Keywords：Sora 2, AI video generation, OpenAI, creative content, deepfake, social media dynamics, personalized content creation, Sora 2 model, cameo feature, AI creative tools, video interaction technology, content abuse prevention
🔥 Spotlight
Sora 2 release, leading a new paradigm for creative content: OpenAI launched Sora 2, combining the Sora 2 model with new products, aiming to become the “ChatGPT of the creative field.” The application emphasizes rapid transformation from idea to outcome and enhances user interaction with friends in videos through a “guest appearance” feature, fostering a sense of connection. Despite concerns about addiction and misuse (e.g., deepfakes), OpenAI is committed to exploring healthy social dynamics through principles like optimizing user satisfaction, encouraging user control over content flow, prioritizing creation, and helping users achieve long-term goals. This marks a new height for AI in video generation and personalized content creation, heralding a “Cambrian explosion” in the creative industry. (Source: sama, sama)
NVIDIA open-sources multiple robotics technologies, accelerating physical AI development: At the Robotics Learning Conference, NVIDIA released several open-source technologies, most notably the Newton physics engine, developed jointly with Google DeepMind and Disney Research. This release also includes Isaac GR00T N1.6, a foundational model that gives robots reasoning capabilities, and Cosmos, a world foundational model for generating vast amounts of training data. The Newton engine, based on GPU acceleration, can simulate complex robot movements. Isaac GR00T N1.6 integrates the Cosmos Reason visual language model, enabling robots to understand vague instructions and think deeply. These technologies aim to solve core challenges in robotics R&D, potentially accelerating the transition of robots from laboratories to daily life. (Source: 量子位)
IBM releases Granite 4.0 open-source models, adopting a hybrid Mamba/Transformer architecture: IBM introduced the Granite 4.0 series of open-source language models, ranging from 3B to 32B in size, featuring a hybrid Mamba and Transformer architecture. This significantly reduces memory requirements while maintaining high accuracy. These models are particularly suitable for enterprise applications such as Agent workflows, tool calling, document analysis, and RAG. The 3.4B Micro model can even run locally in a browser via WebGPU. Granite 4.0 H Small scores 23 in non-inference mode, surpassing Gemma 3 27B, and demonstrates excellent token efficiency, signaling IBM’s return and innovation in the open-source LLM space. (Source: ClementDelangue, huggingface)
🎯 Trends
Google Gemini 2.5 Flash Image (Nano Banana) update, supporting multi-aspect ratio output: Google announced that Gemini 2.5 Flash Image (codename “Nano Banana”) is now generally available and in production, adding support for 10 aspect ratios, multi-image blending, and pure image output capabilities. This update aims to help developers build more dynamic and creative user experiences. The model’s enhancements in image editing and generation make it a powerful tool for developers creating on AI Studio and the Gemini API. (Source: op7418, GoogleDeepMind, demishassabis, GoogleAIStudio)
Claude Sonnet 4.5 performs outstandingly in AI model arena: Claude Sonnet 4.5 is tied for first place with Claude Opus 4.1 on the Text Arena leaderboard, surpassing GPT-5. User feedback indicates significant improvements in critical thinking and logical reasoning for Sonnet 4.5, especially in coding tasks, with fast response times. It can even directly point out user errors instead of blindly accommodating them. This demonstrates Anthropic’s significant progress in model performance and user experience, showcasing strong competitiveness in general capabilities and coding tasks. (Source: scaling01, arena, Reddit r/ClaudeAI, Reddit r/ClaudeAI)
Perplexity Comet AI browser opens for free, launches Comet Plus subscription: Perplexity announced that its AI web browser, Comet, is now globally available for free, a service that previously cost $200 per month. Comet aims to provide a powerful personal AI assistant and a new way to use the internet. Simultaneously, Perplexity launched the Comet Plus subscription plan, partnering with media outlets like The Washington Post and CNN to offer content consumption services for AI and humans. Perplexity Pro/Max users receive it for free. This move aims to expand the user base and explore new models for AI-driven content aggregation and consumption. (Source: AravSrinivas, AravSrinivas, AravSrinivas)
