AI Dynamics

Global AI News Aggregator

@jiqizhixin

  • Error-Entropy Scaling Law Surpasses Traditional Cross-Entropy for LLM Development
    Error-Entropy Scaling Law Surpasses Traditional Cross-Entropy for LLM Development

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    @jiqizhixin

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    Is the fundamental scaling law guiding large language model development broken? Researchers from Tsinghua University have found the answer. They've decomposed cross-entropy loss into three components: Error-Entropy, Self-Alignment, and Confidence, finding that only Error-Entropy truly scales with model size. This new "Error-Entropy scaling law" provides a far more accurate guide for LLM development, outperforming the traditional cross-entropy law, especially for the largest models. Crucial for future AI design. What Scales in Cross-Entropy Scaling Law? Paper: arxiv.org/abs/2510.04067 Code: github.com/yanjx2021/Rethink… Our report: mp.weixin.qq.com/s/ngn6YY6Aj… 📬 #PapersAccepted by Jiqizhixin

    → View original post on X — @jiqizhixin, 2026-04-04 08:49 UTC

  • Shop-R1: AI Framework for Understanding Human Online Shopping Behavior
    Shop-R1: AI Framework for Understanding Human Online Shopping Behavior

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    @jiqizhixin

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    Ever wonder if an AI could truly understand how you shop online? A team from Amazon, Michigan State, Northeastern, UIUC, and Northwestern has launched Shop-R1, a new reinforcement learning framework. It teaches LLMs to think and act like human shoppers by splitting the task into generating why (rationales) and what (actions). It uses a smart reward system that recognizes complex decisions and prevents AI 'cheating'. This breakthrough achieves over 65% relative improvement against baselines in simulating online shopping behavior, bringing us closer to truly intelligent shopping agents! Shop-R1: Rewarding LLMs to Simulate Human Behavior in Online Shopping via Reinforcement Learning Paper: arxiv.org/abs/2507.17842 Project: damon-demon.github.io/shop-r… Our report: mp.weixin.qq.com/s/Dvst0Oirm… 📬 #PapersAccepted by Jiqizhixin

    → View original post on X — @jiqizhixin, 2026-04-04 05:43 UTC

  • Qwen 3.6 Plus Breaks 1 Trillion Tokens Record on OpenRouter
    Qwen 3.6 Plus Breaks 1 Trillion Tokens Record on OpenRouter

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    @jiqizhixin

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    Huge accomplishment! Congrates to @Alibaba_Qwen team. OpenRouter (@OpenRouter) Qwen 3.6 Plus from @Alibaba_Qwen is officially the first model on OpenRouter to break 1 Trillion tokens processed in a single day! At ~1,400,000,000,000 tokens, it’s the strongest full day performance of any new model dropped this year. Congrats to the Qwen team! — https://nitter.net/OpenRouter/status/2040239467865489874#m

    → View original post on X — @jiqizhixin, 2026-04-04 02:47 UTC

  • Intelligent Remote Sensing Agents Transform Earth Observation with AI
    Intelligent Remote Sensing Agents Transform Earth Observation with AI

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    @jiqizhixin

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    What if Earth observation could truly think for itself? A collaborative team from Hong Kong University of Science and Technology, Northwestern Polytechnical University, Tsinghua University, and international partners have released a seminal survey on "Intelligent Remote Sensing Agents." This new paradigm shows how AI agents integrate perception, planning, memory, and tool execution to autonomously achieve complex geospatial understanding. This transforms remote sensing from passive data collection into proactive, intelligent decision support, far surpassing previous capabilities in urban governance, precision agriculture, ecological monitoring, and emergency response. Intelligent Remote Sensing Agents: A Survey Paper: github.com/PolyX-Research/Aw… Repo: github.com/PolyX-Research/Aw… Our report: mp.weixin.qq.com/s/QYAyTjaAa… 📬 #PapersAccepted by Jiqizhixin

    → View original post on X — @jiqizhixin, 2026-04-04 01:40 UTC

  • MoGraphGPT: Creating Interactive 2D Scenes Without Coding

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    @jiqizhixin

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    What if building complex, interactive 2D scenes was as simple as describing them, with no coding needed? Enter MoGraphGPT! This new system leverages modular LLMs, using specialized AIs for individual scene elements and a central AI to manage their interactions, all through an intuitive graphical user interface with auto-generated sliders. It offers precise visual control for scene creation. MoGraphGPT significantly outperforms Cursor Composer, making the creation of multi-element 2D interactive scenes easier, more controllable, and higher performing, all without writing a single line of code. MoGraphGPT: Creating Interactive Scenes Using Modular LLM and Graphical Control Paper: ieeexplore.ieee.org/abstract… Our report: mp.weixin.qq.com/s/objKgAzNO… 📬 #PapersAccepted by Jiqizhixin

    → View original post on X — @jiqizhixin, 2026-04-03 19:50 UTC

  • VLMgineer: AI-Powered Robots Design Their Own Tools
    VLMgineer: AI-Powered Robots Design Their Own Tools

