EgoMemReason is a new benchmark showing that even the best multimodal models achieve only 39.6% accuracy on reasoning tasks that require integrating sparse evidence across days in egocentric video.
MA-EgoQA: Question answer- ing over egocentric videos from multiple embodied agents
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Introduces Personal VCL formalization and benchmark revealing LMM context gaps, plus an Agentic Context Bank baseline that boosts personalized visual reasoning.
Cross-domain memory transfer in coding agents boosts average performance by 3.7% via meta-knowledge like validation routines, with high-level abstractions transferring better than low-level traces.
citing papers explorer
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EgoMemReason: A Memory-Driven Reasoning Benchmark for Long-Horizon Egocentric Video Understanding
EgoMemReason is a new benchmark showing that even the best multimodal models achieve only 39.6% accuracy on reasoning tasks that require integrating sparse evidence across days in egocentric video.
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Personal Visual Context Learning in Large Multimodal Models
Introduces Personal VCL formalization and benchmark revealing LMM context gaps, plus an Agentic Context Bank baseline that boosts personalized visual reasoning.
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Memory Transfer Learning: How Memories are Transferred Across Domains in Coding Agents
Cross-domain memory transfer in coding agents boosts average performance by 3.7% via meta-knowledge like validation routines, with high-level abstractions transferring better than low-level traces.