Search: “people”

arXiv:2610.05501v1 Announce Type: new Abstract: In this study, we propose a novel rule-based interpretable choice model, Logic-Logit, designed to effectively learn and explain human choices. Choice mo

arXiv:2610.03828v1 Announce Type: new Abstract: Quadruped robots entering hospitals, care homes, and quiet offices must be context-appropriate not only in where they move but in how loudly they move:

arXiv:2610.03625v1 Announce Type: new Abstract: Relational databases are among the most widely deployed forms of structured knowledge, and natural language access to them requires grounding language o

arXiv:2610.02886v1 Announce Type: cross Abstract: Language models learn from web documents, some of them false, and false content can reach a model's answer to a factual question and the summaries and

arXiv:2610.02568v1 Announce Type: new Abstract: Personal advice, including relationship advice, now ranks among the most common uses of generative AI. But language models (LMs) exhibit sycophancy: the

arXiv:2610.03025v1 Announce Type: new Abstract: What makes a short story gripping; a news article newsworthy; or a math proof elegant? These constructs resist articulation or verification; their meani

arXiv:2610.01461v1 Announce Type: new Abstract: We often plan ambitiously yet act habitually and wonder, in retrospect, whether we would have planned differently had we known what we would actually do

arXiv:2606.13607v3 Announce Type: replace Abstract: When large language models (LLMs) fail to generalize or make content-sensitive errors in reasoning, it is often taken as evidence that LLMs are not

arXiv:2610.02033v1 Announce Type: new Abstract: Individual mobility trajectories support urban analysis and location-based services, yet most trajectory generators require observations from their depl

arXiv:2505.08894v2 Announce Type: replace-cross Abstract: Large language model (LLM) chatbots are increasingly reaching users through messaging platforms (e.g. WhatsApp). However, these systems remain

arXiv:2609.38437v1 Announce Type: new Abstract: Teleoperation systems are tuned for channel fidelity, while whether the operator experiences the device as part of the body, the Sense of Embodiment (So

arXiv:2609.38371v1 Announce Type: cross Abstract: Existing LLM-driven robot task planners rely on a taken-for-granted assumption of an ideal user whose instructions are clear, complete, and task-focus

arXiv:2609.38574v1 Announce Type: new Abstract: Large language models are frequently proposed as a route to AI-powered services for African communities, but they are least reliable exactly where the n

arXiv:2609.39483v1 Announce Type: new Abstract: Ownership establishes rights over the use, control, and transfer of objects. Understanding these relations is essential for AI systems to interact appro

arXiv:2609.35796v1 Announce Type: new Abstract: Invoices are commercial documents that contain various pieces of information, including the purchased items, time, and total money. Making the extractio

arXiv:2609.36172v1 Announce Type: new Abstract: Figuring out how objects relate to each other, like whether they touch, overlap, stay completely separate or one sits inside another, matters a lot in f

arXiv:2609.36190v1 Announce Type: new Abstract: Scientific figures are designed to communicate information visually, yet MLLMs typically explain them by translating their visual content back into text

arXiv:2609.37708v1 Announce Type: new Abstract: Human social behaviour is not a collection of independent motions, but a jointly organised process in which group dynamics and individual variation cont

arXiv:2609.37861v1 Announce Type: new Abstract: In the Global South, the lower-income countries of Africa, Asia, and Latin America where most of the world's languages are spoken, a deployed text class

arXiv:2609.31896v1 Announce Type: new Abstract: Robots on construction sites must detect workers who are kneeling or bending, which we call low poses. These workers can be lost from training datasets

arXiv:2609.31753v1 Announce Type: cross Abstract: Video anomaly detection for patrol robots and surveillance systems must recognize abnormal interactions among familiar entities. Existing pixel-recons

arXiv:2606.14586v2 Announce Type: replace Abstract: Self-supervised learning can learn useful representations from unlabeled images. Yet these representations remain largely opaque, making it difficul

arXiv:2609.31168v1 Announce Type: new Abstract: My doctoral work aims to explore Audio Emotion Recognition (AER) in the context of atypical listening. This research focuses on auditory hypersensitivit

