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arXiv:2509.09071v4 Announce Type: replace Abstract: Markets increasingly accommodate large language models (LLMs) as autonomous decision-making agents. As this transition occurs, it becomes critical to evaluate...
arXiv:2509.09828v3 Announce Type: replace Abstract: Robust semantic perception for autonomous vehicles relies on effectively combining multiple sensors with complementary strengths and weaknesses. State-of-the-art sensor fusion...
arXiv:2509.09840v3 Announce Type: replace Abstract: AI capabilities for document reader software are usually presented in separate chat interfaces. We explore integrating AI into document comments,...
arXiv:2509.10414v3 Announce Type: replace Abstract: In-context learning (ICL) allows some autoregressive models to solve tasks via next-token prediction and without needing further training. This has...
arXiv:2509.10950v3 Announce Type: replace Abstract: Generative AI (GenAI) is reshaping work, but adoption remains largely individual and experimental rather than coordinated into collaborative work. Whether...
arXiv:2509.11480v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models have emerged as powerful generalist policies for robotic control, yet their performance scaling across model architectures and...
arXiv:2509.11602v2 Announce Type: replace Abstract: A Straight-Line Program (SLP) for a string $T$ is a context-free grammar in Chomsky normal form that derives $T$ only,...
arXiv:2509.13379v3 Announce Type: replace Abstract: Vision-Language Models (VLMs) have achieved remarkable progress in complex visual understanding across scientific and reasoning tasks. While performance benchmarking has...
arXiv:2509.13588v3 Announce Type: replace Abstract: This paper introduces CoBRA, a novel toolkit for systematically specifying agent behavior in LLM-based social simulation. We found that conventional...
arXiv:2509.13805v3 Announce Type: replace Abstract: Foundation models have revolutionized natural language processing through a ``train once, deploy anywhere'' paradigm, where a single pre-trained model adapts...
arXiv:2509.13891v2 Announce Type: replace Abstract: We initiate a study of solving a row/column diagonally dominant (RDD/CDD) linear system $Mx=b$ in sublinear time, with the goal...
arXiv:2509.14088v2 Announce Type: replace Abstract: Modern computing students often rely on both natural-language prompting and manual code editing to solve programming tasks. Yet we still...
arXiv:2509.14260v2 Announce Type: replace Abstract: In experiments spanning more than 100,000 trials across thirteen large language models, we show that several state-of-the-art models presented with...
arXiv:2509.14275v3 Announce Type: replace Abstract: Privacy-preserving adaptation of Large Language Models (LLMs) in sensitive domains (e.g., mental health) requires balancing strict confidentiality with model utility...
arXiv:2509.14278v2 Announce Type: replace Abstract: Large Language Models (LLMs) have achieved remarkable progress in natural language understanding, reasoning, and autonomous decision-making. However, these advancements have...
arXiv:2509.14545v2 Announce Type: replace Abstract: Large language models (LLMs) have emerged as powerful tools for supporting second language acquisition, particularly in simulating interactive dialogues for...
arXiv:2509.14788v2 Announce Type: replace Abstract: Accurate identification of drug-target interactions (DTI) remains a central challenge in computational pharmacology, where sequence-based methods offer scalability. This work...
arXiv:2509.14994v2 Announce Type: replace Abstract: Dynamic time warping (DTW) is widely used to align time series evolving on mismatched timescales, yet most applications reduce alignment...
arXiv:2509.15674v3 Announce Type: replace Abstract: We focus on a binary classification problem in an edge intelligence system where false negatives are more costly than false...
arXiv:2509.15922v2 Announce Type: replace Abstract: In speech enhancement, knowledge distillation (KD) compresses models by transferring a high-capacity teacher's knowledge to a compact student. However, conventional...
arXiv:2509.16495v2 Announce Type: replace Abstract: Efficient parallelism is necessary for achieving low-latency, high-throughput inference with large language models (LLMs). Tensor parallelism (TP) is the state-of-the-art...
arXiv:2509.16891v4 Announce Type: replace Abstract: While Large Language Models (LLMs) have demonstrated impressive reasoning and planning abilities in textual domains and can effectively follow instructions...
arXiv:2509.17052v3 Announce Type: replace Abstract: Large-scale text-to-speech (TTS) systems are limited by the scarcity of clean, multilingual recordings. We introduce Sidon, a fast, open-source speech...
arXiv:2509.17191v2 Announce Type: replace Abstract: Understanding cultural heritage artifacts such as ancient Greek pottery requires expert-level reasoning that remains challenging for current MLLMs due to...