✨Computer-Use Agents as Judges for Generative User Interface

📝 Summary:
This paper introduces a framework where Computer-Use Agents CUA act as judges for coding language models Coder to automatically design GUIs. The goal is to optimize interfaces for CUA efficiency and task solvability, rather than human aesthetics, using a new benchmark called AUI-Gym.

🔹 Publication Date: Published on Nov 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.15567
• PDF: https://arxiv.org/pdf/2511.15567
• Project Page: https://showlab.github.io/AUI/
• Github: https://github.com/showlab/AUI/

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AIAgents #GUIDesign #GenerativeAI #AIevaluation #LanguageModels

✨Budget-Aware Tool-Use Enables Effective Agent Scaling

📝 Summary:
Tool-augmented agents struggle to scale with more tool calls due to a lack of budget awareness. This paper introduces Budget Tracker for continuous budget awareness and BATS for adaptive planning, dynamically adjusting strategy based on remaining resources. These methods significantly improve cos...

🔹 Publication Date: Published on Nov 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.17006
• PDF: https://arxiv.org/pdf/2511.17006

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AIAgents #ToolUse #ResourceManagement #AgentScaling #AIResearch

✨PRInTS: Reward Modeling for Long-Horizon Information Seeking

📝 Summary:
PRInTS is a generative process reward model that improves AI agents information-seeking. It provides dense scoring on step quality and summarizes long trajectories to manage context. PRInTS enhances agent performance, matching or surpassing frontier models with a smaller backbone.

🔹 Publication Date: Published on Nov 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.19314
• PDF: https://arxiv.org/pdf/2511.19314

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#RewardModeling #InformationSeeking #AIagents #GenerativeAI #MachineLearning

✨PC-Agent: A Hierarchical Multi-Agent Collaboration Framework for Complex Task Automation on PC

📝 Summary:
PC-Agent is a hierarchical multi-agent framework improving MLLM-based GUI agents for complex PC tasks. It uses an Active Perception Module and a hierarchical decision-making architecture with Manager, Progress, and Decision agents. A Reflection agent provides feedback. It achieved a 32% task succ...

🔹 Publication Date: Published on Feb 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2502.14282
• PDF: https://arxiv.org/pdf/2502.14282
• Github: https://github.com/X-PLUG/MobileAgent/tree/main/PC-Agent

✨ Spaces citing this paper:
• https://huggingface.co/spaces/junyangwang0410/PC-Agent

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#MultiAgentSystems #AIAgents #MLLMs #PCAutomation #DeepLearning

✨Fara-7B: An Efficient Agentic Model for Computer Use

📝 Summary:
FaraGen creates synthetic datasets for computer use agents, solving a data scarcity problem. This data trains Fara-7B, a small on-device model that perceives computers via screenshots and outperforms larger models on diverse web tasks.

🔹 Publication Date: Published on Nov 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.19663
• PDF: https://arxiv.org/pdf/2511.19663

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AIAgents #OnDeviceAI #SyntheticData #MachineLearning #ComputerVision

✨Agent0-VL: Exploring Self-Evolving Agent for Tool-Integrated Vision-Language Reasoning

📝 Summary:
Agent0-VL is a self-evolving vision-language agent that integrates tool usage into both reasoning and self-evaluation. It uses a Solver and Verifier in a self-evolving cycle for continuous improvement without human annotation or external rewards, achieving a 12.5% performance gain.

🔹 Publication Date: Published on Nov 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.19900
• PDF: https://arxiv.org/pdf/2511.19900

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#AIAgents #VisionLanguage #SelfEvolvingAI #ToolAugmentedAI #AIResearch

✨Latent Collaboration in Multi-Agent Systems

📝 Summary:
LatentMAS enables LLM agents to collaborate directly in latent space, surpassing text-based communication. This boosts reasoning quality, accuracy, and efficiency speed, tokens without extra training.

🔹 Publication Date: Published on Nov 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.20639
• PDF: https://arxiv.org/pdf/2511.20639
• Github: https://github.com/Gen-Verse/LatentMAS

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#LLM #MultiAgentSystems #LatentSpace #AIAgents #ArtificialIntelligence

✨DualVLA: Building a Generalizable Embodied Agent via Partial Decoupling of Reasoning and Action

📝 Summary:
DualVLA tackles action degeneration in VLAs by boosting action performance while retaining reasoning. It uses dual-layer data pruning and dual-teacher adaptive distillation. This balances precise action and multimodal understanding, leading to high success rates.

🔹 Publication Date: Published on Nov 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.22134
• PDF: https://arxiv.org/pdf/2511.22134
• Project Page: https://costaliya.github.io/DualVLA/
• Github: https://costaliya.github.io/DualVLA/

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#EmbodiedAI #VLAs #AIagents #DeepLearning #AIResearch

✨Agentic Policy Optimization via Instruction-Policy Co-Evolution

📝 Summary:
INSPO introduces a novel framework dynamically optimizing instructions within the reinforcement learning loop for autonomous agents. It substantially outperforms static instruction methods in multi-turn reasoning by discovering innovative, strategic reasoning paths.

🔹 Publication Date: Published on Dec 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.01945
• PDF: https://arxiv.org/pdf/2512.01945
• Github: https://github.com/cambridgeltl/inspo

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#ReinforcementLearning #AIAgents #PolicyOptimization #MachineLearning #AI

✨SIMA 2: A Generalist Embodied Agent for Virtual Worlds

📝 Summary:
SIMA 2 is a Gemini-based embodied agent for 3D virtual worlds. It reasons about goals, handles complex instructions, and autonomously learns new skills. This closes the gap with human performance and validates continuous learning agents.

🔹 Publication Date: Published on Dec 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.04797
• PDF: https://arxiv.org/pdf/2512.04797

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

For more data science resources:
✓ https://t.iss.one/DataScienceT

#EmbodiedAI #AI #VirtualWorlds #ReinforcementLearning #AIagents