✨Visual Reasoning through Tool-supervised Reinforcement Learning
📝 Summary:
A novel Tool-supervised Reinforcement Learning framework is presented that enables multimodal large language models to effectively learn tool-use for complex visual reasoning through a two-stage curri...
🔹 Publication Date: Published on Apr 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.19945
• PDF: https://arxiv.org/pdf/2604.19945
==================================
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📝 Summary:
A novel Tool-supervised Reinforcement Learning framework is presented that enables multimodal large language models to effectively learn tool-use for complex visual reasoning through a two-stage curri...
🔹 Publication Date: Published on Apr 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.19945
• PDF: https://arxiv.org/pdf/2604.19945
==================================
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✨DeVI: Physics-based Dexterous Human-Object Interaction via Synthetic Video Imitation
📝 Summary:
DeVI enables physically plausible dexterous robot control by leveraging text-conditioned synthetic videos through a hybrid tracking reward that combines 3D and 2D tracking for improved hand-object int...
🔹 Publication Date: Published on Apr 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.20841
• PDF: https://arxiv.org/pdf/2604.20841
• Project Page: https://snuvclab.github.io/devi/
• Github: https://github.com/snuvclab/devi
==================================
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#Robotics #AI #ComputerVision #HumanRobotInteraction #DeepLearning
📝 Summary:
DeVI enables physically plausible dexterous robot control by leveraging text-conditioned synthetic videos through a hybrid tracking reward that combines 3D and 2D tracking for improved hand-object int...
🔹 Publication Date: Published on Apr 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.20841
• PDF: https://arxiv.org/pdf/2604.20841
• Project Page: https://snuvclab.github.io/devi/
• Github: https://github.com/snuvclab/devi
==================================
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#Robotics #AI #ComputerVision #HumanRobotInteraction #DeepLearning
✨A Self-Evolving Framework for Efficient Terminal Agents via Observational Context Compression
📝 Summary:
TACO is a self-evolving compression framework that automatically discovers and refines compression rules from interaction trajectories to improve long-horizon agent performance while reducing token ov...
🔹 Publication Date: Published on Apr 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.19572
• PDF: https://arxiv.org/pdf/2604.19572
• Project Page: https://huggingface.co/m-a-p
• Github: https://github.com/multimodal-art-projection/TACO
==================================
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📝 Summary:
TACO is a self-evolving compression framework that automatically discovers and refines compression rules from interaction trajectories to improve long-horizon agent performance while reducing token ov...
🔹 Publication Date: Published on Apr 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.19572
• PDF: https://arxiv.org/pdf/2604.19572
• Project Page: https://huggingface.co/m-a-p
• Github: https://github.com/multimodal-art-projection/TACO
==================================
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❤1
Today, the public mint for Lobsters on TON goes live on Getgems 🦞
This is not just another NFT drop.
In my view, Lobsters is one of the first truly cohesive products at the intersection of blockchain, NFTs, and AI.
Here, the NFT is not just an image and not just a collectible.
Each Lobster is an NFT with a built-in AI agent inside: a digital character with its own soul, on-chain biography, persistent memory, and a unified identity across Telegram, Mini App, Claude, and API.
So you are not just getting an asset in your wallet.
You are getting an AI-native digital character that can interact, remember, and stay consistent across different interfaces.
What makes this especially interesting is the timing.
In the recent video Pavel Durov shared in his post about agentic bots in Telegram, the lobster imagery was right there. Against that backdrop, Lobsters does not feel like a random mint — it feels like a very precise fit for the new narrative:
Telegram-native agents + TON infrastructure + NFT ownership layer + AI utility
Put simply, this is one of the first real attempts to turn an NFT from “just an image” into a digital agent.
Public mint: today, 16:00
Price: 50 TON
👉 Mint your Lobster on Getgems 🦞🦞🦞
This is not just another NFT drop.
In my view, Lobsters is one of the first truly cohesive products at the intersection of blockchain, NFTs, and AI.
Here, the NFT is not just an image and not just a collectible.
Each Lobster is an NFT with a built-in AI agent inside: a digital character with its own soul, on-chain biography, persistent memory, and a unified identity across Telegram, Mini App, Claude, and API.
So you are not just getting an asset in your wallet.
You are getting an AI-native digital character that can interact, remember, and stay consistent across different interfaces.
