arXiv AI
Beyond the Timeline: Augmenting Long-Video Memory with Grounded Entity Biographies
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arXiv Computer Vision
Sep 7
ICM-Bench: Person-Level Identity Reasoning in Multimodal Agents with Long-Term Memory
ICM-Bench is a new benchmark for evaluating identity-centric reasoning in multimodal agents with long-term memory. It consists of 839 synthetic video clips totaling 141 minutes and 1,217 open-ended questions about six recurring adults in a one-year life album. The benchmark isolates the ability to maintain recurring person identities and reason over their cross-time relations, and compares various baseline systems, showing that while Gemini 3.1 Pro performs well overall, its accuracy drops on questions requiring long-term identity profiles.
arXiv Computer Vision
Sep 1
Dynamic Hub-and-Spoke Memory for Streaming Video Understanding
arXiv:2608.30294v1 Announce Type: new Abstract: Streaming video understanding requires answering questions at arbitrary times over a continuously growing visual stream. The central challenge is to co...
arXiv AI
Aug 14
EgoMonth: A Month-Level Egocentric Video Benchmark for Long-Term Spatiotemporal Memory
arXiv:2608. 13113v1 Announce Type: cross Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have led to substantial progress in video understanding, accompanied by a growing number of long video benchmarks.
arXiv AI
Aug 3
ViSAGE: Constructing Self-Correcting Memories for Long-Form Video Understanding
arXiv:2607. 28678v1 Announce Type: new Abstract: Multimodal agents operating in long-horizon environments must build and continually update multimedia memories to support entity-consistent, temporally grounded reasoning.
arXiv AI
3d ago
MemLife: Curating and Reasoning over Long-Term Egocentric Video Memories
arXiv:2609.40195v1 Announce Type: cross Abstract: Long-term egocentric video enables personalized AI assistants to reason about daily life. However, as video histories grow to hundreds of hours spann...
arXiv AI
Aug 20
EgoMemReason: A Memory-Driven Reasoning Benchmark for Long-Horizon Egocentric Video Understanding
EgoMemReason is a new benchmark for week‑long egocentric video understanding that focuses on memory‑driven reasoning rather than simple perception tasks. It tests three memory types—entity, event, and behavior—across 500 questions, each requiring evidence from an average of 5.1 video segments and 25.9 hours of backtracking. Evaluation of 17 models shows that even the best achieves only 39.6% accuracy, highlighting the difficulty of long‑horizon memory in multimodal systems.