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arXiv:2603.11067v1 Announce Type: new Abstract: Large language models (LLMs) achieve remarkable performance, yet further gains often require costly training. This has motivated growing interest in …
arXiv:2603.11299v1 Announce Type: new Abstract: This editorial addresses the critical intersection of artificial intelligence (AI) and blockchain technologies, highlighting their contrasting tendencies toward centralization and …
arXiv:2603.11352v1 Announce Type: new Abstract: Transformer-based time series foundation models face a fundamental trade-off in choice of tokenization: point-wise embeddings preserve temporal fidelity but scale …
arXiv:2603.11559v1 Announce Type: new Abstract: Large language models perform reliably when their outputs can be checked: solving equations, writing code, retrieving facts. They perform differently …
arXiv:2603.11709v1 Announce Type: new Abstract: While scaling laws for Large Language Models (LLMs) have been extensively studied along dimensions of model parameters, training data, and …
arXiv:2603.11342v1 Announce Type: new Abstract: The study of the attribution of input features to the output of neural network models is an active area of …
arXiv:2603.11691v1 Announce Type: new Abstract: Offline multi-agent reinforcement learning (MARL) with multi-task datasets is challenging due to varying numbers of agents across tasks and the …
arXiv:2603.11623v1 Announce Type: new Abstract: Topological Data Analysis (TDA) provides powerful tools to explore the shape and structure of data through topological features such as …
arXiv:2603.11689v1 Announce Type: new Abstract: Frontier Multimodal Large Language Models (MLLMs) exhibit remarkable capabilities in Visual-Language Comprehension (VLC) tasks. However, they are often deployed as …
arXiv:2603.11076v1 Announce Type: new Abstract: Recent work synthesizes agentic tasks for post-training tool-using LLMs, yet robust generalization under shifts in tasks and toolsets remains an …
arXiv:2603.11767v1 Announce Type: new Abstract: This paper presents an in-depth analysis of Wikidata qualifiers, focusing on their semantics and actual usage, with the aim of …
arXiv:2603.11445v1 Announce Type: new Abstract: We present Verified Multi-Agent Orchestration (VMAO), a framework that coordinates specialized LLM-based agents through a verification-driven iterative loop. Given a …