✨Self-Distilled RLVR

📝 Summary:
RLSD combines reinforcement learning with verifiable rewards RLVR and self-distillation to overcome sparse feedback. It uses self-distillation for fine-grained update magnitudes and RLVR for reliable update directions. This achieves superior training stability and convergence.

🔹 Publication Date: Published on Apr 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.03128
• PDF: https://arxiv.org/pdf/2604.03128

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#ReinforcementLearning #SelfDistillation #RLVR #MachineLearning #AI

✨Token Warping Helps MLLMs Look from Nearby Viewpoints

📝 Summary:
Token-level warping significantly improves MLLMs ability to reason from nearby viewpoints. It outperforms pixel-wise methods by offering greater stability and semantic coherence during viewpoint transformations. This backward token warping approach enables reliable visual reasoning.

🔹 Publication Date: Published on Apr 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.02870
• PDF: https://arxiv.org/pdf/2604.02870
• Project Page: https://token-warping-mllm.github.io/
• Github: https://github.com/KAIST-Visual-AI-Group/Token-Warping-MLLM

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#MLLMs #TokenWarping #ComputerVision #AI #DeepLearning

✨AgentSocialBench: Evaluating Privacy Risks in Human-Centered Agentic Social Networks

📝 Summary:
AgentSocialBench evaluates privacy in human-centered agentic social networks. It finds multi-agent coordination leads to persistent leakage and an abstraction paradox, showing current LLM agents are insufficient for privacy preservation. New mechanisms are required.

🔹 Publication Date: Published on Apr 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.01487
• PDF: https://arxiv.org/pdf/2604.01487
• Project Page: https://agent-social-bench.github.io/
• Github: https://github.com/kingofspace0wzz/agentsocialbench

✨ Datasets citing this paper:
• https://huggingface.co/datasets/kingofspace0wzz/AgentSocialBench

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

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#AgenticAI #PrivacyRisks #LLMAgents #SocialNetworks #Cybersecurity

✨Do World Action Models Generalize Better than VLAs? A Robustness Study

📝 Summary:
World Action Models WAMs show superior robustness in robot action planning compared to Vision-Language-Action VLAs. WAMs achieve higher success rates on benchmarks under various perturbations, benefiting from video-based dynamic prediction.

🔹 Publication Date: Published on Apr 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.22078
• PDF: https://arxiv.org/pdf/2603.22078

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#Robotics #AI #MachineLearning #Robustness #ComputerVision

✨Communicating about Space: Language-Mediated Spatial Integration Across Partial Views

📝 Summary:
MLLMs struggle with collaborative spatial communication and building shared mental models from partial views. The COSMIC benchmark shows MLLMs achieve only 72 percent accuracy compared to humans 95 percent, performing poorly on relational reasoning and global map building. Models fail to converge...

🔹 Publication Date: Published on Mar 28

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.27183
• PDF: https://arxiv.org/pdf/2603.27183
• Github: https://github.com/ankursikarwar/Cosmic

✨ Datasets citing this paper:
• https://huggingface.co/datasets/mair-lab/Cosmic

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

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#MLLMs #SpatialAI #AIResearch #HumanAICollaboration #ComputerVision

✨Test-Time Scaling Makes Overtraining Compute-Optimal

📝 Summary:
New Train-to-Test T^2 scaling laws optimize model size, training, and inference samples under budget. Considering inference costs, optimal pretraining shifts into an overtraining regime, yielding better performance for modern LLMs.

🔹 Publication Date: Published on Apr 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.01411
• PDF: https://arxiv.org/pdf/2604.01411

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

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#LLM #MachineLearning #AIResearch #ScalingLaws #ModelOptimization

✨Swift-SVD: Theoretical Optimality Meets Practical Efficiency in Low-Rank LLM Compression

📝 Summary:
Swift-SVD is a novel LLM compression framework that provides optimal low-rank approximations. It achieves this by efficiently aggregating covariance and performing a single eigenvalue decomposition, resulting in faster and more accurate compression than existing methods.

