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Toward Knowledge-Centric AI: Discovering, Internalizing, and Using Explicit Knowledge Across Agents and Generations — arXiv:2611.03157

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.03157
License (as archived): CC0-1.0
Archived: 2026-11-03T11:03:40.643Z
SHA-256 of the archived text: 67c258f226810b10ed7cbe9c2a04fc186e18c1ca0702df287aefaf5d1d10cc8d
Stable URL of this page: https://metraipolis.com/documento/dc_a674293dff64

How to cite

Toward Knowledge-Centric AI: Discovering, Internalizing, and Using Explicit Knowledge Across Agents and Generations — arXiv:2611.03157 — arXiv (en), https://arxiv.org/abs/2611.03157 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_a674293dff64 (Metraipolis archive dc_a674293dff64, sha256 67c258f226810b10ed7cbe9c2a04fc186e18c1ca0702df287aefaf5d1d10cc8d)

BibTeX

@misc{dca674293dff64,
  title = {Toward Knowledge-Centric AI: Discovering, Internalizing, and Using Explicit Knowledge Across Agents and Generations — arXiv:2611.03157},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.03157},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_a674293dff64; sha256 67c258f226810b10ed7cbe9c2a04fc186e18c1ca0702df287aefaf5d1d10cc8d},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.03157. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Knowledge is how understanding accumulates across people and generations: durable records that outlive their producers and that others can check, combine, teach, and correct. AI agents are now built and run by many independent users and organizations, and each agent is upgraded, replaced, or retired within months. Yet what an agent learns stays trapped in model parameters, a transient context window, or a system-specific memory store that is appended to and retrieved from rather than processed. We argue that the next generation of AI should be knowledge-centric, built around an explicit, continuously evolving knowledge layer independent of any single agent. In this loop, agents discover knowledge from their environments and trajectories; the layer internalizes it by turning fragmented, heterogeneous records and experience into composable, revisable understanding; and agents across systems and generations use the result and return new outcomes for revision. While discovery and use have advanced rapidly with large language model agents, internalization remains the bottleneck. We argue that internalization should become a persistent object of training and maintenance-centered on condition-preserving conceptualization of events and deductive, inductive, and abductive reasoning across noisy, heterogeneous representations (text, tables, graphs, rules, and programs). To power this layer, we call for knowledge foundation models that provide shared neural capacity for knowledge computation, and outline a research agenda showing how an explicit knowledge layer delivers reuse, cost efficiency, auditability, and continuity across agent generations.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.03157 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T10:59:45.879Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).