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EpiCon presents a shared multimodal memory framework for collective learning among AI agents, enhancing performance across eleven benchmarks without updating host model parameters.
VoxMem is a benchmark for evaluating multimodal memory in Large Audio Language Models, focusing on acoustic evidence types and multi-session memory operations. It reveals significant gaps in current models, such as poor retention of speaker identity and paralinguistic cues compared to semantic content.
Proposes VoxPolyMem, an interaction-aware multimodal memory framework with adaptive agentic retrieval for multi-party spoken conversations, achieving state-of-the-art performance on new benchmarks.
This research paper introduces a parametric multimodal user memory system that improves AI agents' ability to recall users by integrating perceptual data like voice and appearance, using vision-language models and dedicated encoders to surpass text-based methods.
EM^2Mem proposes an event-centric multimodal memory framework that binds heterogeneous evidence to event anchors for compact, generation-ready memory in long-video question answering, improving accuracy and reducing latency.
This paper introduces AdaMM, a framework that complements retrieval-based multimodal memory with analytic memory, enabling filtering, aggregation, ranking, and temporal comparison over accumulated observations. Experiments on MemEye and MemGallery benchmarks show improvements of up to 11.3% and 7.3% respectively.
ViSAGE is a multimodal agentic memory framework for long-form video understanding that builds self-correcting, entity-centric memories via cross-modal binding, bidirectional memory refinement, and multi-agent cross-verification, achieving 5.9% higher accuracy than baselines.