How Should a Camera-First AI Agent Work?

Title:How Should a Camera-First AI Agent Work?

Abstract:Camera-first agents must act on the visual world as it unfolds. We argue that the central obstacle for future camera-first agents is not recognition ability - multimodal models (vision-language and live/streaming alike) generalize well through language and remain the right tool for semantic reasoning - but time. Many visual targets are ephemeral: they appear, are query-relevant, and leave within a short window. Binding memory to the same end-to-end path as reasoning means that by the time a monolithic pipeline completes, the evidence is gone. We formalize this as Ephemeral-Target Interaction (ETI) and a Perceptual Deadline Contract separating a hard memory deadline from a soft reasoning deadline, grounded in the bounded, passive nature of sensory and working memory. We propose a two-deadline architecture: an always-on, bounded, non-semantic fast path; an active remembering mechanism that commits a moment to durable memory before it is overwritten (event-driven in v1); and a deferred deliberation layer (multimodal model + tools). We instantiate the contract on real MOT17 track windows, a consistency check, and calibrate the single-path baseline from measured frontier-model latency: even a short answer takes 1.2-9.3 s, and an agentic identify-and-search longer. Answers can be late; evidence cannot be missed.

8 pages, 4 figures, 3 tables. Position/contract paper on time-critical camera-first agents (systems/timing).Primary cs.AI;

Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Logic in Computer Science (cs.LO)

arXiv:2610.14772 [cs.AI]

(or arXiv:2610.14772v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.14772 Focus to learn more arXiv-issued DOI via DataCite

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