🔹 Publication Date: Published on Apr 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.01609
• PDF: https://arxiv.org/pdf/2604.01609

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

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#LLMCompression #LowRankApproximation #SVD #MachineLearning #AI

✨Salt: Self-Consistent Distribution Matching with Cache-Aware Training for Fast Video Generation

📝 Summary:
The paper introduces Salt, a method for fast video generation. It proposes Self-Consistent Distribution Matching Distillation SC-DMD to improve low-NFE quality by regularizing denoising updates. Cache-Distribution-Aware training further optimizes real-time autoregressive generation using KV cache.

🔹 Publication Date: Published on Apr 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.03118
• PDF: https://arxiv.org/pdf/2604.03118
• Github: https://github.com/XingtongGe/Salt

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

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#VideoGeneration #GenerativeAI #DeepLearning #AIResearch #RealTimeAI

✨VLMs Need Words: Vision Language Models Ignore Visual Detail In Favor of Semantic Anchors

📝 Summary:
VLMs struggle with fine-grained visual tasks for unnamed entities due to their language-centric training. They prioritize mapping visuals to known text, hindering reasoning for novel or unnameable objects. Task-specific finetuning without language priors improves performance, suggesting learned t...

🔹 Publication Date: Published on Apr 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.02486
• PDF: https://arxiv.org/pdf/2604.02486

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#VLMs #ComputerVision #NLP #AIResearch #DeepLearning

✨GrandCode: Achieving Grandmaster Level in Competitive Programming via Agentic Reinforcement Learning

📝 Summary:
GrandCode is a multi-agent reinforcement learning system that achieves grandmaster level in competitive programming. It orchestrates specialized agent modules and uses novel reward optimization techniques. GrandCode consistently beat all human participants, including legendary grandmasters, in li...

🔹 Publication Date: Published on Apr 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.02721
• PDF: https://arxiv.org/pdf/2604.02721
• Project Page: https://deep-reinforce.com/cp.html
• Github: https://github.com/deepreinforce-ai/codeforces

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

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#ReinforcementLearning #CompetitiveProgramming #AI #MultiAgentSystems #DeepLearning

✨DriveDreamer-Policy: A Geometry-Grounded World-Action Model for Unified Generation and Planning

📝 Summary:
DriveDreamer-Policy is a unified driving world-action model. It integrates depth, future video, and motion planning using geometry-aware world representation learning. This improves imagined futures and driving actions, achieving strong performance on navigation benchmarks.

🔹 Publication Date: Published on Apr 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.01765
• PDF: https://arxiv.org/pdf/2604.01765
• Project Page: https://drivedreamer-policy.github.io/
• Github: https://github.com/youngzhou1999/DriveDreamer-Policy

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

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#AutonomousDriving #MotionPlanning #WorldModels #DeepLearning #ComputerVision

✨SpatialEdit: Benchmarking Fine-Grained Image Spatial Editing

📝 Summary:
This paper presents SpatialEdit-Bench, a new benchmark and dataset for fine-grained image spatial editing. It introduces SpatialEdit-16B, a model that substantially outperforms prior methods on spatial manipulation, offering precise control over object layout and camera viewpoints.

🔹 Publication Date: Published on Apr 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04911
• PDF: https://arxiv.org/pdf/2604.04911
• Project Page: https://github.com/EasonXiao-888/SpatialEdit
• Github: https://github.com/EasonXiao-888/SpatialEdit

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

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#ImageEditing #ComputerVision #DeepLearning #AI #Benchmark

✨AURA: Always-On Understanding and Real-Time Assistance via Video Streams

📝 Summary:
AURA is an end-to-end streaming visual interaction framework for continuous video understanding. It enables real-time question answering and proactive responses, improving on current VideoLLMs through integrated context management and optimized deployment.

