Anahtar Kelimeler：Sora 2, AI video oluşturma, OpenAI, derin sahte, yaratıcı içerik, sosyal dinamikler, kişiselleştirilmiş içerik oluşturma, Sora 2 modeli, konuk oyuncu özelliği, AI yaratıcı araçlar, video etkileşim teknolojisi, içerik kötüye kullanımını önleme
🔥 Spotlight
Sora 2 Released, Leading a New Paradigm for Creative Content: OpenAI has launched Sora 2, combining the Sora 2 model with new products, aiming to become the “ChatGPT of the creative domain.” The application emphasizes rapid conversion from idea to outcome and enhances user connection through a “guest appearance” feature, allowing users to interact with friends in videos. Despite concerns about addiction and misuse (e.g., deepfakes), OpenAI is committed to exploring healthy social dynamics through principles such as 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, foreshadowing a “Cambrian explosion” in the creative industry. (Source: sama, sama)
NVIDIA Open-Sources Multiple Robotics Technologies, Accelerating Physical AI Development: NVIDIA unveiled several open-source technologies at the Robotics Learning Conference, most notably the Newton physics engine, developed in collaboration with Google DeepMind and Disney Research. This release also includes Isaac GR00T N1.6, a foundational model that imbues robots with 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 vision-language model, enabling robots to understand ambiguous instructions and engage in deep thinking. These technologies aim to solve core challenges in robotics R&D, potentially significantly accelerating the transition of robots from laboratories to daily life. (Source: 量子位)
IBM Releases Granite 4.0 Open-Source Models, Featuring Hybrid Mamba/Transformer Architecture: IBM has introduced the Granite 4.0 series of open-source language models, ranging in size from 3B to 32B. These models adopt a hybrid Mamba and Transformer architecture, significantly reducing memory requirements while maintaining high accuracy. They 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 browsers via WebGPU. Granite 4.0 H Small scores 23 in non-inference mode, outperforming 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, with added 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 Gemini API. (Source: op7418, GoogleDeepMind, demishassabis, GoogleAIStudio)
Claude Sonnet 4.5 Shows Outstanding Performance in AI Model Arena: Claude Sonnet 4.5 has tied for first place with Claude Opus 4.1 on the Text Arena leaderboard, surpassing GPT-5. User feedback indicates that Sonnet 4.5 has significantly improved in critical thinking and logical reasoning, performing exceptionally well 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, especially in general capabilities and coding tasks. (Source: scaling01, arena, Reddit r/ClaudeAI, Reddit r/ClaudeAI)
Perplexity Comet AI Browser Now Free, Launches Comet Plus Subscription: Perplexity announced that its AI web browser, Comet, is now globally free, having previously cost $200 per month. Comet aims to provide a powerful personal AI assistant and a new way to use the internet. Concurrently, Perplexity has launched the Comet Plus subscription plan, collaborating with media outlets like The Washington Post and CNN to offer content consumption services for both AI and humans, available for free to Perplexity Pro/Max users. This move aims to expand the user base and explore new AI-driven content aggregation and consumption models. (Source: AravSrinivas, AravSrinivas, AravSrinivas)
Future of LLM Architecture: Sparse vs. Linear Attention, Hybrid Architectures May Dominate: The Zhihu community is actively discussing the LLM architectural directions represented by DeepSeek-V3.2-Exp and Qwen3-Next. DeepSeek’s Sparse Attention Path (DSA) emphasizes engineering efficiency, enabling efficient operation 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 two are not in competition; rather, 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 Settings: A study indicates that in data-constrained training scenarios, Diffusion models outperform autoregressive models when computational resources are ample (more training epochs and parameters). By training hundreds of models, the research found that Diffusion models can extract more value from repeated data and exhibit significantly greater robustness 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 when high-quality data is scarce but computational resources are relatively abundant, Diffusion models are a more efficient choice, challenging the traditional notion of autoregressive models’ general superiority. (Source: aihub.org)
HTTP 402 Micropayment Concept Resurges in the AI Era: The “402 Payment Required” micropayment concept, proposed in the HTTP/1.1 protocol in 1996, is gaining renewed attention after thirty years of dormancy, driven by the rise of AI. Traditional advertising models are crumbling under the backdrop of AI-driven atomic consumption, streamed decision-making, and dehumanized entities (M2M economy). AI necessitates micro-payments for every API call, data request, and compute rental. The “three major hurdles” of high traditional credit card transaction costs, fragmented user experience, and lack of technical infrastructure are being overcome one by one by the changes brought by AI. Micropayments are poised to become the payment cornerstone of the AI economy, enabling value to return to its source, resources to flow on demand, and friction-free, millisecond-level settlement across global supply chains. (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 comprehensive solution combining beautiful UI, excellent RAG, deep research, ChatGPT-level web search, and in-depth assistant creation (with file attachments, external tools, and sharing). It supports both proprietary and open-source LLMs