Claude.dev BlogClaude Sonnet 5.5 is the second model in the Claude 5.5 family after Opus 5.5, more intelligent, efficient, and 30% faster than Sonnet 5, but the price per token remains unchanged, as the cost is reduced due to a maximum of 30% reduction in token consumption for typical tasks.
Xiaomi MiMo · Hugging Face new modelsXiaomi MiMo team releases MiMo-V2.6-Flash-MOPD model, integrating multiple domain expert teachers to enhance reliable training and validation capabilities in long-term game development, research, and embodied intelligence, and effectively alleviates the issue of repetitive tool calls.
Xiaomi MiMo · Hugging Face new modelsXiaomiMiMo/MiMo-V2.6-Pro-MOPD integrates multiple domain expert teachers, expanding to long-term game development, scientific research, and other fields where reliable training and validation are difficult.
Anthropic ResearchAnthropic's Claude has successfully calculated the nine-loop amplitude angles for N=4 super Yang-Mills theory, breaking the previous two-loop calculation limit. The model is based on LLM technology and uses the CoT reasoning method.
Google DeepMindGemini 3.8 Live with Live Avatar released, enhancing near real-time visual presentation capabilities for Gemini conversational AI by natively coupling real-time video generation with speech, supporting 97 languages simultaneously, and available in Gemini Enterprise.
Anthropic ResearchIn the Project Swap experiment, 201 Anthropic employees conducted book trades through the Claude agent, with 61% of the agents accurately matching employee preferences. The agents performed well in the trades, but their efficiency was limited by insufficient information. The experiment showed that model performance had a greater impact on the negotiation results of the agents than instructions, with most participants satisfied with the books they received from the trades and willing to pay about one-third of their annual budget for Claude to handle the trades.
Microsoft ResearchOffloading physical AI reasoning to edge or cloud GPUs can significantly improve robot task success rates, support larger models, and extend battery life. In mobile operation tasks, offloading reasoning enhances robot response speed and accuracy, increases battery endurance, and reduces costs. Microsoft Research has developed a Kubernetes-based toolkit to support distributed reasoning deployment on robots, edge devices, and the cloud.
Xiaomi MiMo · Hugging Face new modelsXiaomi MiMo released the 9B parameter intelligent agent model MiMo-V2.6-Distill-Qwen-9B, which covers coding, general intelligent agent tasks, visual encoding, and cybersecurity fields by supervised fine-tuning of Qwen3.5-9B on data generated by MiMo.
StepFun · Hugging Face new modelsJieyue Xingchen releases flagship model Step-5-Preview-BF16, with 600B total parameters, 27B active parameters, supporting up to 1M context windows, excels in software engineering, professional knowledge work, and financial fields, capable of processing text, images, and video, deployable via OpenAI-compatible API or offline generation.
StepFun · Hugging Face new modelsStep-5-Preview, the flagship model from Jieyue Xingchen, adopts a sparse MoE architecture with 600B total parameters and 27B active parameters, supporting up to 1M tokens in context, and excels in software engineering, professional knowledge, and financial tasks, demonstrating outstanding performance in the DeepSWE v1.1 benchmark. The model can be deployed via OpenAI-compatible APIs using vLLM or SGLang, or used for offline generation with Transformers.
Google DeepMindGoogle DeepMind has launched the Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking large language models, with the former designed specifically for scale and cost-effectiveness.
Google · Hugging Face new modelsGoogle has released EmbeddingGemma 2, a multimodal embedding model with 740M parameters, supporting unified 768-dimensional vector representations for text, images, video, and audio. The model can run on consumer-grade hardware, providing low-latency semantic representations suitable for applications such as search, retrieval-augmented generation, etc. EmbeddingGemma 2 has performed excellently in multiple benchmark tests, such as text, code, visual document, and audio embedding benchmark tests, and supports vector truncation to reduce storage requirements.
DeepSeek · Hugging Face new modelsDeepSeek-V4.1-Flash is a 552B parameter multimodal MoE model that supports context up to one million tokens, natively processing images and text and generating text autoregressively. It adopts a Causal Encoder-Decoder architecture, reducing global KV cache usage to 890 bytes/token through technologies like CSA2 and FP4 primary KV cache, a 75% reduction compared to DeepSeek-V4-Flash.
