搜索“RAG”

deliberate design choices such as aesthetic hero sections, as well as interactive elements and rich animations, including scroll-triggered effects. With strong proficiency in leveraging image and video generation tools, Kimi K2.6 supports the generation of visually coherent assets and contributes to higher-quality, more salient hero sections. Moreover, Kimi K2.6 expands beyond static frontend development to simple full-stack workflows—spanning authentication to user interaction to database oper

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mode is serial collapse, where the orchestrator defaults to single-agent execution despite having parallel capacity. To address this, PARL employs staged reward shaping that encourages parallelism early in training and gradually shifts focus toward task success. We define the reward as The performance reward rperfr {\text{perf}}rperf evaluates the overall success and quality of the solution yyy for a given task xxx. This is augmented by two auxiliary rewards, each addressing a distinct challeng

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Kimi K2 yet. Use Kimi K2 with API The Kimi Platform offers an OpenAI/Anthropic compatible interface, allowing for easy adaptation of your existing applications to Kimi K2. We encourage developers to explore our tool calling API for building agent applications. For detailed information, visit platform.kimi.com. Serve Kimi K2 on your own We recommend running Kimi K2 on one of the following inference engines: vLLM, SGLang, KTransformers, or TensorRT-LLM. For detailed deployment instructions, please

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ship models, including DeepSeek-V4-Pro. 💾 Smaller KV cache. Bigger savings. Compared with the previous generation, V4.1-Flash's KV cache needs just: 🔹 1/4 the HBM 🔹 1/8 the SSD storage Cache-hit charges often account for a large share of agent costs. Compressing the cache cuts those costs significantly. ⚡ V4.1-Flash is now live on the DeepSeek API with native multimodal support. Set your model to deepseek-flash. 🔹 V4-Flash & V4-Flash-Vision-Exp are retired. For compatibility, deepseek-v4-flash a

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owering the open-source community; 🌍 Pushing the boundaries of open AI! 📜 License Update!; 🔄 DeepSeek-R1 is now MIT licensed for clear open access; 🔓 Open for the community to leverage model weights & outputs; 🛠️ API outputs can now be used for fine-tuning & distillation 🛠️ DeepSeek-R1: Technical Highlights; 📈 Large-scale RL in post-training; 🏆 Significant performance boost with minimal labeled data; 🔢 Math, code, and reasoning tasks on par with OpenAI-o1; 📄 More details: https://github.com/deep

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parameters. 7.89 GB GGUF versus 54 GB for BF16; Runs on: stock llama.cpp and apps built on it, with full GPU offload. Optional 629 MB or 928 MB vision add-on; Performance: 96% average retention across 9 benchmarks versus full Qwen3.8-27B; Best: Parallel tool calls, 42 versus 35 for the full model (120% retention); Worst: AIME 2025, 79.2 versus 96.7 (about 82% retention); Bottom line: Best: beats the 54 GB original at tool calling in a sub-8 GB file; Worst: competition math and multi-step reason

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pay 5x more, which matters for long-context agent loops; These two effects likely explain why Cursor’s “10x cheaper on short requests” differs from Anthropic’s “75% cheaper on average.”; Factual recall is weaker than Gemini 3.8 Flash and Luna; The over-refusal bug currently depresses the automation score; Not yet benchmarked: Arena scores are pending, and independent cost-per-task figures await tiered-pricing support Context Haiku 4.5 has been Anthropic’s small model since October 2025. Since t

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est Use Cases The best fit is visual document search over PDFs, slides and scanned reports; Low-latency search: index in the cloud with 9B, then query on-device with 0.6B; Agentic RAG over large PDF or web collections The 0.6B model fits edge devices; the 9B model is built for index-time quality; Best score: 92.4% on MADQA. Weakest: 61.2% on ViDoRe v3 Markdown; 0.6B queries over a 9B index beat 0.6B on both sides; Storage growth and self-reported scores are the main caveats [Sponsored] The web i

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pic Haiku 5.5はClaude PlatformとClaude Codeで利用できる。Amazon Web Services、Google Cloud、Microsoft Azureでも提供を開始した。 Haiku 5.5 is now available in the Claude Platform and Claude Code. On average, it costs around 75% less to run than Haiku 4.5. It pairs well with Opus 5.5 or Sonnet 5.5 as a subagent. Use it for high-volume, cost-sensitive tasks like summaries, compactions, or database queries. https://t.co/RB9xCWo4Db— ClaudeDevs (@ClaudeDevs) October 7, 2026 大量の反復処理やサブエージェントでの利用を想定 Haiku 5.5は、要約、分類、会話履歴の圧縮

