✨Learning Humanoid End-Effector Control for Open-Vocabulary Visual Loco-Manipulation

📝 Summary:
HERO enables humanoid robots to perform object manipulation in diverse real-world environments. It combines accurate end-effector control, trained in simulation, with open-vocabulary vision for generalization, reducing tracking error by 3.2x.

🔹 Publication Date: Published on Feb 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.16705
• PDF: https://arxiv.org/pdf/2602.16705
• Project Page: https://hero-humanoid.github.io/
• Github: https://hero-humanoid.github.io/

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Towards a Science of AI Agent Reliability

📝 Summary:
Traditional benchmark evaluations of AI agents fail to capture critical reliability issues, prompting the development of comprehensive metrics that assess consistency, robustness, predictability, and ...

🔹 Publication Date: Published on Feb 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.16666
• PDF: https://arxiv.org/pdf/2602.16666
• Project Page: https://hal.cs.princeton.edu/reliability

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨World Action Models are Zero-shot Policies

📝 Summary:
DreamZero is a World Action Model that leverages video diffusion to enable better generalization of physical motions across novel environments and embodiments compared to vision-language-action models...

🔹 Publication Date: Published on Feb 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.15922
• PDF: https://arxiv.org/pdf/2602.15922
• Project Page: https://dreamzero0.github.io/
• Github: https://github.com/dreamzero0/dreamzero

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Multi-agent cooperation through in-context co-player inference

📝 Summary:
Sequence models enable cooperative behavior emergence in multi-agent reinforcement learning through in-context learning without hardcoded assumptions or timescale separation. AI-generated summary Achi...

🔹 Publication Date: Published on Feb 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.16301
• PDF: https://arxiv.org/pdf/2602.16301

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨SLA2: Sparse-Linear Attention with Learnable Routing and QAT

📝 Summary:
SLA2 improves sparse-linear attention in diffusion models by introducing a learnable router, direct attention formulation, and quantization-aware fine-tuning for enhanced efficiency and quality. AI-ge...

🔹 Publication Date: Published on Feb 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.12675
• PDF: https://arxiv.org/pdf/2602.12675
• Project Page: https://github.com/thu-ml/SLA

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Learning Situated Awareness in the Real World

📝 Summary:
SAW-Bench presents a new benchmark for evaluating egocentric situated awareness in multimodal foundation models through real-world video datasets with human-annotated question-answer pairs, focusing o...

🔹 Publication Date: Published on Feb 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.16682
• PDF: https://arxiv.org/pdf/2602.16682
• Project Page: https://sawbench.github.io/

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨MMA: Multimodal Memory Agent

📝 Summary:
Multimodal Memory Agent (MMA) improves long-horizon agent performance by dynamically scoring memory reliability and handling visual biases in retrieval-augmented systems. AI-generated summary Long-hor...

🔹 Publication Date: Published on Feb 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.16493
• PDF: https://arxiv.org/pdf/2602.16493
• Github: https://github.com/AIGeeksGroup/MMA

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨RynnBrain: Open Embodied Foundation Models

📝 Summary:
RynnBrain is an open-source spatiotemporal foundation model for embodied intelligence that unifies perception, reasoning, and planning capabilities across multiple scales and task-specific variants. A...

🔹 Publication Date: Published on Feb 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.14979
• PDF: https://arxiv.org/pdf/2602.14979
• Project Page: https://alibaba-damo-academy.github.io/RynnBrain.github.io/
• Github: https://github.com/alibaba-damo-academy/RynnBrain

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Empty Shelves or Lost Keys? Recall Is the Bottleneck for Parametric Factuality

📝 Summary:
LLMs demonstrate near-complete factual encoding but struggle with retrieval accessibility, where errors stem from access limitations rather than knowledge gaps, with reasoning improving recall of enco...

🔹 Publication Date: Published on Feb 15

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.14080
• PDF: https://arxiv.org/pdf/2602.14080

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Optimizing Few-Step Generation with Adaptive Matching Distillation

📝 Summary:
Adaptive Matching Distillation AMD improves generative model training by detecting and escaping unstable optimization regions. It uses reward proxies to correct trajectories, boosting sample fidelity and training robustness across generation tasks.

🔹 Publication Date: Published on Feb 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.07345
• PDF: https://arxiv.org/pdf/2602.07345

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨BiManiBench: A Hierarchical Benchmark for Evaluating Bimanual Coordination of Multimodal Large Language Models

📝 Summary:
BiManiBench evaluates multimodal large language models on bimanual robotic tasks, revealing limitations in spatial grounding and control despite strong high-level reasoning capabilities. AI-generated ...

🔹 Publication Date: Published on Feb 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.08392
• PDF: https://arxiv.org/pdf/2602.08392
• Project Page: https://bimanibench.github.io/
• Github: https://github.com/bimanibench/BiManiBench

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

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#AI #DataScience #MachineLearning #HuggingFace #Research

✨Visual Memory Injection Attacks for Multi-Turn Conversations

📝 Summary:
Visual Memory Injection VMI covertly manipulates generative vision-language models using images. These images trigger specific manipulative responses only with certain prompts in multi-turn conversations, showing large-scale user manipulation is feasible.