🔹 Publication Date: Published on Apr 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04184
• PDF: https://arxiv.org/pdf/2604.04184
• Project Page: https://aurateam2026.github.io
• Github: https://github.com/aurateam2026/AURA

🔹 Models citing this paper:
• https://huggingface.co/aurateam/AURA

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

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#VideoUnderstanding #RealTimeAI #VideoLLM #ComputerVision #DeepLearning

✨ClawArena: Benchmarking AI Agents in Evolving Information Environments

📝 Summary:
ClawArena evaluates AI agents' ability to maintain accurate beliefs in dynamic, multi-source information environments through diverse professional scenarios and evaluation methods. AI-generated summar...

🔹 Publication Date: Published on Apr 5

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04202
• PDF: https://arxiv.org/pdf/2604.04202
• Github: https://github.com/aiming-lab/ClawArena

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Less Detail, Better Answers: Degradation-Driven Prompting for VQA

📝 Summary:
Visual question answering performance is enhanced by strategically reducing image fidelity to focus models on essential structural information rather than distracting details. AI-generated summary Rec...

🔹 Publication Date: Published on Apr 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04838
• PDF: https://arxiv.org/pdf/2604.04838
• Project Page: https://hhx-jpg.github.io/ddp/
• Github: https://github.com/ziplab/DDP

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Vero: An Open RL Recipe for General Visual Reasoning

📝 Summary:
Vero is an open vision-language model family that achieves state-of-the-art visual reasoning performance through scaled reinforcement learning data across diverse tasks, demonstrating that broad data ...

🔹 Publication Date: Published on Apr 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04917
• PDF: https://arxiv.org/pdf/2604.04917
• Project Page: https://vero-reasoning.github.io/

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

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#VisualReasoning #ReinforcementLearning #VisionLanguageModels #AIResearch #DeepLearning

✨Memory Intelligence Agent

📝 Summary:
Memory Intelligence Agent framework integrates non-parametric and parametric memory systems with reinforcement learning to enable efficient reasoning and autonomous evolution in open-world environment...

🔹 Publication Date: Published on Apr 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04503
• PDF: https://arxiv.org/pdf/2604.04503
• Github: https://github.com/ECNU-SII/MIA

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨TriAttention: Efficient Long Reasoning with Trigonometric KV Compression

📝 Summary:
To overcome LLM KV cache bottlenecks, TriAttention leverages stable pre-RoPE Q/K vector concentration and a trigonometric series to accurately estimate key importance. It matches full attention accuracy with 10.7x memory reduction or 2.5x higher throughput.

🔹 Publication Date: Published on Apr 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04921
• PDF: https://arxiv.org/pdf/2604.04921
• Project Page: https://weianmao.github.io/tri-attention-project-page/
• Github: https://github.com/WeianMao/triattention

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨MinerU2.5-Pro: Pushing the Limits of Data-Centric Document Parsing at Scale

📝 Summary:
Training data engineering and optimized strategies improve document parsing performance without architectural changes, achieving state-of-the-art results on OmniDocBench v1.6. AI-generated summary Cur...

🔹 Publication Date: Published on Apr 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04771
• PDF: https://arxiv.org/pdf/2604.04771

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨LightThinker++: From Reasoning Compression to Memory Management

📝 Summary:
LightThinker and LightThinker++ enable efficient large language model reasoning through dynamic compression and adaptive memory management, significantly reducing computational overhead while maintain...

🔹 Publication Date: Published on Apr 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.03679
• PDF: https://arxiv.org/pdf/2604.03679

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨SkillX: Automatically Constructing Skill Knowledge Bases for Agents

📝 Summary:
SkillX is an automated framework that creates reusable skill libraries for LLM agents through hierarchical skill design, iterative refinement, and exploratory expansion to improve generalization and e...

🔹 Publication Date: Published on Apr 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.04804
• PDF: https://arxiv.org/pdf/2604.04804
• Github: https://github.com/zjunlp/SkillX

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

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#AI #DataScience #MachineLearning #HuggingFace #Research