and can be self-hosted with a single command. The release of Onyx fills a gap in existing open-source chat tools regarding feature integration, offering developers and users a full-featured 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 against 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 has released the Claude Agent SDK, offering the same core tools, context management system, and permissions framework as Claude Code. Developers can use this SDK to build custom AI agents, enabling functionalities such as prompt-based UI planning, document retrieval, and API calls. The SDK supports built-in tools (e.g., Task, Grep, WebFetch) and custom tools, and can be integrated with MCP. Despite limitations like 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 has launched 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 such as Llama and Qwen, including large MoE models, and enables efficient resource sharing through LoRA fine-tuning. Tinker aims to make LLM post-training and RL research more accessible for researchers and developers, lowering the barrier to entry. (Source: thinkymachines, TheTuringPost)
Hex Tech Integrates Agent Features to Enhance 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 reliable data work. These features, through an Agentic approach, enhance the efficiency of data processing and analysis, enabling more people to utilize AI for complex data tasks. (Source: sarahcat21)
Yupp.ai Launches “Help Me Choose” Feature, Utilizing AI Committee for Multi-Perspective Decision Making: Yupp.ai has launched a new feature, “Help Me Choose,” which helps users synthesize different perspectives and obtain optimal answers from an “AI committee” by having multiple AIs critique 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 Time Series Analysis AI Agent: TimeSeriesScientist (TSci) is the first LLM-driven general-purpose framework for time series forecasting agents. It comprises four specialized agents: Curator, Planner, Forecaster, and Reporter, responsible for data diagnosis, model selection, fit validation, and report generation, respectively. TSci aims to address the limitations of traditional models in handling diverse, noisy data. Through transparent natural language reasoning and comprehensive reporting, it transforms forecasting workflows into an interpretable, scalable white-box system, reducing prediction 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 issues in long-context code generation through a two-stage strategy. It first performs coarse-grained compression, identifying and retaining instruction-relevant functions, 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 up to 5.6x without performance degradation, thereby 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 offers an excellent opportunity for students and practitioners of machine learning/deep learning, whether starting from scratch or filling knowledge gaps. (Source: Reddit r/MachineLearning, jeremyphoward)
RLP: Reinforcement as a Pretraining Objective to Enhance 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, with rewards based on its information gain for predicting future tokens. 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 actions, 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 breaking through the performance bottlenecks of small reasoning LLMs. (Source: omarsar0)
“LoRA Without Regret”: A Guide to Matching LoRA Fine-tuning Performance with Full Fine-tuning: @thinkymachines published an article on “LoRA Without Regret,” exploring 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 guidelines for achieving this goal and identifies a “low regret zone” where choosing LoRA fine-tuning will not lead to regret. (Source: ben_burtenshaw, TheTuringPost)
MixtureVitae: An Open Web-Scale Pretraining Dataset for High-Quality Instruction 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 licensed datasets on standard benchmarks, showing strong performance particularly on math/code tasks, demonstrating its potential as a practical and legally low-risk cornerstone for training LLMs. (Source: HuggingFace Daily Papers)
CLUE: A Non-Parametric Verification Framework Based on Hidden State Clustering to Improve LLM Output Correctness: CLUE (Clustering and Experience-based Verification) proposes a non-parametric verification framework that assesses the correctness of LLM outputs by analyzing the trajectories of their internal hidden states. The research found that the correctness of a solution is encoded as geometrically separable features within the hidden activation trajectories. CLUE significantly improves LLM accuracy on benchmarks like AIME and GPQA without requiring trained parameters, by summarizing reasoning trajectories into hidden state differences and classifying them based on the nearest centroid distance to “success” and “failure” clusters formed from past experiences. (Source: HuggingFace Daily Papers)
TOUCAN: Synthesizing 1.5 Million Tool Agent Trajectories 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 Reasoning from Experience to Enhance 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 mixed policy objective to balance exploration and experience exploitation. 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 demonstrated stable training on both 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 research 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 RLVR-based post-training strategies, providing verifiable rewards for region alignment, trajectory consistency, and output format, thereby enhancing reasoning robustness and execution accuracy. VLA-R1 demonstrates exceptional generalization capabilities and real-world performance across various evaluations, aiming to advance the field of embodied AI. (Source: HuggingFace Daily Papers)
