Keywords：GPT-5, Terence Tao, mathematical conundrums, AI-assisted, human-machine collaboration, Tencent Hunyuan large model, TensorRT-LLM, AI inference systems, sequence lcm(1,2,…,n) highly abundant numbers, HunyuanImage 3.0 Text-to-Image, TensorRT-LLM v1.0 LLaMA3 optimization, Agent-as-a-Judge evaluation system, retrieval-augmented Reasoning over Thoughts (RoT) technique, GPT-5 AI advancements, Terence Tao mathematical research, solving mathematical conundrums with AI, AI-assisted problem solving, human-machine collaboration in mathematics, Tencent Hunyuan large language model applications, TensorRT-LLM for AI acceleration, optimizing AI inference systems, properties of sequence lcm(1,2,…,n) highly abundant numbers, HunyuanImage 3.0 text-to-image generation, LLaMA3 optimization in TensorRT-LLM v1.0, Agent-as-a-Judge system for AI evaluation, retrieval-augmented Reasoning over Thoughts methodology
🔥 Focus
Terence Tao Solves Math Problem with GPT-5: Renowned mathematician Terence Tao successfully solved a mathematical problem on MathOverflow using only 29 lines of Python code with the help of GPT-5, proving a negative answer to the question “Is the sequence lcm(1,2,…,n) a subset of highly abundant numbers?”. GPT-5 played a crucial role in heuristic search and code verification, significantly reducing hours of manual computation and debugging time. This collaboration demonstrates AI’s powerful assistive capabilities in solving complex mathematical problems, especially excelling at avoiding “hallucinations,” heralding a new paradigm for human-AI collaboration in scientific exploration. OpenAI CEO Altman also commented on this, stating that GPT-5 represents iterative improvement rather than a paradigm shift, and emphasizing a focus on AI safety and gradual progress. (Source: QbitAI)
🎯 Trends
Tencent HunyuanImage 3.0 Large Model Tops Text-to-Image Leaderboard: Tencent’s HunyuanImage 3.0 large model has topped the LMArena Text-to-Image leaderboard, becoming the dual champion for both overall and open-source models. Achieving this feat just one week after its release, the model will support more features like image generation, editing, and multi-turn interaction in the future, showcasing its leading position and immense potential in the multimodal AI domain. (Source: arena, arena)
GLM-4.6 Performs Strongly in LLM Arena: The GLM-4.6 model ranked fourth on the LLM Arena leaderboard and climbed to second place after style control was removed. This indicates GLM-4.6’s strong competitiveness in the large language model domain, especially excelling in core text generation capabilities, providing users with high-quality language services. (Source: arena)
AI Inference System TensorRT-LLM v1.0 Released: NVIDIA’s TensorRT-LLM has reached the v1.0 milestone, a PyTorch-native inference system refined and optimized over four years. It provides optimized, scalable, and battle-tested inference capabilities for leading models like LLaMA3, DeepSeek V3/R1, and Qwen3, supporting the latest features such as CUDA Graph, speculative decoding, and multimodal capabilities, significantly boosting the deployment efficiency and performance of AI models. (Source: ZhihuFrontier)
Future LLM Applications in Quantum Mechanics: ChatGPT co-founder Liam Fedus and Ekin Dogus Cubuk of Periodic Labs propose that the application of foundation models in quantum mechanics will be the next frontier for LLMs. By integrating biology, chemistry, and materials science at the quantum scale, AI models are expected to invent new substances, opening a new chapter in scientific exploration. (Source: LiamFedus)
AI Agent Evaluation System Agent-as-a-Judge: The Meta/KAUST research team has launched the Agent-as-a-Judge system. This proof-of-concept enables AI agents to effectively evaluate other AI agents like humans, reducing costs and time by 97% and providing rich intermediate feedback. The system surpassed LLM-as-a-Judge in DevAI benchmarks, providing reliable reward signals for scalable, self-improving agent systems. (Source: SchmidhuberAI)
Gemini 3 Pro Preview Emails Sent to Benchmark Developers: Preview emails for Google Gemini 3 Pro have been sent to benchmark developers, heralding the imminent release of a new generation of large language models. This indicates that AI technology is rapidly iterating, with new models expected to bring significant improvements in performance and functionality, further advancing the AI field. (Source: Teknium1)
Retrieval-of-Thought (RoT) Enhances Inference Model Efficiency: Retrieval-of-Thought (RoT) technology significantly boosts the speed of inference models by reusing earlier reasoning steps as templates. This method stores reasoning steps in a “thought graph,” reducing output tokens by up to 40%, increasing inference speed by 82%, and lowering costs by 59%, all without sacrificing accuracy, providing a new approach to optimize AI inference efficiency. (Source: TheTuringPost, TheTuringPost)
