✨BiCLIP: Domain Canonicalization via Structured Geometric Transformation

📝 Summary:
BiCLIP adapts vision-language models to specialized domains using a simple bilinear transformation. It aligns multimodal features via geometric canonicalization, leveraging few-shot samples as anchors. This achieves state-of-the-art results on multiple benchmarks with extreme simplicity.

🔹 Publication Date: Published on Mar 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.08942
• PDF: https://arxiv.org/pdf/2603.08942
• Project Page: https://quantitativeimaginglaboratory.github.io/BilinearCLIP/
• Github: https://github.com/QuantitativeImagingLaboratory/BilinearCLIP

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✨Micro-Diffusion Compression -- Binary Tree Tweedie Denoising for Online Probability Estimation

📝 Summary:
Midicoth enhances compression efficiency by applying a micro-diffusion denoising layer to refine probability estimates in adaptive statistical models, addressing limitations in sparse data scenarios t...

🔹 Publication Date: Published on Mar 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.08771
• PDF: https://arxiv.org/pdf/2603.08771
• Github: https://github.com/robtacconelli/midicoth

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✨Multi-Head Low-Rank Attention

📝 Summary:
Multi-Head Low-Rank Attention addresses long-context inference bottlenecks in large language models by enabling efficient 4-way tensor parallelism decoding through partitionable latent states. AI-gene...

🔹 Publication Date: Published on Mar 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2603.02188
• PDF: https://arxiv.org/pdf/2603.02188
• Project Page: https://songtaoliu0823.github.io/mlra/
• Github: https://github.com/SongtaoLiu0823/MLRA

🔹 Models citing this paper:
• https://huggingface.co/Soughing/MLRA

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✨OpenClaw-RL: Train Any Agent Simply by Talking

📝 Summary:
OpenClaw-RL unifies policy learning from all live next-state signals across diverse interaction modalities. It asynchronously recovers evaluative and directive information, enabling agents to improve simply by being used.

🔹 Publication Date: Published on Mar 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.10165
• PDF: https://arxiv.org/pdf/2603.10165
• Github: https://github.com/Gen-Verse/OpenClaw-RL

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✨RetroAgent: From Solving to Evolving via Retrospective Dual Intrinsic Feedback

📝 Summary:
RetroAgent enhances LLM-based agents through online reinforcement learning with self-reflection mechanisms that provide both numerical and language-based intrinsic feedback for improved exploration an...

🔹 Publication Date: Published on Mar 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.08561
• PDF: https://arxiv.org/pdf/2603.08561
• Github: https://github.com/zhangxy-2019/RetroAgent

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✨EmboAlign: Aligning Video Generation with Compositional Constraints for Zero-Shot Manipulation

📝 Summary:
A data-free framework aligns video generative model outputs with vision-language model constraints for improved robotic manipulation, achieving higher success rates through constraint-guided selection...

🔹 Publication Date: Published on Mar 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.05757
• PDF: https://arxiv.org/pdf/2603.05757

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✨V_{0.5}: Generalist Value Model as a Prior for Sparse RL Rollouts

📝 Summary:
Adaptive value estimation method combines pretrained prior with empirical rollouts using real-time statistical testing to reduce variance and improve reinforcement learning performance under sparse sa...

🔹 Publication Date: Published on Mar 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.10848
• PDF: https://arxiv.org/pdf/2603.10848

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✨Just-in-Time: Training-Free Spatial Acceleration for Diffusion Transformers

📝 Summary:
Diffusion Transformers face high computational costs during iterative sampling, which this work addresses by introducing a spatial-domain acceleration framework that uses sparse anchor tokens and dete...

🔹 Publication Date: Published on Mar 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.10744
• PDF: https://arxiv.org/pdf/2603.10744

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✨CLIPO: Contrastive Learning in Policy Optimization Generalizes RLVR

📝 Summary:
Contrastive Learning mechanism integrated into Policy Optimization enhances LLM reasoning by regularizing correct reasoning paths and reducing hallucinations. AI-generated summary Reinforcement Learni...

🔹 Publication Date: Published on Mar 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.10101
• PDF: https://arxiv.org/pdf/2603.10101
• Github: https://github.com/Qwen-Applications/CLIPO

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✨Code-Space Response Oracles: Generating Interpretable Multi-Agent Policies with Large Language Models

📝 Summary:
Code-Space Response Oracles replace traditional neural network policies with human-readable code generated by large language models, enabling interpretable and explainable multi-agent reinforcement le...

🔹 Publication Date: Published on Mar 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.10098
• PDF: https://arxiv.org/pdf/2603.10098

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✨Bootstrapping Exploration with Group-Level Natural Language Feedback in Reinforcement Learning

📝 Summary:
Language feedback is leveraged in reinforcement learning to improve exploration efficiency and sample utilization through grouped critique aggregation and joint generation-refinement optimization. AI-...

🔹 Publication Date: Published on Mar 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04597
• PDF: https://arxiv.org/pdf/2603.04597
• Github: https://github.com/LuckyyySTA/GOLF

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✨RbtAct: Rebuttal as Supervision for Actionable Review Feedback Generation

📝 Summary:
Researchers developed RbtAct, a method that uses rebuttal responses to improve the actionability of AI-generated peer-review feedback by training a language model to produce specific, implementable co...

