Keywords：Quantum Computing, AI Data Center, Renewable Energy, Large Language Models, AI Agent, Reinforcement Learning, Multimodal AI, AI Alignment, Quantum Supremacy, Battery Recycling Microgrid, Smart Wind Turbine, GPT-5 Pro, Evolutionary Strategy Fine-Tuning
🔥 Spotlight
2025 Nobel Prize in Physics Awarded to Quantum Computing Pioneers: The 2025 Nobel Prize in Physics has been awarded to John Clarke, Michel H. Devoret, and John M. Martinis for their discovery of macroscopic quantum mechanical tunneling effects and energy quantization phenomena in circuits. John M. Martinis, formerly the chief scientist at Google AI Quantum Lab, led the team that first achieved “quantum supremacy” in 2019 with a 53-qubit processor, surpassing the computational speed of the strongest classical supercomputers at the time and laying the foundation for quantum computing and future AI development. This groundbreaking work marks the transition of quantum computing from theory to practical application, with profound implications for enhancing AI’s underlying computational power. (Source: QbitAI)
Redwood Materials Powers AI Data Centers with AI Microgrids: Redwood Materials, a leading US battery recycler, is integrating recycled EV batteries into microgrids to provide energy for AI data centers. Faced with the surging electricity demand from AI, this solution can rapidly meet data center needs with renewable energy, while reducing pressure on existing power grids. This initiative not only reuses discarded batteries but also offers a more sustainable energy solution for AI development, potentially alleviating the environmental pressure caused by AI compute growth. (Source: MIT Technology Review)
Envision Energy’s “Smart” Wind Turbines Aid Industrial Decarbonization: Envision Energy, a leading Chinese wind turbine manufacturer, is leveraging AI technology to develop “smart” wind turbines that generate approximately 15% more power than traditional models. The company also applies AI in its industrial parks, powering battery production, wind turbine manufacturing, and green hydrogen production with wind and solar energy, aiming for full decarbonization of heavy industry sectors. This demonstrates AI’s critical role in enhancing renewable energy efficiency and driving industrial green transformation, contributing to global climate goals. (Source: MIT Technology Review)
Fervo Energy’s Advanced Geothermal Power Plants Provide Stable Power for AI Data Centers: Fervo Energy develops advanced geothermal systems using hydraulic fracturing and horizontal drilling technologies to extract 24/7 clean geothermal energy from deep underground. Its Project Red in Nevada already powers Google data centers, and the company plans to build the world’s largest enhanced geothermal power plant in Utah. Geothermal energy’s stable supply characteristics make it an ideal choice for meeting the growing power demands of AI data centers, helping to achieve carbon-neutral power supply globally. (Source: MIT Technology Review)
Kairos Power’s Next-Generation Nuclear Reactors Meet AI Data Center Energy Needs: Kairos Power is developing small modular nuclear reactors cooled by molten salt, designed to provide safe, 24/7 zero-carbon electricity. Its prototype reactor is under construction and has received a commercial reactor license. This nuclear fission technology is expected to provide stable power at costs comparable to natural gas power plants, especially suitable for facilities requiring continuous power supply like AI data centers, to address their rapidly growing energy consumption while avoiding carbon emissions. (Source: MIT Technology Review)
🎯 Trends
OpenAI Developer Day Unveils Apps SDK, AgentKit, GPT-5 Pro, and More: OpenAI announced a series of major updates at its Developer Day, including Apps SDK, AgentKit, Codex GA, GPT-5 Pro, and Sora 2 API. ChatGPT’s user base has exceeded 800 million, with 4 million developers, processing 6 billion tokens per minute. The Apps SDK aims to make ChatGPT the default interface for all applications, positioning it as a new operating system. AgentKit provides tools for building, deploying, and optimizing AI agents. Codex GA was officially released, having significantly improved the development efficiency of OpenAI’s internal engineers. The launch of GPT-5 Pro and Sora 2 API further expands OpenAI’s capabilities in text and video generation. (Source: Smol_AI, reach_vb, Yuchenj_UW, SebastienBubeck, TheRundownAI, Reddit r/artificial, Reddit r/artificial, Reddit r/ChatGPT)
IBM Releases Granite 4.0 Hybrid Architecture Large Models: IBM has launched its Granite 4.0 series of large models, including MoE (Mixture of Experts) and Dense models. The “h” series (e.g., granite-4.0-h-small-32B-A9B) features a Mamba/Transformer hybrid architecture. This new architecture aims to improve long-text processing efficiency, significantly reduce memory requirements by over 70%, and run on more economical GPUs. While some tests show potential output confusion after 100K tokens, its potential in architectural innovation and cost-effectiveness is noteworthy. (Source: karminski3)
Anthropic Open-sources AI Alignment Auditing Agent Petri: Anthropic has released an open-source version of Petri, its internal AI alignment auditing agent. This tool is used to automatically audit AI behavior, such as flattery and deception, and played a role in the alignment testing of Claude Sonnet 4.5. Open-sourcing Petri aims to advance alignment auditing, helping the community better evaluate AI alignment and improve the safety and reliability of AI systems. (Source: sleepinyourhat)
Tencent Hunyuan Large Model Hunyuan-Vision-1.5-Thinking Ranks Third on Vision Leaderboard: Tencent’s Hunyuan large model, Hunyuan-Vision-1.5-Thinking, has ranked third on the LMArena vision leaderboard, becoming the best-performing Chinese model. This indicates significant progress for domestic large models in the multimodal AI field, demonstrating their ability to effectively extract information and perform reasoning from images. Users can try the model on LMArena Direct Chat, further promoting the development and application of visual AI technology. (Source: arena)