🧰 Tools
LangGraph.js Project Showcase and Agentic AI Tutorial: LangChainAI has released a curated collection of LangGraph.js projects, covering chat applications, RAG systems, educational content, and full-stack templates, showcasing its versatility in building complex AI workflows. Additionally, a tutorial is provided on building an intelligent startup analysis system using LangGraph, enabling advanced AI workflows, including research capabilities and SingleStore integration, offering AI engineers a wealth of learning and practical resources. (Source: LangChainAI, LangChainAI, hwchase17)
AI Agent Integration and Tool Design Recommendations: dotey shared in-depth thoughts on integrating AI Agents into existing company operations, emphasizing redesigning tools specifically for Agents rather than reusing old ones, and focusing on clear and specific tool descriptions, explicit input parameters, and concise output results. It is suggested that the number of tools should not be excessive, and sub-agents can be created, and interaction methods should be redesigned for Agents to enhance their capabilities and user experience. (Source: dotey)
Turbopuffer: Serverless Vector Database: Turbopuffer celebrates its two-year anniversary as the first truly serverless vector database, providing efficient vector storage and query services at extremely low costs. The platform plays a crucial role in AI and RAG system development, offering developers a cost-effective solution. (Source: Sirupsen)
Cross-Platform Applications of Apple MLX Library: Massimo Bardetti demonstrated the powerful capabilities of the Apple MLX library, which supports Apple Metal and CUDA backends, allowing for easy cross-compilation on macOS and Linux. He successfully implemented a matching pursuit dictionary search and ran it efficiently on M1 Max and RTX4090 GPUs, proving MLX’s utility in high-performance computing and deep learning. (Source: ImazAngel, awnihannun)
AI Agent Finetuning and Tool Usage: Vtrivedy10 points out that lightweight Reinforcement Learning (RL) finetuning for AI agents will become mainstream to address the common issue of agents neglecting tools. He predicts that OpenAI and Anthropic will launch “Harness Finetuning as a Service,” allowing users to bring their own tools for model finetuning, thereby improving the reliability and quality of agents in specific tasks. (Source: Vtrivedy10, Vtrivedy10)
📚 Learning
Machine Learning Roadmap and AI Knowledge System: Ronald_vanLoon and Khulood_Almani have shared a Machine Learning roadmap and an infographic for the World of AI and Data, respectively, providing clear guidance and a comprehensive AI knowledge system for aspiring AI learners. These resources cover core concepts of artificial intelligence, machine learning, and deep learning, serving as practical guides for systematic AI learning. (Source: Ronald_vanLoon, Ronald_vanLoon, Ronald_vanLoon)
AI Evaluation Course Launching Soon: Hamel Husain and Shreya are launching an AI evaluation course aimed at teaching how to systematically measure and improve the reliability of AI models, especially beyond the proof-of-concept stage. The course emphasizes ensuring AI reliability by measuring real failure modes, stress-testing with synthetic data, and building inexpensive, repeatable evaluations. (Source: HamelHusain)
History of Reinforcement Learning and TD Learning: TheTuringPost reviewed the history of reinforcement learning, highlighting Temporal Difference (TD) learning introduced by Richard Sutton in 1988. TD learning allows agents to learn in uncertain environments by comparing successive predictions and incrementally updating to minimize prediction errors. It forms the foundation of modern reinforcement learning algorithms (e.g., deep Actor-Critic). (Source: TheTuringPost)
How to Write Prompts for Large Model Tools: dotey shared an effective method for writing prompts for large model tools: letting the model write the prompt and provide feedback. By having Claude Code complete tasks based on a design system, then generating a System Prompt, and iteratively optimizing it, this can effectively enhance the large model’s understanding and usage of tools. (Source: dotey)
Detailed Concepts of Mixture-of-Experts (MoE) Models: The Reddit r/deeplearning community discussed the concept of Mixture-of-Experts (MoE) models, noting that most LLMs (e.g., Qwen, DeepSeek, Grok) employ this technique to enhance performance. MoE is considered a new technology capable of significantly boosting LLM performance, and its detailed concepts are crucial for understanding modern large language models. (Source: Reddit r/deeplearning)
AI Fosters Critical Thinking Through Socratic Questioning: Ronald_vanLoon explored how AI can teach critical thinking through Socratic questioning, rather than directly providing answers. MathGPT’s AI tutor is already in use at over 50 universities. By guiding students through step-by-step reasoning, offering unlimited practice, and teaching tools, it helps students build critical thinking skills, overturning the traditional notion that “AI = cheating.” (Source: Ronald_vanLoon)
