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Most developers assume the APIs they depend on are stable. The data says otherwise. KushoAI's State...

arXiv:2604.11828v1 Announce Type: new Abstract: Science is widely regarded as humanity's most reliable method for uncovering truths about the natural world. Yet

arXiv:2604.11914v1 Announce Type: new Abstract: Self-monitoring capabilities -- metacognition, self-prediction, and subjective duration -- are often proposed as

arXiv:2604.11924v1 Announce Type: new Abstract: While LLMs hold significant potential to transform scientific research, we advocate for their use to augment and

arXiv:2604.11969v1 Announce Type: new Abstract: We introduce ArcDeck, a multi-agent framework that formulates paper-to-slide generation as a structured narrativ

arXiv:2604.11978v1 Announce Type: new Abstract: Large language model (LLM) agents perform strongly on short- and mid-horizon tasks, but often break down on long

arXiv:2604.12007v1 Announce Type: new Abstract: Agent memory systems accumulate experience but currently lack a principled operational metric for memory quality

arXiv:2604.12016v1 Announce Type: new Abstract: Large language models map semantically related prompts to similar internal representations -- a phenomenon inter

arXiv:2604.12019v1 Announce Type: new Abstract: Although artificial intelligence (AI) agents are increasingly proposed to support potentially longitudinal healt

arXiv:2604.12025v1 Announce Type: new Abstract: The Semantic Web standardizes concept meaning for humans and machines, enabling machine-operable content and con

arXiv:2604.12034v1 Announce Type: new Abstract: Retrieval-Augmented Generation remains the dominant pattern for giving LLMs persistent memory, but a visible clu

arXiv:2604.12066v1 Announce Type: new Abstract: Large language models can increasingly adapt educational tasks to learners characteristics. In the present study

arXiv:2604.12081v1 Announce Type: new Abstract: Memory is fundamental to social interaction, enabling humans to recall meaningful past experiences and adapt the

arXiv:2604.12096v1 Announce Type: new Abstract: On online advertising platforms, newly introduced promotional ads face the cold-start problem, as they lack suff

arXiv:2604.12102v1 Announce Type: new Abstract: We introduce compute-grounded reasoning (CGR), a design paradigm for spatial-aware research agents in which ever

arXiv:2604.12116v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as tool-augmented agents capable of executing system-leve

arXiv:2604.12126v1 Announce Type: new Abstract: Large Language Models (LLMs) have significantly advanced tool-augmented agents, enabling autonomous reasoning vi

arXiv:2604.12129v1 Announce Type: new Abstract: The transition from stateless model inference to stateful agentic execution is reshaping the systems assumptions

arXiv:2604.12133v1 Announce Type: new Abstract: Historical approaches to Table Representation Learning (TRL) have largely adopted the sequential paradigms of Na

arXiv:2604.12138v1 Announce Type: new Abstract: RAG systems have transformed how LLMs access external knowledge, but we find that current implementations exhibi

arXiv:2604.12161v1 Announce Type: new Abstract: Tumor boards are multidisciplinary conferences dedicated to producing actionable patient care recommendations wi

arXiv:2604.12167v1 Announce Type: new Abstract: We present (Experience-Modulated Biologically-inspired Emergent Reasoning), a hybrid cognitive architecture that

arXiv:2604.12176v1 Announce Type: new Abstract: Relational reasoning is the ability to infer relations that jointly bind multiple entities, attributes, or varia

arXiv:2604.12177v1 Announce Type: new Abstract: LLM-based agents can execute actions that are syntactically valid, user-sanctioned, and semantically appropriate