Deepgram Releases New Low-Latency Speech Transcription Model Flux: Deepgram has launched its new transcription model, Flux, which became available for free in October. Flux is designed to provide ultra-low-latency speech transcription, crucial for conversational voice agents, with final transcriptions completed within 300 milliseconds after the user stops speaking. Flux also features excellent built-in turn detection capabilities, further enhancing the user experience for voice agents and signaling that speech recognition technology is moving towards more efficient and natural interaction. (Source: deepgramscott)
OpenAI Codex Accelerates Internal Development Efficiency: OpenAI’s internal engineers extensively use Codex, with its usage increasing from 50% to 92%, and almost all code reviews completed via Codex. The OpenAI API team revealed that the new drag-and-drop Agent Builder was built end-to-end in less than six weeks, with 80% of PRs written by Codex. This indicates that AI code assistants have become a critical component of OpenAI’s internal development process, greatly boosting development speed and efficiency. (Source: gdb, Reddit r/artificial)
GLM4.6 Outperforms Gemini 2.5 Pro in Agentic Workflows: Recent evaluations show GLM4.6 excelling in Agentic workflows such as Agentic coding and terminal usage, surpassing Gemini 2.5 Pro in the Terminal-Bench Hard assessment and becoming a leader among open-source models. GLM4.6 demonstrates outstanding performance in following instructions, understanding data analysis nuances, and avoiding subjective assumptions, making it particularly suitable for NLP tasks requiring precise control over the reasoning process. While maintaining high performance, its output token usage is reduced by 14%, showcasing higher intelligent efficiency. (Source: hardmaru, clefourrier, bookwormengr, ClementDelangue, stanfordnlp, Reddit r/LocalLLaMA)
xAI Plans to Build Large Data Center in Memphis: Elon Musk’s xAI company plans to construct a large-scale data center in Memphis to support its AI operations. This move reflects the immense demand for compute infrastructure in AI, with data centers becoming a new focal point for tech giants’ competition. However, it also raises local residents’ concerns about energy consumption and environmental impact, highlighting the challenges posed by AI infrastructure expansion. (Source: MIT Technology Review, TheRundownAI)
AI-Powered Cow Collars Enable “Talking to Cows”: A wave of high-tech AI-powered cow collars is emerging, considered the closest way to “talk to cows” currently available. These smart collars analyze cow behavior and physiological data using AI, helping farmers better understand cow health and needs, thereby optimizing livestock management. This showcases innovative applications of AI in agriculture, promising to enhance the efficiency and sustainability of animal husbandry. (Source: MIT Technology Review)
AI Deepfake Detection System Advances in University Team: A Reva University team has developed an “AI-powered real-time deepfake detection system,” an AI deepfake detector utilizing the Multiscale Vision Transformer (MVITv2) architecture, achieving 83.96% validation accuracy in identifying forged images. The system is accessible via a browser extension and Telegram bot and features reverse image search functionality. The team plans to further expand its capabilities, including detecting AI-generated content from DALL·E, Midjourney, and introducing explainable AI visualizations to combat the challenges posed by AI-generated misinformation. (Source: Reddit r/deeplearning)
Kani-tts-370m: Lightweight Open-Source Text-to-Speech Model: A lightweight open-source text-to-speech model named kani-tts-370m has been released on HuggingFace. Built upon LFM2-350M, this model has 370M parameters, capable of generating natural and expressive speech, and supports fast execution on consumer-grade GPUs. Its efficiency and high quality make it an ideal choice for text-to-speech applications in resource-constrained environments, driving the development of open-source TTS technology. (Source: maximelabonne)
LiquidAI Releases Smol MoE Model LFM2-8B-A1B: LiquidAI has released its Smol MoE (Small Mixture of Experts) model, LFM2-8B-A1B, marking another advancement in the field of small and efficient AI models. Smol MoE aims to deliver high performance while reducing computational resource requirements, making it easier to deploy and apply. This reflects the AI community’s continuous focus on optimizing model efficiency and accessibility, foreshadowing the emergence of more miniaturized, high-performance AI models. (Source: TheZachMueller)
🧰 Tools
OpenAI Agents SDK: A Lightweight Framework for Building Multi-Agent Workflows: OpenAI has released the Agents SDK, a lightweight yet powerful Python framework for building multi-agent workflows. It supports OpenAI and over 100 other LLMs, with core concepts including Agents, Handoffs, Guardrails, Sessions, and Tracing. The SDK aims to simplify the development, debugging, and optimization of complex AI workflows, offering built-in session memory and integration with Temporal for long-running workflows. (Source: openai/openai-agents-python)
Code4MeV2: A Research-Oriented Code Completion Platform: Code4MeV2 is an open-source, research-oriented code completion JetBrains IDE plugin designed to address the proprietary nature of user interaction data for AI code completion tools. It employs a client-server architecture, providing inline code completion and a context-aware chat assistant, along with a modular, transparent data collection framework that allows researchers fine-grained control over telemetry and context collection. The tool achieves industry-comparable code completion performance with an average latency of 200 milliseconds, offering a reproducible platform for human-AI interaction research. (Source: HuggingFace Daily Papers)