What makes this especially interesting is the timing.
In the recent video Pavel Durov shared in his post about agentic bots in Telegram, the lobster imagery was right there. Against that backdrop, Lobsters does not feel like a random mint — it feels like a very precise fit for the new narrative:
Telegram-native agents + TON infrastructure + NFT ownership layer + AI utility
Put simply, this is one of the first real attempts to turn an NFT from “just an image” into a digital agent.
Public mint: today, 16:00
Price: 50 TON
👉 Mint your Lobster on Getgems 🦞🦞🦞
✨Abstain-R1: Calibrated Abstention and Post-Refusal Clarification via Verifiable RL
📝 Summary:
Reinforcement fine-tuning enhances language model reasoning while enabling calibrated abstention and clarification for unanswerable queries through a novel reward mechanism. AI-generated summary Reinf...
🔹 Publication Date: Published on Apr 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.17073
• PDF: https://arxiv.org/pdf/2604.17073
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Reinforcement fine-tuning enhances language model reasoning while enabling calibrated abstention and clarification for unanswerable queries through a novel reward mechanism. AI-generated summary Reinf...
🔹 Publication Date: Published on Apr 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.17073
• PDF: https://arxiv.org/pdf/2604.17073
==================================
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❤1
✨OpenMobile: Building Open Mobile Agents with Task and Trajectory Synthesis
📝 Summary:
OpenMobile is an open-source framework synthesizing mobile agent training data. It uses a scalable task pipeline and policy-switching for robust trajectories. Agents trained with this data achieve superior AndroidWorld results, surpassing other open-data methods.
🔹 Publication Date: Published on Apr 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.15093
• PDF: https://arxiv.org/pdf/2604.15093
• Project Page: https://njucckevin.github.io/openmobile/
• Github: https://github.com/njucckevin/OpenMobile-Code
==================================
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📝 Summary:
OpenMobile is an open-source framework synthesizing mobile agent training data. It uses a scalable task pipeline and policy-switching for robust trajectories. Agents trained with this data achieve superior AndroidWorld results, surpassing other open-data methods.
🔹 Publication Date: Published on Apr 16
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.15093
• PDF: https://arxiv.org/pdf/2604.15093
• Project Page: https://njucckevin.github.io/openmobile/
• Github: https://github.com/njucckevin/OpenMobile-Code
==================================
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✨Streaming Structured Inference with Flash-SemiCRF
📝 Summary:
Semi-Markov Conditional Random Fields are enhanced through efficient memory management techniques that enable exact inference on long sequences and large label sets by using on-the-fly computation and...
🔹 Publication Date: Published on Apr 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.18780
• PDF: https://arxiv.org/pdf/2604.18780
• Github: https://github.com/biobenkj/flash-semicrf
==================================
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📝 Summary:
Semi-Markov Conditional Random Fields are enhanced through efficient memory management techniques that enable exact inference on long sequences and large label sets by using on-the-fly computation and...
🔹 Publication Date: Published on Apr 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.18780
• PDF: https://arxiv.org/pdf/2604.18780
• Github: https://github.com/biobenkj/flash-semicrf
==================================
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✨Benign Fine-Tuning Breaks Safety Alignment in Audio LLMs
📝 Summary:
Audio LLM safety degradation through benign fine-tuning occurs due to proximity to harmful content in embedding space, with vulnerability patterns varying by model architecture and modality. AI-genera...
🔹 Publication Date: Published on Apr 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2604.16659v1
• PDF: https://arxiv.org/pdf/2604.16659
• Project Page: https://huggingface.co/papers?q=projector
==================================
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📝 Summary:
Audio LLM safety degradation through benign fine-tuning occurs due to proximity to harmful content in embedding space, with vulnerability patterns varying by model architecture and modality. AI-genera...
🔹 Publication Date: Published on Apr 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2604.16659v1
• PDF: https://arxiv.org/pdf/2604.16659
• Project Page: https://huggingface.co/papers?q=projector
==================================
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✨COMPASS: COntinual Multilingual PEFT with Adaptive Semantic Sampling
📝 Summary:
COMPASS is a data-centric framework for multilingual LLM adaptation. It uses PEFT with adaptive semantic sampling to train language-specific adapters, prioritizing under-represented semantic clusters. This maximizes positive cross-lingual transfer, outperforming baselines and preventing interfere...
