✨4DGS360: 360° Gaussian Reconstruction of Dynamic Objects from a Single Video

📝 Summary:
4DGS360 presents a diffusion-free approach for 360° dynamic object reconstruction using 3D-native initialization and a 3D tracker called AnchorTAP3D to improve geometric consistency and handle occlusi...

🔹 Publication Date: Published on Mar 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.21618
• PDF: https://arxiv.org/pdf/2603.21618
• Project Page: https://jaewon040.github.io/4dgs360/

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✨Why Does Self-Distillation (Sometimes) Degrade the Reasoning Capability of LLMs?

📝 Summary:
Self-distillation in large language models can degrade mathematical reasoning performance by suppressing uncertainty expression, particularly affecting out-of-distribution tasks. AI-generated summary ...

🔹 Publication Date: Published on Mar 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.24472
• PDF: https://arxiv.org/pdf/2603.24472
• Project Page: https://beanie00.notion.site/why-does-self-distillation-degrade-reasoning
• Github: https://github.com/beanie00/self-distillation-analysis

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✨T-MAP: Red-Teaming LLM Agents with Trajectory-aware Evolutionary Search

📝 Summary:
T-MAP, a trajectory-aware evolutionary search method, discovers adversarial prompts that bypass safety measures and achieve harmful outcomes through tool interactions in LLM agents. AI-generated summa...

🔹 Publication Date: Published on Mar 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.22341
• PDF: https://arxiv.org/pdf/2603.22341
• Github: https://github.com/pwnhyo/T-MAP

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✨UniFunc3D: Unified Active Spatial-Temporal Grounding for 3D Functionality Segmentation

📝 Summary:
UniFunc3D enables 3D scene functionality segmentation by treating multimodal large language models as active observers that perform joint semantic, temporal, and spatial reasoning through adaptive fra...

🔹 Publication Date: Published on Mar 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.23478
• PDF: https://arxiv.org/pdf/2603.23478
• Project Page: https://jiaying.link/unifunc3d/

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✨EVA: Efficient Reinforcement Learning for End-to-End Video Agent

📝 Summary:
EVA is an RL framework enabling efficient, adaptive video understanding by autonomously deciding what and how to watch. It uses iterative planning to handle long video sequences. EVA significantly outperforms existing MLLM and adaptive agent methods on multiple video benchmarks.

🔹 Publication Date: Published on Mar 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.22918
• PDF: https://arxiv.org/pdf/2603.22918
• Project Page: https://huggingface.co/WRHC/EfficientVideoAgent/
• Github: https://github.com/wangruohui/EfficientVideoAgent

🔹 Models citing this paper:
• https://huggingface.co/WRHC/EfficientVideoAgent

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✨PLDR-LLMs Reason At Self-Organized Criticality

📝 Summary:
PLDR-LLMs exhibit reasoning capabilities at self-organized criticality through metastable steady states that mirror second-order phase transitions, enabling generalization without benchmark evaluation...

🔹 Publication Date: Published on Mar 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.23539
• PDF: https://arxiv.org/pdf/2603.23539
• Project Page: https://huggingface.co/fromthesky
• Github: https://github.com/burcgokden/PLDR-LLM-Self-Organized-Criticality

🔹 Models citing this paper:
• https://huggingface.co/fromthesky/PLDR-LLM-v51-SOC-110M-1
• https://huggingface.co/fromthesky/PLDR-LLM-v51-SOC-110M-2
• https://huggingface.co/fromthesky/PLDR-LLM-v51-SOC-110M-3

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✨Qworld: Question-Specific Evaluation Criteria for LLMs

📝 Summary:
Qworld is a new method that generates question-specific evaluation criteria for LLMs using recursive expansion trees. It decomposes questions into fine-grained criteria, enabling more insightful and granular assessment of LLM capabilities by adapting to each question's context. This approach reve...

🔹 Publication Date: Published on Mar 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.23522
• PDF: https://arxiv.org/pdf/2603.23522
• Project Page: https://qworld.openscientist.ai/
• Github: https://github.com/mims-harvard/qworld

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#LLMEvaluation #LargeLanguageModels #AIResearch #NLP #MachineLearning

✨Understanding the Challenges in Iterative Generative Optimization with LLMs

📝 Summary:
Generative optimization with LLMs is often brittle due to implicit design choices about artifact modification and learning evidence. These hidden decisions, such as starting artifact or batching, critically determine success across applications. Making these choices explicit is crucial for wider ...

🔹 Publication Date: Published on Mar 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.23994
• PDF: https://arxiv.org/pdf/2603.23994

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#LLMs #GenerativeAI #Optimization #AIResearch #MachineLearning

✨Internal Safety Collapse in Frontier Large Language Models

📝 Summary:
Frontier LLMs suffer Internal Safety Collapse, continuously generating harmful content under specific task conditions, even for benign tasks. A new framework triggers this vulnerability, yielding 95% safety failure rates and revealing inherent unsafe capabilities despite alignment efforts.

🔹 Publication Date: Published on Mar 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.23509
• PDF: https://arxiv.org/pdf/2603.23509
• Project Page: https://wuyoscar.github.io/ISC-Bench
• Github: https://github.com/wuyoscar/ISC-Bench

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#AISafety #LLM #AIAlignment #MachineLearning #AIResearch

✨MACRO: Advancing Multi-Reference Image Generation with Structured Long-Context Data

📝 Summary:
To advance multi-reference image generation, this paper introduces MacroData, a large-scale dataset providing structured long-context supervision. It also proposes MacroBench, a standardized benchmark for evaluation. Fine-tuning on MacroData significantly improves generation performance.

🔹 Publication Date: Published on Mar 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.25319
• PDF: https://arxiv.org/pdf/2603.25319
• Project Page: https://macro400k.github.io/
• Github: https://github.com/HKU-MMLab/Macro

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

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