搜索“DeepSeek”

from labs.scale.com. Claude Opus 4.7, MiniMax-M2.7, and MiniMax-M3 used Mini-SWE-Agent as scaffolding, with evaluation logic aligned with the official method. NL2Repo: Scores for DeepSeek-V4-pro, Kimi-k2.6, and GLM-5.1 are taken from https://qwen.ai/blog?id=qwen3.7. Other models were evaluated on internal infrastructure with a sandbox configured as 1C2G and a 4-hour timeout. Claude Opus 4.7, MiniMax-M2.7, MiniMax-M3, and Gemini 3.1 Pro used Claude Code scaffolding; GPT-5.5 used Codex scaffoldin

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ing ownership of their models. They're fine-tuning with their own data, running RL, and tuning inference for their own latency, throughput, and cost needs. Open-weight models from DeepSeek, Qwen and others have reached production quality, and inference engines like vLLM and SGLang have matured alongside them. For the first time, the full stack to own and serve your models is there, without sacrificing capability. “Modal powers both our reinforcement learning infrastructure and production inferen

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livered the final 1.2x improvement in the last hours. We also ran the same task with several other models under identical conditions. GLM 5.1 reached 7.3x; Kimi K2.6 reached 5.0x; DeepSeek V4 Pro reached 3.3x; Qwen3.6-Plus reached 1.1x. Models that stopped early did so because the agent issued no tool calls for five consecutive rounds — the model concluded it could no longer make progress and voluntarily ended the session. In addition to achieving strong kernel generation results on PPUs, Qwen3.

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came from treating inference as a full-stack problem: profiling end-to-end, identifying the most expensive operations, and eliminating them one by one. ThunderMLA. Kimi K2.5 uses DeepSeek's Multi-head Latent Attention (MLA) architecture. Standard implementations run two separate kernel launches per decode step. Our ThunderMLA — part of our ThunderKittens kernel library — fuses these into a single megakernel, eliminating launch overhead and the tail effects between them. On representative decode

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e task is fundamentally broken. It serves as a first sanity check for task solvability when the synthesizer creates a new task instance. Task Evolution: Difficulty Tiers Following DeepSeek-V3.2's methodology (arxiv:2512.02556), we find that iteratively evolving a task from trivial to challenging is substantially easier than one-shotting a challenging task. Hence, the synthesizer first constructs a simple seed task following the spec outlined above and then iteratively increases its difficulty by

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point that provides high quality multi-lingual transcription at an optimized speed. Then a large language model translates that transcript. Here we leverage the latest advances of Deepseek V4 Pro as default translator. We also enable the user's input of a predefined list of translation rules to maintain the faithfulness and accuracy. Finally, the TTS model generates translated speech, allowing users to specify their desired voice characteristics in plain text. Together-hosted Cartesia’s Sonic 3

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thinking variants): Qwen: Qwen3, Qwen3.5, Qwen3-Next; GLM: GLM-4.7, GLM-5, GLM-5.1; Kimi: Kimi-K2, Kimi-K2.5, Kimi-K2.6; Nemotron: Nemotron-3; Minimax: Minimax-M2.5, Minimax-M2.7; Deepseek: Deepseek-v3.2, Deepseek-v4 For each model (except Deepseek-v3.2 and Deepseek-v4), TITO is verified to handle the following combinations of message roles a harness may append after the first assistant turn: {tool}: harnesses that only inject tool outputs; {tool, user}: harnesses that also inject User-role mess

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TSP, a next-generation inference algorithm designed for scalable long-context and agentic inference. Zyphra Cloud also powers agentic inference for large-scale open models such as DeepSeek and Kimi on AMD Instinct MI355X GPUs through serverless endpoints. These systems and software optimizations now directly power Zyphra Cloud and are delivered as a service, enabling customers to deploy and scale custom AMD infrastructure reliably for production AI workloads. Availability AMD Instinct MI355 GPU

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是对该领域的综述，部分是对自适应并行推理的观点阐述。作者之一（Tony Lian）共同领导了 ThreadWeaver（Lian et al., 2025），这是下文讨论的方法之一。作者力求以各方法自身的立场来呈现每一种方法。 动机 近年来，LLM 推理能力的进展在很大程度上由推理时扩展驱动，此外还有数据和参数扩展（OpenAI et al., 2024; DeepSeek-AI et al., 2025）。显式输出推理 token（通过中间步骤、回溯和探索）的模型如今在数学、编程和智能体基准测试中占据主导地位。这些行为使模型能够探索替代假设、纠正先前的错误并综合得出结论，而不是只锁定于单一解法（Wen et al., 2025). 问题在于，顺序推理会随探索量的增加而线性扩展。 扩展顺序推理 token 是有代价的，因为模型有超出有效上下文限制的风险（Hsieh et al., 2024）。中间探索路径的累积使模型在关注其上下文中的信息时难以在干扰项之间进行区分，从而导致模型性能下降，这也被称为 上下文腐化 (Hong, Troynikov and Huber, 2025）。延迟也会

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tches or exceeds open-weight models such as Nemotron-3-Nano-30B-A3B and Mistral-Small-4-119B while remaining competitive with first-generation frontier, reasoning models including DeepSeek-R1-0528, Gemini-2.5-Pro. Alongside ZAYA1-8B, Zyphra introduces Markovian RSA, a novel test-time compute methodology that combines parallel trace generation with fixed-length context chunking to enable unbounded reasoning while keeping memory costs constant. With this methodology, ZAYA1-8B approaches or exceeds

