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Wonder is a general-purpose video world model that enables real-time, camera-controllable world exploration from an image or conditional video. It introduces camera conditioning via dense coordinate fields, a sparse attention memory mechanism, and techniques to improve distillation, allowing minute-scale video generation at 16 FPS.
This paper introduces a proactive memory agent that operates alongside a standard action agent to selectively inject memory-grounded reminders during long-horizon tasks, mitigating behavioral state decay. Experiments on Terminal-Bench and τ²-Bench show significant improvements in pass@1, and the approach is demonstrated with both weak and strong action agents.
This paper introduces AutoMMemo, a framework that enables multimodal agents to automatically design memory mechanisms (expressible as executable memo programs) for learning from multimodal interaction trajectories, outperforming no-memory and fixed-memory baselines on GUI/Web navigation and visual reasoning benchmarks.
The paper introduces δ-mem, a lightweight memory mechanism that enhances large language models by augmenting a frozen attention backbone with a compact associative memory state. It demonstrates improved performance on memory-heavy benchmarks with minimal computational overhead.