VOGUE: Visual Uncertainty Guided Exploration to Enhance Multimodal Reasoning: VOGUE (Visual Uncertainty Guided Exploration) is a novel method that addresses challenges in multimodal LLM (MLLM) exploration by shifting exploration from the output (text) space to the input (visual) space. It treats images as stochastic contexts, quantifies policy sensitivity to visual perturbations, and uses this signal to shape learning objectives, combining token entropy rewards and 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 have launched “solveit,” a new development environment and programming paradigm course. The course aims to help programmers better leverage AI to solve problems, avoid AI-induced frustrations, 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 formed 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 Top AI Application, User Spending Report Highlights Its Growth: An AI application spending report released in collaboration by a16z and 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 to Accelerate AI Compute Infrastructure Development: Modal has received investment aimed at redefining AI compute infrastructure and accelerating the company’s product launch. Investor Jake Paul stated that Modal’s innovations in AI compute infrastructure will help businesses bring products to market faster. (Source: mervenoyann, sarahcat21, charles_irl)
🌟 Community
Sora 2 Release Sparks Discussion on Quality, Ethics, and Social Impact: OpenAI’s Sora 2 release has ignited widespread discussion regarding the quality, ethics, and social impact of AI-generated content (“slop”). The community is concerned that tools like Sora 2 could lead to a proliferation of low-quality content and ethical risks concerning 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 highlights that AI can simulate cognitive empathy, and the comfort it generates is real, prompting deep reflection on the boundaries of “humanity.” Analysis of numerous AI model user queries also reveals that humans use AI to address cognitive overload, seek non-judgmental “mirrors” for self-understanding, and explore existential meaning. (Source: Reddit r/ChatGPT, Reddit r/ChatGPT, Reddit r/artificial)
AI Agent Workflow Optimization and the Risk of “Blind Goal-Directedness”: Social media is abuzz with discussions on optimizing AI agent workflows, emphasizing the importance of “context engineering” over simple prompt engineering, including streamlining prompts, tool selection, and pruning historical messages. 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%), underscoring the necessity for stronger interventions during training and inference phases. (Source: scottastevenson, omarsar0, Vtrivedy10, dotey, HuggingFace Daily Papers)
AI Ethics and Governance: Challenges of Data Bias, Privacy, and Model Security: Italy has become the first EU country to pass comprehensive AI regulatory laws, sparking discussions about balancing AI development with economic growth. Google is 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 gaps and annotation biases, leading to inaccurate diagnoses, revealing issues of fairness and accuracy in clinical AI. AI safety, privacy protection, and misinformation governance remain key community concerns, and researchers are 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 Scenarios”: Social media is saturated with claims that AI will “destroy humanity” or “take all jobs,” leading to public “fatigue” with such information. Comments suggest that while experts like Hinton, Bengio, Sutskever, and even Altman have expressed concerns, excessive fear-mongering can be counterproductive, desensitizing people when genuine attention is needed. Concurrently, some view this as a propaganda tool, arguing that the true challenge lies in the productivity revolution brought by AI, rather than simple “destruction.” (Source: Reddit r/ArtificialInteligence)
Discussion on AI Models Identifying Errors in Wikipedia Articles: 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 replaced by automation. New curricula and skills will focus on reference literacy, taste cultivation, constraint design, and knowing when to abandon and deliver. This implies that 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)
“The 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 learning (on-the-job learning) rather than solely relying on pre-trained 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 notes that LLMs have developed world representations, and test-time fine-tuning 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 regarding Claude’s “real name” and model naming conventions themselves. This reflects the community’s increasing anthropomorphization 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 revolves around the power demands of AI data centers. Although a single 1GW data center (like XAI’s Colossous-2) consumes a small percentage of electricity at a global or national level, its demand for massive power and cooling within a small footprint poses significant challenges to traditional power grids. This indicates that the bottleneck for AI development is not total electricity 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 to developers. (Source: Ronald_vanLoon)
Head-Mounted “Window Mode” Achieves 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, thereby achieving a true glasses-free 3D experience. (Source: janusch_patas)
LLM Token Decomposition Research: How Models Understand Unseen Token Sequences: A new study investigates 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 research found that LLMs are surprisingly capable of doing this, and can even modify tokenization at inference time for performance gains. This reveals the deep mechanisms of LLMs in processing subword units and internal representations. (Source: teortaxesTex)