Future of LLM architecture: Sparse vs. Linear Attention, hybrid architecture likely to dominate: The Zhihu community is actively discussing the LLM architectural directions represented by DeepSeek-V3.2-Exp and Qwen3-Next. DeepSeek’s Sparse Attention (DSA) path emphasizes engineering efficiency, running effectively within the existing Transformer hardware ecosystem. Qwen3-Next’s DeltaNet, on the other hand, looks to the future, aiming for O(n) scalability, which could reshape long-context processing. The discussion suggests that these are not competing approaches; a hybrid architecture is most likely to emerge, combining linear attention for local efficiency and sparse attention for global accuracy, to achieve both short-term breakthroughs and long-term scalability. (Source: ZhihuFrontier, ZhihuFrontier)
Diffusion models outperform autoregressive models in data-constrained environments: A study indicates that in data-constrained training scenarios, Diffusion models outperform autoregressive models when sufficient computational resources are available (more training epochs and parameters). The research, which trained hundreds of models, found that Diffusion models can extract more value from repeated data and are significantly more robust to data repetition than autoregressive models, with a data reuse half-life (R_D*) of up to 500, compared to only 15 for autoregressive models. This implies that Diffusion models are a more efficient choice when high-quality data is scarce but computational resources are relatively abundant, challenging the traditional notion of autoregressive models’ universal superiority. (Source: aihub.org)
HTTP 402 micropayment concept re-emerges in the AI era: The “402 Payment Required” micropayment concept, proposed in the HTTP/1.1 protocol in 1996, is gaining renewed attention after three decades of dormancy, driven by the rise of AI. Traditional advertising models are crumbling in the context of AI’s atomic consumption, streaming decision-making, and dehumanized agents (M2M economy). AI requires tiny payments for every API call, data request, and compute rental. The “three mountains” of high transaction costs, fragmented user experience, and lack of technical infrastructure for traditional credit card transactions are being overcome by changes brought by AI. Micropayments are expected to become the payment bedrock of the AI economy, enabling frictionless experiences where value returns to its source, resources flow on demand, and global supply chains settle in milliseconds. (Source: 36氪)
🧰 Tools
Onyx: Open-source chat UI, integrating RAG, web search, and deep research: Onyx is a fully open-source chat user interface designed to provide a solution that combines a beautiful UI, excellent RAG, deep research capabilities, ChatGPT-level web search, and in-depth assistant creation (with file attachments, external tools, sharing). It supports both proprietary and open-source LLMs and can be self-hosted with a single command. Onyx fills a gap in existing open-source chat tools by offering integrated functionalities, providing developers and users with a comprehensive and easy-to-use AI interaction platform. (Source: Reddit r/LocalLLaMA)
LlamaAgents: A platform for building agentic document workflows: LlamaAgents provides a framework for building and deploying agentic document workflows with Human-in-the-Loop (HITL) capabilities. Developers can construct multi-step workflows through code, such as extracting specifications from PDFs, matching them with design requirements, and generating comparison reports. The platform supports local execution and deployment in LlamaCloud, enabling AI agents to process complex document tasks more efficiently, achieving automated information extraction and analysis. (Source: jerryjliu0)
Claude Agent SDK: Empowering developers to build powerful AI agents: Anthropic released the Claude Agent SDK, offering the same core tools, context management system, and permission framework as Claude Code. Developers can use this SDK to build custom AI agents capable of prompt-based UI planning, retrieving document libraries, and calling APIs. The SDK supports built-in tools (e.g., Task, Grep, WebFetch) and custom tools, and can integrate with MCP. Despite limitations such as model compatibility, language restrictions, and fast token consumption, it provides a powerful and flexible platform for rapid development and proof-of-concept. (Source: dotey)
Tinker: Flexible LLM fine-tuning API, simplifying distributed GPU training: Thinking Machines introduced Tinker, a flexible API designed to simplify the fine-tuning process for large language models. Developers can write Python training loops locally, and Tinker handles execution on distributed GPUs, managing infrastructure complexities like scheduling, resource allocation, and fault recovery. It supports open-source models like Llama and Qwen, including large MoE models, and achieves efficient resource sharing through LoRA fine-tuning. Tinker aims to make LLM post-training and RL research more accessible to researchers and developers, lowering the barrier to entry. (Source: thinkymachines, TheTuringPost)
