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Perplexity AI Open-Sources Unigram Tokenizer That Achieves 5x Lower p50 Latency Than Hugging Face tokenizers Crate

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Perplexity AI’s research team reimplemented their Unigram tokenizer from scratch in Rust and open-sourced the code in pplx-garden, their inference technology repository. At production input lengths, the new encoder cuts p50 latency by roughly 5x versus the Hugging Face tokenizers crate, ~2x versus SentencePiece (C++), and ~1.5x versus IREE’s tokenizer (C), with zero steady-state heap […]

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Sakana AI Proposes DiffusionBlocks: a Block-wise Training Framework That Converts Residual Networks into Independently Trainable Denoising Modules

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Researchers from Sakana AI and the University of Tokyo propose DiffusionBlocks. It trains transformer-based networks one block at a time. Training memory is reduced by a factor of B, where B is the number of blocks. Performance is maintained across diverse architectures. The Memory Problem in Neural Network Training End-to-end backpropagation requires storing intermediate activations

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NVIDIA Releases Polar, a Token-Faithful Rollout Framework for GRPO Training Across Codex, Claude Code, and Qwen Code

agentic ai, ai, AI (Artificial Intelligence), AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine Learning, New Releases, Open Source, Staff, Tech News, Technology

Reinforcement learning for language agents is growing more complex. Agents now manage multi-turn tool use, long-running contexts, and multi-agent orchestration. The main engineering challenge is connecting existing agent software to training pipelines without breaking how those tools work. NVIDIA’s research team introduced Polar, a rollout framework that lets researchers run reinforcement learning over any agent

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https://vllm.ai/blog/2026-05-26-eagle-3-1

Meet EAGLE 3.1: The Speculative Decoding Algorithm That Fixes Attention Drift in LLM Inference

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Speculative decoding is a technique for speeding up large language model inference. A small, fast draft model proposes several tokens. The large target model verifies them in parallel. If accepted, inference is faster. If rejected, the system falls back gracefully. EAGLE Team, vLLM Team, and TorchSpec Team has launched the EAGLE series including EAGLE 1,

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MEMO: A Modular Framework for Training a Dedicated Memory Model on New Knowledge Without Modifying LLM Parameters

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Large language models become static after pretraining. Their knowledge does not update as the world changes. Retraining a full LLM is too expensive at modern scales. Fine-tuning risks degrading previously learned knowledge. Retrieval-augmented generation (RAG) struggles when answers require reasoning across many documents. A team of researchers from the National University of Singapore, MIT CSAIL,

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Together AI Open-Sources OSCAR: An Attention-Aware 2-Bit KV Cache Quantization System for Long-Context LLM Serving

Long-context inference makes the KV cache one of the main costs of serving LLMs. During autoregressive decoding, the cache grows with context length, batch size, and model depth. At high batch sizes and long contexts with 100K tokens across dozens of concurrent requests the KV cache consumes a large fraction of GPU memory. Compressing it

Together AI Open-Sources OSCAR: An Attention-Aware 2-Bit KV Cache Quantization System for Long-Context LLM Serving Read More »

Best Authentication Platforms for AI Agents and MCP Servers in 2026

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The Model Context Protocol has moved from Anthropic’s internal experiment to a de facto industry standard at a speed few integration protocols have matched. Since its launch in November 2024, MCP has grown explosively: OpenAI adopted it in March 2025, Microsoft announced support in Copilot Studio in March 2025, and by late 2025 combined Python

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WorkOS Releases auth.md: An Open Agent Registration Protocol Built on OAuth Standards

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For years, authentication on the web followed one design assumption: a human sits behind a browser. Click a button. Fill out a form. Verify an email. Copy an API key and paste it somewhere else. That model does not work when the user is delegating work to an agent. Agents are already writing code, opening

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Build a Complete Langfuse Observability and Evaluation Pipeline for Tracing, Prompt Management, Scoring, and Experiments

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In this tutorial, we implement the Langfuse (an open-source LLM engineering platform) pipeline for tracing, prompt management, scoring, datasets, and experiments. We build a complete workflow that works with either a real OpenAI key or a deterministic mock LLM, so we can understand every major Langfuse feature without depending on paid model access. We start

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Microsoft Research Releases Webwright: A Terminal-Native Web Agent Framework That Scores 60.1% on Odysseys, Up from Base GPT-5.4’s 33.5%

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Most web agents today drive a browser one action at a time. The model receives the current page state — as a screenshot or DOM text — and predicts the next click, keypress, or scroll. This action-at-a-time design made sense when language models had limited reasoning ability. As models have become more capable at writing

Microsoft Research Releases Webwright: A Terminal-Native Web Agent Framework That Scores 60.1% on Odysseys, Up from Base GPT-5.4’s 33.5% Read More »