Generative Retrieval (GR) encompasses two primary paradigms: Generative Document Retrieval (GDR), which directly generates document identifiers, and Retrieval-Augmented Generation (RAG), which synthesizes reliable responses from retrieved evidence. Despite their promising performance, most existing GR approaches assume static corpora. In GDR, corpus knowledge is encoded directly into model parameters, meaning that integrating new documents requires expensive retraining while risking catastrophic forgetting. Non-stationarity also arises along the temporal dimension: in RAG systems, facts evolve and queries frequently contain temporal constraints. When temporally misaligned evidence is retrieved, generation quality degrades regardless of the LLM's reasoning capability, demonstrating that the retriever forms a central bottleneck under evolving knowledge. Beyond textual corpora, modern Information Retrieval (IR) increasingly operates over multimodal collections that combine text, images, and other modalities. These corpora are likewise dynamic and subject to continuous distributional shifts. In summary, real-world information retrieval systems are characterized by multiple forms of non-stationarity: text corpus growth, temporal evolution, and multimodal corpus growth. This thesis investigates principled and efficient continual learning (CL) mechanisms for GR under these realistic conditions, with a unifying hypothesis that scalable and efficient retriever adaptation is essential to maintaining effective GR.

Continually updating model-based indexes in generative retrieval with new documents remains challenging, as full retraining is computationally expensive and impractical under resource constraints.We propose MixLoRA-DSI, a novel framework that combines an expandable mixture of Low-Rank Adaptation experts with a layer-wise out-of-distribution (OOD)driven expansion strategy.Instead of allocating new experts for each new corpus, our proposed expansion strategy enables sublinear parameter growth by selectively introducing new experts only when significant number of OOD documents are detected.Experiments on NQ320k and MS MARCO Passage demonstrate that MixLoRA-DSI outperforms full-model update baselines, with minimal parameter overhead and substantially lower training costs. 1