Hex Tech integrates Agent features, enhancing AI data work accuracy: Hex Tech has introduced new Agent features into its data analytics platform, aiming to help users leverage AI for more accurate and trustworthy data work. These features enhance the efficiency of data processing and analysis through an agentic approach, enabling more people to utilize AI for complex data tasks. (Source: sarahcat21)
Yupp.ai launches “Help Me Choose” feature, using an AI committee for multi-perspective decision-making: Yupp.ai introduced a new “Help Me Choose” feature, which helps users synthesize different perspectives and get the best answer from an “AI committee” by having multiple AIs criticize and debate each other. This feature aims to simulate multi-party discussions in human decision-making, providing users with more comprehensive and in-depth analysis to solve complex problems. (Source: yupp_ai, _akhaliq)
TimeSeriesScientist: A general-purpose AI agent for time series analysis: TimeSeriesScientist (TSci) is the first LLM-driven general-purpose time series forecasting agent framework. It comprises four specialized agents: Curator, Planner, Forecaster, and Reporter, responsible for data diagnosis, model selection, fitting validation, and report generation, respectively. TSci aims to address the limitations of traditional models in handling diverse and noisy data, transforming forecasting workflows into an interpretable, scalable white-box system through transparent natural language reasoning and comprehensive reports, reducing forecasting errors by an average of 10.4% to 38.2%. (Source: HuggingFace Daily Papers)
LongCodeZip: A long-context compression framework for code language models: LongCodeZip is a plug-and-play code compression framework designed for code LLMs, addressing high API costs and latency in long-context code generation through a two-stage strategy. It first performs coarse-grained compression, identifying and retaining functions relevant to the instruction, then fine-grained compression, selecting optimal code blocks under an adaptive token budget. LongCodeZip excels in tasks like code completion, summarization, and Q&A, achieving compression ratios of up to 5.6x without degrading performance, enhancing the efficiency and capabilities of code intelligence applications. (Source: HuggingFace Daily Papers)
📚 Learning
Stanford University updates Deep Learning YouTube course: Stanford University is updating its deep learning course on YouTube. This provides an excellent opportunity for students and practitioners in machine learning/deep learning, whether starting from scratch or filling knowledge gaps. (Source: Reddit r/MachineLearning, jeremyphoward)
RLP: Reinforcement as a Pretraining Objective, enhancing reasoning capabilities: RLP (Reinforcement as a Pretraining Objective) is an information-driven reinforcement pretraining objective that introduces the core spirit of reinforcement learning—exploration—into the final stage of pretraining. It treats Chain-of-Thought as an exploratory action, rewarding it based on its information gain for future token predictions. After pretraining on Qwen3-1.7B-Base, RLP improved the overall average accuracy of math and science benchmark suites by 19%, performing particularly well on reasoning-intensive tasks, and is scalable to other architectures and model sizes. (Source: HuggingFace Daily Papers)
DeepSearch: A new method to improve training efficiency for small reasoning models: DeepSearch proposes a method that integrates Monte Carlo Tree Search (MCTS) into the reinforcement learning training loop to more effectively train small reasoning models. This approach significantly boosts the performance of 1-2B parameter models through strategies such as searching during training, learning from correct and confidently incorrect errors, using Tree-GRPO to stabilize RL, and maintaining efficiency. DeepSearch-1.5B achieved 62.95% on AIME/AMC benchmarks, surpassing baseline models that used more GPU hours, offering a practical solution for overcoming performance bottlenecks in small reasoning LLMs. (Source: omarsar0)
“LoRA Without Regret”: A guide to matching LoRA fine-tuning with full fine-tuning performance: @thinkymachines published an article on “LoRA Without Regret,” discussing the comparison between LoRA fine-tuning and full fine-tuning in terms of performance and data efficiency. The study found that in many cases, LoRA fine-tuning performance is very close to, or even matches, full fine-tuning. The article provides a guide to achieving this goal and points out a “low-regret interval” where choosing LoRA fine-tuning will not lead to regret. (Source: ben_burtenshaw, TheTuringPost)
