Quality follows upgrading
Academic
arXiv:2603.18016v1 Announce Type: new Abstract: Speculative decoding (SD) accelerates large language model inference by using a smaller draft model to propose draft tokens that are …
arXiv:2603.18197v1 Announce Type: new Abstract: Recent studies reveal gaps in delegating critical tasks to agentic AI that accesses websites on the user's behalf, primarily due …
arXiv:2603.18353v1 Announce Type: new Abstract: Language models encode task-relevant knowledge in internal representations that far exceeds their output performance, but whether mechanistic interpretability methods can …
arXiv:2603.18761v1 Announce Type: new Abstract: Standard attention mechanisms in transformers are limited by their pairwise formulation, which hinders the modeling of higher-order dependencies among tokens. …
arXiv:2603.18563v1 Announce Type: new Abstract: AI agents are increasingly deployed in interactive economic environments characterized by repeated AI-AI interactions. Despite AI agents' advanced capabilities, empirical …
arXiv:2603.18614v1 Announce Type: new Abstract: Tool-augmented large language models (LLMs) must tightly couple multi-step reasoning with external actions, yet existing benchmarks often confound this interplay …
arXiv:2603.18104v1 Announce Type: new Abstract: Prevailing AI training infrastructure assumes reverse-mode automatic differentiation over IEEE-754 arithmetic. The memory overhead of training relative to inference, optimizer …
arXiv:2603.18273v1 Announce Type: new Abstract: In this technical report, we present the Educational Data Mining Automated Research System (EDM-ARS), a domain-specific multi-agent pipeline that automates …
arXiv:2603.18495v1 Announce Type: new Abstract: Recent advances in Vision-Language Models (VLMs) have enabled video-instructed robotic programming, allowing agents to interpret video demonstrations and generate executable …
arXiv:2603.18528v1 Announce Type: new Abstract: Text-to-image models produce images that align well with natural language prompts, but compositional generation has long been a central challenge. …
arXiv:2603.18331v1 Announce Type: new Abstract: Deep neural networks (DNNs) have achieved remarkable empirical success, yet the absence of a principled theoretical foundation continues to hinder …
arXiv:2603.18712v1 Announce Type: new Abstract: The task of multi-channel time series forecasting is ubiquitous in numerous fields such as finance, supply chain management, and energy …