Tootfinder

TComQA: Extracting Temporal Commonsense from Text
Lekshmi R Nair, Arun Sankar, Koninika Pal
https://arxiv.org/abs/2508.15274 https://arxiv.org/pdf/2508.152…

TComQA: Extracting Temporal Commonsense from Text
Understanding events necessitates grasping their temporal context, which is often not explicitly stated in natural language. For example, it is not a trivial task for a machine to infer that a museum tour may last for a few hours, but can not take months. Recent studies indicate that even advanced large language models (LLMs) struggle in generating text that require reasoning with temporal commonsense due to its infrequent explicit mention in text. Therefore, automatically mining temporal commo…

Empty-Signal Detection: Proof-of-Principle Scheme for Arbitrarily Long-Distance Quantum Communication
Hao Shu
https://arxiv.org/abs/2509.15884 https://arxi…

Empty-Signal Detection: Proof-of-Principle Scheme for Arbitrarily Long-Distance Quantum Communication
Secure quantum communication, particularly quantum key distribution (QKD), has remained a central research focus for more than four decades and continues to attract significant attention in recent years. However, due to the unavoidable dark counts in single-photon detectors (SPDs), it has long been constrained by the rapid increase of the quantum bit error rate (QBER) induced by empty signals at the receiver, which prevents it from extending to arbitrarily long distances. To address this challe…

MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers
Ziyang Luo, Zhiqi Shen, Wenzhuo Yang, Zirui Zhao, Prathyusha Jwalapuram, Amrita Saha, Doyen Sahoo, Silvio Savarese, Caiming Xiong, Junnan Li
https://arxiv.org/abs/2508.14704

MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers
The Model Context Protocol has emerged as a transformative standard for connecting large language models to external data sources and tools, rapidly gaining adoption across major AI providers and development platforms. However, existing benchmarks are overly simplistic and fail to capture real application challenges such as long-horizon reasoning and large, unfamiliar tool spaces. To address this critical gap, we introduce MCP-Universe, the first comprehensive benchmark specifically designed to…

Beyond Visual Line of Sight: UAVs with Edge AI, Connected LLMs, and VR for Autonomous Aerial Intelligence
Andres Navarro, Carlos de Quinto, Jos\'e Alberto Hern\'andez
https://arxiv.org/abs/2507.15049

Beyond Visual Line of Sight: UAVs with Edge AI, Connected LLMs, and VR for Autonomous Aerial Intelligence
Unmanned Aerial Vehicles are reshaping Non-Terrestrial Networks by acting as agile, intelligent nodes capable of advanced analytics and instantaneous situational awareness. This article introduces a budget-friendly quadcopter platform that unites 5G communications, edge-based processing, and AI to tackle core challenges in NTN scenarios. Outfitted with a panoramic camera, robust onboard computation, and LLMs, the drone system delivers seamless object recognition, contextual analysis, and immers…

Identifying Algorithmic and Domain-Specific Bias in Parliamentary Debate Summarisation
Eoghan Cunningham, James Cross, Derek Greene
https://arxiv.org/abs/2507.14221

Identifying Algorithmic and Domain-Specific Bias in Parliamentary Debate Summarisation
The automated summarisation of parliamentary debates using large language models (LLMs) offers a promising way to make complex legislative discourse more accessible to the public. However, such summaries must not only be accurate and concise but also equitably represent the views and contributions of all speakers. This paper explores the use of LLMs to summarise plenary debates from the European Parliament and investigates the algorithmic and representational biases that emerge in this context.…

It Depends: Resolving Referential Ambiguity in Minimal Contexts with Commonsense Knowledge
Lukas Ellinger, Georg Groh
https://arxiv.org/abs/2509.16107 https://

It Depends: Resolving Referential Ambiguity in Minimal Contexts with Commonsense Knowledge
Ambiguous words or underspecified references require interlocutors to resolve them, often by relying on shared context and commonsense knowledge. Therefore, we systematically investigate whether Large Language Models (LLMs) can leverage commonsense to resolve referential ambiguity in multi-turn conversations and analyze their behavior when ambiguity persists. Further, we study how requests for simplified language affect this capacity. Using a novel multilingual evaluation dataset, we test DeepS…

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…

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…

Prompt Commons: Collective Prompting as Governance for Urban AI
Rashid Mushkani
https://arxiv.org/abs/2509.12415 https://arxiv.org/pdf/2509.12415

Prompt Commons: Collective Prompting as Governance for Urban AI
Large Language Models (LLMs) are entering urban governance, yet their outputs are highly sensitive to prompts that carry value judgments. We propose Prompt Commons - a versioned, community-maintained repository of prompts with governance metadata, licensing, and moderation - to steer model behaviour toward pluralism. Using a Montreal dataset (443 human prompts; 3,317 after augmentation), we pilot three governance states (open, curated, veto-enabled). On a contested policy benchmark, a single-au…

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…