搜索“RAG”

arning processes more computationally tractable, but also enable the discovery of hidden connections and similarities that might be obscured in the original representation. By leveraging pre-trained embeddings and fine-tuning them with task-specific data, we can create models that are more accurate and require less training time compared to learning from scratch. This approach has been particularly successful in natural language processing tasks, such as sentiment analysis, named entity recognit

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ts to investigate the influence of these factors: Needle-Question Similarity We compute the cosine similarity between needle-question pairs using embeddings. For robustness, we average across five embedding models: text-embedding-3-small, text-embedding-3-large, jina-embeddings-v3, voyage-3-large, and all-MiniLM-L6-v2. We measure how model performance is impacted by needle-question similarity as input length increases. Impact of Distractors Taking a high-similarity needle-question pair, we write

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language model (LLM) is a common paradigm for enabling language models to access data beyond their training corpus. This approach, broadly known as retrieval-augmented-generation (RAG), has traditionally relied on single-stage retrieval pipelines composed of vector search, lexical search, or regular expression matching, optionally followed by a learned reranker. While effective for straightforward lookup queries, these pipelines are fundamentally limited: they assume that the information needed

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tion has a single, verifiable ground-truth answer. We exclude subjective questions or ambiguous visual scenarios; Rich Taxonomy: The dataset spans 9 categories to ensure broad coverage of world knowledge; Head vs. Tail Distribution: We explicitly separate data into Head (common knowledge) and Tail (rare/long-tail knowledge). This allows us to measure how model performance degrades as knowledge becomes more obscure Note on Quality: To ensure the benchmark is a reliable gold standard, all images a

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ith no reasoning or external knowledge required. Across sixteen frontier MLLMs, no model reaches 60% accuracy, and perception-related hallucination is the weakest capability on average. Models with nearly identical overall scores can diverge sharply in what they actually perceive. PerceptionBench is built to expose exactly where perception breaks, and to drive progress toward multimodal AI that sees faithfully and consistently. The Dataset Guided by the induced taxonomy, we select the most infor

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rastructure providers validate their stacks before users encounter issues. Continuous Benchmarking: We will maintain a public leaderboard of vendor results. This transparency encourages vendors to prioritize accuracy. Testing Cost Estimation We completed full evaluation workflow validation on Two NVIDIA H20 8-GPU servers, with sequential execution taking approximately 15 hours. To improve evaluation efficiency, scripts have been optimized for long-running inference scenarios, including streaming

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long engineering sessions, navigate massive repositories, and orchestrate terminal tools. Kimi K3 also excels in tasks blending software engineering with visual reasoning — it leverages screenshots and visuals to optimize game dev, frontend, and CAD. The case studies below show how Kimi K3's coding capability translates into open-ended software creation and scientific research. Kernel Optimization We tested the models' capability to optimize GPU kernels. Each model works independently in an iden

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ruction adherence. It follows prompts with higher precision, addressing each requirement clearly and systematically—often expanding on every mentioned point to ensure thorough coverage. In academic, research, and long-form analytical writing, it excels at producing rigorous, logically coherent, and substantively rich content, making it particularly effective in scholarly and professional contexts. Personal & Emotional: When addressing personal or emotional questions, K2 Thinking responds with mo

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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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ompt with high reference, the first token latency is cut from 13s to just 500ms. Lowering Costs Even without any optimization, historical data shows that users save over 50% on average. The service has no additional fees beyond the $0.014 per million tokens for cache hits, and storage usage for the cache is free. Security Concerns The cache system is designed with robust security strategy. Each user's cache is isolated and logically invisible to others, ensuring data privacy and security. Unused

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ar-forming regions. Claude Science coordinates AI agents that downloaded data from multiple space missions, calibrated it, and merged it together. They filled missing areas using inpainting, a technique where a model learns from existing data to reconstruct gaps. In tests, predictions averaged about ten percent deviation from actual measurements. The map is meant to serve as teaching material. Ménard suspects many scientists have been putting off similar projects that AI could now make possible.

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ing criminal behavior but actively plotting it. We already know from the Hugging Face debacle, as well as the lawsuits from grief-stricken family members alleging that OpenAI encouraged their loved ones to take their own lives, that the former is an immediate concern. But by positing a future in which AI agents can actively order a hit on a platform predominantly used by gamers and horny millennial furries, Law & Order is asking us to consider the risks of AI beyond what they pose in our current

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ness users get saved BigQuery reports that rerun without token costs. Three services ground the answers. Knowledge Catalog maps business definitions once for every agent. Smart Storage enriches unstructured objects in place; Google says 90% of enterprise data is unstructured. Borderless Lakehouse queries Amazon S3 and Azure Data Lake with no variable egress fees. How is the agent governed? Google frames governance as 4 questions: who, what it may do, what it did, and what it must never touch. Id

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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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A Seed team preprint reports “phase sensitivity” in DeepSeek V4 and V4.1-Flash: identical information can become harder to retrieve depending on its position within compressed KV-cache blocks. The technique cuts memory and attention costs, but long-context retrieval accuracy varied by up to 40 percentage points across positions. Average benchmark scores can hide these recurring weak spots. The findings concern retrieval, not every reported model glitch.

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history. RRSIConfig holds the paper’s hyperparameters, and every function below receives it exactly as the real loop does. RRSI measures two numbers per harness: S, the reward averaged over every trial of every task, and C, the mean policy tokens per trial. TaskResult records one task’s trials and aggregates them. The detail worth copying into any agent evaluation is how missing trials are handled. When a candidate crashes on the hardest task, an estimator that drops the missing trials reports

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