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    @jiqizhixin

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    Can AI truly empower robots to invent their own solutions? George Jiayuan Gao, Tianyu Li, and colleagues from UPenn present VLMgineer. This framework leverages Vision Language Models (VLMs) to brainstorm initial tool designs and action plans. It then refines these ideas using evolutionary search in simulation, optimizing both the tool's geometry and how the robot uses it. VLMgineer consistently outperforms existing human-crafted tools and VLM-generated designs from human specifications across diverse, challenging everyday manipulation tasks, transforming complex robotics problems into straightforward executions. VLMgineer: Vision Language Models as Robotic Toolsmiths Project: vlmgineer.github.io Paper: arxiv.org/abs/2507.12644 Our report: mp.weixin.qq.com/s/FXdeQhAeq… 📬 #PapersAccepted by Jiqizhixin

    → View original post on X — @jiqizhixin, 2026-04-03 14:45 UTC

  • HACRL: AI Agents Learn Together Without Losing Autonomy
    HACRL: AI Agents Learn Together Without Losing Autonomy

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    @jiqizhixin

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    What if diverse AI agents could mutually learn and improve without sacrificing their autonomy? Researchers from Beihang University, Bytedance China, Tsinghua University, and Peking University have just unveiled Heterogeneous Agent Collaborative Reinforcement Learning (HACRL)! This innovative framework allows different types of AI agents to share verified learning experiences during training, creating a bidirectional flow of knowledge to enhance performance for everyone. Unlike other multi-agent systems, it requires no coordinated deployment and fosters true peer-to-peer growth, not one-way teaching. Their HACPO algorithm consistently boosts all participating agents, outperforming GSPO by 3.3% on diverse reasoning benchmarks while dramatically cutting training data costs in half. Heterogeneous Agent Collaborative Reinforcement Learning Paper: arxiv.org/abs/2603.02604 Github Page: zzx-peter.github.io/hacrl/ Huggingface: huggingface.co/papers/2603.0… Our report: mp.weixin.qq.com/s/ggzim_4Pc… 📬 #PapersAccepted by Jiqizhixin

    → View original post on X — @jiqizhixin, 2026-04-03 12:43 UTC

  • Streamo: Real-time Streaming Video LLM for Intelligent Assistance
    Streamo: Real-time Streaming Video LLM for Intelligent Assistance

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    @jiqizhixin

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    What if an AI could truly understand live video streams and act as your intelligent assistant, in real-time? Researchers from Hong Kong Baptist University and Tencent Youtu Lab just unveiled a major step forward! They present Streamo, a real-time streaming video LLM. It's trained on a new, massive instruction dataset (Streamo-Instruct-465K) to enable unified understanding across many streaming video tasks. Streamo excels at real-time narration, complex action understanding, event captioning, and time-sensitive Q&A. It bridges the gap between static video analysis and genuinely interactive, intelligent multimodal AI assistants in continuous streams! Streaming Instruction Tuning Project: jiaerxia.github.io/Streamo/ Code: github.com/maifoundations/St… Our report: mp.weixin.qq.com/s/Q28azqwk-… 📬 #PapersAccepted by Jiqizhixin

    → View original post on X — @jiqizhixin, 2026-04-03 03:36 UTC

  • Anthropic Research: Emotion Concepts in Large Language Models
    Anthropic Research: Emotion Concepts in Large Language Models

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    @jiqizhixin

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    Interesting 🤔 Anthropic (@AnthropicAI) New Anthropic research: Emotion concepts and their function in a large language model. All LLMs sometimes act like they have emotions. But why? We found internal representations of emotion concepts that can drive Claude’s behavior, sometimes in surprising ways. — https://nitter.net/AnthropicAI/status/2039749628737019925#m

    → View original post on X — @jiqizhixin, 2026-04-03 01:30 UTC

  • Action-to-Action Flow Matching: Ultra-Fast Robot Control Method
    Action-to-Action Flow Matching: Ultra-Fast Robot Control Method

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    @jiqizhixin

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    What if real-time robot control didn't have to wait for slow, iterative action generation? MARS Lab at Nanyang Technological University (Jindou Jia et al.) introduces Action-to-Action Flow Matching (A2A). This novel method uses a robot's own historical actions to directly predict the next move, skipping the slow, random noise sampling of traditional diffusion models. A2A enables lightning-fast, single-step action generation (0.56 ms!), vastly outperforming existing methods in speed, training efficiency, robustness to visual noise, and generalization to unseen configurations. It even shows versatility in video generation! Action-to-Action Flow Matching Website: lorenzo-0-0.github.io/A2A_Fl…  arXiv: arxiv.org/pdf/2602.07322  Code: github.com/JIAjindou/A2A_Flo… Our report: mp.weixin.qq.com/s/mrSUcVLUA… 📬 #PapersAccepted by Jiqizhixin

    → View original post on X — @jiqizhixin, 2026-04-02 18:30 UTC