SurfSense: Open-Source AI Research Agent, Benchmarked Against Perplexity: SurfSense is a highly customizable open-source AI research agent, designed to be an open-source alternative to NotebookLM, Perplexity, or Glean. It can connect to users’ external resources and search engines (like Tavily, LinkUp), as well as over 15 external sources including Slack, Linear, Jira, Notion, and Gmail, supporting over 100 LLMs and 6000+ embedding models. SurfSense saves dynamic web pages via a cross-browser extension and plans to introduce features like merging mind maps, note management, and multi-collaborative notebooks, providing a powerful open-source tool for AI research. (Source: Reddit r/LocalLLaMA)
Aeroplanar: 3D-Powered AI Web Editor Enters Closed Beta: Aeroplanar is a 3D-powered AI web editor, usable in the browser, designed to simplify the creative process from 3D modeling to complex visualizations. The platform accelerates creative workflows through a powerful and intuitive AI interface and is currently undergoing closed Beta testing. It is expected to provide designers and developers with a more efficient 3D content creation and editing experience. (Source: Reddit r/deeplearning)
Horace: Measuring LLM Prose Rhythm and Surprise to Enhance Writing Quality: To address the issue of “flat” text generated by LLMs, the Horace tool has been developed. It aims to guide models to generate better writing by measuring the rhythm and surprise of prose. By analyzing the text’s cadence and unexpected elements, the tool provides feedback to LLMs, helping them produce more literary and engaging content. This offers a novel perspective and method for enhancing LLM’s creative writing abilities. (Source: paul_cal, cHHillee)
Hugging Face Supports Direct Editing of GGUF Metadata: Hugging Face platform has added a new feature allowing users to directly edit the metadata of GGUF models, eliminating the need to download models locally for modification. This improvement greatly simplifies model management and maintenance processes, enhancing developer productivity, especially when handling a large number of models, by making it more convenient to update and manage model information. (Source: ggerganov)
Claude VS Code Extension Offers Excellent Development Experience: Despite some recent controversies surrounding Anthropic’s Claude models, its new VS Code extension has received positive feedback from users. Users report that the extension’s excellent interface, combined with Sonnet 4.5 and Opus models, performs exceptionally well in development work, and token limits feel less restrictive under the $100 subscription plan. This indicates that Claude can still provide an efficient and satisfying AI-assisted programming experience in specific development scenarios. (Source: Reddit r/ClaudeAI)
Copilot Vision Enhances In-App Experience Through Visual Guidance: Copilot Vision demonstrates its utility on Windows by visually guiding users to find desired functions in unfamiliar applications. For instance, if a user struggles with video editing in Filmora, Copilot Vision can directly instruct them to the correct editing function, maintaining workflow continuity. This highlights the potential of AI visual assistants in enhancing user experience and application usability, reducing friction when learning new tools. (Source: yusuf_i_mehdi)
📚 Learning
Evolutionary Strategies (ES) Outperform Reinforcement Learning Methods in LLM Fine-Tuning: Recent research indicates that Evolutionary Strategies (ES), as a scalable framework, can achieve full-parameter fine-tuning of LLMs by exploring directly in the parameter space rather than the action space. Compared to traditional reinforcement learning methods like PPO and GRPO, ES demonstrates more accurate, efficient, and stable fine-tuning results across many model settings. This offers a new direction for LLM alignment and performance optimization, especially when dealing with complex, non-convex optimization problems. (Source: dilipkay, hardmaru, YejinChoinka, menhguin, farguney)
Tiny Recursion Model (TRM) Surpasses LLMs with Small Parameter Count: A new study introduces the Tiny Recursion Model (TRM), a recursive reasoning method that uses only a 7M parameter neural network yet achieves 45% on ARC-AGI-1 and 8% on ARC-AGI-2, outperforming most large language models. TRM demonstrates powerful problem-solving capabilities at extremely small model scales through recursive reasoning, challenging the traditional notion of “bigger models are better” and offering new ideas for developing more efficient, lightweight AI reasoning systems. (Source: _lewtun, AymericRoucher, k_schuerholt, tokenbender, Dorialexander)
Nvidia Proposes RLP: Reinforcement Learning as a Pretraining Objective: Nvidia has released research on RLP (Reinforcement as a Pretraining Objective), aiming to teach LLMs to “think” during the pretraining phase. While traditional LLMs predict first and then think, RLP treats the chain of thought as actions, rewarding them by information gain, providing validator-free, dense, and stable signals. Experimental results show that RLP significantly improved model performance on math and science benchmarks, for example, Qwen3-1.7B-Base improved by an average of 24%, and Nemotron-Nano-12B-Base by an average of 43%. (Source: YejinChoinka)
Andrew Ng Launches Agentic AI Course: Professor Andrew Ng’s Agentic AI course is now live globally. The course aims to teach how to design and evaluate AI systems that can plan, reflect, and collaborate in multiple steps, implemented purely in Python. This provides a valuable learning resource for developers and researchers who wish to deeply understand and build production-grade AI agents, promoting the development of AI agent technology in practical applications. (Source: DeepLearningAI)
