March 2026

arXiv:2603.26687v1 Announce Type: new Abstract: Hybrid aerial–ground robots offer both traversability and endurance, but stair-like discontinuities create a trade-off: wheels alone often stall at edges, while flight is energy-hungry for small height gains. We propose an energy-aware reinforcement learning framework that trains a single continuous policy to coordinate propellers, wheels, and tilt servos without predefined aerial and ground modes. We train policies from proprioception and a local height scan in Isaac Lab with parallel environments, using hardware-calibrated thrust/power […]

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arXiv:2603.26686v1 Announce Type: new Abstract: Distributed multi-robot systems for the home often require robots to operate out of the user’s sight, creating a state awareness gap that can diminish trust and perceived transparency and control. This paper investigates whether real-time, socially mediated state externalization can bridge this gap without compromising task performance. We developed a system where a co-located social mediator robot (Pepper) externalizes the hidden execution states of an out-of-sight mobile manipulator (Stretch~3) for voice-driven object retrieval […]

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arXiv:2603.26685v1 Announce Type: new Abstract: Recent advances in computer vision facilitate fully automatic extraction of object-centric relational representations from visual-inertial data. These state representations, dubbed 3D scene graphs, are a hierarchical decomposition of real-world scenes with a dense multiplex graph structure. While 3D scene graphs claim to promote efficient task planning for robot systems, they contain numerous objects and relations when only small subsets are required for a given task. This magnifies the state space that task planners […]

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arXiv:2603.26684v1 Announce Type: new Abstract: Multi-Agent Path Finding (MAPF) requires collision-free trajectories for multiple agents on a shared graph, often with the objective of minimizing the sum-of-costs (SOC). Many optimal and bounded-suboptimal solvers rely on time-expanded models and centralized conflict resolution, which limits scalability in large or dense instances. We propose a hybrid prioritized framework that separates geometric planning from execution-time conflict resolution. In the first stage, Geometric Conflict Preemption (GCP) plans agents sequentially with A* on the […]

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arXiv:2603.26683v1 Announce Type: new Abstract: Retrieving relevant evidence from visually rich documents such as textbooks, technical reports, and manuals is challenging due to long context, complex layouts, and weak lexical overlap between user questions and supporting pages. We propose LITTA, a query-expansion-centric retrieval framework for evidence page retrieval that improves multimodal document retrieval without retriever retraining. Given a user query, LITTA generates complementary query variants using a large language model and retrieves candidate pages for each variant using […]

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arXiv:2603.26682v1 Announce Type: new Abstract: The “gender” of intelligent agents, virtual characters, social robots, and other agentic machines has emerged as a fundamental topic in studies of people’s interactions with computers. Perceptions of agent gender can help explain user attitudes and behaviours — from preferences to toxicity to stereotyping — across a variety of systems and contexts of use. Yet, standards in capturing perceptions of agent gender do not exist. A scoping review was conducted to clarify how […]

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arXiv:2603.26681v1 Announce Type: new Abstract: Social media platforms play an increasingly important role in shaping political discussion and information flows. This study examines the structure of participation and attention in Arabic-language discourse about Hezbollah on X (formerly Twitter). Using a dataset of 15,767 tweets posted by 8,148 users between March 1 and March 8, 2026, the analysis investigates how engagement is distributed across participants and whether certain types of accounts play a disproportionate role in attracting attention. The […]

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arXiv:2603.26680v1 Announce Type: new Abstract: As Large Language Models (LLMs) evolve into lifelong AI assistants, LLM personalization has become a critical frontier. However, progress is currently bottlenecked by the absence of a gold-standard evaluation benchmark. Existing benchmarks either overlook personalized information management that is critical for personalization or rely heavily on synthetic dialogues, which exhibit an inherent distribution gap from real-world dialogue. To bridge this gap, we introduce AlpsBench, An LLM PerSonalization benchmark derived from real-world human-LLM dialogues. […]

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arXiv:2603.26679v1 Announce Type: new Abstract: Large-enrollment university courses face persistent challenges in providing timely and scalable instructional support. While generative AI holds promise, its effective use depends on reliability and pedagogical alignment. We present a human-centered case study of AI-assisted support in a Calculus I course, implemented in close collaboration with the course instructor. We developed a system to answer students’ questions on a discussion forum, fine-tuning a lightweight language model on 2,588 historical student-instructor interactions. The model […]

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arXiv:2603.26678v1 Announce Type: new Abstract: AI and renewable energy are increasingly framed as a “power couple” — the idea that surging AI electricity demand will accelerate clean-energy investment — yet concerns persist that AI will instead entrench fossil-fuel carbon lock-in. We reconcile these views by modeling the equilibrium interaction between AI growth and renewable investment. In a parsimonious game, a policymaker invests in renewable capacity available to AI and an AI developer chooses capability; the equilibrium depends on […]

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