✨SDF-Net: Structure-Aware Disentangled Feature Learning for Opticall-SAR Ship Re-identification

📝 Summary:
SDF-Net improves optical-SAR ship re-identification by leveraging stable ship geometry despite radiometric differences. It extracts scale-invariant structural features and disentangles modality-invariant and modality-specific cues to enhance discrimination.

🔹 Publication Date: Published on Mar 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12588
• PDF: https://arxiv.org/pdf/2603.12588
• Github: https://github.com/cfrfree/SDF-Net

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

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✨Do You See What I Am Pointing At? Gesture-Based Egocentric Video Question Answering

📝 Summary:
EgoPointVQA presents a dataset and benchmark for gesture-grounded egocentric question answering, along with Hand Intent Tokens (HINT) that encode 3D hand keypoints to improve pointing intent interpret...

🔹 Publication Date: Published on Mar 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12533
• PDF: https://arxiv.org/pdf/2603.12533
• Project Page: https://yuuraa.github.io/papers/choi2026egovqa/
• Github: https://github.com/Yuuraa/EgoPointVQA

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

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✨Compression Favors Consistency, Not Truth: When and Why Language Models Prefer Correct Information

📝 Summary:
Language models prefer correct information from a 'Compression-Consistency Principle': next-token prediction favors shorter, more internally consistent data. Truth bias is a compression side effect, not inherent truth-seeking, emerging when false alternatives are hard to compress.

🔹 Publication Date: Published on Mar 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.11749
• PDF: https://arxiv.org/pdf/2603.11749
• Github: https://github.com/Rai220/compression-drives-truth/blob/master/paper_v2.md

✨ Datasets citing this paper:
• https://huggingface.co/datasets/krestnikov/compression-drives-truth

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

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✨Taking Shortcuts for Categorical VQA Using Super Neurons

📝 Summary:
This paper introduces Super Neurons SNs, scalar activations replacing Sparse Attention Vectors SAVs for Vision Language Model classification. SNs enable extreme early exiting from shallow layers, improving classification performance and achieving up to 5.10x speedup.

🔹 Publication Date: Published on Mar 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.10781
• PDF: https://arxiv.org/pdf/2603.10781

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

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✨AI Can Learn Scientific Taste

📝 Summary:
RLCF trains AI to judge and propose high-impact research. Scientific Judge models preferences; Scientific Thinker proposes ideas. AI learns this capability, outperforming SOTA LLMs.

🔹 Publication Date: Published on Mar 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.14473
• PDF: https://arxiv.org/pdf/2603.14473
• Project Page: https://tongjingqi.github.io/AI-Can-Learn-Scientific-Taste/
• Github: https://github.com/tongjingqi/AI-Can-Learn-Scientific-Taste

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

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✨Safe and Scalable Web Agent Learning via Recreated Websites

📝 Summary:
VeriEnv enables safe and scalable training of web agents by creating synthetic, verifiable environments from real websites through language model-based cloning. AI-generated summary Training autonomou...

🔹 Publication Date: Published on Mar 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.10505
• PDF: https://arxiv.org/pdf/2603.10505
• Project Page: https://huggingface.co/spaces/hyungjoochae/verienv-project-page
• Github: https://github.com/kyle8581/VeriEnv

✨ Spaces citing this paper:
• https://huggingface.co/spaces/hyungjoochae/verienv-project-page

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✨FlashMotion: Few-Step Controllable Video Generation with Trajectory Guidance

📝 Summary:
Recent advances in trajectory-controllable video generation have achieved remarkable progress. Previous methods mainly use adapter-based architectures for precise motion control along predefined traje...

🔹 Publication Date: Published on Mar 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12146
• PDF: https://arxiv.org/pdf/2603.12146
• Project Page: https://quanhaol.github.io/flashmotion-site/
• Github: https://github.com/quanhaol/FlashMotion

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✨FineRMoE: Dimension Expansion for Finer-Grained Expert with Its Upcycling Approach

📝 Summary:
As revealed by the scaling law of fine-grained MoE, model performance ceases to be improved once the granularity of the intermediate dimension exceeds the optimal threshold, limiting further gains fro...

🔹 Publication Date: Published on Mar 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.13364
• PDF: https://arxiv.org/pdf/2603.13364
• Project Page: https://github.com/liaoning97/FineRMoE
• Github: https://github.com/liaoning97/FineRMoE

🔹 Models citing this paper:
• https://huggingface.co/NingLiao/FineRMoE-26.65B-A7.94B
• https://huggingface.co/NingLiao/FineRMoE-1.68B-A0.65B
• https://huggingface.co/NingLiao/FineRMoE-5.64B-A1.85B

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

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✨ViFeEdit: A Video-Free Tuner of Your Video Diffusion Transformer

📝 Summary:
Diffusion Transformers (DiTs) have demonstrated remarkable scalability and quality in image and video generation, prompting growing interest in extending them to controllable generation and editing ta...

🔹 Publication Date: Published on Mar 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.15478
• PDF: https://arxiv.org/pdf/2603.15478
• Github: https://github.com/Lexie-YU/ViFeEdit

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✨Grounding World Simulation Models in a Real-World Metropolis

📝 Summary:
Seoul World Model SWM renders video simulations of actual cities, not imagined environments. It grounds autoregressive video generation using real street-view images, overcoming data challenges. SWM generates spatially faithful, long-horizon urban videos for diverse camera paths and scenarios.

🔹 Publication Date: Published on Mar 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.15583
• PDF: https://seoul-world-model.github.io/SWM_paper.pdf
• Project Page: https://seoul-world-model.github.io/

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✨Panoramic Affordance Prediction

📝 Summary:
Affordance prediction serves as a critical bridge between perception and action in embodied AI. However, existing research is confined to pinhole camera models, which suffer from narrow Fields of View...