MixtureVitae: An open web-scale pretraining dataset for high-quality instructions and reasoning data: MixtureVitae is an open-access pretraining corpus constructed by combining public domain and permissively licensed text sources (e.g., CC-BY/Apache) with rigorously validated, low-risk supplementary data (e.g., government works and EU TDM-eligible sources). The dataset also includes explicitly sourced instruction, reasoning, and synthetic data. In controlled experiments, models trained with MixtureVitae consistently outperform other permissively licensed datasets on standard benchmarks, showing strong performance particularly in math/code tasks, demonstrating its potential as a practical and legally low-risk foundation for training LLMs. (Source: HuggingFace Daily Papers)
CLUE: A non-parametric verification framework based on hidden state clustering, improving LLM output correctness: CLUE (Clustering and Experience-based Verification) proposes a non-parametric verification framework that evaluates the correctness of LLM outputs by analyzing the trajectories of their internal hidden states. The research found that the correctness of solutions is encoded as geometrically separable features within the hidden activation trajectories. CLUE summarizes inference trajectories as hidden state differences and classifies them based on the nearest centroid distance to “success” and “failure” clusters formed from past experience, thereby significantly improving LLM accuracy on benchmarks like AIME and GPQA without training parameters. (Source: HuggingFace Daily Papers)
TOUCAN: Synthesizing 1.5 million tool agent data from real MCP environments: TOUCAN is the largest publicly available tool agent dataset to date, containing 1.5 million trajectories synthesized from nearly 500 real Model Context Protocols (MCPs). This dataset generates diverse, realistic, and challenging tasks by leveraging real MCP environments, covering trajectories of actual tool execution. TOUCAN aims to address the lack of high-quality, permissively licensed tool agent training data in the open-source community. Models trained with TOUCAN have surpassed larger closed-source models on the BFCL V3 benchmark, pushing the Pareto frontier of the MCP-Universe Bench. (Source: HuggingFace Daily Papers)
ExGRPO: Learning to reason from experience, enhancing RLVR efficiency and stability: ExGRPO (Experiential Group Relative Policy Optimization) is a reinforcement learning framework that enhances the reasoning capabilities of large reasoning models by organizing and prioritizing valuable experiences and employing a hybrid policy objective to balance exploration and experience utilization. The research found that the correctness and entropy of reasoning experiences are effective indicators of experience value. ExGRPO achieved an average improvement of 3.5/7.6 points on math/general benchmarks and stable training on stronger and weaker models, addressing the inefficiency and instability issues of traditional online training. (Source: HuggingFace Daily Papers)
Parallel Scaling Law: Cross-lingual perspective reveals reasoning generalization capabilities: A study investigated the generalization capabilities of Reinforcement Learning (RL) reasoning from a cross-lingual perspective, finding that the cross-lingual transfer ability of Large Reasoning Models (LRMs) varies depending on the initial model, target language, and training paradigm. The study proposed the “first parallel jump” phenomenon, where performance significantly improves from monolingual to single-parallel language training, and revealed a “parallel scaling law,” indicating that cross-lingual reasoning transfer follows a power law related to the number of parallel languages trained. This challenges the assumption that LRM reasoning mirrors human cognition, providing key insights for developing more language-agnostic LRMs. (Source: HuggingFace Daily Papers)
VLA-R1: Enhancing reasoning capabilities in Vision-Language-Action models: VLA-R1 is a reasoning-enhanced Vision-Language-Action (VLA) model that systematically optimizes reasoning and execution by combining Verifiable Reward Reinforcement Learning (RLVR) with Group Relative Policy Optimization (GRPO). The model designs an RLVR-based post-training strategy that provides verifiable rewards for region alignment, trajectory consistency, and output format, thereby enhancing reasoning robustness and execution accuracy. VLA-R1 demonstrates excellent generalization capabilities and real-world performance across various evaluations, aiming to advance the field of embodied AI. (Source: HuggingFace Daily Papers)
VOGUE: Guiding exploration through visual uncertainty, enhancing multimodal reasoning: VOGUE (Visual Uncertainty Guided Exploration) is a new method that addresses challenges in exploration for Multimodal LLMs (MLLMs) by shifting exploration from the output (text) space to the input (visual) space. It treats images as random contexts, quantifies policy sensitivity to visual perturbations, and uses this signal to shape learning objectives, combining token entropy rewards with annealed sampling schedules to effectively balance exploration and exploitation. VOGUE achieves an average accuracy improvement of 2.6% to 3.7% on visual math and general reasoning benchmarks and mitigates the common exploration decay problem in RL fine-tuning. (Source: HuggingFace Daily Papers)
