Tootfinder

OmniEAR: Benchmarking Agent Reasoning in Embodied Tasks
Zixuan Wang, Dingming Li, Hongxing Li, Shuo Chen, Yuchen Yan, Wenqi Zhang, Yongliang Shen, Weiming Lu, Jun Xiao, Yueting Zhuang
https://arxiv.org/abs/2508.05614

OmniEAR: Benchmarking Agent Reasoning in Embodied Tasks
Large language models excel at abstract reasoning but their capacity for embodied agent reasoning remains largely unexplored. We present OmniEAR, a comprehensive framework for evaluating how language models reason about physical interactions, tool usage, and multi-agent coordination in embodied tasks. Unlike existing benchmarks that provide predefined tool sets or explicit collaboration directives, OmniEAR requires agents to dynamically acquire capabilities and autonomously determine coordinati…

Quantifying Gender Stereotypes in Japan between 1900 and 1999 with Word Embeddings
Shintaro Sakai, Haewoon Kwak, Jisun An, Akira Matsui
https://arxiv.org/abs/2510.03905 https://…

Quantifying Gender Stereotypes in Japan between 1900 and 1999 with Word Embeddings
We quantify the evolution of gender stereotypes in Japan from 1900 to 1999 using a series of 100 word embeddings, each trained on a corpus from a specific year. We define the gender stereotype value to measure the strength of a word's gender association by computing the difference in cosine similarity of the word to female- versus male-related attribute words. We examine trajectories of gender stereotype across three traditionally gendered domains: Home, Work, and Politics, as well as occupatio…

Toward Low-Latency End-to-End Voice Agents for Telecommunications Using Streaming ASR, Quantized LLMs, and Real-Time TTS
Vignesh Ethiraj, Ashwath David, Sidhanth Menon, Divya Vijay
https://arxiv.org/abs/2508.04721

Toward Low-Latency End-to-End Voice Agents for Telecommunications Using Streaming ASR, Quantized LLMs, and Real-Time TTS
We introduce a low-latency telecom AI voice agent pipeline for real-time, interactive telecommunications use, enabling advanced voice AI for call center automation, intelligent IVR (Interactive Voice Response), and AI-driven customer support. The solution is built for telecom, combining four specialized models by NetoAI: TSLAM, a 4-bit quantized Telecom-Specific Large Language Model (LLM); T-VEC, a Telecom-Specific Embedding Model; TTE, a Telecom-Specific Automatic Speech Recognition (ASR) mode…

Jazzaria – Empty Words (Full Thoughts)
#acoustic

PersONAL: Towards a Comprehensive Benchmark for Personalized Embodied Agents
Filippo Ziliotto, Jelin Raphael Akkara, Alessandro Daniele, Lamberto Ballan, Luciano Serafini, Tommaso Campari
https://arxiv.org/abs/2509.19843

PersONAL: Towards a Comprehensive Benchmark for Personalized Embodied Agents
Recent advances in Embodied AI have enabled agents to perform increasingly complex tasks and adapt to diverse environments. However, deploying such agents in realistic human-centered scenarios, such as domestic households, remains challenging, particularly due to the difficulty of modeling individual human preferences and behaviors. In this work, we introduce PersONAL (PERSonalized Object Navigation And Localization, a comprehensive benchmark designed to study personalization in Embodied AI. Ag…

MLSD: A Novel Few-Shot Learning Approach to Enhance Cross-Target and Cross-Domain Stance Detection
Parush Gera, Tempestt Neal
https://arxiv.org/abs/2509.03725 https://

MLSD: A Novel Few-Shot Learning Approach to Enhance Cross-Target and Cross-Domain Stance Detection
We present the novel approach for stance detection across domains and targets, Metric Learning-Based Few-Shot Learning for Cross-Target and Cross-Domain Stance Detection (MLSD). MLSD utilizes metric learning with triplet loss to capture semantic similarities and differences between stance targets, enhancing domain adaptation. By constructing a discriminative embedding space, MLSD allows a cross-target or cross-domain stance detection model to acquire useful examples from new target domains. We …

Empathy in Explanation
Katherine M. Collins, Kartik Chandra, Adrian Weller, Jonathan Ragan-Kelley, Joshua B. Tenenbaum
https://arxiv.org/abs/2507.21081 https://

Empathy in Explanation
Why do we give the explanations we do? Recent work has suggested that we should think of explanation as a kind of cooperative social interaction, between a why-question-asker and an explainer. Here, we apply this perspective to consider the role that emotion plays in this social interaction. We develop a computational framework for modeling explainers who consider the emotional impact an explanation might have on a listener. We test our framework by using it to model human intuitions about how …

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…

Confidence-Guided Error Correction for Disordered Speech Recognition
Abner Hernandez, Tom\'as Arias Vergara, Andreas Maier, Paula Andrea P\'erez-Toro
https://arxiv.org/abs/2509.25048

Confidence-Guided Error Correction for Disordered Speech Recognition
We investigate the use of large language models (LLMs) as post-processing modules for automatic speech recognition (ASR), focusing on their ability to perform error correction for disordered speech. In particular, we propose confidence-informed prompting, where word-level uncertainty estimates are embedded directly into LLM training to improve robustness and generalization across speakers and datasets. This approach directs the model to uncertain ASR regions and reduces overcorrection. We fine-…