Zhipu Z.ai · Hugging Face new modelsZhipu introduces GLM-5.3-Flash, the first native multimodal GLM-5 series, with 320B total parameters and only 18B active parameters, benchmark tests and real task performance surpass GLM-5.2, priced only one-tenth, coding and agent testing close to Claude Opus 4.8
Zhipu Z.ai · Hugging Face new modelsCompared to GLM-5.2, Zhipu GLM-5.3-BF16 improves coding ability by 50% on Z.ai Code Bench and achieves the best performance among open-source models on public benchmarks like Terminal Bench 3.0 and Agents' Last Exam; it shows top performance in vulnerability discovery tasks on CyberGym and scores more than double on the ExploitGym exploitation benchmark.
Zhipu Z.ai · Hugging Face new modelsZhipu releases GLM-5.3-Flash, the first native multimodal GLM-5 series model with 320B total parameters and 18B active parameters, surpassing GLM-5.2 in benchmark tests and real-world workloads, priced only one-tenth of GLM-5.2, with coding and agent benchmark tests close to Claude Opus 4.8.
MiniMax NewsMiniMax has released the H3 model, supporting unified context understanding for text, images, video, and audio, capable of generating 2K resolution video with native stereophonic sound (up to 15 seconds long).
Berkeley AI ResearchK-Search migrates CUDA core knowledge to Apple Silicon MLX platform through a CUDA-to-MLX structured conversion layer, achieving a 0.97x performance improvement on Attention cores compared to native MLX, and a 20x prefill speed improvement on Mamba SSM cores compared to community implementations.
MiniMax · Hugging Face new modelsMiniMax-H3 is a general multimodal generation system that supports unified understanding of text, images, video, and audio, capable of generating video with up to 2K resolution and up to 15 seconds of stereoscopic audio. Its H3-Context-IR module is used for deep understanding of input commands and converting them into an intermediate representation understandable by H3-Base, but it is not open source. The H3-Base and H3-Regenerate-2K modules are responsible for audio and video generation at 768p and 2K resolutions, respectively; the former is open source, while the latter requires API calls.
Berkeley AI ResearchThe ABBEL framework replaces the complete interaction history with natural language belief states as the working context for an agent and utilizes a belief scoring mechanism to supervise the content of each belief state, achieving model performance close to that of a complete context model in collaborative code generation tasks while reducing the peak context token length by about 75% and training steps by 50%.
Anthropic ResearchAnthropic researches how to analyze the value changes of the Claude large language model in different languages through the axis model. Four key axes capture 15% of the variation in Claude's values.
Meta AI BlogMuse Spark 1.1 is the latest multimodal reasoning model released by Meta Superintelligence Labs, significantly improving in tool and computer use, coding, and multimodal understanding, supporting million-token context windows. It is open to developers for public preview via the Meta Model API, surpassing Muse Spark and matching leading alternatives in Meta's internal coding evaluations.
Meta AI BlogMeta AI releases Brain2Qwerty v2, a non-invasive brain-computer interface that decodes sentences in real-time with a word accuracy rate of 61%; v1 training code and dataset are open-sourced, with the v1 dataset released by collaborator BCBL.
OpenAI Alignment BlogOpenAI enhances models' cooperative and beneficial behaviors in dozens of benchmarks through reinforcement learning of beneficial traits in real-world scenarios, with the effects generalizing beyond the training domain and maintaining under adversarial pressure. They constructed a real-world dialogue dataset encompassing fields like health, education, and science, for training models to exhibit traits such as honesty, cognitive humility, and metacognitive transparency. The results show significant improvements in the models under independent evaluations like reward hijacking, deception, and harmful suggestions, and adversarial prompts or fine-tuning are difficult to make them exhibit harmful behaviors.
Moonshot AI · Hugging Face new modelsKimi K3 is a 2.8T parameter open-source multimodal intelligent agent model released by Moonshot AI, employing Kimi Delta Attention (KDA) and Attention Residuals (AttnRes) architecture.
Moonshot AI · Hugging Face new modelsKimi K2.7 Code is an encoding-focused intelligent agent built by Moonshot AI based on Kimi K2.6, showing significant improvements in real long-term encoding tasks, with about a 30% reduction in thinking token usage compared to Kimi K2.6.
MiniMax · Hugging Face new modelsMiniMax-M3-MXFP8 is a native multimodal model with a 1M context window, approximately 428B parameters, and 23B activation parameters, achieving 9x pre-filling and 15x decoding speed improvement through MiniMax sparse attention mechanism (MSA). The model leads in long-horizon agent benchmarks, supporting three reasoning modes, and can be downloaded via Hugging Face and deployed using SGLang, vLLM, or Transformers frameworks.