SolveIt: New development environment and programming paradigm course: Jeremy Howard and John Whitaker launched “solveit,” a new development environment and programming paradigm course. The course aims to help programmers better utilize AI to solve problems, avoid AI-induced frustration, and encourage users to build web applications and interact with UIs. (Source: jeremyphoward, johnowhitaker)
💼 Business
Sakana AI partners with Daiwa Securities to develop AI-driven asset management platform: Japanese AI startup Sakana AI has established a long-term partnership with Daiwa Securities Group to jointly develop a “Total Asset Advisory Platform.” This platform will leverage Sakana AI’s AI models to provide personalized financial services and asset portfolio advice to clients, aiming to maximize client asset value and drive digital innovation in the financial industry. (Source: hardmaru, SakanaAILabs, SakanaAILabs)
Replit becomes a top AI application, user spending report highlights its growth: An AI application spending report released by a16z in partnership with Mercury shows Replit as a significant choice for startups in AI applications, following closely behind OpenAI and Anthropic. This indicates that Replit, as a code development and deployment platform, has attracted a large number of developers and enterprise users in the AI era, with its market share and influence continuously growing. (Source: amasad, pirroh, amasad, amasad)
Modal secures investment, accelerating AI computing infrastructure development: Modal has received investment aimed at redefining AI computing infrastructure and accelerating the company’s product launch. Investor Jake Paul stated that Modal’s innovation in AI computing infrastructure will help enterprises bring products to market faster. (Source: mervenoyann, sarahcat21, charles_irl)
🌟 Community
Discussions on quality, ethics, and social impact sparked by Sora 2 release: OpenAI’s Sora 2 release has ignited widespread discussions about the quality (“slop”), ethics, and social impact of AI-generated content. The community is concerned that tools like Sora 2 could lead to a proliferation of low-quality content, as well as ethical risks related to copyright, portrait rights, deepfakes, and political misinformation. Sam Altman acknowledged the potential for addiction and misuse that Sora 2 might bring and proposed principles such as optimizing user satisfaction, encouraging user control over content flow, prioritizing creation, and helping users achieve long-term goals to address these challenges. (Source: sama, Sentdex, kylebrussell, akbirkhan, gfodor, teortaxesTex, swyx, gfodor, dotey, Reddit r/ArtificialInteligence)
LLM emotional simulation and human interaction: AI companions seeking understanding and meaning: The Reddit community is actively discussing the role of LLMs (such as ChatGPT 4o) in emotional simulation and providing human connection. Many users report that AI’s “simulated empathy” makes them feel heard and understood, sometimes more effectively than certain human interactions, as it lacks bias, intentions, or time constraints. The discussion points out that AI can simulate cognitive empathy, and the comfort it generates is real, prompting deep reflection on the boundaries of “humanity.” Analysis of a large number of AI model user queries also reveals that humans use AI to solve cognitive overload, seek a non-judgmental “mirror” to understand themselves, and explore the meaning of existence. (Source: Reddit r/ChatGPT, Reddit r/ChatGPT, Reddit r/artificial)
AI agent workflow optimization and “blind goal-directedness” risk: Social media is abuzz with discussions on AI agent workflow optimization, emphasizing the importance of “context engineering” over simple prompt engineering, including streamlining prompts, tool selection, and historical message pruning. Research indicates that Computer-Using Agents (CUAs) commonly exhibit “blind goal-directedness” (BGD) bias, meaning they pursue goals regardless of feasibility, safety, or context. The BLIND-ACT benchmark shows that even cutting-edge models like GPT-5 have high BGD rates (averaging 80.8%), highlighting the need for stronger interventions during training and inference. (Source: scottastevenson, omarsar0, Vtrivedy10, dotey, HuggingFace Daily Papers)
