Information Transfer
The Anna Karenina Principle (AKP) holds that success requires satisfying a small set of essential conditions, whereas failure takes diverse forms. We test AKP, its reverse, and two further patterns described as ordered and noisy across novels, online encyclopedias, research papers, and movies. Texts are represented as sequences of functional blocks, and convergence is assessed in transition order and position. Results show that structural principles vary by medium: novels follow reverse AKP in …

Dynamics of Quantum Analogs of Classical Impact Oscillators
Arnab Acharya, Titir Mukherjee, Deepshikha Singh, Soumitro Banerjee
https://arxiv.org/abs/2509.12835 https://

Dynamics of Quantum Analogs of Classical Impact Oscillators
This paper investigates the dynamics of quantum analogs of classical impact oscillators to explore how complex nonlinear behaviors manifest in quantum systems. While classical impact oscillators exhibit chaos and bifurcations, quantum systems, governed by linear equations, appear to forbid such dynamics. Through simulations of unforced, forced, and dissipative quantum oscillators, we uncover quasiperiodicity, strange nonchaotic dynamics, and even chaos in the presence of dissipation. Using entr…

Quality Assessment of Tabular Data using Large Language Models and Code Generation
Ashlesha Akella, Akshar Kaul, Krishnasuri Narayanam, Sameep Mehta
https://arxiv.org/abs/2509.10572

Quality Assessment of Tabular Data using Large Language Models and Code Generation
Reliable data quality is crucial for downstream analysis of tabular datasets, yet rule-based validation often struggles with inefficiency, human intervention, and high computational costs. We present a three-stage framework that combines statistical inliner detection with LLM-driven rule and code generation. After filtering data samples through traditional clustering, we iteratively prompt LLMs to produce semantically valid quality rules and synthesize their executable validators through code-g…

Toward Ownership Understanding of Objects: Active Question Generation with Large Language Model and Probabilistic Generative Model
Saki Hashimoto, Shoichi Hasegawa, Tomochika Ishikawa, Akira Taniguchi, Yoshinobu Hagiwara, Lotfi El Hafi, Tadahiro Taniguchi
https://arxiv.org/abs/2509.12754

Toward Ownership Understanding of Objects: Active Question Generation with Large Language Model and Probabilistic Generative Model
Robots operating in domestic and office environments must understand object ownership to correctly execute instructions such as ``Bring me my cup.'' However, ownership cannot be reliably inferred from visual features alone. To address this gap, we propose Active Ownership Learning (ActOwL), a framework that enables robots to actively generate and ask ownership-related questions to users. ActOwL employs a probabilistic generative model to select questions that maximize information gain, thereby …

Decoding Plastic Toxicity: An Intelligent Framework for Conflict-Aware Relational Metapath Extraction from Scientific Abstracts
Sudeshna Jana, Manjira Sinha, Tirthankar Dasgupta
https://arxiv.org/abs/2509.11330

Decoding Plastic Toxicity: An Intelligent Framework for Conflict-Aware Relational Metapath Extraction from Scientific Abstracts
The widespread use of plastics and their persistence in the environment have led to the accumulation of micro- and nano-plastics across air, water, and soil, posing serious health risks including respiratory, gastrointestinal, and neurological disorders. We propose a novel framework that leverages large language models to extract relational metapaths, multi-hop semantic chains linking pollutant sources to health impacts, from scientific abstracts. Our system identifies and connects entities acr…

Growing Perspectives: Modelling Embodied Perspective Taking and Inner Narrative Development Using Large Language Models
Sabrina Patania, Luca Annese, Anna Lambiase, Anita Pellegrini, Tom Foulsham, Azzurra Ruggeri, Silvia Rossi, Silvia Serino, Dimitri Ognibene
https://arxiv.org/abs/2509.11868

Growing Perspectives: Modelling Embodied Perspective Taking and Inner Narrative Development Using Large Language Models
Language and embodied perspective taking are essential for human collaboration, yet few computational models address both simultaneously. This work investigates the PerspAct system [1], which integrates the ReAct (Reason and Act) paradigm with Large Language Models (LLMs) to simulate developmental stages of perspective taking, grounded in Selman's theory [2]. Using an extended director task, we evaluate GPT's ability to generate internal narratives aligned with specified developmental stages, a…

Multi-Robot Task Planning for Multi-Object Retrieval Tasks with Distributed On-Site Knowledge via Large Language Models
Kento Murata, Shoichi Hasegawa, Tomochika Ishikawa, Yoshinobu Hagiwara, Akira Taniguchi, Lotfi El Hafi, Tadahiro Taniguchi
https://arxiv.org/abs/2509.12838

Multi-Robot Task Planning for Multi-Object Retrieval Tasks with Distributed On-Site Knowledge via Large Language Models
It is crucial to efficiently execute instructions such as "Find an apple and a banana" or "Get ready for a field trip," which require searching for multiple objects or understanding context-dependent commands. This study addresses the challenging problem of determining which robot should be assigned to which part of a task when each robot possesses different situational on-site knowledge-specifically, spatial concepts learned from the area designated to it by the user. We propose a task plannin…

PAC: Pronunciation-Aware Contextualized Large Language Model-based Automatic Speech Recognition
Li Fu, Yu Xin, Sunlu Zeng, Lu Fan, Youzheng Wu, Xiaodong He
https://arxiv.org/abs/2509.12647

PAC: Pronunciation-Aware Contextualized Large Language Model-based Automatic Speech Recognition
This paper presents a Pronunciation-Aware Contextualized (PAC) framework to address two key challenges in Large Language Model (LLM)-based Automatic Speech Recognition (ASR) systems: effective pronunciation modeling and robust homophone discrimination. Both are essential for raw or long-tail word recognition. The proposed approach adopts a two-stage learning paradigm. First, we introduce a pronunciation-guided context learning method. It employs an interleaved grapheme-phoneme context modeling …