Tootfinder

McLaurin hold-in ends with deal worth up to $96M https://www.espn.com/nfl/story/_/id/46071301/sources-commanders-wr-terry-mclaurin-agree-3-year-deal

DESAMO: A Device for Elder-Friendly Smart Homes Powered by Embedded LLM with Audio Modality
Youngwon Choi, Donghyuk Jung, Hwayeon Kim
https://arxiv.org/abs/2508.18918 https://…

DESAMO: A Device for Elder-Friendly Smart Homes Powered by Embedded LLM with Audio Modality
We present DESAMO, an on-device smart home system for elder-friendly use powered by Audio LLM, that supports natural and private interactions. While conventional voice assistants rely on ASR-based pipelines or ASR-LLM cascades, often struggling with the unclear speech common among elderly users and unable to handle non-speech audio, DESAMO leverages an Audio LLM to process raw audio input directly, enabling a robust understanding of user intent and critical events, such as falls or calls for he…

Benchmarking Training Paradigms, Dataset Composition, and Model Scaling for Child ASR in ESPnet
Anyu Ying, Natarajan Balaji Shankar, Chyi-Jiunn Lin, Mohan Shi, Pu Wang, Hye-jin Shim, Siddhant Arora, Hugo Van hamme, Abeer Alwan, Shinji Watanabe
https://arxiv.org/abs/2508.16576

Benchmarking Training Paradigms, Dataset Composition, and Model Scaling for Child ASR in ESPnet
Despite advancements in ASR, child speech recognition remains challenging due to acoustic variability and limited annotated data. While fine-tuning adult ASR models on child speech is common, comparisons with flat-start training remain underexplored. We compare flat-start training across multiple datasets, SSL representations (WavLM, XEUS), and decoder architectures. Our results show that SSL representations are biased toward adult speech, with flat-start training on child speech mitigating the…

Amari Cooper, Raiders agree to deal: Las Vegas reuniting with veteran WR on one-year contract, per report

https://www.cbssports.com/nfl/news/amari-c

UserRL: Training Interactive User-Centric Agent via Reinforcement Learning
Cheng Qian, Zuxin Liu, Akshara Prabhakar, Jielin Qiu, Zhiwei Liu, Haolin Chen, Shirley Kokane, Heng Ji, Weiran Yao, Shelby Heinecke, Silvio Savarese, Caiming Xiong, Huan Wang
https://arxiv.org/abs/2509.19736

UserRL: Training Interactive User-Centric Agent via Reinforcement Learning
Reinforcement learning (RL) has shown promise in training agentic models that move beyond static benchmarks to engage in dynamic, multi-turn interactions. Yet, the ultimate value of such agents lies in their ability to assist users, a setting where diversity and dynamics of user interaction pose challenges. In this work, we propose UserRL, a unified framework for training and evaluating user-centric abilities through standardized gym environments paired with simulated users. We systematically v…

H-PRM: A Pluggable Hotword Pre-Retrieval Module for Various Speech Recognition Systems
Huangyu Dai, Lingtao Mao, Ben Chen, Zihan Wang, Zihan Liang, Ying Han, Chenyi Lei, Han Li
https://arxiv.org/abs/2508.18295

H-PRM: A Pluggable Hotword Pre-Retrieval Module for Various Speech Recognition Systems
Hotword customization is crucial in ASR to enhance the accuracy of domain-specific terms. It has been primarily driven by the advancements in traditional models and Audio large language models (LLMs). However, existing models often struggle with large-scale hotwords, as the recognition rate drops dramatically with the number of hotwords increasing. In this paper, we introduce a novel hotword customization system that utilizes a hotword pre-retrieval module (H-PRM) to identify the most relevant …

Apple and Home Office agree to drop legal claim over encryption backdoor
https://www.computerweekly.com/news/366632561/Apple-and-Home-Office-agree-to-drop-legal-claim-over-encryption-backdoor

Apple and Home Office agree to drop legal claim over encryption backdoor
Investigatory Powers Tribunal rules that Apple’s appeal against the Home Office will no longer proceed because of a ‘change in circumstances’

A Set of Generalized Components to Achieve Effective Poison-only Clean-label Backdoor Attacks with Collaborative Sample Selection and Triggers
Zhixiao Wu, Yao Lu, Jie Wen, Hao Sun, Qi Zhou, Guangming Lu
https://arxiv.org/abs/2509.19947

A Set of Generalized Components to Achieve Effective Poison-only Clean-label Backdoor Attacks with Collaborative Sample Selection and Triggers
Poison-only Clean-label Backdoor Attacks aim to covertly inject attacker-desired behavior into DNNs by merely poisoning the dataset without changing the labels. To effectively implant a backdoor, multiple \textbf{triggers} are proposed for various attack requirements of Attack Success Rate (ASR) and stealthiness. Additionally, sample selection enhances clean-label backdoor attacks' ASR by meticulously selecting ``hard'' samples instead of random samples to poison. Current methods 1) usually han…

Chiefs acquire DT Derrick Nnadi from Jets, per source: Why they're bringing him back https://www.nytimes.com/athletic/6572749/2025/08/24/derrick-nnadi-trade-chiefs-jets/

UserRL: Training Interactive User-Centric Agent via Reinforcement Learning
Cheng Qian, Zuxin Liu, Akshara Prabhakar, Jielin Qiu, Zhiwei Liu, Haolin Chen, Shirley Kokane, Heng Ji, Weiran Yao, Shelby Heinecke, Silvio Savarese, Caiming Xiong, Huan Wang
https://arxiv.org/abs/2509.19736

UserRL: Training Interactive User-Centric Agent via Reinforcement Learning
Reinforcement learning (RL) has shown promise in training agentic models that move beyond static benchmarks to engage in dynamic, multi-turn interactions. Yet, the ultimate value of such agents lies in their ability to assist users, a setting where diversity and dynamics of user interaction pose challenges. In this work, we propose UserRL, a unified framework for training and evaluating user-centric abilities through standardized gym environments paired with simulated users. We systematically v…