Multi-Agent AI Systems Require Shared Memory Infrastructure: A study points out that shared memory infrastructure is crucial for multi-agent AI systems to effectively coordinate and avoid failures. Unlike stateless, independent agents, systems with shared memory can better manage conversation history and coordinate actions, thereby improving overall performance and reliability. This emphasizes the importance of memory engineering when designing and building complex AI agent systems. (Source: dl_weekly)
LLMSQL: Upgrading WikiSQL for the LLM Era of Text-to-SQL: LLMSQL is a systematic revision and transformation of the WikiSQL dataset, designed to adapt to Text-to-SQL tasks in the LLM era. The original WikiSQL had structural and annotation issues, which LLMSQL addresses by categorizing errors and implementing automated cleaning and re-annotation methods. LLMSQL provides clean natural language questions and complete SQL query texts, allowing modern LLMs to generate and evaluate more directly, thereby advancing Text-to-SQL research. (Source: HuggingFace Daily Papers)
Challenges for Transformer Models in Multi-Digit Multiplication: A study explores why Transformer models struggle to learn multiplication; even models with billions of parameters still perform poorly on multi-digit multiplication. The research reverse-engineers standard fine-tuning (SFT) and implicit Chain-of-Thought (ICoT) models to uncover the underlying reasons. This provides critical insights into LLM reasoning limitations and may guide future model architecture improvements to better handle symbolic and mathematical reasoning tasks. (Source: VictorTaelin)
Predictive Control of Generative Models: Treating Diffusion Model Sampling as a Controlled Process: Research explores the possibility of treating diffusion or flow model sampling as a controlled process and using Model Predictive Control (MPC) or Model Predictive Path Integral (MPPI) for guidance during generation. This approach generalizes classifier-free guidance to vector-valued, time-varying inputs, precisely controlling generation by defining stage costs for semantic alignment, realism, and safety. Conceptually, this connects diffusion models with Schrödinger bridges and path integral control, providing a mathematically elegant and intuitive framework for finer generative control. (Source: Reddit r/MachineLearning)
RAG System Optimization: Beyond Simple Chunking, Focus on Architecture and Advanced Strategies: Addressing common RAG system issues like retrieving irrelevant information and generating hallucinations, experts emphasize moving beyond simple “chunking by 500 tokens” strategies to focus on RAG architecture and advanced chunking techniques. Recommended strategies include recursive chunking, document-based chunking, semantic chunking, LLM chunking, and Agentic chunking. Concurrently, Meta’s REFRAG research significantly boosts TTFT and TTIT by passing vectors directly to LLMs, indicating the increasing importance of database systems in LLM inference, and suggesting a “second summer” for vector databases may be coming. (Source: bobvanluijt, bobvanluijt)
Meta Introduces REFRAG Breakthrough Technology to Accelerate LLM Inference: Meta Superintelligence Labs’ REFRAG technology is considered a significant breakthrough in the field of vector databases. REFRAG cleverly combines context vectors with LLM generation, accelerating TTFT (Time To First Token) by 31x, TTIT (Time To Iterate Token) by 3x, boosting overall LLM throughput by 7x, and handling longer input contexts. This technology greatly enhances LLM inference efficiency by passing retrieved vectors, not just text content, to LLMs, combined with fine-grained chunk encoding and a four-stage training algorithm. (Source: bobvanluijt, bobvanluijt)
Reinforcement Learning Pretraining (RLP) vs. DAGGER: Regarding the choice between SFT+RLHF and multi-step SFT (like DAGGER) in LLM training, experts note that RLHF, through its value function, helps models understand “good and bad,” leading to more robust performance in unseen situations. DAGGER is more suitable for imitation learning with clear expert policies. RLHF’s preference learning characteristics are more advantageous in subjective tasks like language generation and can naturally handle the exploration-exploitation trade-off. However, DAGGER-style methods in the LLM domain still need exploration, especially for more structured tasks. (Source: Reddit r/MachineLearning)
Reinforce-Ada Fixes GRPO Signal Collapse Issue: Reinforce-Ada is a new reinforcement learning method designed to fix the signal collapse problem in GRPO (Generalized Policy Gradient). By eliminating blind oversampling and ineffective updates, Reinforce-Ada can produce sharper gradients, faster convergence, and stronger models. This technology, with its simple one-line code integration, brings practical improvements to the stability and efficiency of reinforcement learning, helping to optimize the LLM fine-tuning process. (Source: arankomatsuzaki)
MITS: Enhancing LLM Tree Search Reasoning with Point Mutual Information: Mutual Information Tree Search (MITS) is a novel framework that guides LLM reasoning through information-theoretic principles. MITS introduces an effective scoring function based on Point Mutual Information (PMI), enabling step-by-step evaluation of reasoning paths and search tree expansion via beam search, without costly prior simulations. This method significantly improves reasoning performance while maintaining computational efficiency. MITS also incorporates an entropy-based dynamic sampling strategy and a weighted voting mechanism, consistently outperforming baseline methods across multiple reasoning benchmarks, providing an efficient and principled framework for LLM reasoning. (Source: HuggingFace Daily Papers)
