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arXiv:2603.01189v1 Announce Type: new Abstract: This paper presents an empirically grounded agent-based model capturing trust dynamics, workload distribution, and collaborative performance in human-robot teams. The...
arXiv:2603.01190v1 Announce Type: new Abstract: Unlike autoregressive models, which generate tokens sequentially and benefit from reasoning-before-answering strategies such as Chain-of-Thought, Masked Diffusion Language Models (MDLMs)...
arXiv:2603.01191v1 Announce Type: new Abstract: The success of randomized range finders (RRFs) is typically analyzed via the singular value gaps of a target matrix $A$....
arXiv:2603.01193v1 Announce Type: new Abstract: Training neural PDE solvers is often bottlenecked by expensive data generation or unstable physics-informed neural network (PINN) that involves challenging...
arXiv:2603.01194v1 Announce Type: new Abstract: Human perceive the 3D world through 2D observations from limited viewpoints. While recent feed-forward generalizable 3D reconstruction models excel at...
arXiv:2603.01195v1 Announce Type: new Abstract: The effectiveness of multimodal instruction tuning depends not only on dataset scale, but critically on whether training samples genuinely require...
arXiv:2603.01198v1 Announce Type: new Abstract: Data center cooling systems consume significant auxiliary energy, yet optimization studies rarely quantify the gap between theoretically optimal and operationally...
arXiv:2603.01201v1 Announce Type: new Abstract: In this paper, we study incremental LTLf synthesis -- a form of reactive synthesis where the goals are given incrementally...
arXiv:2603.01202v1 Announce Type: new Abstract: This study examines how vulnerability is produced for human operators of Tesla's Full Self-Driving (FSD), a Level 2 semi-autonomous vehicle...
arXiv:2603.01203v1 Announce Type: new Abstract: AI agents are increasingly developed and evaluated on benchmarks relevant to human work, yet it remains unclear how representative these...
arXiv:2603.01204v1 Announce Type: new Abstract: As AI systems approach superhuman capabilities, scalable oversight increasingly relies on LLM-as-a-judge frameworks where models evaluate and guide each other's...
arXiv:2603.01205v1 Announce Type: new Abstract: Open-world promptable 3D semantic segmentation remains brittle as semantics are inferred in the input sensor coordinates. Yet, humans, in contrast,...
arXiv:2603.01206v1 Announce Type: new Abstract: We introduce a new family of priority-queue data structures: partition-based simple heaps. The structures consist of $O(\log n)$ doubly-linked lists;...
arXiv:2603.01209v1 Announce Type: new Abstract: Tool-augmented LLMs are increasingly deployed as agents that interleave natural-language reasoning with executable Python actions, as in CodeAct-style frameworks. In...
arXiv:2603.01212v1 Announce Type: new Abstract: Comparative opinion mining involves comparing products from different reviews. However, transformer-based models designed for this task often lack transparency, which...
arXiv:2603.01213v1 Announce Type: new Abstract: Large language models are increasingly deployed as cooperating agents, yet their behavior in adversarial consensus settings has not been systematically...
arXiv:2603.01214v1 Announce Type: new Abstract: Opinion modeling aims to capture individual or group political preferences, enabling applications such as digital democracies, where models could help...
arXiv:2603.01220v1 Announce Type: new Abstract: Generative models, like the one in ChatGPT, are powered by their training data. The models are simply next-word predictors, based...
arXiv:2603.01221v1 Announce Type: new Abstract: Multi-Agent Debate (MAD) has shown promise in leveraging collective intelligence to improve reasoning and reduce hallucinations, yet it remains unclear...
arXiv:2603.01222v1 Announce Type: new Abstract: Quantum federated learning (QFL) combines the robust data processing of quantum computing with the privacy-preserving features of federated learning (FL)....
arXiv:2603.01223v1 Announce Type: new Abstract: Reinforcement learning (RL) for mathematical reasoning can suffer from reward sparsity: for challenging problems, LLM fails to sample any correct...
arXiv:2603.01224v1 Announce Type: new Abstract: Pre-trained general-purpose Vision-Language Models (VLM) hold the potential to enhance intuitive human-machine interactions due to their rich world knowledge and...
arXiv:2603.01225v1 Announce Type: new Abstract: Hateful memes often require compositional multimodal reasoning: the image and text may appear benign in isolation, yet their interaction conveys...
arXiv:2603.01227v1 Announce Type: new Abstract: We propose the Lattice Representation Hypothesis of large language models: a symbolic backbone that grounds conceptual hierarchies and logical operations...