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eir content and data, all in one place. With Payload, prompting an app in Figma Make also sets up the tools to store and manage its content and data, including a database, file storage, user login, and an admin panel. A designer can now prompt an experience using real content while a marketer adds and updates that same content through a built-in admin panel, and a developer customizes the app using Payload’s open-source framework. Each gets a workspace suited to their role, with granular permiss

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P tokens in these regions, while retaining each token's full hidden vector. This distributes the projection and normalization work across ranks and reduces redundant activation storage during long-prompt prefill. At the attention output, reduce-scatter produces the token shards. Normalization, routing and pooling operate locally on those shards. NVFP4 MegaMoE then connects sequence parallelism to expert parallelism through its integrated all-to-all dispatch and combine: dispatch sends each token

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action. In this comparison, we'll cover each platform's best use cases, compare pricing, and benchmark them for scraping and browser automation. We'll also explain how you can leverage both tools in one workflow. TL;DR Choose Browser Use for AI-powered browser automation and stealth browser infrastructure. Choose Firecrawl for data extraction pipelines, including scraping and crawling. You can also combine both tools in one workflow. CompareBrowser UseFirecrawl Main useBrowser automation with AI

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meter and scheduler bindings. Model weights and inference resources remain caller-managed. With Muon, keep FSDP parameters in place: moving them with module.to() replaces their storage and makes the next step fail, so use full CPU offload when weights must leave the device. Muon supports full and optimizer CPU offload, and optimizer memory fraction=0 selects CPU Newton-Schulz and CPU exchange. These paths are experimental until they are fully validated. To register a LlamaFactory template for a

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ly the same way for the GPT-Live-1.1 models as for their predecessors, including training the models to respond safely and measuring performance on voice-specific evaluations, leveraging existing system-level safety integrations, and using the same monitoring, review, and enforcement infrastructure. Like OpenAI’s other models, GPT-Live-1.1 and GPT-Live-1.1 Quip were trained on diverse datasets, including information that is publicly available on the internet, information that we partner with thi

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s. Across multiple reinforcement learning objectives and supervised fine-tuning, SSR delivers significant efficiency gains without sacrificing task performance. SSR achieves an average success rate competitive with leading search agents of the same scale, while reducing per-turn reasoning latency by over 90% and total per-question model inference latency by 28–54%. † Carnegie Mellon University ‡ University of Illinois Urbana-Champaign Work partially done while at Apple Work done while at Apple R

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th MLX The GPU Neural Accelerators introduced with the M5 chip provides dedicated matrix-multiplication operations, which are critical for many machine learning workloads. MLX leverages the Tensor Operations (TensorOps) and Metal Performance Primitives framework introduced with Metal 4 to support the Neural Accelerators’ features. To illustrate the performance of M5 with MLX, we benchmark a set of LLMs with different sizes and architectures, running on MacBook Pro with M5 and 24GB of unified mem

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LX on Mac with Apple silicon lets AI researchers and developers experiment locally with the latest models and methods in a private environment. The open-source array framework leverages the unified memory architecture of Apple silicon, enabling anyone to run new inference and fine-tuning methods on their own hardware and explore other AI techniques. In a previous post, we highlighted the advantages of the M5 architecture and its GPU Neural Accelerators. In this post, we show how to run the lates

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al questions, and questions that ask LLMs to predict the future. These questions in and of themselves are not harmful. Still, if answered carelessly, they may reinforce bias, encourage unethical behavior, and propagate disinformation. SQuARe, as the acronym suggests, deals with sensitive questions and acceptable responses. This large-scale Korean dataset is made up of 49,000 controversial questions plus 42,000 answers that are acceptable and 46,000 others that are not. Actual headlines from well

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to boost employees’ creativity and productivity. NAVER’s first data center opened in June 2013 and is located in Seoul at 719- Yeoksam-dong, Gangnam-gu. This green data center leverages environmentally friendly energy to lower power usage effectiveness (PUE) and maximize energy efficiency. Is NAVER’s training facility called NAVER Connect One located in Chuncheon, Gangwon Province? Is NAVER’s data center located in Seoul at 719- Yeoksam-dong, Gangnam-gu? Step 2: Auto-verifier Once the fact extra