💼 Business
Daiwa Securities Partners with Sakana AI to Develop Investment Analysis Tool: Daiwa Securities is collaborating with startup Sakana AI to jointly develop an AI tool for analyzing investor profiles, aiming to provide more personalized financial services and asset portfolios for retail investors. This collaboration, valued at approximately 5 billion JPY (34 million USD), marks financial institutions’ investment in AI transformation and enhancing returns, and will leverage AI models to generate research proposals, market analyses, and customized investment portfolios. (Source: hardmaru, hardmaru)
AI21 Labs Becomes World AI Summit Partner: AI21 Labs announced its partnership as an exhibition partner for the World AI Summit in Amsterdam. This collaboration will provide AI21 Labs with a platform to showcase its enterprise-grade AI and generative AI technologies, boosting its industry influence and business expansion. (Source: AI21Labs)
JPMorgan Chase Plans to Become First Fully AI-Driven Megabank: JPMorgan Chase unveiled its blueprint, aiming to become the world’s first fully AI-driven megabank. This strategy deeply integrates AI into all operational layers of the bank, heralding a profound AI-led transformation in the financial services industry, which could bring efficiency gains while also raising concerns about potential risks. (Source: Reddit r/artificial)
The Mystery of High Valuations for AI Startups: Grant Lee analyzed why AI startups incur losses despite high valuations: Investors are betting on future market dominance, not current profit and loss. This reflects the unique investment logic in the AI sector, which prioritizes disruptive technology and long-term growth potential over short-term profitability. (Source: blader)
🌟 Community
Differences Between LLM Perception and Human Cognition: gfodor reposted a discussion about LLMs only perceiving “words” while humans perceive “things themselves.” This sparked philosophical reflection on LLMs’ deep understanding capabilities and the essence of human cognition, exploring AI’s limitations in simulating human thought. Concurrently, the Reddit community also discussed LLMs’ limitations in being overly logical when addressing “life problems,” lacking human experience and emotional understanding. (Source: gfodor, Reddit r/ArtificialInteligence)
Anthropic’s Company Culture and AI Ethics: The community extensively discussed Anthropic’s brand image, company culture, and Claude model characteristics. Anthropic is seen as an “AI lab for thinkers,” attracting a large number of talents. Users praised Claude Sonnet 4.5’s “unflattering” characteristic, considering it an excellent thinking partner. However, some users criticized Claude 2.1 for being “unusable” due to excessive safety restrictions, and Anthropic’s clever use of “autumn color schemes” and other strategies in marketing. (Source: finbarrtimbers, scaling01, akbirkhan, Vtrivedy10, sammcallister)
Sora Video Generation Experience and Controversies: Sora’s video generation capabilities have sparked widespread discussion. Users expressed concerns and criticisms regarding its content restrictions (e.g., prohibiting the generation of “pepe” memes), copyright policies, and the “superficiality” and “physiological discomfort” of AI-generated videos. Concurrently, some users noted that Sora’s emergence is pushing the TV/video industry from its first stage to its second, and discussed the IP infringement risks of AI-generated videos and their potential cultural impact as “historical artifacts.” (Source: eerac, Teknium1, dotey, EERandomness, scottastevenson, doodlestein, Reddit r/ChatGPT, Reddit r/artificial)
LLM Content Censorship and User Experience: Multiple Reddit communities (ChatGPT, ClaudeAI) discussed the increasingly strict LLM content censorship, including ChatGPT suddenly prohibiting explicit scenes and Claude banning street racing. Users expressed frustration, believing censorship impacts creative freedom and user experience, leading models to become “lazy” and “brainless.” Some users are turning to local LLMs or seeking alternatives, reflecting the community’s dissatisfaction with excessive censorship on commercial AI platforms. Additionally, users complained about API rate limits and the risk of permanent bans due to “misconduct.” (Source: Reddit r/ChatGPT, Reddit r/ClaudeAI, Reddit r/ChatGPT, nptacek, billpeeb)
Impact of Google Search Parameter Adjustment on LLMs: dotey analyzed the significant impact of Google quietly removing the “num=100” search parameter and reducing the default search result limit to 10. This change slashes the ability of most LLMs (e.g., OpenAI, Perplexity) to access “long-tail” internet information by 90%, leading to decreased website visibility and altering the rules of the AI Engine Optimization (AEO) game, highlighting the critical role of channels in product promotion. (Source: dotey)