🔹 Publication Date: Published on Mar 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.09723
• PDF: https://arxiv.org/pdf/2603.09723
• Github: https://github.com/formula12/RbtAct

✨ Datasets citing this paper:
• https://huggingface.co/datasets/shwu22/RMR-75K

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✨V2M-Zero: Zero-Pair Time-Aligned Video-to-Music Generation

📝 Summary:
V2M-Zero enables video-to-music generation with improved temporal alignment by using modality-specific event curves derived from pretrained encoders, achieving superior audio quality and synchronizati...

🔹 Publication Date: Published on Mar 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.11042
• PDF: https://arxiv.org/pdf/2603.11042
• Project Page: https://genjib.github.io/v2m_zero/
• Github: https://genjib.github.io/v2m_zero/

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✨Can Large Language Models Keep Up? Benchmarking Online Adaptation to Continual Knowledge Streams

📝 Summary:
OAKS is a new benchmark to test how LLMs adapt to real-time, evolving information streams. Current models struggle significantly, showing delays and distraction in tracking dynamic knowledge.

🔹 Publication Date: Published on Mar 8

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.07392
• PDF: https://arxiv.org/pdf/2603.07392
• Github: https://github.com/kaistAI/OAKS

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✨CodePercept: Code-Grounded Visual STEM Perception for MLLMs

📝 Summary:
MLLMs struggle with STEM visual reasoning due to perceptual limitations rather than reasoning deficiencies, and enhancing perception through code-as-perception paradigms improves performance. AI-gener...

🔹 Publication Date: Published on Mar 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.10757
• PDF: https://arxiv.org/pdf/2603.10757
• Github: https://github.com/TongkunGuan/Qwen-CodePercept

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✨MA-EgoQA: Question Answering over Egocentric Videos from Multiple Embodied Agents

📝 Summary:
Multi-agent systems require understanding multiple long-horizon egocentric videos simultaneously, necessitating new benchmarks and models for system-level comprehension. AI-generated summary As embodi...

🔹 Publication Date: Published on Mar 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.09827
• PDF: https://arxiv.org/pdf/2603.09827
• Project Page: https://ma-egoqa.github.io/
• Github: https://github.com/KangsanKim07/MA-EgoQA

✨ Datasets citing this paper:
• https://huggingface.co/datasets/KangsanKim71/MA-EgoQA

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✨Any to Full: Prompting Depth Anything for Depth Completion in One Stage

📝 Summary:
A novel one-stage depth completion framework that uses scale-prompting adaptation of pretrained monocular depth estimation models to handle varying depth sparsity and irregular distributions more effi...

🔹 Publication Date: Published on Mar 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.05711
• PDF: https://arxiv.org/pdf/2603.05711

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✨LLM2Vec-Gen: Generative Embeddings from Large Language Models

📝 Summary:
LLM2Vec-Gen introduces a self-supervised method for text embedding that represents model responses through trainable special tokens, achieving superior performance on MTEB while reducing harmful conte...

🔹 Publication Date: Published on Mar 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.10913
• PDF: https://arxiv.org/pdf/2603.10913
• Project Page: https://mcgill-nlp.github.io/llm2vec-gen/
• Github: https://github.com/McGill-NLP/llm2vec-gen

🔹 Models citing this paper:
• https://huggingface.co/McGill-NLP/LLM2Vec-Gen-Qwen3-06B
• https://huggingface.co/McGill-NLP/LLM2Vec-Gen-Qwen3-17B
• https://huggingface.co/McGill-NLP/LLM2Vec-Gen-Qwen3-4B

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✨TADA: A Generative Framework for Speech Modeling via Text-Acoustic Dual Alignment

📝 Summary:
A novel tokenization scheme synchronizes acoustic features with text tokens in TTS systems, enabling unified modeling and reduced hallucinations through flow matching and text-only guidance. AI-genera...

🔹 Publication Date: Published on Feb 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.23068
• PDF: https://arxiv.org/pdf/2602.23068
• Project Page: https://www.hume.ai/blog/opensource-tada
• Github: https://github.com/HumeAI/tada

🔹 Models citing this paper:
• https://huggingface.co/HumeAI/tada-1b
• https://huggingface.co/HumeAI/tada-3b-ml
• https://huggingface.co/HumeAI/tada-codec

✨ Spaces citing this paper:
• https://huggingface.co/spaces/HumeAI/tada

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✨Flash-KMeans: Fast and Memory-Efficient Exact K-Means

📝 Summary:
Flash-kmeans enables efficient online k-means clustering on GPUs through novel kernel-level optimizations that eliminate I/O bottlenecks and atomic write contention. AI-generated summary k-means has h...

🔹 Publication Date: Published on Mar 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.09229
• PDF: https://arxiv.org/pdf/2603.09229
• Github: https://github.com/svg-project/flash-kmeans

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✨ReMix: Reinforcement routing for mixtures of LoRAs in LLM finetuning

📝 Summary:
Researchers address imbalance in routing weights of Mixture-of-LoRAs models by proposing Reinforcement Routing (ReMix), which uses non-learnable weights and reinforcement learning techniques to improv...

🔹 Publication Date: Published on Mar 10

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.10160
• PDF: https://arxiv.org/pdf/2603.10160

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