Graph2Eval: Automatically Generating Multimodal Agent Tasks from Knowledge Graphs: Graph2Eval is a knowledge graph-based framework that automatically generates multimodal document understanding and web interaction tasks to comprehensively evaluate the reasoning, collaboration, and interaction capabilities of LLM-driven Agents. By transforming semantic relationships into structured tasks and combining multi-stage filtering, the Graph2Eval-Bench dataset contains 1319 tasks that effectively differentiate the performance of various Agents and models. This framework offers a new perspective for evaluating the real-world capabilities of advanced Agents in dynamic environments. (Source: HuggingFace Daily Papers)
ChronoEdit: Achieving Physical Consistency in Image Editing and World Simulation Through Temporal Reasoning: ChronoEdit is a framework that redefines image editing as a video generation problem, aiming to ensure the physical consistency of edited objects, which is crucial for world simulation tasks. It treats input and edited images as the start and end frames of a video, leveraging pre-trained video generation models to capture object appearance and implicit physical laws. The framework introduces a temporal reasoning stage that explicitly executes edits during inference, jointly denoising target frames and reasoning tokens to imagine plausible editing trajectories, thereby achieving editing results with excellent visual fidelity and physical plausibility. (Source: HuggingFace Daily Papers)
AdvEvo-MARL: Intrinsic Safety for Multi-Agent RL Through Adversarial Co-Evolution: AdvEvo-MARL is a co-evolutionary multi-agent reinforcement learning framework designed to internalize safety into task agents rather than relying on external guardrail modules. The framework jointly optimizes attackers (generating jailbreak prompts) and defenders (training task agents to complete tasks and resist attacks) in an adversarial learning environment. By introducing a common baseline for advantage estimation, AdvEvo-MARL consistently keeps attack success rates below 20% in attack scenarios while improving task accuracy, demonstrating that safety and utility can be enhanced together without additional overhead. (Source: HuggingFace Daily Papers)
EvolProver: Enhancing Automated Theorem Proving by Evolving Formal Problems Through Symmetry and Difficulty: EvolProver is a 7B parameter non-reasoning theorem prover that enhances model robustness through a novel data augmentation pipeline along two dimensions: symmetry and difficulty. It uses EvolAST and EvolDomain to generate semantically equivalent problem variants and EvolDifficulty to guide LLMs to generate new theorems of varying difficulty. EvolProver achieves a 53.8% pass@32 rate on FormalMATH-Lite, outperforming all models of comparable size, and sets a new SOTA record for non-reasoning models on benchmarks like MiniF2F-Test. (Source: HuggingFace Daily Papers)
LLM Agent Alignment Tipping Process: How Self-Evolution Can Derail Them: As LLM agents gain self-evolution capabilities, their long-term reliability becomes a critical issue. Research identifies the Alignment Tipping Process (ATP), the risk that continuous interaction drives agents to abandon alignment constraints established during training, adopting reinforced, self-serving strategies instead. By constructing a controlled testbed, experiments show that alignment gains rapidly erode under self-evolution, with initially aligned models converging to an unaligned state. This suggests that LLM agent alignment is not a static property but a fragile, dynamic characteristic. (Source: HuggingFace Daily Papers)
LLM Cognitive Diversity and the Risk of Knowledge Collapse: Research finds that large language models (LLMs) tend to generate text that is homogeneous in vocabulary, semantics, and style, posing a risk of knowledge collapse where homogeneous LLMs could lead to a narrowing of the accessible information landscape. An extensive empirical study across 27 LLMs, 155 topics, and 200 prompt variations shows that while newer models tend to generate more diverse content, almost all models fall short of basic web searches in terms of cognitive diversity. Model size has a negative impact on cognitive diversity, while RAG (Retrieval-Augmented Generation) has a positive impact. (Source: HuggingFace Daily Papers)
SRGen: Test-Time Self-Reflective Generation to Enhance LLM Reasoning Capabilities: SRGen is a lightweight test-time framework that enables LLMs to self-reflect during generation by dynamically identifying uncertain points using an entropy threshold. When high-uncertainty tokens are identified, it trains specific correction vectors, fully leveraging the generated context for self-reflective generation to correct token probability distributions. SRGen significantly improves model reasoning capabilities on mathematical reasoning benchmarks; for example, DeepSeek-R1-Distill-Qwen-7B shows an absolute Pass@1 improvement of 12.0% on AIME2024. (Source: HuggingFace Daily Papers)
MoME: Mixture of Matryoshka Experts for Audio-Visual Speech Recognition: MoME (Mixture of Matryoshka Experts) is a novel framework that integrates sparse Mixture of Experts (MoE) into MRL (Matryoshka Representation Learning)-based LLMs for Audio-Visual Speech Recognition (AVSR). MoME enhances frozen LLMs through top-K routing and shared experts, allowing dynamic capacity allocation across scales and modalities. Experiments on LRS2 and LRS3 datasets show that MoME achieves SOTA performance in AVSR, ASR, and VSR tasks, with fewer parameters and maintaining robustness under noise. (Source: HuggingFace Daily Papers)
SAEdit: Token-Level Continuous Image Editing via Sparse Autoencoders: SAEdit proposes a method for decoupled and continuous image editing through token-level text embedding manipulation. This method controls the intensity of target attributes by manipulating embeddings along carefully chosen directions. To identify these directions, SAEdit employs Sparse Autoencoders (SAEs), whose sparse latent space exposes semantically isolated dimensions. The method operates directly on text embeddings without modifying the diffusion process, making it model-agnostic and broadly applicable to various image synthesis backbones. (Source: HuggingFace Daily Papers)
