✨TAPFormer: Robust Arbitrary Point Tracking via Transient Asynchronous Fusion of Frames and Events

📝 Summary:
TAPFormer is a new transformer framework for robust arbitrary point tracking. It uses Transient Asynchronous Fusion to bridge low-rate frames and high-rate events, and Cross-modal Locally Weighted Fusion for adaptive attention. This method significantly outperforms existing trackers.

🔹 Publication Date: Published on Mar 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04989
• PDF: https://arxiv.org/pdf/2603.04989
• Project Page: https://tapformer.github.io/
• Github: https://github.com/ljx1002/TAPFormer

🔹 Models citing this paper:
• https://huggingface.co/ljx1002/tapformer

✨ Datasets citing this paper:
• https://huggingface.co/datasets/ljx1002/tapformer

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

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#PointTracking #Transformers #ComputerVision #EventCameras #DeepLearning

✨MWM: Mobile World Models for Action-Conditioned Consistent Prediction

📝 Summary:
MWM improves action-conditioned rollout consistency for navigation world models. It uses a two-stage training approach and Inference-Consistent State Distillation to achieve robust, efficient planning with higher visual fidelity and success.

🔹 Publication Date: Published on Mar 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.07799
• PDF: https://arxiv.org/pdf/2603.07799
• Project Page: https://aigeeksgroup.github.io/MWM
• Github: https://aigeeksgroup.github.io/MWM

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

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✨HY-WU (Part I): An Extensible Functional Neural Memory Framework and An Instantiation in Text-Guided Image Editing

📝 Summary:
Foundation models require adaptive architectures to handle evolving objectives and user needs, leading to the development of HY-WU, a memory-first framework that generates instance-specific weight upd...

🔹 Publication Date: Published on Mar 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.07236
• PDF: https://arxiv.org/pdf/2603.07236
• Project Page: https://tencent-hy-wu.github.io/
• Github: https://github.com/Tencent-Hunyuan/HY-WU

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

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✨Making LLMs Optimize Multi-Scenario CUDA Kernels Like Experts

📝 Summary:
This paper introduces CUDAMaster, a multi-agent, hardware-aware system for general-purpose automated GPU kernel optimization across diverse scenarios including ML and scientific computing. It achieves significant speedups, often matching or exceeding commercial libraries like cuBLAS.

🔹 Publication Date: Published on Mar 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.07169
• PDF: https://arxiv.org/pdf/2603.07169

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

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#LLMs #GPUOptimization #CUDA #HighPerformanceComputing #MachineLearning

✨NLE: Non-autoregressive LLM-based ASR by Transcript Editing

📝 Summary:
NLE is a non-autoregressive ASR system that uses a bidirectional LLM editor for conditional transcript editing, enabling parallel prediction. It achieves strong accuracy and a 27x speedup over AR baselines, making it suitable for real-time use.

🔹 Publication Date: Published on Mar 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.08397
• PDF: https://arxiv.org/pdf/2603.08397

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

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✨PresentBench: A Fine-Grained Rubric-Based Benchmark for Slide Generation

📝 Summary:
Slides serve as a critical medium for conveying information in presentation-oriented scenarios such as academia, education, and business. Despite their importance, creating high-quality slide decks re...

🔹 Publication Date: Published on Mar 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.07244
• PDF: https://arxiv.org/pdf/2603.07244

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

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✨Agentic Planning with Reasoning for Image Styling via Offline RL

📝 Summary:
This paper presents an agentic offline reinforcement learning framework for complex image styling. It uses structured planning with chain-of-thought reasoning and a tool library to decompose editing tasks. This approach significantly improves performance over direct prompting, validated by human ...

🔹 Publication Date: Published on Mar 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.07148
• PDF: https://arxiv.org/pdf/2603.07148

✨ Datasets citing this paper:
• https://huggingface.co/datasets/subhojyoti1990/image-agent-styling

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

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✨Sparse-BitNet: 1.58-bit LLMs are Naturally Friendly to Semi-Structured Sparsity

📝 Summary:
Sparse-BitNet demonstrates that 1.58-bit quantization works better with N:M sparsity than full-precision models, achieving stable training and improved efficiency across different scales and regimes. ...

🔹 Publication Date: Published on Mar 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.05168
• PDF: https://arxiv.org/pdf/2603.05168
• Github: https://github.com/AAzdi/Sparse-BitNet

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

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✨MedSteer: Counterfactual Endoscopic Synthesis via Training-Free Activation Steering

📝 Summary:
MedSteer is a training-free framework for generating counterfactual medical images. It steers diffusion model activations along pathology vectors to modify concepts while preserving underlying image structure. This method outperforms existing techniques in concept modification and significantly i...

🔹 Publication Date: Published on Mar 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.07066
• PDF: https://arxiv.org/pdf/2603.07066
• Github: https://github.com/phamtrongthang123/medsteer

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

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✨Scaling Data Difficulty: Improving Coding Models via Reinforcement Learning on Fresh and Challenging Problems

📝 Summary:
A four-stage data processing framework with LLM-based difficulty filtering creates a high-quality code generation dataset that significantly improves model performance on challenging problems. AI-gene...

🔹 Publication Date: Published on Mar 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.07779
• PDF: https://arxiv.org/pdf/2603.07779
• Project Page: https://github.com/ZongqianLi/MicroCoder/blob/main/README.md

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

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✨Breaking Training Bottlenecks: Effective and Stable Reinforcement Learning for Coding Models

📝 Summary:
MicroCoder-GRPO enhances code generation through improved policy optimization with innovations in truncation masking, temperature selection, and loss function adjustments, achieving superior performan...