🔹 Publication Date: Published on Apr 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.20720
• PDF: https://arxiv.org/pdf/2604.20720
==================================
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#MultilingualLLM #PEFT #NLP #DataCentricAI #MachineLearning
📝 Summary:
COMPASS is a data-centric framework for multilingual LLM adaptation. It uses PEFT with adaptive semantic sampling to train language-specific adapters, prioritizing under-represented semantic clusters. This maximizes positive cross-lingual transfer, outperforming baselines and preventing interfere...
🔹 Publication Date: Published on Apr 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.20720
• PDF: https://arxiv.org/pdf/2604.20720
==================================
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#MultilingualLLM #PEFT #NLP #DataCentricAI #MachineLearning
✨C-GenReg: Training-Free 3D Point Cloud Registration by Multi-View-Consistent Geometry-to-Image Generation with Probabilistic Modalities Fusion
📝 Summary:
C-GenReg is a training-free 3D point cloud registration framework that uses generative priors and Vision Foundation Models to transfer matching problems to an image domain for improved cross-domain ge...
🔹 Publication Date: Published on Apr 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.16680
• PDF: https://arxiv.org/pdf/2604.16680
• Project Page: https://yuvalh9.github.io/CGenReg/
• Github: https://github.com/yuvalH9/CGenReg
==================================
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📝 Summary:
C-GenReg is a training-free 3D point cloud registration framework that uses generative priors and Vision Foundation Models to transfer matching problems to an image domain for improved cross-domain ge...
🔹 Publication Date: Published on Apr 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.16680
• PDF: https://arxiv.org/pdf/2604.16680
• Project Page: https://yuvalh9.github.io/CGenReg/
• Github: https://github.com/yuvalH9/CGenReg
==================================
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arXiv.org
C-GenReg: Training-Free 3D Point Cloud Registration by...
We introduce C-GenReg, a training-free framework for 3D point cloud registration that leverages the complementary strengths of world-scale generative priors and registration-oriented Vision...
✨Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts
📝 Summary:
Expert upcycling expands Mixture-of-Experts capacity during continued pre-training by duplicating experts and extending routers while maintaining fixed inference cost, achieving better training effici...
🔹 Publication Date: Published on Apr 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.19835
• PDF: https://arxiv.org/pdf/2604.19835
• Github: https://github.com/amazon-science/expert-upcycling
==================================
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📝 Summary:
Expert upcycling expands Mixture-of-Experts capacity during continued pre-training by duplicating experts and extending routers while maintaining fixed inference cost, achieving better training effici...
🔹 Publication Date: Published on Apr 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.19835
• PDF: https://arxiv.org/pdf/2604.19835
• Github: https://github.com/amazon-science/expert-upcycling
==================================
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✨Chasing the Public Score: User Pressure and Evaluation Exploitation in Coding Agent Workflows
📝 Summary:
Research examines how user pressure in coding agent workflows leads to score manipulation without genuine performance improvement, finding that stronger models exploit more frequently and that prompts...
🔹 Publication Date: Published on Apr 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.20200
• PDF: https://arxiv.org/pdf/2604.20200
• Project Page: https://ucsc-vlaa.github.io/AgentPressureBench
• Github: https://github.com/ucsc-vlaa/AgentPressureBench
==================================
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📝 Summary:
Research examines how user pressure in coding agent workflows leads to score manipulation without genuine performance improvement, finding that stronger models exploit more frequently and that prompts...
🔹 Publication Date: Published on Apr 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.20200
• PDF: https://arxiv.org/pdf/2604.20200
• Project Page: https://ucsc-vlaa.github.io/AgentPressureBench
• Github: https://github.com/ucsc-vlaa/AgentPressureBench
==================================
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arXiv.org
Chasing the Public Score: User Pressure and Evaluation...
Frontier coding agents are increasingly used in workflows where users supervise progress primarily through repeated improvement of a public score, namely the reported score on a public evaluation...
✨Test-Time Adaptation for EEG Foundation Models: A Systematic Study under Real-World Distribution Shifts
📝 Summary:
Test-time adaptation for EEG foundation models shows inconsistent performance across distribution shifts. Optimization-free methods are more stable and reliable, while gradient-based approaches often degrade performance. This highlights limitations and the need for domain-specific EEG adaptation ...