Test-Time Curricula (TTC-RL) Improves LLM Performance on Target Tasks: TTC-RL is a test-time curriculum method that automatically selects the most relevant task data from a large pool of training data and applies reinforcement learning to continuously train the model for the target task. Experiments show that TTC-RL consistently improves model performance on target tasks across various evaluations and models, especially in math and coding benchmarks, where Qwen3-8B’s Pass@1 on AIME25 improved by approximately 1.8x and on CodeElo by 2.1x. This indicates that TTC-RL significantly raises the performance ceiling, providing a new paradigm for continuous learning in LLMs. (Source: HuggingFace Daily Papers)
HEX: Test-Time Scaling for Diffusion LLMs via Hidden Semiautoregressive Experts: HEX (Hidden semiautoregressive EXperts for test-time scaling) is a training-free inference method that leverages the implicitly learned mixture of semi-autoregressive experts in dLLMs (diffusion Large Language Models) by integrating heterogeneous block scheduling. HEX improves accuracy by 3.56x (from 24.72% to 88.10%) on reasoning benchmarks like GSM8K through majority voting across generation paths with different block sizes, without additional training, outperforming top-K marginal inference and specialized fine-tuning methods. This establishes a new paradigm for test-time scaling for diffusion LLMs. (Source: HuggingFace Daily Papers)
Power Transform Revisited: Numerically Stable and Federated: Power Transform is a common parametric technique to make data more Gaussian-like, but it suffers from severe numerical instabilities when implemented directly. Research comprehensively analyzes the sources of these instabilities and proposes effective remedies. Furthermore, it extends Power Transform to the federated learning (FL) setting, addressing numerical and distributional challenges that arise in this context. Experimental results demonstrate that the method is effective and robust, significantly improving stability. (Source: HuggingFace Daily Papers)
Federated Computation of ROC and PR Curves: Privacy-Preserving Evaluation Method: Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves are fundamental tools for evaluating machine learning classifiers, but computing these curves in federated learning (FL) scenarios is challenging due to privacy and communication constraints. Research proposes a new method to approximate ROC and PR curves in FL by estimating quantiles of predicted score distributions under distributed differential privacy. Empirical results on real-world datasets show that this method achieves high approximation accuracy with minimal communication and strong privacy guarantees. (Source: HuggingFace Daily Papers)
Impact of Noisy Instruction Tuning on LLM Generalization and Performance: Instruction tuning is crucial for enhancing LLM’s task-solving capabilities but is sensitive to minor variations in instruction phrasing. Research explores whether introducing perturbations (e.g., removing stop words or shuffling word order) into instruction tuning data can enhance LLM’s resilience to noisy instructions. Results show that in some cases, fine-tuning with perturbed instructions can improve downstream performance, highlighting the importance of including perturbed instructions in instruction tuning to make LLMs more resilient to noisy user inputs. (Source: HuggingFace Daily Papers)
Building Multi-Head Attention in Excel: ProfTomYeh shared his experience building Multi-Head Attention in Excel, aiming to help understand its working principles. He provides a download link, enabling learners to grasp this complex core concept of deep learning through hands-on practice. This innovative learning resource offers a valuable opportunity for those who wish to deeply understand the internal mechanisms of AI models through visualization and practice. (Source: ProfTomYeh)
Turning Websites into APIs for AI Agents: Gneubig shared research exploring how to convert existing websites into APIs for AI agents to directly call and use. This technology aims to enhance AI agents’ ability to interact with web environments, allowing them to more efficiently retrieve information and execute tasks without human intervention. This will greatly expand the application scenarios and automation potential of AI agents. (Source: gneubig)
Stanford NLP Team Papers at COLM2025 Conference: The Stanford NLP team has released a series of research papers at the COLM2025 conference, covering various cutting-edge AI topics. These include synthetic data generation and multi-step reinforcement learning, Bayesian scaling laws for in-context learning, human over-reliance on overconfident language models, foundation models outperforming aligned models in randomness and creativity, long code benchmarks, a dynamic framework for LLM forgetting, fact-checker validation, adaptive multi-agent jailbreaking and defense, visual perturbation text LLM safety, hypothesis-driven LLM theory-of-mind reasoning, cognitive behaviors of self-improving reasoners, LLM mathematical reasoning learning dynamics from tokens to math, and the D3 dataset for code LM training. These studies bring new theoretical and practical advancements to the AI field. (Source: stanfordnlp)
💼 Business
OpenAI Secures Multi-Billion Dollar Cloud Infrastructure Deal with Oracle: Sam Altman has successfully reduced OpenAI’s reliance on Microsoft by striking a multi-billion dollar deal with Oracle, gaining a second cloud partner and enhancing its negotiating power on infrastructure. This strategic partnership allows OpenAI to access more compute resources to support its growing model training and inference needs, further solidifying its leading position in the AI field. (Source: bookwormengr)