🔹 Publication Date: Published on Mar 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.07777
• PDF: https://arxiv.org/pdf/2603.07777
• Project Page: https://github.com/ZongqianLi/MicroCoder/blob/main/README.md
• Github: https://github.com/ZongqianLi/MicroCoder

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

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✨Retrieval-Augmented Generation for Predicting Cellular Responses to Gene Perturbation

📝 Summary:
PT-RAG framework improves prediction of cellular responses to genetic perturbations by using differentiable, cell-type-aware retrieval combined with generative modeling, outperforming existing methods...

🔹 Publication Date: Published on Mar 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.07233
• PDF: https://arxiv.org/pdf/2603.07233
• Github: https://github.com/difra100/PT-RAG_ICLR

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

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✨Training-free Latent Inter-Frame Pruning with Attention Recovery

📝 Summary:
LIPAR reduces video generation latency by skipping redundant latent patches. It uses Attention Recovery to maintain quality, boosting throughput by 1.45x without extra training.

🔹 Publication Date: Published on Mar 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.05811
• PDF: https://arxiv.org/pdf/2603.05811
• Project Page: https://dennismenn.github.io/lipar/
• Github: https://github.com/DennisMenn/lipar

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

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✨LiveWorld: Simulating Out-of-Sight Dynamics in Generative Video World Models

📝 Summary:
LiveWorld addresses the out-of-sight dynamics problem in video world models by introducing a persistent global state representation that maintains continuous evolution of dynamic entities beyond the o...

🔹 Publication Date: Published on Mar 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.07145
• PDF: https://arxiv.org/pdf/2603.07145
• Project Page: https://zichengduan.github.io/LiveWorld/index.html

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

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✨Variational Flow Maps: Make Some Noise for One-Step Conditional Generation

📝 Summary:
Variational Flow Maps VFMs introduce a novel framework for fast, high-fidelity conditional image generation. VFMs learn an optimal initial noise distribution to respect observations and data priors, accelerating sampling over iterative models. This allows well-calibrated conditional samples in si...

🔹 Publication Date: Published on Mar 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.07276
• PDF: https://arxiv.org/pdf/2603.07276
• Github: https://github.com/abbasmammadov/VFM

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✨ByteFlow: Language Modeling through Adaptive Byte Compression without a Tokenizer

📝 Summary:
ByteFlow Net presents a tokenizer-free hierarchical architecture that enables language models to learn adaptive segmentation of raw byte streams through compression-driven methods while maintaining a ...

🔹 Publication Date: Published on Mar 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.03583
• PDF: https://arxiv.org/pdf/2603.03583

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

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✨CAST: Modeling Visual State Transitions for Consistent Video Retrieval

📝 Summary:
Current video retrieval often lacks context, leading to inconsistent narratives. CAST is a new plug-and-play adapter that predicts state-conditioned visual history to improve video consistency. It enhances retrieval performance and temporal coherence in video generation.

🔹 Publication Date: Published on Mar 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.08648
• PDF: https://arxiv.org/pdf/2603.08648
• Project Page: https://ucsc-vlaa.github.io/CAST/

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✨Free Lunch for Pass@k? Low Cost Diverse Sampling for Diffusion Language Models

📝 Summary:
Diffusion language models suffer from redundant sampling, but a novel technique that repels samples from each other's feature space improves diversity and performance on code generation and math probl...

🔹 Publication Date: Published on Mar 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04893
• PDF: https://arxiv.org/pdf/2603.04893
• Project Page: https://sean-lamont.github.io/odd/
• Github: https://github.com/sean-lamont/odd

✨ Spaces citing this paper:
• https://huggingface.co/spaces/sean-lamont/ODD-Demo

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✨Reading, Not Thinking: Understanding and Bridging the Modality Gap When Text Becomes Pixels in Multimodal LLMs

📝 Summary:
Multimodal LLMs struggle to process text from images compared to textual tokens, a modality gap influenced by rendering quality. This gap mainly stems from amplified reading errors. A self-distillation method, using pure text reasoning traces with image inputs, effectively improves visual text un...

🔹 Publication Date: Published on Mar 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.09095
• PDF: https://arxiv.org/pdf/2603.09095

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

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✨Omni-Diffusion: Unified Multimodal Understanding and Generation with Masked Discrete Diffusion

📝 Summary:
Omni-Diffusion introduces the first any-to-any multimodal language model based on mask-based discrete diffusion models, unifying text, speech, and image processing in a single framework. AI-generated ...

🔹 Publication Date: Published on Mar 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.06577
• PDF: https://arxiv.org/pdf/2603.06577
• Project Page: https://omni-diffusion.github.io/
• Github: https://github.com/VITA-MLLM/Omni-Diffusion

🔹 Models citing this paper:
• https://huggingface.co/lijiang/Omni-Diffusion

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

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✨The Reasoning Trap -- Logical Reasoning as a Mechanistic Pathway to Situational Awareness

📝 Summary:
The RAISE framework demonstrates how advances in logical reasoning capabilities within large language models can lead to increasingly sophisticated forms of situational awareness, potentially resultin...

🔹 Publication Date: Published on Mar 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.09200
• PDF: https://arxiv.org/pdf/2603.09200

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

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