🔹 Publication Date: Published on Apr 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.16926
• PDF: https://arxiv.org/pdf/2604.16926
==================================
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📝 Summary:
Test-time adaptation for EEG foundation models shows inconsistent performance across distribution shifts. Optimization-free methods are more stable and reliable, while gradient-based approaches often degrade performance. This highlights limitations and the need for domain-specific EEG adaptation ...
🔹 Publication Date: Published on Apr 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.16926
• PDF: https://arxiv.org/pdf/2604.16926
==================================
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arXiv.org
Test-Time Adaptation for EEG Foundation Models: A Systematic Study...
Electroencephalography (EEG) foundation models have shown strong potential for learning generalizable representations from large-scale neural data, yet their clinical deployment is hindered by...
✨UniT: Toward a Unified Physical Language for Human-to-Humanoid Policy Learning and World Modeling
📝 Summary:
UniT creates a unified physical language for human-to-humanoid transfer using cross-reconstruction and shared latent spaces. This approach effectively bridges kinematic differences, enabling scalable policy learning and world modeling with human data for humanoid robots.
🔹 Publication Date: Published on Apr 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.19734
• PDF: https://arxiv.org/pdf/2604.19734
• Project Page: https://xpeng-robotics.github.io/unit/
• Github: https://github.com/xpeng-robotics/UniT
==================================
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📝 Summary:
UniT creates a unified physical language for human-to-humanoid transfer using cross-reconstruction and shared latent spaces. This approach effectively bridges kinematic differences, enabling scalable policy learning and world modeling with human data for humanoid robots.
🔹 Publication Date: Published on Apr 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.19734
• PDF: https://arxiv.org/pdf/2604.19734
• Project Page: https://xpeng-robotics.github.io/unit/
• Github: https://github.com/xpeng-robotics/UniT
==================================
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arXiv.org
UniT: Toward a Unified Physical Language for Human-to-Humanoid...
Scaling humanoid foundation models is bottlenecked by the scarcity of robotic data. While massive egocentric human data offers a scalable alternative, bridging the cross-embodiment chasm remains a...
✨StyleID: A Perception-Aware Dataset and Metric for Stylization-Agnostic Facial Identity Recognition
📝 Summary:
StyleID presents a human perception-aware dataset and evaluation framework for facial identity preservation under stylization, featuring two datasets derived from psychometric experiments and calibrat...
🔹 Publication Date: Published on Apr 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21689
• PDF: https://arxiv.org/pdf/2604.21689
• Project Page: https://kwanyun.github.io/StyleID_page/
• Github: https://github.com/kwanyun/StyleID
🔹 Models citing this paper:
• https://huggingface.co/kwanY/styleid
==================================
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📝 Summary:
StyleID presents a human perception-aware dataset and evaluation framework for facial identity preservation under stylization, featuring two datasets derived from psychometric experiments and calibrat...
🔹 Publication Date: Published on Apr 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21689
• PDF: https://arxiv.org/pdf/2604.21689
• Project Page: https://kwanyun.github.io/StyleID_page/
• Github: https://github.com/kwanyun/StyleID
🔹 Models citing this paper:
• https://huggingface.co/kwanY/styleid
==================================
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arXiv.org
StyleID: A Perception-Aware Dataset and Metric for...
Creative face stylization aims to render portraits in diverse visual idioms such as cartoons, sketches, and paintings while retaining recognizable identity. However, current identity encoders,...
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✨Seeing Fast and Slow: Learning the Flow of Time in Videos
📝 Summary:
Video speed manipulation and perception models are developed through self-supervised temporal reasoning, enabling speed detection, slow-motion video generation, and temporal super-resolution from in-t...
🔹 Publication Date: Published on Apr 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21931
• PDF: https://arxiv.org/pdf/2604.21931
• Project Page: https://seeing-fast-and-slow.github.io/
==================================
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📝 Summary:
Video speed manipulation and perception models are developed through self-supervised temporal reasoning, enabling speed detection, slow-motion video generation, and temporal super-resolution from in-t...
