✨Unified Spatio-Temporal Token Scoring for Efficient Video VLMs

📝 Summary:
STTS is a lightweight module for efficiently pruning vision tokens across vision transformer and language models in video VLMs. It achieves 62% efficiency gains with only a 0.7% performance drop by learning spatio-temporal token scoring without text conditioning.

🔹 Publication Date: Published on Mar 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.18004
• PDF: https://arxiv.org/pdf/2603.18004
• Github: https://github.com/allenai/STTS

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✨MosaicMem: Hybrid Spatial Memory for Controllable Video World Models

📝 Summary:
Video diffusion models use hybrid spatial memory to maintain consistency under camera motion and enable long-term scene editing and navigation. AI-generated summary Video diffusion models are moving b...

🔹 Publication Date: Published on Mar 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.17117
• PDF: https://arxiv.org/pdf/2603.17117
• Project Page: https://mosaicmem.github.io/mosaicmem/
• Github: https://mosaicmem.github.io/mosaicmem/

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✨Stereo World Model: Camera-Guided Stereo Video Generation

📝 Summary:
StereoWorld is a camera-conditioned stereo world model that generates stereo videos end-to-end using RGB modality while maintaining geometric consistency and efficiency through novel attention mechani...

🔹 Publication Date: Published on Mar 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.17375
• PDF: https://arxiv.org/pdf/2603.17375
• Project Page: https://sunyangtian.github.io/StereoWorld-web/
• Github: https://github.com/SunYangtian/StereoWorld

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✨When AI Navigates the Fog of War

📝 Summary:
Large language models demonstrate varying capabilities in reasoning about unfolding geopolitical conflicts, showing strategic realism in structured settings but inconsistent performance in complex pol...

🔹 Publication Date: Published on Mar 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.16642
• PDF: https://arxiv.org/pdf/2603.16642
• Project Page: https://www.war-forecast-arena.com/
• Github: https://github.com/xirui-li/war-test

✨ Datasets citing this paper:
• https://huggingface.co/datasets/AIcell/war-test-dataset

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✨AdaMem: Adaptive User-Centric Memory for Long-Horizon Dialogue Agents

📝 Summary:
AdaMem is an adaptive memory framework for dialogue agents that organizes conversation history into multiple memory types and uses conditional retrieval to improve long-horizon reasoning and user mode...

🔹 Publication Date: Published on Mar 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.16496
• PDF: https://arxiv.org/pdf/2603.16496

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✨LaDe: Unified Multi-Layered Graphic Media Generation and Decomposition

📝 Summary:
LaDe is a latent diffusion framework that generates layered media designs with flexible layer counts and semantic meaning from natural language prompts, supporting text-to-image, text-to-layers, and m...

🔹 Publication Date: Published on Mar 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.17965
• PDF: https://arxiv.org/pdf/2603.17965

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✨Complementary Reinforcement Learning

📝 Summary:
Complementary RL enables efficient agent learning by synchronizing experience extraction with policy optimization through dual objectives that evolve together during training. AI-generated summary Rei...

🔹 Publication Date: Published on Mar 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.17621
• PDF: https://arxiv.org/pdf/2603.17621
• Github: https://github.com/pUmpKin-Co/ComplementaryRL

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✨MetaClaw: Just Talk -- An Agent That Meta-Learns and Evolves in the Wild

📝 Summary:
MetaClaw is a continual meta-learning framework for LLM agents that evolves policies and reusable skills. It enables zero-downtime skill adaptation and opportunistic policy optimization during inactive periods. This boosts agent accuracy and robustness, scaling to production LLMs.

🔹 Publication Date: Published on Mar 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.17187
• PDF: https://arxiv.org/pdf/2603.17187
• Github: https://github.com/aiming-lab/MetaClaw

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✨Efficient Exploration at Scale

📝 Summary:
An online learning algorithm for reinforcement learning from human feedback that achieves significant data efficiency improvements through incremental model updates, reward uncertainty modeling, and i...

🔹 Publication Date: Published on Mar 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.17378
• PDF: https://arxiv.org/pdf/2603.17378

==================================

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✨LoST: Level of Semantics Tokenization for 3D Shapes

📝 Summary:
Level-of-Semantics Tokenization (LoST) improves 3D shape generation by ordering tokens based on semantic salience and using a novel relational alignment loss for better reconstruction and efficiency. ...

🔹 Publication Date: Published on Mar 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.17995
• PDF: https://arxiv.org/pdf/2603.17995
• Project Page: https://lost3d.github.io/

==================================

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