Tootfinder

Towards Adaptive External Communication in Autonomous Vehicles: A Conceptual Design Framework
Tram Thi Minh Tran, Judy Kay, Stewart Worrall, Marius Hoggenmueller, Callum Parker, Xinyan Yu, Julie Stephany Berrio Perez, Mao Shan, Martin Tomitsch
https://arxiv.org/abs/2508.12518

Towards Adaptive External Communication in Autonomous Vehicles: A Conceptual Design Framework
External Human-Machine Interfaces (eHMIs) are key to facilitating interaction between autonomous vehicles and external road actors, yet most remain reactive and do not account for scalability and inclusivity. This paper introduces a conceptual design framework for adaptive eHMIs-interfaces that dynamically adjust communication as road actors vary and context shifts. Using the cyber-physical system as a structuring lens, the framework comprises three layers: Input (what the system detects), Proc…

Long-context Reference-based MT Quality Estimation
Sami Ul Haq, Chinonso Cynthia Osuji, Sheila Castilho, Brian Davis
https://arxiv.org/abs/2509.13980 https://

Long-context Reference-based MT Quality Estimation
In this paper, we present our submission to the Tenth Conference on Machine Translation (WMT25) Shared Task on Automated Translation Quality Evaluation. Our systems are built upon the COMET framework and trained to predict segment-level Error Span Annotation (ESA) scores using augmented long-context data. To construct long-context training data, we concatenate in-domain, human-annotated sentences and compute a weighted average of their scores. We integrate multiple human judgment datasets…

Reasoning Efficiently Through Adaptive Chain-of-Thought Compression: A Self-Optimizing Framework
Kerui Huang, Shuhan Liu, Xing Hu, Tongtong Xu, Lingfeng Bao, Xin Xia
https://arxiv.org/abs/2509.14093

Reasoning Efficiently Through Adaptive Chain-of-Thought Compression: A Self-Optimizing Framework
Chain-of-Thought (CoT) reasoning enhances Large Language Models (LLMs) by prompting intermediate steps, improving accuracy and robustness in arithmetic, logic, and commonsense tasks. However, this benefit comes with high computational costs: longer outputs increase latency, memory usage, and KV-cache demands. These issues are especially critical in software engineering tasks where concise and deterministic outputs are required. To investigate these trade-offs, we conduct an empirical study base…

Conducting Mission-Critical Voice Experiments with Automated Speech Recognition and Crowdsourcing
Jan Janak, Kahlil Dozier, Lauren Berny, Liang Hu, Dan Rubenstein, Charles Jennings, Henning Schulzrinne
https://arxiv.org/abs/2509.13724

Conducting Mission-Critical Voice Experiments with Automated Speech Recognition and Crowdsourcing
Mission-critical voice (MCV) communications systems have been a critical tool for the public safety community for over eight decades. Public safety users expect MCV systems to operate reliably and consistently, particularly in challenging conditions. Because of these expectations, the Public Safety Communications Research (PSCR) Division of the National Institute of Standards and Technology (NIST) has been interested in correlating impairments in MCV communication systems and public safety user…

Agentic AI Frameworks: Architectures, Protocols, and Design Challenges
Hana Derouiche, Zaki Brahmi, Haithem Mazeni
https://arxiv.org/abs/2508.10146 https://

Agentic AI Frameworks: Architectures, Protocols, and Design Challenges
The emergence of Large Language Models (LLMs) has ushered in a transformative paradigm in artificial intelligence, Agentic AI, where intelligent agents exhibit goal-directed autonomy, contextual reasoning, and dynamic multi-agent coordination. This paper provides a systematic review and comparative analysis of leading Agentic AI frameworks, including CrewAI, LangGraph, AutoGen, Semantic Kernel, Agno, Google ADK, and MetaGPT, evaluating their architectural principles, communication mechanisms, m…

Spatial Balancing: Harnessing Spatial Reasoning to Balance Scientific Exposition and Narrative Engagement in LLM-assisted Science Communication Writing
Kexue Fu, Jiaye Leng, Yawen Zhang, Jingfei Huang, Yihang Zuo, Runze Cai, Zijian Ding, Ray LC, Shengdong Zhao, Qinyuan Lei
https://arxiv.org/abs/2509.13742

Spatial Balancing: Harnessing Spatial Reasoning to Balance Scientific Exposition and Narrative Engagement in LLM-assisted Science Communication Writing
Balancing scientific exposition and narrative engagement is a central challenge in science communication. To examine how to achieve balance, we conducted a formative study with four science communicators and a literature review of science communication practices, focusing on their workflows and strategies. These insights revealed how creators iteratively shift between exposition and engagement but often lack structured support. Building on this, we developed SpatialBalancing, a co-writing syste…

Attention-Enhanced Learning for Sensing-Assisted Long-Term Beam Tracking in mmWave Communications
Mengyuan Ma, Nhan Thanh Nguyen, Nir Shlezinger, Yonina C. Eldar, Markku Juntti
https://arxiv.org/abs/2509.11725

Attention-Enhanced Learning for Sensing-Assisted Long-Term Beam Tracking in mmWave Communications
Beam training and prediction in millimeter-wave communications are highly challenging due to fast time-varying channels and sensitivity to blockages and mobility. In this context, infrastructure-mounted cameras can capture rich environmental information that can facilitate beam tracking design. In this work, we develop an efficient attention-enhanced machine learning model for long-term beam tracking built upon convolutional neural networks and gated recurrent units to predict both current and …

Designing LLMs for cultural sensitivity: Evidence from English-Japanese translation
Helene Tenzer, Oumnia Abidi, Stefan Feuerriegel
https://arxiv.org/abs/2509.11921 https://

Designing LLMs for cultural sensitivity: Evidence from English-Japanese translation
Large language models (LLMs) are increasingly used in everyday communication, including multilingual interactions across different cultural contexts. While LLMs can now generate near-perfect literal translations, it remains unclear whether LLMs support culturally appropriate communication. In this paper, we analyze the cultural sensitivity of different LLM designs when applied to English-Japanese translations of workplace e-mails. Here, we vary the prompting strategies: (1) naive "just translat…

Onboard Neuromorphic Split Computing via Optical Links for LEO Remote Sensing
Zihang Song, Petar Popovski
https://arxiv.org/abs/2507.08490 https://

Onboard Neuromorphic Split Computing via Optical Links for LEO Remote Sensing
Low Earth orbit (LEO) satellite constellations increasingly require onboard intelligence under strict power and communication constraints. This paper proposes a neuromorphic split computing framework tailored for hierarchical LEO systems, where edge satellites perform event-driven sensing using dynamic vision sensors (DVS) and lightweight spiking neural network (SNN) encoders, while core satellites conduct inference using a powerful SNN decoder. A learned spike mapping scheme enables direct tra…

PRELUDE: A Benchmark Designed to Require Global Comprehension and Reasoning over Long Contexts
Mo Yu, Tsz Ting Chung, Chulun Zhou, Tong Li, Rui Lu, Jiangnan Li, Liyan Xu, Haoshu Lu, Ning Zhang, Jing Li, Jie Zhou
https://arxiv.org/abs/2508.09848

PRELUDE: A Benchmark Designed to Require Global Comprehension and Reasoning over Long Contexts
We introduce PRELUDE, a benchmark for evaluating long-context understanding through the task of determining whether a character's prequel story is consistent with the canonical narrative of the original book. Our task poses a stronger demand for global comprehension and deep reasoning than existing benchmarks -- as the prequels are not part of the original story, assessing their plausibility typically requires searching and integrating information that is only indirectly related. Empirically, 8…