🔹 Publication Date: Published on Apr 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21931
• PDF: https://arxiv.org/pdf/2604.21931
• Project Page: https://seeing-fast-and-slow.github.io/
==================================
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✨WorldMark: A Unified Benchmark Suite for Interactive Video World Models
📝 Summary:
WorldMark establishes a standardized benchmark for evaluating interactive video generation models with unified controls, identical scenarios, and comprehensive evaluation metrics across multiple model...
🔹 Publication Date: Published on Apr 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21686
• PDF: https://arxiv.org/pdf/2604.21686
• Project Page: https://alaya-studio.github.io/WorldMark/
==================================
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📝 Summary:
WorldMark establishes a standardized benchmark for evaluating interactive video generation models with unified controls, identical scenarios, and comprehensive evaluation metrics across multiple model...
🔹 Publication Date: Published on Apr 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21686
• PDF: https://arxiv.org/pdf/2604.21686
• Project Page: https://alaya-studio.github.io/WorldMark/
==================================
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✨Context Unrolling in Omni Models
📝 Summary:
Omni is a unified multimodal model trained on diverse data types that enables context unrolling for improved reasoning across heterogeneous modalities. AI-generated summary We present Omni, a unified ...
🔹 Publication Date: Published on Apr 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21921
• PDF: https://arxiv.org/pdf/2604.21921
• Project Page: https://omni-model.com/
==================================
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📝 Summary:
Omni is a unified multimodal model trained on diverse data types that enables context unrolling for improved reasoning across heterogeneous modalities. AI-generated summary We present Omni, a unified ...
🔹 Publication Date: Published on Apr 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21921
• PDF: https://arxiv.org/pdf/2604.21921
• Project Page: https://omni-model.com/
==================================
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arXiv.org
Context Unrolling in Omni Models
We present Omni, a unified multimodal model natively trained on diverse modalities, including text, images, videos, 3D geometry, and hidden representations. We find that such training enables...
✨UniGenDet: A Unified Generative-Discriminative Framework for Co-Evolutionary Image Generation and Generated Image Detection
📝 Summary:
A unified generative-discriminative framework is proposed that enables co-evolutionary image generation and detection through symbiotic attention mechanisms and unified fine-tuning algorithms. AI-gene...
🔹 Publication Date: Published on Apr 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21904
• PDF: https://arxiv.org/pdf/2604.21904
• Project Page: https://ivg-yanranzhang.github.io/UniGenDet/
• Github: https://github.com/Zhangyr2022/UniGenDet
==================================
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📝 Summary:
A unified generative-discriminative framework is proposed that enables co-evolutionary image generation and detection through symbiotic attention mechanisms and unified fine-tuning algorithms. AI-gene...
🔹 Publication Date: Published on Apr 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21904
• PDF: https://arxiv.org/pdf/2604.21904
• Project Page: https://ivg-yanranzhang.github.io/UniGenDet/
• Github: https://github.com/Zhangyr2022/UniGenDet
==================================
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arXiv.org
UniGenDet: A Unified Generative-Discriminative Framework for...
In recent years, significant progress has been made in both image generation and generated image detection. Despite their rapid, yet largely independent, development, these two fields have evolved...
✨VLAA-GUI: Knowing When to Stop, Recover, and Search, A Modular Framework for GUI Automation
📝 Summary:
VLAA-GUI is a modular GUI agent framework that addresses early stopping and repetitive loop issues through integrated components for verification, loop breaking, and search capabilities. AI-generated ...
🔹 Publication Date: Published on Apr 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21375
• PDF: https://arxiv.org/pdf/2604.21375
• Project Page: https://ucsc-vlaa.github.io/VLAA-GUI/
• Github: https://github.com/UCSC-VLAA/VLAA-GUI
==================================
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📝 Summary:
VLAA-GUI is a modular GUI agent framework that addresses early stopping and repetitive loop issues through integrated components for verification, loop breaking, and search capabilities. AI-generated ...
🔹 Publication Date: Published on Apr 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21375
• PDF: https://arxiv.org/pdf/2604.21375
• Project Page: https://ucsc-vlaa.github.io/VLAA-GUI/
• Github: https://github.com/UCSC-VLAA/VLAA-GUI
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arXiv.org
VLAA-GUI: Knowing When to Stop, Recover, and Search, A Modular...
Autonomous GUI agents face two fundamental challenges: early stopping, where agents prematurely declare success without verifiable evidence, and repetitive loops, where agents cycle through the...