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Large Language Models (LLMs) owe much of their success to next-token prediction (NTP), but their autoregressive (AR) structure requires slow, sequential token generation. To overcome this bottleneck, we introduce diff... Context & related coverage →
GPT‑6 Astra GPT-6 Astra is "rolling out today to a limited set of organizations and over the coming days will become available to all ChatGPT Plus, Pro, Business, and Enterprise users, as well as through the OpenAI AP... Context & related coverage →
Repository-level software engineering benchmarks have significantly advanced the evaluation of coding agents, but existing benchmarks primarily measure whether generated patches pass functional tests and overlook revi... Context & related coverage →
For scientific progress, we need benchmarks that test the limits of state-of-the-art models, and evaluation methods that inform us about failure cases. As models get stronger, standard benchmarks for machine translati... Context & related coverage →
Retrieval-augmented generation (RAG) complements parametric models with retrieved external evidence. The same idea is attractive for continuous-output regression, but directly reusing retrieved target values is often... Context & related coverage →
Understanding the frequency of factual errors in chatbot-generated text and evaluating systems that detect these errors is critical for determining chatbot safety. Yet factual-error detection is often treated as a sin... Context & related coverage →
A guide on scaling agents in Europe & the Middle East to see how Schneider Electric, Vodafone, and monday.com are approaching production AI at scale, from establishing shared agent platforms and LLMOps practices to de... Context & related coverage →
Shopify's engineering introduced gisting, a novel technique for compressing long LLM prompts into a smaller set of learned "gist" tokens, improving throughput and reducing inference cost. By Sergio De Simone Context & related coverage →
MCP support now lives in langchain.mcp, built on FastMCP for the 2026-07-28 spec, with elicitation handled as a LangGraph interrupt and tool lists cached. Context & related coverage →
We spent 20B+ tokens of GPT-6 Astra to explore everything. Here’s our learnings. Context & related coverage →