✨BERTology of Molecular Property Prediction

📝 Summary:
Researchers systematically investigate how dataset size, model size, and standardization impact chemical language model performance in molecular property prediction. This study provides numerical evidence to understand mechanisms affecting performance and resolve inconsistent literature results.

🔹 Publication Date: Published on Mar 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.13627
• PDF: https://arxiv.org/pdf/2603.13627
• Github: https://github.com/molssi-ai/bertology

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#MolecularPropertyPrediction #ChemicalLanguageModels #BERT #DeepLearning #Cheminformatics

✨V-Co: A Closer Look at Visual Representation Alignment via Co-Denoising

📝 Summary:
Pixel-space diffusion models can be enhanced through visual co-denoising techniques that incorporate pretrained visual features, with systematic analysis revealing key architectural and training compo...

🔹 Publication Date: Published on Mar 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.16792
• PDF: https://arxiv.org/pdf/2603.16792

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✨ECG-Reasoning-Benchmark: A Benchmark for Evaluating Clinical Reasoning Capabilities in ECG Interpretation

📝 Summary:
W h i l e M u l t i m o d a l L a r g e L a n g u a g e M o d e l s ( M L L M s ) s h o w p r o m i s i n g p e r f o r m a n c e i n a u t o m a t e d e l e c t r o c a r d i o g r a m i n t e r p r ...

🔹 Publication Date: Published on Mar 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.14326
• PDF: https://arxiv.org/pdf/2603.14326

✨ Datasets citing this paper:
• https://huggingface.co/datasets/Jwoo5/ECG-Reasoning-Benchmark

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✨Residual Stream Duality in Modern Transformer Architectures

📝 Summary:
The residual stream in Transformers can be viewed through a two-axis framework where sequence position and layer depth provide different pathways for information flow, with causal depth-wise residual ...

🔹 Publication Date: Published on Mar 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.16039
• PDF: https://arxiv.org/pdf/2603.16039
• Project Page: https://github.com/yifanzhang-pro/residual-stream-duality
• Github: https://github.com/yifanzhang-pro/residual-stream-duality

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✨ARISE: Agent Reasoning with Intrinsic Skill Evolution in Hierarchical Reinforcement Learning

📝 Summary:
A hierarchical reinforcement learning framework named ARISE employs a skill management system to improve mathematical reasoning in language models through reusable strategies and structured skill libr...

🔹 Publication Date: Published on Mar 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.16060
• PDF: https://arxiv.org/pdf/2603.16060
• Github: https://github.com/Skylanding/ARISE

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✨MDM-Prime-v2: Binary Encoding and Index Shuffling Enable Compute-optimal Scaling of Diffusion Language Models

📝 Summary:
MDM-Prime-v2 enhances masked diffusion language models with Binary Encoding and Index Shuffling. It is 21.8 times more compute-efficient than autoregressive models, achieving significantly better perplexity and zero-shot accuracy.

🔹 Publication Date: Published on Mar 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.16077
• PDF: https://arxiv.org/pdf/2603.16077
• Project Page: https://chen-hao-chao.github.io/mdm-prime-v2/
• Github: https://github.com/chen-hao-chao/mdm-prime-v2

🔹 Models citing this paper:
• https://huggingface.co/chen-hao-chao/mdm-prime-v2-c4
• https://huggingface.co/chen-hao-chao/mdm-prime-v2-slimpajama

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✨Mamba: Linear-Time Sequence Modeling with Selective State Spaces

📝 Summary:
Mamba is a novel SSM that outperforms Transformers by enabling content-based reasoning through selective state spaces. It offers 5x faster inference, linear scaling, and achieves state-of-the-art results across language, audio, and genomics, even matching larger Transformers.

🔹 Publication Date: Published on Dec 1, 2023

🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/mamba-linear-time-sequence-modeling-with-selective-state-spaces
• PDF: https://arxiv.org/pdf/2312.00752
• Github: https://github.com/state-spaces/mamba

🔹 Models citing this paper:
• https://huggingface.co/tiiuae/falcon-mamba-7b
• https://huggingface.co/state-spaces/mamba-2.8b-slimpj
• https://huggingface.co/tiiuae/falcon-mamba-7b-instruct

✨ Datasets citing this paper:
• https://huggingface.co/datasets/huaXiaKyrie/up
• https://huggingface.co/datasets/Sherirto/BD4UI

✨ Spaces citing this paper:
• https://huggingface.co/spaces/FallnAI/Quantize-HF-Models
• https://huggingface.co/spaces/openfree/LLM_Quantization
• https://huggingface.co/spaces/seawolf2357/LLM_Quantization

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✨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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✨RAMP: Reinforcement Adaptive Mixed Precision Quantization for Efficient On Device LLM Inference

📝 Summary:
Reinforcement learning-based mixed precision quantization method achieves superior compression efficiency and model performance for large language models through adaptive bit width assignment and nove...

🔹 Publication Date: Published on Mar 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.17891
• PDF: https://arxiv.org/pdf/2603.17891

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✨PRISM: Demystifying Retention and Interaction in Mid-Training

📝 Summary:
Mid-training design choices significantly improve reasoning performance in large language models, with optimal results achieved when reinforcement learning is applied to models that have been pre-trai...

🔹 Publication Date: Published on Mar 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.17074
• PDF: https://arxiv.org/pdf/2603.17074

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