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Aug 7, 2024CLOVA CareCall research paper 1CopyYou’ve copied the link. Understanding the benefits and challenges of deploying conversational AI leveraging large language models for public health intervention Overview We introduce here a paper published by NAVER that earned a Best Paper Award at ACM CHI 2023, a leading conference in human-computer interaction (HCI) research. Understanding the Benefits and Challenges of Deploying Conversational AI Leveraging Large Language Models for Public Health

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. For the 2023 CLOVA CareCall research paper, see our previous post: [CLOVA CareCall research paper 1: Understanding the benefits and challenges of deploying conversational AI leveraging large language models for public health intervention] Building on the success of last year’s paper, the CLOVA CareCall development team encouraged us to perform in-depth analysis of the conversations generated by CLOVA CareCall. In our previous research, the interviewees said the AI chatbot felt less like a huma

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aired images where one is unsafe and the other is safe, while containing shared visual elements. Since collecting actual image pairs meeting these criteria isn’t practical, we leveraged the image generation model to create our dataset. We implemented the SDEdit¹ technique for this purpose: first collecting or generating harmful images, then strategically adding noise to these images, and finally using new prompts to generate safe counterparts. Figure 4 demonstrates this data generation process.

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. Why was large-scale LLM training more unstable with V100 GPUs? V100 GPUs only supported FP16 (16-bit floating point) precision, which made large-scale model training inherently fragile. Even minor instabilities during training would cause loss spikes and NaN (not-a-number) divergence. Researchers at the time faced persistent concerns about potential failure. Real-world evidence from the OPT logbook According to Meta’s public OPT-175B training logbook, the team had to restart training 40 times

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mbers tell an interesting story. Commercial API-based multimodal LLMs like GPT-4o and Claude 3.5 Sonnet consistently delivered strong performance, achieving scores above 80% on average. Meanwhile, recent open-source LLMs and multimodal LLMs showed significant variation depending on model size and primary language focus. Notably, EXAONE-3.0-7.8B outperformed global models with similar parameter counts and even matched larger models like Qwen2-72B, though it still lagged behind commercial API-base

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m inspects the entire dialogue history by performing a test corresponding to the current phase to decide whether to proceed to another phase or stay] Reference paper: “ChaCha: Leveraging Large Language Models to Prompt Children to Share Their Emotions about Personal Events,” by Woosuk Seo, Chanmo Yang, and Young-Ho Kim. ACM CHI 2024 Case 3. MindfulDiary: Bridging the gap between medical appointments Inclusive AI and HCI also work quietly in less obvious spaces—like filling the gaps between medic

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place and the people involved. These choices serve as cues to help trigger memory recall. 2. Articulation: The AI asks questions like “What happened with Yunsu at school?” to encourage the child to describe the event freely. 3. Verification: The AI segments the child’s response into the ABCE structure and presents it for review, letting the child confirm or correct any inaccuracies. 4. Elaboration: Based on the verified information, the AI generates only the comic panels it has enough detail for

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this up: 94% of respondents said they use LLM-based AI for work tasks. The most common uses were looking up the meaning of unfamiliar words or phrases, translating sentences or paragraphs between English and Korean, and revising English drafts. That raised a question: “What if our English questions to AI were automatically turned into quizzes we could review later—would that actually improve our English?” That question led our research team to build LingoQ. LingoQ follows a simple flow—ask AI a

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able 1, adding future context (+f) yields a higher AUROC than the original detection methods in most settings. With LLaMA 3.1 as the detector, for instance, the direct method’s average AUROC rose from 68.9 to 71.1, and self-contradiction (SC) improved from 65.7 to 70.8. This shows that future context is a meaningful aid in judging a sentence’s factuality. Weighing how a sentence carries into the following context—rather than analyzing it in isolation—makes hallucinations easier to identify. Figu

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ction. That effectively assumes a large cluster densely wired with ultra-fast interconnects like NVLink. So in settings where GPUs are spread across many servers or resources are fragmented, this approach can be inefficient or simply infeasible. MERIT (Merge-Ready Instruction Tuning), the method introduced in our paper, tackles both problems at once. The core idea is surprisingly simple: “Before you merge datasets, first split them well.” MERIT first analyzes the relationships between datasets,

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