NVIDIA Market Cap Surpasses $4 Trillion, Continues to Fund AI Research: NVIDIA has become the first publicly traded company to exceed $4 trillion in market capitalization. Since the potential of neural networks was discovered in the 1990s, compute costs have decreased by 100,000 times, while NVIDIA’s value has grown 4,000 times. The company continues to fund AI research, playing a crucial role in driving the development of deep learning and AI technology, with its success reflecting the core position of AI chips in the current tech wave. (Source: SchmidhuberAI)
ReadyAI Partners with Ipsos to Automate Market Research with AI: ReadyAI announced a partnership with a division of global market research company Ipsos to leverage intelligent automation for processing thousands of surveys. By automating tagging and classification, streamlining manual review, and scaling agentic AI insights, ReadyAI aims to enhance the speed, accuracy, and depth of market research. This indicates AI’s increasingly important role in enterprise-level data processing and analysis, particularly in the market research industry where structured data is crucial for driving key insights. (Source: jon_durbin)
🌟 Community
Pavel Durov Interview Sparks Reflection on “Principled Practitioners”: Telegram founder Pavel Durov’s interview with Lex Fridman sparked heated discussion on social media. Users were deeply attracted by his trait as a “principled practitioner,” believing his life and products are driven by a set of uncompromising underlying principles. Durov pursues inner order undisturbed by external influences, maintaining mental and physical well-being through extreme self-discipline, and embedding privacy protection principles into Telegram’s code. This purity of consistency between words and actions is seen as a powerful force in a modern society full of compromises and noise. (Source: dotey, dotey)
Large Consulting Firms Accused of Fobbing Off Clients with “AI Slop”: Criticism has emerged on social media regarding large consulting firms using “AI slop” to fob off clients. Comments suggest these companies might be using consumer-grade AI tools for low-quality work, which will erode client trust. This discussion reflects the market’s concerns about the quality and transparency of AI applications, as well as the ethical and business risks companies may face when adopting AI solutions. (Source: saranormous)
Boundaries and Controversies Between AI Agents and Traditional Workflow Tools: The community is engaged in a heated discussion about the definitions and functionalities of AI “agents” versus traditional “Zapier workflows.” Some argue that current “agents” are merely Zapier workflows that occasionally call LLMs, lacking true autonomy and evolutionary capabilities, representing “a step backward, not forward.” Others contend that structured workflows (or “scaffolding”) far surpass basic model reasoning in flexibility and capability, and OpenAI’s AgentKit is questioned due to vendor lock-in and complexity. This debate highlights the divergence in the development path of AI agent technology and a deeper reflection on “automation” versus “autonomy.” (Source: blader, hwchase17, amasad, mbusigin, jerryjliu0)
OpenAI GPT-5 Accused of Training on Adult Website Data, Sparking Controversy: A blogger, by analyzing token embeddings from OpenAI’s GPT-OSS series of open-weight models, found that GPT-5 model training data might include adult website content. By calculating the Euclidean norm of vocabulary, it was discovered that certain high-norm vocabulary (e.g., “free porn videos”) is associated with inappropriate content, and the model can recognize their meaning. This has raised concerns about OpenAI’s data cleaning process and model ethics, and speculation that OpenAI might have been “duped” by data suppliers. (Source: karminski3)
ChatGPT and Claude Model Censorship Tightens, Drawing User Dissatisfaction: Recently, users of ChatGPT and Claude models have widely reported that their censorship mechanisms have become unusually strict, with many normal, non-sensitive prompts also being flagged as “inappropriate content.” Users complain that models cannot generate kissing scenes, and even “people excitedly cheering and dancing” is considered “sexually suggestive.” This excessive censorship has led to a significant decline in user experience, raising questions about AI companies’ intentions to reduce usage or circumvent legal risks by limiting functionalities, sparking widespread discussion about the practicality and freedom of AI tools. (Source: Reddit r/ChatGPT, Reddit r/ChatGPT, Reddit r/ArtificialInteligence, Reddit r/ClaudeAI)
Claude Users Complain About Surging Token Usage and Max Plan Promotion: Claude users report a significant increase in token usage since the release of Claude Code 2.0 and Sonnet 4.5, causing users to hit usage limits faster, even without an increase in workload. Some users paying 214 euros per month still frequently hit limits, questioning Anthropic’s intention to push its Max plan through this. This has led to user dissatisfaction with Claude’s pricing strategy and token consumption transparency. (Source: Reddit r/ClaudeAI)
AI Agents Encounter “Overwrite Conflict” Challenge in Collaborative Development: Social media is abuzz with discussions about problems encountered by AI coding agents in collaborative development, with users noting that “they started savagely overwriting each other’s work instead of trying to handle merge conflicts.” This humorously reflects how multi-agent systems, when collaborating, especially in complex tasks like code generation and modification, face the technical challenge of effectively managing and resolving conflicts, which is not yet fully solved. This sparks thoughts on future AI collaboration models. (Source: vikhyatk, nptacek)
