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arXiv:2412.00435v2 Announce Type: replace Abstract: Large Language Models (LLMs) have opened transformative possibilities for human-robot collaboration. However, enabling real-time collaboration requires both low latency and...
arXiv:2412.17316v3 Announce Type: replace Abstract: The Rotary Position Embedding (RoPE) mechanism has become a powerful enhancement to the Transformer architecture, which enables models to capture...
arXiv:2501.01202v2 Announce Type: replace Abstract: Autism Spectrum Disorder (ASD) is a chronic neurodevelopmental condition characterized by repetitive behaviors and impairments in social and communication skills....
arXiv:2501.02729v2 Announce Type: replace Abstract: Jacobi diffusion is a representative diffusion process whose solution is bounded in a domain under certain conditions about drift and...
arXiv:2501.06510v2 Announce Type: replace Abstract: This paper proposes two cooperative optimal output tracking (COOT) algorithms based on policy iteration (PI) for discrete-time multi-agent systems with...
arXiv:2501.09493v4 Announce Type: replace Abstract: Conversational recommender systems (CRSs) integrate both recommendation and dialogue tasks, making their evaluation uniquely challenging. Existing approaches primarily assess CRS...
arXiv:2501.10688v3 Announce Type: replace Abstract: Looped Transformers have shown exceptional neural algorithmic reasoning capability in simulating traditional graph algorithms, but their application to more complex...
arXiv:2501.13736v3 Announce Type: replace Abstract: We study a quantity called discrete layered entropy, which approximates the Shannon entropy within a logarithmic gap. Compared to the...
arXiv:2501.14271v3 Announce Type: replace Abstract: Meta-learning enables models to rapidly adapt to new tasks by leveraging prior experience, but its adaptation mechanisms remain opaque, especially...
arXiv:2501.18581v4 Announce Type: replace Abstract: Bias-variance decompositions are widely used to understand the generalization performance of machine learning models. While the squared error loss permits...
arXiv:2502.00662v2 Announce Type: replace Abstract: Existing vision-language model (VLM)-based methods for out-of-distribution (OOD) detection typically rely on similarity scores between input images and in-distribution (ID)...
arXiv:2502.04192v4 Announce Type: replace Abstract: Multiple works have emerged to push the boundaries of multi-modal large language models (MLLMs) towards pixel-level understanding. The current trend...
arXiv:2502.05850v3 Announce Type: replace Abstract: This paper presents a unified framework for codifying and automating optimization strategies to efficiently deploy deep neural networks (DNNs) on...
arXiv:2502.05853v2 Announce Type: replace Abstract: This paper introduces a novel finite Zak transform (FZT)-aided framework for constructing multiple zero-correlation zone (ZCZ) sequence sets with optimal...
arXiv:2502.06574v3 Announce Type: replace Abstract: Semivalue-based data valuation uses cooperative-game theory intuitions to assign each data point a value reflecting its contribution to a downstream...
arXiv:2502.07687v2 Announce Type: replace Abstract: We consider the possible role of current large language models (LLMs) in the study of human linguistic cognition. We focus...
arXiv:2502.11242v5 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly embedded in child-facing contexts such as education, companionship, creative tools, but their deployment raises...
arXiv:2502.11740v2 Announce Type: replace Abstract: Recent MLLMs have shown emerging visual understanding and reasoning abilities after being pre-trained on large-scale multimodal datasets. Unlike pre-training, where...
arXiv:2502.12945v3 Announce Type: replace Abstract: In an era where micro-videos dominate platforms like TikTok and YouTube, AI-generated content is nearing cinematic quality. The next frontier...
arXiv:2502.13321v2 Announce Type: replace Abstract: Trust biases how users rely on AI recommendations in AI-assisted decision-making tasks, with low and high levels of trust resulting...
arXiv:2502.13358v5 Announce Type: replace Abstract: Large Language Models (LLMs) have significantly advanced natural language processing, demonstrating strong capabilities in tasks such as text generation, summarization,...
arXiv:2502.16944v2 Announce Type: replace Abstract: In this paper, we explore how directly pretraining a value model simplifies and stabilizes reinforcement learning from human feedback (RLHF)....
arXiv:2502.18334v4 Announce Type: replace Abstract: Graph-based learning excels at capturing interaction patterns in diverse domains like recommendation, fraud detection, and particle physics. However, its performance...
arXiv:2502.18536v3 Announce Type: replace Abstract: Visual Question Answering requires models to generate accurate answers by integrating visual and textual understanding. However, VQA models still struggle...