January 2026

arXiv:2601.09856v1 Announce Type: new Abstract: Motivated by the vision of integrating mobile robots closer to humans in warehouses, hospitals, manufacturing plants, and the home, we focus on robot navigation in dynamic and spatially constrained environments. Ensuring human safety, comfort, and efficiency in such settings requires that robots are endowed with a model of how humans move around them. Human motion prediction around robots is especially challenging due to the stochasticity of human behavior, differences in user preferences, and […]

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arXiv:2601.09855v1 Announce Type: new Abstract: Sequential test-time scaling is a promising training-free method to improve large reasoning model accuracy, but as currently implemented, significant limitations have been observed. Inducing models to think for longer can increase their accuracy, but as the length of reasoning is further extended, it has also been shown to result in accuracy degradation and model instability. This work presents a novel sequential test-time scaling method, Min-Seek, which improves model accuracy significantly over a wide […]

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arXiv:2601.09853v1 Announce Type: new Abstract: Real-world health questions from patients often unintentionally embed false assumptions or premises. In such cases, safe medical communication typically involves redirection: addressing the implicit misconception and then responding to the underlying patient context, rather than the original question. While large language models (LLMs) are increasingly being used by lay users for medical advice, they have not yet been tested for this crucial competency. Therefore, in this work, we investigate how LLMs react to […]

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arXiv:2601.09852v1 Announce Type: new Abstract: Language places subtle constraints on how we make inductive inferences. Developmental evidence by Gelman et al. (2002) has shown children (4 years and older) to differentiate among generic statements (“Bears are daxable”), universally quantified NPs (“all bears are daxable”) and indefinite plural NPs (“some bears are daxable”) in extending novel properties to a specific member (all > generics > some), suggesting that they represent these types of propositions differently. We test if these […]

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arXiv:2601.09851v1 Announce Type: new Abstract: Multimodal video captioning condenses dense footage into a structured format of keyframes and natural language. By creating a cohesive multimodal summary, this approach anchors generative AI in rich semantic evidence and serves as a lightweight proxy for high-efficiency retrieval. However, traditional metrics like BLEU or ROUGE fail to quantify information coverage across disparate modalities, such as comparing a paragraph of text to a sequence of keyframes. To address this, we propose the Video […]

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arXiv:2601.09849v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed to support human decision-making. This use of LLMs has concerning implications, especially when their prescriptions affect the welfare of others. To gauge how LLMs make social decisions, we explore whether five leading models produce sensible strategies in the repeated prisoner’s dilemma, which is the main metaphor of reciprocal cooperation. First, we measure the propensity of LLMs to cooperate in a neutral setting, without using language reminiscent […]

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arXiv:2601.09842v1 Announce Type: new Abstract: Teaching software development basics to hundreds of students in a frontal setting is cost-efficient and thus still common in universities. However, in a large lecture hall, students can easily get bored, distracted, and disengaged. The frontal setting can also frustrate lecturers since interaction opportunities are limited and hard to scale. Fun activities can activate students and, if well designed, can also help remember and reflect on abstract software development concepts. We present a […]

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arXiv:2601.09841v1 Announce Type: new Abstract: When training machine learning (ML) models for potential deployment in a healthcare setting, it is essential to ensure that they do not replicate or exacerbate existing healthcare biases. Although many definitions of fairness exist, we focus on path-specific causal fairness, which allows us to better consider the social and medical contexts in which biases occur (e.g., direct discrimination by a clinician or model versus bias due to differential access to the healthcare system) […]

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arXiv:2601.09839v1 Announce Type: new Abstract: Lazy evaluation is a powerful technique that can optimize code execution by deferring evaluations until their results are required, thus enhancing efficiency. In most modern programming languages, like R, lazy evaluation is commonly applied to function arguments. However, the application of lazy evaluation in SAS has not been extensively explored. This paper focuses on the mechanisms of lazy evaluation in SAS MACROs and R functions, offering a comparative analysis of the underlying principles […]

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arXiv:2601.09838v1 Announce Type: new Abstract: Introduction: Socially assistive robots hold promise for enhancing therapeutic engagement in paediatric clinical settings. However, their successful implementation requires not only technical robustness but also context-sensitive, co-designed solutions. This paper presents Mobirobot, a socially assistive robot developed to support mobilisation in children recovering from trauma, fractures, or depressive disorders through personalised exercise programmes. Methods: An agile, human-centred development approach guided the iterative design of Mobirobot. Multidisciplinary clinical teams and end users were involved […]

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