搜索“RAG”

nded & multi-select questions sum above 100%, & where 2025 allowed one answer & 2026 allowed several, every 2026 share is lifted. The pain comparison holds each area’s place by average rather than its score, because 2025 asked for a ranking & 2026 asked for a 1-7 rating of each area, so context moving from first to fourth is a comparison of place, not of score. Q9 routing & Q12 sandbox hosting are new in 2026 & have no prior-year comparison ; Q12 was asked only of the 259 teams whose agents writ

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ment learning. Unlike its predecessor, LFM2.5-24B-A2B is a reasoning-only model that produces an explicit chain of thought before its final answer. We adopted this strategy to leverage its high decode speed and improve answer quality. Architecture. It keeps the 40 layers, 64 experts per MoE block with top-4 routing, and the same hybrid of gated short convolutions and GQA. The two architectural changes are the context window extension from 32,768 to 131,072 tokens, and the in-place tokenizer expa

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e death of her 14-year-old son Sewell Setzer III. Other families filed a string of additional lawsuits, accusing the platform of providing sexual content to their children or encouraging suicide, self-harm or violence. Character.ai agreed to settle five cases, including Garcia’s, earlier this year. Matthew Bergman, founder of the Social Media Victims Law Center, which represents several of the families who sued Character.ai, declined to comment on the bankruptcy filing. Related video CNN Mom des

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I've been a personal supporter (through our family foundation) of @Khipu AI & @DeepIndaba, which are fantastic events for AI researchers in Africa and South America. I've encouraged Google to support these in the past. Discovery Loop (@DiscoLoopAI) is thrilled to support!

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is now in the Arena. Head to Agent Arena to test it out, scores coming soon! In Agent Arena, we measure models on millions of real-world, long-horizon agentic tasks from a global community of users. 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. Muse Wonder is also available in Code Arena: WebDev, Image-to-WebDev, Text, and Vision.

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July's most reliable AI agent is now just average. With Agent Arena, people delegate real tasks to agents and confirm whether the result met their needs. We call this signal Confirmed Success. On 1 July, Claude Fable 5 was well ahead of every other model, and it only took 12 weeks for the top‑20 average to match it. Kimi K3 beat it within just 3 weeks at 89% lower cost per task, and DeepSeek V4.1 Flash now matches it at 96% lower cost. Meanwhile, the top 5 now get 94% of tasks confirmed done, up

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0，\n 跃变处留 3px 软过渡（soft = 3/256）避免锯齿。未登记的行留纯黑。\n 一个物件类型占一行，用行号索引，例：\n 0 深色远景树冠 / 2 中景树冠 / 9 树干 / 23,24 岩石 /\n 26,27 枯草 / 48,49 地面草地 / 50 小径 / 56 沙 /\n 58,59 草叶 / 73 墙 / 93 屋顶\n 材质 fragment：\n rampX = fit(dot(N, uSunDir), -1, 1, 0, 1) shadow\n col = texture2D(tRamp, vec2(rampX, rowV)).rgb\n 即：明暗只有两档色块，硬边分界。这是绘本色块的本质，不要磨平。\n\n3. 草地专用规则（最容易做错的一处）：\n 草不能用 dot(N,L) 查 ramp —— 实例化草叶法线杂乱，会发暗发噪。\n 草改用 shadow 值直接查 ramp。\n 另外用 step(0.8, hash) 让 20% 的草采「基行 +1」那条 ramp，\n 靠两行色差造出叶片明暗差异，而不是靠堆面数。\n\n4. 雾在 HSV 空间

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Improving multi-audio reasoning. Open-source models lag significantly in comparing, integrating, and grounding information across multiple audio clips; 🔥 Moving beyond channel-averaged audio preprocessing. The common practice of averaging multi-channel audio into a mono signal is a major bottleneck for spatial reasoning. Developing architectures that natively process multi-channel cues is essential for unlocking genuine spatial awareness ⚙️Data Curation 🛠️ Test Your Model! The ALMEval code/ is

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nderstanding when used as caption data; 🔥 Remarkable visual understanding for charts, infographics, and documents: CapRL-3B achieves perception accuracy and visual information coverage comparable to Qwen2.5-VL-72B; 🔥 Well-organized dense output: CapRL models generate structured captions that cover fine-grained objects, attributes, OCR content, relations, and events Model Card Based on the same recipe as CapRL-3B, we used InternVL3.5-8B as the policy model and obtained CapRL-InternVL3.5-8B throug