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the performance of models many times its size such as Mistral-Small-4-119B, and remaining competitive with substantially larger first-generation frontier reasoning models such as DeepSeek-R1-0528, Gemini-2.5-Pro and Claude 4.5 Sonnet. With our novel Markovian-RSA test-time compute methodology, we achieve significant additional performance gains — exceeding Claude 4.5 Sonnet and GPT-5-High on HMMT'25 (89.6 vs 88.3) and closing in on frontier open-weight models such as DeepSeek-V3.2 on mathematic

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e can’t easily model progress over time. On the other hand, we can look at some of the research being done to get a feel for the progress. Some of the types of work include: Using DeepSeek’s models to try to build better GPU kernels ( 400 ), automating the conversion of PyTorch modules to CUDA code ( 401 ), Meta using LLMs to automate the generation of optimized Triton kernels for use within its infrastructure ( 439 ), using LLMs to help write kernels for non-standard hardware like Huawei’s Asce

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pool and we continue to the next all gather immediately. Other Quantization and Post Processes Not all necessary tensors are registered in SGLang's model.named parameters(). For example, with the DeepSeekV3 model lineage, the MLA contains local tensors w kc and w vc, which are generated after all weights are fully loaded in a post weight load() call. SGLang contains a huge number of custom quantization and hardware-specific logic that depends on device and can not be executed on our CPU replica.

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eneck in RL post-training — delivering up to 50% speedup without touching model outputs. Reinforcement learning has become the cornerstone of modern LLM post-training. Models like DeepSeek-R1 owe their reasoning capabilities to RL fine-tuning. But as models grow larger, a critical bottleneck has emerged: the rollout phase. In RL training, the model must generate complete responses to every prompt in a batch before the next training step can begin. The slowest generation determines total step tim

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d Head Attention, a novel attention mechanism with no clear precedent, and record 62 invented Bigram Hash Embedding, uniquely combining a 2017 hash embeddings idea with ideas from DeepSeek’s Engram Early progress was largely about catching up to the frontier research; later records increasingly invented new ideas. Early records (first 20 records, May 2024 – Jan 2025) were mostly imported or adapted to catch up to frontier research, applying techniques like RoPE, ReLU², QK-norm, and FlexAttention

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l 25 evaluated models, with specific icons marking the positions of a subset of frontier models (Anthropic Claude 4 Sonnet, Google Gemini 3 Pro, OpenAI GPT 5.1, Mistral Large, and DeepSeek R1). The validity of assessing LLM dispositions via self-reported agreement with questionnaire statements remains an active area of research. While some researchers question the construct validity of this approach, others argue that specific prompting frameworks enable reliable assessment. While settling this

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B (Feb 13, 2026); Qwen 3.5 (Feb 15, 2026); Ant Group’s Ling 2.5 1T & Ring 2.5 1T (Feb 16, 2026); Cohere’s Tiny Aya (Feb 17, 2026); Update 1: Sarvam 30B and 105B (Mar 6, 2026) (PS: DeepSeek V4 will be added once released.) Since there’s a lot of ground to cover, I will be referencing my previous The Big LLM Architecture Comparison article for certain technical topics (like Mixture-of-Experts, QK-Norm, Multi-head Latent Attention, etc.) throughout this article for background information to avoid r

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the history to keep the context within limits. BrowseComp: we tested two strategies, simple context-folding achieved a score of 69.0, while using the same discard-all strategy as DeepSeek-V3.2 and Kimi K2.5 achieved 78.6. WideSearch: we use a 256k context window without any context management. MMLU-ProX: we report the averaged accuracy on 29 languages. WMT24++: a harder subset of WMT24 after difficulty labeling and rebalancing; we report the averaged scores on 55 languages using XCOMET-XXL. MAX

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o do one thing at enormous scale, and that thing is becoming less differentiated over time. For instance, Kimi K2.5, Minimax 2.5 and GLM-5 were all released in the last week, with Deepseek-V4 and Qwen-3.5 on the horizon. The specialisation game, in contrast, requires doing a thousand things at small scale, and each of those things creates compounding value that’s hard to replicate. OpenAI offering an API fine-tuning endpoint is to genuine specialisation what a hotel concierge recommending a rest

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长程执行：通过大规模 fully-async agentic RL 训练，显著提升针对复杂任务的长程自主执行能力，使得 Ring-2.5-1T 可以轻松适配 Claude Code 等智能体编程框架和 OpenClaw 个人 AI 助理。 1. 深度思考与长程执行 为评估 Ring-2.5-1T 的深度思考和长程执行能力，我们选取了具有代表性的开源思考模型（DeepSeek-v3.2-Thinking、Kimi-K2.5-Thinking）和闭源API（GPT-5.2-thinking-high、Gemini-3.0-Pro-preview-thinking-high、Claude-Opus-4.5-Extended-Thinking）作为参考。 Ring-2.5-1T 在数学、代码、逻辑等高难推理任务（IMOAnswerBench、AIME 26、HMMT 25、LiveCodeBench、ARC-AGI-V2）和智能体搜索、软件工程、工具调用等长程任务执行（Gaia2-search、Tau2-bench、SWE-Bench Verified）上均达到了开源领先水平。 我们还额外测试

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