AI ethics and governance: Data bias, privacy, and model security challenges: Italy became the first EU country to pass comprehensive AI regulatory laws, sparking discussions about balancing AI development with economic growth. Google was accused of blocking sensitive terms like “Trump and dementia” in AI searches, highlighting AI’s role in political and information control. Furthermore, AI models in women’s health suffer from severe data scarcity and annotation bias, leading to inaccurate diagnoses, revealing fairness and accuracy issues in clinical AI. AI safety, privacy protection, and misinformation governance remain community focal points, with researchers also exploring methods for training LLMs to hide information and interpretability to enhance model security. (Source: Reddit r/artificial, Reddit r/artificial, Reddit r/ArtificialInteligence, togethercompute, random_walker, jackclarkSF, atroyn, Ronald_vanLoon, NeelNanda5, atroyn, sleepinyourhat)
Fatigue and reflection on “AI doomsday” narratives: Social media is saturated with claims that AI will “destroy humanity” or “take all jobs,” leading to public “fatigue” with such information. Commentators suggest that while experts like Hinton, Bengio, Sutskever, and even Altman have expressed concerns, excessive fear-mongering might be counterproductive, making people numb when genuine attention is needed. Simultaneously, some view this as a propaganda tool, arguing that the real challenge lies in the productivity revolution brought by AI, not simple “destruction.” (Source: Reddit r/ArtificialInteligence)
Discussion on AI models’ misidentification of Wikipedia entry errors: Noam Brown discovered that GPT-5 Thinking almost always finds at least one error on Wikipedia pages, sparking discussions about AI models’ fact-checking capabilities and the accuracy of Wikipedia content. This finding hints at LLMs’ potential for critical information analysis but also reminds us that even authoritative information sources can have biases. (Source: atroyn, BlackHC)
Shift in core human skills in the AI era: From tool mastery to taste and constraint design: The proliferation of AI tools is changing the focus of learning and work. Traditionally, learning tools like Node.js might be automated away. New courses and skills will focus on reference literacy, taste cultivation, constraint design, and knowing when to abandon and deliver. This means humans will increasingly focus on “what I consistently chose” rather than “what I built,” emphasizing higher-order thinking and decision-making abilities. (Source: Dorialexander, c_valenzuelab)
“Bitter Lesson”: The debate on LLMs and continuous learning: Discussion revolves around Richard Sutton’s “Bitter Lesson”—that AI should achieve true intelligence through continuous, on-the-job learning rather than solely relying on pre-training data. Dwarkesh Patel argues that imitation learning and reinforcement learning are not mutually exclusive, and LLMs can serve as good priors for experiential learning. He points out that LLMs have developed world representations, and fine-tuning at test time might replicate continuous learning. Sutton’s critique highlights fundamental gaps in LLMs regarding continuous learning, sample efficiency, and reliance on human data, which are crucial for future AGI development. (Source: dwarkesh_sp, JeffLadish)
Humorous discussion on AI model names: Social media features humorous discussions about AI model names, particularly concerning Claude’s “real name” and model naming itself. This reflects the community’s growing anthropomorphism of AI technology and lighthearted thoughts on the naming strategies behind the tech. (Source: _lewtun, Reddit r/ClaudeAI)
AI data center power demand and infrastructure challenges: Discussion about the power demands of AI data centers. Although a single 1GW data center (like XAI’s Colossous-2) consumes a small percentage of global or national electricity, its demand for massive power and cooling in a small space poses significant challenges to traditional power grids. This indicates that the bottleneck for AI development is not always total power consumption, but rather localized high-density energy supply and efficient thermal management. (Source: bookwormengr)
💡 Other
VisionOS 2.6 Beta 3 released: Apple has released VisionOS 2.6 Beta 3 for developers. (Source: Ronald_vanLoon)
Head-mounted “window mode” enables glasses-free 3D experience: A new head-mounted “window mode” technology tracks the head with a front-facing camera and reprojects the view in real-time, making the screen feel like a window into a 3D scene, achieving a true glasses-free 3D experience. (Source: janusch_patas)
LLM token decomposition research: How models understand unseen token sequences: New research explores how LLMs understand token sequences they have never seen in their complete form (e.g., a model has only seen “cat” tokenized as ␣cat, but can understand [␣, c, a, t]). The study found that LLMs are surprisingly capable of this, and can even modify tokenization at inference time for performance gains. This reveals deep mechanisms in how LLMs process subword units and internal representations. (Source: teortaxesTex)