🔹 Publication Date: Published on Feb 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.15927
• PDF: https://arxiv.org/pdf/2602.15927
• Github: https://github.com/chs20/visual-memory-injection

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

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#VMI #VisionLanguageModels #AISecurity #AIManipulation #GenerativeAI

✨Agent S2: A Compositional Generalist-Specialist Framework for Computer Use Agents

📝 Summary:
Agent S2 is a new compositional framework for computer use agents. It uses Mixture-of-Grounding and Proactive Hierarchical Planning to achieve state-of-the-art performance across various benchmarks and operating systems, significantly improving automation.

🔹 Publication Date: Published on Apr 1, 2025

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2504.00906
• PDF: https://arxiv.org/pdf/2504.00906
• Project Page: https://www.simular.ai/articles/agent-s2-technical-review
• Github: https://github.com/simular-ai/Agent-S

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#AI #AIagents #Automation #MachineLearning #ComputerScience

✨MAEB: Massive Audio Embedding Benchmark

📝 Summary:
MAEB is a large-scale audio benchmark evaluating 50+ models across 30 diverse tasks. No single model dominates; strengths vary significantly between speech and environmental sound tasks. Performance on MAEB highly correlates with audio large language model performance.

🔹 Publication Date: Published on Feb 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.16008
• PDF: https://arxiv.org/pdf/2602.16008
• Project Page: https://mteb-leaderboard.hf.space/?benchmark_name=MAEB%28beta%29

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

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#AudioAI #Benchmarking #AudioEmbeddings #SpeechProcessing #AudioLLMs

✨CADEvolve: Creating Realistic CAD via Program Evolution

📝 Summary:
CADEvolve presents an evolution-based pipeline using VLM-guided edits to generate complex CAD programs from simple primitives. It creates a large dataset of 1.3 million scripts, enabling fine-tuned VLMs to achieve state-of-the-art Image2CAD performance.

🔹 Publication Date: Published on Feb 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.16317
• PDF: https://arxiv.org/pdf/2602.16317
• Github: https://github.com/zhemdi/CADEvolve

🔹 Models citing this paper:
• https://huggingface.co/kulibinai/cadevolve-rl1

✨ Datasets citing this paper:
• https://huggingface.co/datasets/kulibinai/cadevolve

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

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#CAD #ProgramEvolution #VLMs #Image2CAD #AI

✨Reinforced Fast Weights with Next-Sequence Prediction

📝 Summary:
REFINE is an RL framework that improves fast weight models for long-context tasks. It uses next-sequence prediction NSP instead of next-token prediction, enhancing long-range dependency capture. Experiments show it consistently outperforms supervised fine-tuning.

🔹 Publication Date: Published on Feb 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.16704
• PDF: https://arxiv.org/pdf/2602.16704
• Github: https://github.com/princetonvisualai/ReFINE/

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#ReinforcementLearning #MachineLearning #DeepLearning #NLP #AI

✨Learning Personalized Agents from Human Feedback

📝 Summary:
PAHF enables AI agents to continually personalize through explicit user memory and dual feedback. It rapidly adapts to changing user preferences by integrating pre-action clarification and post-action updates, significantly reducing personalization error and improving learning speed.

🔹 Publication Date: Published on Feb 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.16173
• PDF: https://arxiv.org/pdf/2602.16173
• Project Page: https://personalized-ai.github.io/
• Github: https://github.com/facebookresearch/PAHF

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#AI #Personalization #HumanAIInteraction #MachineLearning #AIAgents

✨OPBench: A Graph Benchmark to Combat the Opioid Crisis

📝 Summary:
OPBench is the first comprehensive graph benchmark to systematically evaluate graph learning methods for the opioid crisis. It includes five diverse datasets across three domains and a unified framework, providing insights for future research.

🔹 Publication Date: Published on Feb 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.14602
• PDF: https://arxiv.org/pdf/2602.14602
• Github: https://github.com/Tianyi-Billy-Ma/OPBench

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#OpioidCrisis #GraphLearning #DataScience #MachineLearning #PublicHealth

✨Unified Latents (UL): How to train your latents

📝 Summary:
Unified Latents UL learns joint latent representations using diffusion prior regularization and decoding. It achieves competitive FID of 1.4 on ImageNet-512 with fewer training FLOPs and sets a new state of the art FVD of 1.3 on Kinetics-600.

🔹 Publication Date: Published on Feb 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.17270
• PDF: https://arxiv.org/pdf/2602.17270

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#GenerativeAI #DiffusionModels #LatentSpace #ImageGeneration #VideoGeneration

✨Arcee Trinity Large Technical Report

📝 Summary:
Arcee Trinity introduces sparse Mixture-of-Experts models Nano, Mini, Large with up to 400B total parameters. They feature advanced attention, novel normalization, and sigmoid MoE routing, trained on massive token datasets.

🔹 Publication Date: Published on Feb 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.17004
• PDF: https://arxiv.org/pdf/2602.17004

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#MixtureOfExperts #LargeLanguageModels #SparseModels #DeepLearning #AI

✨TactAlign: Human-to-Robot Policy Transfer via Tactile Alignment

📝 Summary:
TactAlign transfers human tactile demonstrations to robots with different embodiments. It aligns human and robot tactile signals into a shared latent space without paired data, improving policy transfer for contact-rich tasks and enabling zero-shot transfer.

🔹 Publication Date: Published on Feb 14

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.13579
• PDF: https://arxiv.org/pdf/2602.13579
• Project Page: https://yswi.github.io/tactalign/

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#Robotics #TactileRobotics #PolicyTransfer #HRI #AI