arXiv:2607. 05456v1 Announce Type: new Abstract: While recent advances in large language models have enabled end-to-end automated manuscript generation, existing systems suffer from three critical deficiencies: (i) generated claims are not deterministically grounded in verifiable literature, (ii) ex

📰미디어arXiv cs.AI

원문

21시간 전

From Graphs to Gradients: Physics-Inspired Structural Attribution for Cyber-Physical IoT Systems and Beyond

arXiv:2607. 05563v1 Announce Type: new Abstract: Interpretable explanation methods in Artificial Intelligence aim to uncover the underlying causes and their effects, enabling a deeper understanding of why a system behaves in a certain way under different inputs. Unlike traditional explainability met

📰미디어arXiv cs.AI

원문

21시간 전

Memory in the Loop: In-Process Retrieval as ExtendedWorking Memory for Language Agents

arXiv:2607. 05690v1 Announce Type: new Abstract: Language agents run a loop - observe, reason, act - but the memory they reason over sits outside it: a store queried at most once per turn. We study the regime where memory moves inside the loop, read and written on every step. The obstacle has always

📰미디어arXiv cs.AI

원문

Native-speed vLLM transformers modeling backend

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Data for Agents

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From Graphs to Gradients: Physics-Inspired Structural Attribution for Cyber-Physical IoT Systems and Beyond

Memory in the Loop: In-Process Retrieval as ExtendedWorking Memory for Language Agents