Advancements in large language models (LLM) have driven the emergence of
complex new systems to provide access to information, that we will collectively
refer to as modular generative information access (GenIA) systems. They
integrate a broad and evolving range of specialized components, including LLMs,
retrieval models, and a heterogeneous set of sources and tools. While
modularity offers flexibility, it also raises critical challenges: How can we
systematically characterize the space of possible modules and their
interactions? How can we automate and optimize interactions among these
heterogeneous components? And, how do we enable this modular system to
dynamically adapt to varying user query requirements and evolving module
capabilities? In this perspective paper, we argue that the architecture of
future modular generative information access systems will not just assemble
powerful components, but enable a self-organizing system through real-time
adaptive orchestration — where components’ interactions are dynamically
configured for each user input, maximizing information relevance while
minimizing computational overhead. We give provisional answers to the questions
raised above with a roadmap that depicts the key principles and methods for
designing such an adaptive modular system. We identify pressing challenges, et
propose avenues for addressing them in the years ahead. This perspective urges
the IR community to rethink modular system designs for developing adaptive,
self-optimizing, and future-ready architectures that evolve alongside their
rapidly advancing underlying technologies.

Cet article explore les excursions dans le temps et leurs implications.

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