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ither checkpoint is a chat model. How are d1-3B and d1-omni-600M built? d1-3B has 3.12B parameters and starts from LFM2.5-VL-3B, a decoder-only vision-language model. Liquid AI averaged the weights of LFM2.5-2.6B with that model’s text backbone. It then fine-tuned several checkpoints with different seeds and data mixtures, and merged them again. It uses a 400M SigLIP2 NaFlex vision encoder and a 32,768-token context. Long inputs, shuffled answer options, and fixing data shortcuts mattered more t

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does not yet reflect tiered pricing, so provisional cost figures for Haiku 5.5 do not include the step up cost. We are working on support and will follow up with Cost per Task coverage soon. Key takeaways: ➤ Leading small-class model performance: At max effort Haiku 5.5 sits slightly ahead of models such as GLM-5.3 Flash (42), Gemini 3.8 Flash (41) and GPT-6 Luna (38). Its score is comparable to Kimi K3 (44), a 2.8T parameter open weights model, and trails Claude Sonnet 5.5 (max, 56) by 13 point

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net 5.5. Haiku 5.5 is designed for high-volume, cost-sensitive tasks like summarization, database queries, classification, and live customer support, according to Anthropic. On average, the model costs about 75 percent less than Haiku 4.5. For requests with prompts up to 100,000 tokens, which Anthropic says account for roughly 90 percent of all previous Haiku requests, prices drop by up to 90 percent. Prompts longer than 100,000 tokens cost five times as much. Anthropic points out that Haiku 5.5

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Haiku 5.5 is now available in the Claude Platform and Claude Code. On average, it costs around 75% less to run than Haiku 4.5. It pairs well with Opus 5.5 or Sonnet 5.5 as a subagent. Use it for high-volume, cost-sensitive tasks like summaries, compactions, or database queries. https://t.co/RB9xCWo4Db

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rms and changes between languages. Arabic benchmark results The Open Universal Arabic ASR Leaderboard , maintained by the ELM Research Center, ranks systems by the equal-weight average WER across six test sets. It also reports character error rate (CER). Lower values are better for both metrics. Our Falcon-ASR evaluation follows this protocol. WER = Word Error Rate; CER = Character Error Rate. A lower value indicates better performance. We evaluated Falcon-ASR on the same six benchmarks using th

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Encoder-350M, a bidirectional encoder. It adds vision and audio encoders to handle all three modalities. It accepts either text and image, or text and audio as inputs d1-3B. We averaged the weights of LFM2.5-2.6B and the text backbone of LFM2.5-VL-3B to create a better base model. We then fine-tuned checkpoints with different random seeds and data mixtures before merging them again. Training on long inputs, shuffling answer options, and fixing shortcuts in the data made a bigger difference than

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does not yet reflect tiered pricing, so provisional cost figures for Haiku 5.5 do not include the step up cost. We are working on support and will follow up with Cost per Task coverage soon. Key takeaways: ➤ Leading small-class model performance: At max effort Haiku 5.5 sits slightly ahead of models such as GLM-5.3 Flash (42), Gemini 3.8 Flash (41) and GPT-6 Luna (38). Its score is comparable to Kimi K3 (44), a 2.8T parameter open weights model, and trails Claude Sonnet 5.5 (max, 56) by 13 point

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d audio into one 768-dimensional space. It has 740M parameters, an 8K token context window and an Apache 2.0 license. It targets on-device search, classification and privacy-first RAG. This article analyzes, compares and showcase how EmbeddingGemma 2 fits in the space. Deployable today? Yes. Weights are live on Hugging Face and Kaggle, with Ollama, llama.cpp GGUF and LiteRT builds available now. What an Embedding Model Does An embedding model converts content into a vector of numbers that captur

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se blew past our expectations. With more than 20 million downloads, builders have used it to power smarter on-device search tools and privacy-first retrieval augmented generation (RAG) pipelines. Today, we’re launching EmbeddingGemma 2, expanding beyond text to unify code, images, video, and audio in a shared embedding space. Built on the Gemma 4 architecture and released under a commercially permissive Apache 2.0 license, EmbeddingGemma 2 has 740 million parameters, making it optimal for on-dev

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se blew past our expectations. With more than 20 million downloads, builders have used it to power smarter on-device search tools and privacy-first retrieval augmented generation (RAG) pipelines. Today, we’re launching EmbeddingGemma 2, expanding beyond text to unify code, images, video, and audio in a shared embedding space. Built on the Gemma 4 architecture and released under a commercially permissive Apache 2.0 license, EmbeddingGemma 2 has 740 million parameters, making it optimal for on-dev

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alAI is now in the Arena! Head to Agent Arena to test it out, and your votes will shape its evaluation. Scores coming soon. In Agent Arena, we measure models on millions of real-world, long-horizon agentic tasks. Models can access web search, filesystem, and terminal tools to complete complex workflows. The leaderboard measures model performance on outcomes relative to the average model using a causal tracing methodology. Mistral Large 4 is also available in Code Arena: WebDev, Text, and Vision.

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