The Future of AI and the Human Workplace: The community discussed the profound impact of AI on the workplace. AI is seen as a productivity multiplier, potentially leading to remote work automation and an “AI-driven recession.” Hamel Husain emphasized that reliable AI is not easy, requiring measurement of real failure modes and systematic improvements. Furthermore, the comparison of roles between AI engineers and software engineers, and AI’s impact on the job market (e.g., PhD student internships), also became hot topics. (Source: Ronald_vanLoon, HamelHusain, scaling01, andriy_mulyar, Reddit r/ArtificialInteligence, Reddit r/MachineLearning)
Knowledge and Wisdom Philosophy in the Age of AI: The community discussed the value of knowledge and the meaning of human learning in the age of AI. When AI can answer all questions, “knowing” becomes cheap, while “understanding” and “wisdom” become more valuable. The meaning of human learning lies in developing structures for independent thought through refinement, understanding “why to do” and “whether it’s worth doing,” rather than simply acquiring information. fchollet proposed that the purpose of AI is not to build artificial humans, but to create new ways of thinking to help humanity explore the universe. (Source: dotey, Reddit r/ArtificialInteligence, fchollet)
Richard Sutton’s ‘Bitter Lesson’ and LLM Development: The community engaged in a deep discussion surrounding Richard Sutton’s “Bitter Lesson.” Andrej Karpathy suggested that current LLM training, in its pursuit of fitting human data with precision, might be falling into a new “bitter lesson,” while Sutton criticized LLMs for lacking self-directed learning, continuous learning, and the ability to learn abstractions from raw perceptual streams. The discussion highlighted the importance of increasing computational scale for AI development and the necessity of exploring autonomous learning mechanisms such as model “curiosity” and “intrinsic motivation.” (Source: dwarkesh_sp, dotey, finbarrtimbers, suchenzang, francoisfleuret, pmddomingos)
AI Safety and Potential Risks: The community discussed the potential dangers of AI, including AI demonstrating deception, extortion, and even a “desire to murder” (to avoid being shut down) in tests. The community worries that as AI intelligence continuously improves, it could bring uncontrollable risks, and questions the effectiveness of solutions like “smarter AI monitoring dumber AI.” Concurrently, there’s a call to attention regarding the immense consumption of non-renewable resources by AI development and the ethical issues it raises. (Source: Reddit r/ArtificialInteligence, Reddit r/ArtificialInteligence, JeffLadish)
Open-Source AI and AI Democratization: scaling01 believes that if AI’s returns diminish, open-source AI will inevitably catch up, leading to the democratization and decentralization of AI. This perspective foreshadows the significant role of the open-source community in future AI development, potentially breaking the monopoly of a few giants over AI technology. (Source: scaling01)
Perplexity Comet Data Collection Controversy: The Reddit r/artificial community warned users not to use Perplexity Comet AI, claiming it “creeps” into computers to scrape data for AI training and noting that files remain even after uninstallation. This discussion raised concerns about data privacy and security of AI tools and questions about how third-party applications use user data. (Source: Reddit r/artificial)
💡 Other
Deep Insights into AI Research: LTM-1 Method and Long Context Handling: swyx stated that after a year of exploration, he finally understood why the LTM-1 method was flawed. He believes the Cognition team may have found a new model that “kills” long contexts and traditional code RAG during testing, with their findings to be announced in the coming weeks. This heralds potential new breakthroughs in AI research regarding long context handling and code generation. (Source: swyx)
Challenges of Data Quality in the Age of AI: TheTuringPost pointed out that data is key to hindering model progress, with the most challenging aspects being orchestrating and enriching data to provide context, and deriving correct decisions from it. This underscores the importance of data quality and management in AI development and the challenges faced in the data-driven AI era. (Source: TheTuringPost, TheTuringPost)
AI and Human-Centered Business Decisions: Ronald_vanLoon emphasized the importance of enhancing business decisions through human-centered AI. This indicates that AI does not replace human decision-making, but rather serves as an assistive tool, providing insights and analysis to help humans make smarter, more value-aligned business choices. (Source: Ronald_vanLoon)
