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Seeing, Maintaining, and Learning: The Evolution of Multimodal Agent Memory — arXiv:2610.19366

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.19366
License (as archived): CC0-1.0
Archived: 2026-10-15T20:01:13.945Z
SHA-256 of the archived text: c0321d2ff8de91557a488a0e8cb5ce6c426adea2f427320db34357c3b44d57f0
Stable URL of this page: https://metraipolis.com/documento/dc_aaffc4ea137b

How to cite

Seeing, Maintaining, and Learning: The Evolution of Multimodal Agent Memory — arXiv:2610.19366 — arXiv (en), https://arxiv.org/abs/2610.19366 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_aaffc4ea137b (Metraipolis archive dc_aaffc4ea137b, sha256 c0321d2ff8de91557a488a0e8cb5ce6c426adea2f427320db34357c3b44d57f0)

BibTeX

@misc{dcaaffc4ea137b,
  title = {Seeing, Maintaining, and Learning: The Evolution of Multimodal Agent Memory — arXiv:2610.19366},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.19366},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_aaffc4ea137b; sha256 c0321d2ff8de91557a488a0e8cb5ce6c426adea2f427320db34357c3b44d57f0},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.19366. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Multimodal models and agents have emerged to enable AI systems to learn from and interact with the real world more like humans by integrating information across multiple modalities. However, enabling multimodal agents to continually acquire useful information from complex, heterogeneous multimodal data remains a significant challenge as they move toward long-horizon tasks and lifelong settings. Addressing this challenge requires effective memory mechanisms that can maintain continuity across sessions, adapt to changing users and environments, and refine memory-management policies through accumulated experience. We introduce a two-axis analytical framework for multimodal agent memory structured around two key questions: how multimodal information is formed and represented (Axis I, Formation and Representation), and how its management adapts with accumulated experience (Axis II, Memory Management). Axis I, Formation and Representation, characterizes what information is retained and how it is encoded (e.g., modality-specific embeddings, textual and structured records, latent and parametric representations) to support subsequent reasoning and action. Axis II, Memory Management, distinguishes three tiers: T1 Static Memory reuses fixed memory; T2 Dynamic Memory updates memory through a stable policy; and T3 Self-Evolving Memory updates memory through an evolving policy. Based on this framework, we synthesize recurring design patterns and derive principles for balancing source fidelity with abstraction, maintaining temporal validity and provenance, grounding memory use in recoverable evidence, and evolving memory policies over time. Furthermore, we discuss the challenges and potential future directions for the memory mechanism of multimodal agents.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL); Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2610.19366 — 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 2026-10-15T19:35:48.216Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).