arXiv:2511. 17162v2 Announce Type: replace Abstract: The Belief-Desire-Intention (BDI) model is a cornerstone for representing rational agency in artificial intelligence and cognitive sciences.
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arXiv:2608.22974v1 Announce Type: new Abstract: Large language model (LLM) agents rely heavily on knowledge encoded in model parameters or presented as unstructured context. In domain-specific tasks,...
The paper proposes a four‑dimensional formal framework—Semantic Expressivity, Agentic Discoverability, Task‑Relative Grounding, and Epistemic Trust Scope—to extend current KG metadata standards (VoID and DCAT). It introduces the Agentic Affordance Profile (AAP), a semantic layer that enables agents to select, compose, and diagnose failures in knowledge graphs at planning time. A scholarly‑search example illustrates the framework and outlines a five‑point research agenda for scaling AAP‑based affordance matching.
arXiv:2605. 22093v3 Announce Type: replace Abstract: Knowledge graphs have become the primary vehicle for data integration and are critical to the success of modern AI, but the diversity of KG modelling practices, from lightweight vocabularies to richly axiomatised ontologies, makes integration and reuse expensive and brittle.
arXiv:2508. 01858v3 Announce Type: replace-cross Abstract: Multimodal large-scale models have significantly advanced the development of web agents, enabling perception and interaction with digital environments akin to human cognition.
arXiv:2604. 00555v5 Announce Type: replace Abstract: Enterprise adoption of Large Language Models (LLMs) is constrained by hallucination, domain drift, and the inability to enforce regulatory compliance at the reasoning level.
arXiv:2608. 09848v1 Announce Type: new Abstract: The development of embodied Intelligent Virtual Agents (IVAs) that have cognitive capabilities in real-time interactive virtual environments remains a challenge, even with today's advancements in technology.