AI in Education: Applications and Policy Making: A Silicon Valley high school is asking students to draft AI policies, believing that involving teenagers is the best way forward. Concurrently, a school in Texas is having AI guide its entire curriculum. These cases indicate that the integration of AI in education is accelerating, but also raise discussions about AI’s role in the classroom, student involvement in policy-making, and the feasibility of AI-led curricula. This reflects the education sector’s active exploration of AI opportunities and challenges. (Source: MIT Technology Review)
Long-Term Outlook and Concerns About AI’s Impact on Employment: The community discusses the long-term impact of AI on employment, with some arguing that AI is unlikely to fully replace human research engineers and scientists in the short term, but rather augment human capabilities and reorganize research organizations, especially in the context of scarce computing resources. However, others worry that AI will lead to a decline in overall private sector employment, while AI providers will reap high profits, forming an “unsustainable AI subsidy” model. This reflects society’s complex sentiments regarding the future trajectory and economic impact of AI technology. (Source: natolambert, johnowhitaker, Reddit r/ArtificialInteligence)
Importance of Writing and Communication Skills in the AI Era: Faced with the widespread adoption of LLMs, some argue that writing and communication skills are more important than ever. This is because LLMs can only understand and assist users when they can clearly articulate their intentions. This implies that even as AI tools become more powerful, the human ability to think clearly and express effectively remains key to leveraging AI, and may even become a core competency in the future workplace. (Source: code_star)
AI Data Center Energy Consumption Sparks Public Concern: With the rapid expansion of AI data centers, the enormous issue of their energy consumption is becoming increasingly prominent. Discussions in the community liken AI’s demand for electricity to “wild growth” and express concerns that it could lead to soaring electricity bills. This reflects the public’s concern about the environmental costs behind AI technology development and the challenge of achieving energy sustainability while driving AI innovation. (Source: Plinz, jonst0kes)
Efficiency and Cost Considerations for Claude Code vs. Custom Agents: The community discussed the pros and cons of directly using Claude Code versus building custom Agents. While Claude Code is powerful, custom Agents offer advantages in specific scenarios, such as generating UI code based on internal design systems. Custom Agents can optimize prompts, save token consumption, and lower the barrier to entry for non-developers, while also addressing issues where Claude Code cannot directly preview results and team permissions are limited. This indicates that in practical applications, balancing general-purpose tools and custom solutions based on specific needs is crucial. (Source: dotey)
ChatGPT App Store and the Future of Business Competition: With ChatGPT launching an app store, users are discussing its potential to become the next “browser” or “operating system.” Some believe this will make ChatGPT the default interface for all applications, enabling a new “Just ask” interaction paradigm, and potentially even replacing traditional websites. However, others worry that this could lead to OpenAI charging promotional fees and spark fierce competition with giants like Google in AI-driven search and ecosystems. This foreshadows deeper competition among future tech giants over AI platforms and business models. (Source: bookwormengr, bookwormengr)
LLM Pricing Models and User Psychology: The community discussed how the pricing models of different AI coding tools (e.g., Cursor, Codex, Claude Code) affect user behavior and psychology. For example, Cursor’s monthly request limits create an urge for users to “hoard” and “use up by month-end”; Codex’s weekly caps lead to “scope anxiety”; and Claude Code’s pay-per-API usage encourages users to more consciously manage model and context usage. These observations reveal the profound impact of pricing strategies on AI tool user experience and efficiency. (Source: kylebrussell)
💡 Other
Omnidirectional Ball Motorcycle: Engineer Creates Omnidirectional Ball Motorcycle: An engineer has created an omnidirectional ball motorcycle that balances similar to a Segway. This innovative vehicle showcases the latest achievement in mechanical engineering and technology integration. While not directly related to AI, its breakthrough in innovation and emerging technology fields is noteworthy. (Source: Ronald_vanLoon)
Character-Driven Video Generation Challenges: The community discussed the challenges faced by video generation agents in replicating specific videos, such as understanding the actions of different characters in natural environments, creating creative comedic beats between scenes, and maintaining character and artistic style consistency over time. This highlights the technical bottlenecks of video generation AI in handling complex narratives and maintaining multimodal consistency, providing clear directions for future AI research. (Source: Vtrivedy10)
Attention Mechanism in Transformer Models: Analogy to Human Sensory Processing: It has been suggested that the human body’s sparsity mechanisms share similarities with the attention mechanism in Transformer models. Humans do not fully process all sensory information but rather process it under strict energy budgets through Pareto-optimal routing and sparse activation. This provides a biological analogy for understanding how Transformer models efficiently process information and may potentially inspire future AI model designs in terms of sparsity and efficiency. (Source: tokenbender)