🔹 Publication Date: Published on Mar 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.15558
• PDF: https://arxiv.org/pdf/2603.15558
• Project Page: https://zixinzhang02.github.io/Panoramic-Affordance-Prediction/
• Github: https://zixinzhang02.github.io/Panoramic-Affordance-Prediction/

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✨MMOU: A Massive Multi-Task Omni Understanding and Reasoning Benchmark for Long and Complex Real-World Videos

📝 Summary:
Multimodal Large Language Models (MLLMs) have shown strong performance in visual and audio understanding when evaluated in isolation. However, their ability to jointly reason over omni-modal (visual, ...

🔹 Publication Date: Published on Mar 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.14145
• PDF: https://arxiv.org/pdf/2603.14145
• Project Page: https://huggingface.co/datasets/nvidia/MMOU

✨ Datasets citing this paper:
• https://huggingface.co/datasets/nvidia/MMOU

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✨Mind the Shift: Decoding Monetary Policy Stance from FOMC Statements with Large Language Models

📝 Summary:
Federal Open Market Committee (FOMC) statements are a major source of monetary-policy information, and even subtle changes in their wording can move global financial markets. A central task is therefo...

🔹 Publication Date: Published on Mar 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.14313
• PDF: https://arxiv.org/pdf/2603.14313
• Project Page: https://yixuantt.github.io/DeltaConsistent/
• Github: https://github.com/yixuantt/DeltaConsistentScoring

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✨OpenSeeker: Democratizing Frontier Search Agents by Fully Open-Sourcing Training Data

📝 Summary:
Deep search capabilities have become an indispensable competency for frontier Large Language Model (LLM) agents, yet the development of high-performance search agents remains dominated by industrial g...

🔹 Publication Date: Published on Mar 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.15594
• PDF: https://github.com/rui-ye/OpenSeeker/blob/main/assets/OpenSeeker.pdf
• Github: https://github.com/rui-ye/OpenSeeker

🔹 Models citing this paper:
• https://huggingface.co/OpenSeeker/OpenSeeker-v1-30B-SFT

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✨WebVR: Benchmarking Multimodal LLMs for WebPage Recreation from Videos via Human-Aligned Visual Rubrics

📝 Summary:
Existing web-generation benchmarks rely on text prompts or static screenshots as input. However, videos naturally convey richer signals such as interaction flow, transition timing, and motion continui...

🔹 Publication Date: Published on Mar 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.13391
• PDF: https://arxiv.org/pdf/2603.13391
• Project Page: https://webvr-benchmark.github.io/
• Github: https://github.com/broalantaps/WebVR

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✨Attention Residuals

📝 Summary:
Residual connections with PreNorm are standard in modern LLMs, yet they accumulate all layer outputs with fixed unit weights. This uniform aggregation causes uncontrolled hidden-state growth with dept...

🔹 Publication Date: Published on Mar 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.15031
• PDF: https://arxiv.org/pdf/2603.15031

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✨HSImul3R: Physics-in-the-Loop Reconstruction of Simulation-Ready Human-Scene Interactions

📝 Summary:
HSImul3R presents a unified framework for 3D reconstruction of human-scene interactions that bridges the perception-simulation gap through physics-grounded bidirectional optimization and reinforcement...

🔹 Publication Date: Published on Mar 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.15612
• PDF: https://arxiv.org/pdf/2603.15612
• Project Page: https://yukangcao.github.io/HSImul3R/
• Github: https://yukangcao.github.io/HSImul3R/

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✨Code-A1: Adversarial Evolving of Code LLM and Test LLM via Reinforcement Learning

📝 Summary:
Reinforcement learning for code generation relies on verifiable rewards from unit test pass rates. Yet high-quality test suites are scarce, existing datasets offer limited coverage, and static rewards...

🔹 Publication Date: Published on Mar 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.15611
• PDF: https://arxiv.org/pdf/2603.15611
• Project Page: https://zju-real.github.io/Code-A1/
• Github: https://github.com/ZJU-REAL/Code-A1

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✨EvoClaw: Evaluating AI Agents on Continuous Software Evolution

📝 Summary:
With AI agents increasingly deployed as long-running systems, it becomes essential to autonomously construct and continuously evolve customized software to enable interaction within dynamic environmen...

🔹 Publication Date: Published on Mar 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.13428
• PDF: https://arxiv.org/pdf/2603.13428
• Project Page: https://evo-claw.com/

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✨MoKus: Leveraging Cross-Modal Knowledge Transfer for Knowledge-Aware Concept Customization

📝 Summary:
Knowledge-aware concept customization binds textual knowledge to visual concepts through a two-stage framework that learns visual anchors and updates textual knowledge for high-fidelity generation, su...

🔹 Publication Date: Published on Mar 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12743
• PDF: https://arxiv.org/pdf/2603.12743
• Project Page: https://chenyangzhu1.github.io/MoKus/
• Github: https://github.com/HKUST-LongGroup/MoKus

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✨TERMINATOR: Learning Optimal Exit Points for Early Stopping in Chain-of-Thought Reasoning

📝 Summary:
TERMINATOR is an early-exit method for large reasoning models to prevent overthinking during Chain-of-Thought reasoning. It learns optimal exit points by predicting the first arrival of the final answer. This reduces reasoning length by 14%-55% without performance loss.

🔹 Publication Date: Published on Mar 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.12529
• PDF: https://arxiv.org/pdf/2603.12529
• Project Page: https://terminator-llm.github.io/
• Github: https://terminator-llm.github.io

🔹 Models citing this paper:
• https://huggingface.co/acnagle/Terminator-Qwen3-8B
• https://huggingface.co/acnagle/Terminator-Qwen3-14B

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