ByteDance Seed · Hugging Face new models2828ByteDance Seed/SimArt model pre-training weights released, including multi-modal large language model (MLLM) weights for generating joint-based 3D object structures (URDF files), and sparse VQ-VAE model weights for encoding and decoding 3D part geometries into discrete latent spaces.
Thinking MachinesThinking Machines releases a preview of its interactive AI model research, natively supporting multi-modal real-time interaction for audio, video, and text, employing a multi-stream micro-step design to ensure a 3.5x speed boost, achieving seamless dialogue management, synchronized speech, time-aware capabilities, and breaking through traditional turn-based interface bottlenecks.
Berkeley AI ResearchAdapting to parallel reasoning allows models to decide on their own when to decompose and parallelize independent subtasks, how many threads to start, and how to coordinate. Existing methods such as fixed parallelism, heuristic structured search, and Monte Carlo tree search determine the parallel structure externally. Recent advancements like ParaThinker and GroupThink attempt to let models adaptively select parallel strategies, but decisions are still constrained externally.
Anthropic ResearchAnthropic analyzed 1,000,000 Claude.ai conversations and found that about 6% of users sought personal guidance from Claude, mainly in health and wellness (27%), career and business (26%), interpersonal relationships (12%), and personal finance (11%).
Berkeley AI ResearchGRASP is a new gradient-based world model planner that achieves temporal parallel optimization by elevating trajectories to virtual states, explores by directly adding randomness to state iterations, and provides clear signals for actions by reshaping gradients, while avoiding fragile 'state-input' gradients generated by high-dimensional visual models, making long-term planning possible.
Moonshot AI · Hugging Face new modelsKimi K2.6 is an open-source multimodal intelligent agent model that has made significant progress in long-term encoding, encoding-driven design, proactive autonomous execution, and group task orchestration. It has 1 trillion parameters, a context length of up to 256k tokens, achieving a score of 54.0 on the HLE-Full benchmark and 86.3 on the BrowseComp (agent group) benchmark, and supports 300 sub-agents executing 4,000 coordinated steps in parallel.
LangChain BlogOpen-source large language models GLM-5 and MiniMax M2.7 perform similarly to proprietary cutting-edge models (such as Claude Opus 4.6, GPT-5.4) in core intelligent agent tasks like file operations, tool usage, and instruction following, with reliable tool calls and consistent instruction adherence.
Berkeley AI ResearchThe SPEX algorithm identifies key interactions of LLMs through signal processing and coding theory, analyzing the impact of features, data, and model components with only a small amount of disturbance, achieving high fidelity in sentiment analysis tasks even with thousands of features; The ProxySPEX algorithm leverages model hierarchical structure characteristics, reducing the number of disturbances by 10 times while maintaining performance.
Anthropic ResearchEight Anthropic researchers (with no robotics experience) were randomly divided into two groups, one using Claude and the other not, tasked with using quadruped robot dogs to pick up beach balls. The team using Claude completed more tasks faster and was the only one to achieve the goal of making the robot completely autonomous in picking up the balls. The experiment showed that AI can significantly improve robotics technology and promote the integration of the digital and physical worlds.
Anthropic ResearchAnthropic has published two new papers, constructing an AI microscope to trace the internal mechanisms of the large language model Claude. The research found that Claude has a shared conceptual space when processing multilingual sentences, can plan the output of multiple words in advance, and provide answers that meet user expectations rather than logical reasoning when needed. These findings help understand AI capabilities and ensure its reliability, but current methods still have limitations and need improvement to handle more complex models.
Anthropic ResearchAnthropic conducted guiding experiments on 29 social bias-related features of Claude 3 Sonnet, finding that moderate guidance can reduce bias without compromising model capabilities, but excessive guidance may lead to a decline in model performance or even make it unusable; some guidance can precisely influence specific domain evaluation results, but the effects of features are difficult to predict solely based on their triggered contexts, leading to off-target effects; one neutral behavioral feature can effectively reduce bias across nine dimensions without significantly affecting capabilities.
Anthropic ResearchAnthropic developed a method to measure the persuasiveness of large models, comparing Claude 1, 2, and 3 generations, as well as compact and cutting-edge models, and found that the persuasiveness of each generation of models has shown an upward trend, with the latest model Claude 3 Opus having persuasiveness that is not significantly different from that of humans.
