✨Agentic-MME: What Agentic Capability Really Brings to Multimodal Intelligence?

📝 Summary:
Agentic-MME introduces a process-verified benchmark for multimodal agentic capabilities. It evaluates tool usage and efficiency using real-world tasks and stepwise checkpoints, revealing models struggle with complex multimodal problem-solving.

🔹 Publication Date: Published on Apr 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.03016
• PDF: https://arxiv.org/pdf/2604.03016
• Project Page: https://agenticmme.github.io/

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#AgenticAI #MultimodalAI #AIEvaluation #AIResearch #Benchmarks

✨AgentHazard: A Benchmark for Evaluating Harmful Behavior in Computer-Use Agents

📝 Summary:
Computer-use agents pose unique safety risks as harm can emerge from sequences of individually benign actions. AgentHazard is a benchmark with 2,653 instances to evaluate this. Experiments reveal current systems are highly vulnerable, showing model alignment alone doesnt ensure agent safety.

🔹 Publication Date: Published on Apr 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.02947
• PDF: https://arxiv.org/pdf/2604.02947
• Project Page: https://yunhao-feng.github.io/AgentHazard/

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#AISafety #AgentAI #AIVulnerability #AIethics #AIbenchmark

✨CoME-VL: Scaling Complementary Multi-Encoder Vision-Language Learning

📝 Summary:
CoME-VL fuses contrastive and self-supervised vision encoders to improve vision-language models. It uses entropy-guided aggregation and RoPE-enhanced attention for better visual understanding and grounding, outperforming single-encoder baselines.

🔹 Publication Date: Published on Apr 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.03231
• PDF: https://arxiv.org/pdf/2604.03231
• Project Page: https://mbzuai-oryx.github.io/CoME-VL/
• Github: https://github.com/mbzuai-oryx/CoME-VL

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#VisionLanguage #MultimodalAI #ComputerVision #MachineLearning #DeepLearning

✨InCoder-32B-Thinking: Industrial Code World Model for Thinking

📝 Summary:
Industrial software development lacks expert reasoning traces for hardware constraints, so a model was trained on error-driven reasoning chains and domain-specific execution traces to generate high-qu...

🔹 Publication Date: Published on Apr 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.03144
• PDF: https://arxiv.org/pdf/2604.03144

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#AI #CodeGeneration #IndustrialAI #WorldModels #SoftwareDevelopment

✨Xpertbench: Expert Level Tasks with Rubrics-Based Evaluation

📝 Summary:
XpertBench introduces a benchmark with 1346 expert-curated tasks across 80 domains for evaluating LLMs on complex professional cognition. It uses ShotJudge for scalable human-aligned assessment. Current LLMs achieve only a 66 percent peak success, revealing a significant expert-gap.

🔹 Publication Date: Published on Mar 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.02368
• PDF: https://arxiv.org/pdf/2604.02368

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#LLM #AIEvaluation #Benchmarking #ArtificialIntelligence #ProfessionalAI

✨MetaChain: A Fully-Automated and Zero-Code Framework for LLM Agents

📝 Summary:
MetaChain is a fully automated, zero-code framework enabling non-technical users to create and deploy LLM agents via natural language. It offers superior performance for multi-agent tasks and retrieval-augmented generation, surpassing current methods.

🔹 Publication Date: Published on Feb 9, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2502.05957
• PDF: https://arxiv.org/pdf/2502.05957
• Github: https://github.com/HKUDS/MetaChain

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#LLMAgents #NoCode #AI #RAG #AIAutomation

✨A Simple Baseline for Streaming Video Understanding

📝 Summary:
A simple sliding-window approach outperforms complex memory-based streaming video methods by using only recent frames. It demonstrates a trade-off between real-time perception and long-term memory, suggesting benchmarks should separate these abilities.

🔹 Publication Date: Published on Apr 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.16655
• PDF: https://arxiv.org/pdf/2604.02317
• Project Page: https://simple-stream.github.io/
• Github: https://simple-stream.github.io/

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#VideoUnderstanding #StreamingAI #ComputerVision #RealTimeAI #MachineLearning

✨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