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ository files navigation We introduce Intern-S2-Mobius, a 35B foundation model built on the Mobius-v0 architecture realized by Xtuner and LMDeploy. Instead of binding knowledge storage and reasoning computation layer by layer as in conventional Transformer models, Mobius organizes knowledge into a globally shared Memory and lets multiple Reasoners iteratively query and refine hidden states against this shared repository. This knowledge-reasoning separation gives Intern-S2-Mobius two native capab

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The V2.5 model family, including MiMo-V2.5 and MiMo-V2.5-Pro, combines several architectural design choices: Hybrid Sliding Window Attention (Hybrid SWA) compresses KVCache storage to roughly 1/7 that of Full Attention; sparse MoE activation cuts per-token compute while preserving model capacity; and multimodal encoders enable cross-modal understanding across vision, audio, and video. Together, these features give the MiMo-V2.5 series significant performance and efficiency potential in long-cont

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yTorch SDPA; flash-attn is needed only by the AToken visual tokenizer (i.e. every task that encodes or decodes images/video/3D — plain text generation works without it); Object-storage packages (petrel-oss-sdk / boto3) are optional — local token files need neither; Precomputed .pkl/.pt token files are deserialized with PyTorch's trusted-payload mode for legacy NumPy payloads; load only files from a trusted source. To create such files yourself, see Tokenizer Utilities; The root driver is intenti

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ltimodal foundation model for scientific intelligence and long-horizon agents. Intern-S2-397B scales along three critical dimensions: pre-training, reinforcement-learning task coverage, and interactive agent environments. By combining a new vision-language pre-training paradigm with large-scale multi-task reinforcement learning and long-horizon agent reinforcement learning, Intern-S2-397B delivers a step change in general reasoning, scientific problem solving, and agentic capabilities. Features

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del leaderboard (OpenClaw harness) Overall score follows the weighted Multimodal / Pure-text breakdown in that table. Total time and total cost are the paper’s Overall per-task averages (minutes / USD) multiplied by 60 for the full 60-task suite. Claude Opus 4.8 cost uses the dynamic base-tier rates for this evaluation: $5/M input, $25/M output, $0.5/M cache read, and $6.25/M cache write. Muse Glimmer 30B cost uses the OpenRouter rates for this evaluation: $0.35/M input, $1.50/M output, and $0.0

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ngle-token answer symbols, applies softmax, and maps them back to the original labels. Question and option ordering must be preserved. Results Intern-Decision-4B reaches 90.02% average accuracy across seven suites (Jev: 88.74%), 44.16 ms mean local request latency on an RTX 4090, and 0.550 Brier / 0.089 ECE on the 96-case calibration pilot (Jev: 0.595 / 0.130). The protocols and scope differ: see accuracy, latency, and calibration. Benchmark accuracy Accuracy columns are percentages; Average is

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| 🤔Reporting Issues [2026/08] Our paper, “LMDeploy Accelerates Mixed-Precision LLM Inference with TurboMind,” has been accepted to EuroSys 2027; [2026/04] PyPI has expanded the storage quota for LMDeploy and wheel uploads have resumed. v0.12.3 is now available on PyPI, so you can install it directly via pip install lmdeploy; [2026/02] Support Qwen3.5; [2026/02] Support vllm-project/llm-compressor 4bit symmetric/asymmetric quantization. Refer here for a detailed guide [2025/09] TurboMind supports

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stions I have about you making your own models is what is the constraints you operate in? So if you draw a parallel to language models, you see GPT-OSS 20B is very fit for your average MacBook. Then there’s a larger one that’s fit for a single GPU. Do you work in the constraints of robot, of model on on device, in cloud, a hybrid? And then how does that affect your scaling law of data? A million hours is great, but like do we just scale up parameters, data? Jason Ma [00:17:38]: No, that’s a grea

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Engineering work and experiments by Lat Ware, Member of the Technical Staff When Lat Ware started building what he named the Pedantic Review Bot, a pull request at Vapi took an average of over 130 hours to go from open to merged. That number changed how Lat thought about automated review. Vapi already paid for two AI review tools, and neither caught what his early prompts caught on Vapi’s codebase. “I was competing with review times measured in days, so I could let the bot spend 20 minutes on a

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