Mneme: An Odyssey Through Reconstructive Memory for Personal Deep Image Search

Image retrieval enables users to find relevant photos in large image collections, such as their photo albums. Common embedding-based methods rank images independently by query–image similarity, but personal album queries may identify targets through contextual clues found in other photos. An effective search must therefore recover cross-photo relations and use them to locate and verify target images. To this end, we introduce Mneme, a memory-augmented agent for reconstructive search over personal photo collections. Rather than using memory only to preserve or reuse agent search experience, Mneme reconstructs the visual history itself as a persistent relational memory. Its Album Memory Graph links grounded photo evidence, recurring visual instances, and overlapping episodic contexts, while Self-Evolving Search Skills induce and iteratively refine reusable search guidance from same-query success–failure trajectory contrasts on auxiliary tasks. At inference time, a dual-route strategy combines memory-guided reconstruction with memory-independent evidence search, and reconciles their candidates against raw images and metadata. Experiments on DISBench and CamRoll show that Mneme consistently outperforms strong retrieval and memory-augmented baselines across both tested backbones. Ablations further indicate that persistent album memory alone is insufficient; its benefits emerge when combined with reliable short-term memory, learned memory search guidance, and complementary memory-guided and direct search.