arXiv:2609.30334v1 Announce Type: cross Abstract: AI development agents are increasingly used to support and partially automate software architecture tasks. To explore how practitioners perceive this

arXiv:2609.28758v1 Announce Type: cross Abstract: We describe our submission to the Unsupervised Speech in the Wild (UPS) Challenge at Interspeech 2026, a bidirectional Mamba-2 (BiMamba2) encoder trai

arXiv:2609.29672v1 Announce Type: new Abstract: Artificial intelligence helps education most where an essential provision has been rationed by cost. For language learners that provision is a teacher's

arXiv:2609.29718v1 Announce Type: cross Abstract: Coding agents are beginning to act in the visual world. They now build webpages, GUIs, games, 3D scenes, diagrams, and documents. Success in such visu

arXiv:2609.29547v1 Announce Type: cross Abstract: Agentic AI is sold on a simple promise: the system keeps acting when the user stops watching. That promise creates an accountability inversion. As ste

arXiv:2609.27690v1 Announce Type: new Abstract: Researchers in industry and academia use synthetic survey respondents powered by large language models as substitutes for human samples. These synthetic

arXiv:2609.28317v1 Announce Type: new Abstract: Turn-taking prediction is especially relevant for social robots that act as mediators in human-human interaction, where the expected action is often not

arXiv:2609.26865v1 Announce Type: cross Abstract: Conversational AI systems can pose safety risks to their users such as hallucination, sycophancy, overconfidence, and anthropomorphism, but these risk

arXiv:2609.26214v1 Announce Type: new Abstract: We present a methodology that places a 3D digital twin (DT) of the environment as the main enabler behind the development of radio sensing at scale. The

arXiv:2507.14077v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI) algorithms are a critical part of state-of-the-art digital health technology for diabetes management. Yet, access

arXiv:2510.14827v3 Announce Type: replace Abstract: Safe and efficient robot operation in complex human environments can benefit from good models of site-specific motion patterns. Maps of Dynamics (Mo

arXiv:2609.26233v1 Announce Type: new Abstract: Text-to-video (T2V) services inherit their safety stack from image generation, pairing a keyword prompt filter with a per-frame checker that blocks a cl

arXiv:2609.24718v1 Announce Type: new Abstract: While a centralized approach involving patient consent to collect and analyze data centrally would theoretically offer the best data quality and predict

arXiv:2609.22099v1 Announce Type: new Abstract: Cooking is a complex process that transforms raw ingredients into delicious and nutritious dishes, yet the recipes that encode this process remain large

arXiv:2609.23178v1 Announce Type: new Abstract: Language models are appealing tools for research on the past. But to trust the evidence a model provides, researchers need to know whether its responses

arXiv:2609.22813v1 Announce Type: new Abstract: We present commonsense ranked search (CoRS), a novel path planner that turns an abstract instruction into a route that follows commonsense. While existi

arXiv:2609.23755v1 Announce Type: new Abstract: Egocentric human data provide a principled source of supervision for learning dexterous robot manipulation. Unlike prior approaches that often collect s

arXiv:2509.08494v2 Announce Type: replace-cross Abstract: As humans delegate more tasks and decisions to artificial intelligence (AI), we risk losing control of our individual and collective futures.

arXiv:2609.22607v1 Announce Type: new Abstract: We argue here that the current dominant practice in LLM human simulation: prompting instruction-tuned assistant language models to role-play personas, i

arXiv:2609.24055v1 Announce Type: new Abstract: Robots can recover from failures by asking bystanders for help, but effective human-in-the-loop recovery requires communication that accounts for differ

arXiv:2609.22261v1 Announce Type: new Abstract: Lexicon-driven misogyny detectors cannot, by construction, distinguish a slur used against a woman from the same slur mentioned in counter-speech ('don'

arXiv:2609.21465v1 Announce Type: cross Abstract: We define OmniVChat (Omni Video Chat) as the task of native audio-visual dialogue between a user and an omni model. In OmniVChat, omni models directly

arXiv:2609.19803v1 Announce Type: new Abstract: Multi-agent heterogeneous air-ground robot teams are attractive for open world search, with applications for reconnaissance, urban search and rescue mis