Deep Learning, NLP, NMT, AI, ML
At a glance Today’s critical services and applications are often highly concurrent, using hundreds of threads. They also operate at large memory scales, frequently hundreds of gigabytes, especially when using large language models. mimalloc is an open-source, modern, scalable memory allocator that is a drop-in replacement
Read moreMicrosoft releases a lightweight foundation model that can predict AC optimal power flow in milliseconds, boosting efficiency and unlocking cost savings in grid analysis. At a glance Microsoft introduces GridSFM, a small foundation model that approximates AC optimal power flow in milliseconds, unlocking decisions
Read moreInteractive Quiz ⋅ 8 QuestionsBy Joseph Peart Share In this quiz, you’ll test your understanding of The LEGB Rule & Understanding Python Scope. By working through this quiz, you’ll revisit how Python resolves names using the LEGB rule, what the local, enclosing, global, and built-in scopes look like in practice, and how the global and nonlocal statements let you reach across scope boundaries. The quiz contains 8 questions and there is no time limit. You’ll get 1 point for each […]
Read moreInteractive Quiz ⋅ 9 QuestionsBy Joseph Peart Share In this quiz, you’ll test your understanding of Building Type-Safe LLM Agents With Pydantic AI. By working through this quiz, you’ll revisit how Pydantic AI returns structured outputs from LLMs, how validation retries improve reliability, how tools and function calling work, how dependency injection flows through RunContext, and what trade-offs to expect when running agents in production. The quiz contains 9 questions and there is no time limit. You’ll get 1 point […]
Read morePydantic AI is a Python framework for building LLM agents that return validated, structured outputs using Pydantic models. Instead of parsing raw strings from LLMs, you get type-safe objects with automatic validation. If you’ve used FastAPI or Pydantic before, then you’ll recognize the familiar pattern of defining schemas with type hints and letting the framework handle the type validation for you. By the end of this video course, you’ll understand that: Pydantic AI uses BaseModel classes to define structured outputs […]
Read moreAt a glance Experimental validation: Using high-throughput screening with MatterSim-v1, we previously identified tetragonal tantalum phosphorus (TaP) as a potential high-performance thermal conductor. Now we have experimentally synthesized it and measured its thermal conductivity (152 W/m/K) to be close to the thermal
Read moreAt a glance AI agents are moving into social contexts. When agents manage calendars, negotiate purchases, or interact with other agents on a user’s behalf, they need more than task competence—they need social reasoning. SocialReasoning-Bench evaluates that ability. The benchmark tests whether an agent can negotiate for
Read more
“When a measure becomes a target, it ceases to be a good measure.” (Goodhart’s Law) TLDR: Appen Inc. and DataoceanAI have provided high-quality English ASR datasets covering scripted and conversational speech over multiple accents. To prevent potential risks of benchmaxxing or test-set contamination, we will keep these datasets private for a high-quality measure of performance on multiple tasks. We’re not updating the average WER at this time: by default, the leaderboard’s Average WER remains computed on public datasets only. You […]
Read more
PipelineRL uses vLLM as the inference engine for rollout generation. The inference engine samples tokens and returns token logprobs; the trainer uses those logprobs to compute policy ratios, KL, clip rate, entropy, and reward. Any discrepancy in how those logprobs are computed can change the training dynamics. This is the train-inference mismatch we needed to eliminate during the vLLM V0 to V1 migration. TL;DR. vLLM V1 matched our vLLM V0 reference after we fixed four things: processed rollout logprobs, V1-specific […]
Read more
🧠 Models: https://huggingface.co/collections/allenai/emo | 📄 Tech report: https://allenai.org/papers/emo | 💻 Code: https://github.com/allenai/EMO | 📊 Visualization: https://emovisualization.netlify.app/ Today we’re releasing EMO, a new mixture-of-experts (MoE) model pretrained end-to-end so that modular structure emerges directly from the data without relying on human-defined priors. EMO lets you use a small subset of its experts – just 12.5% of the total – for a given task while keeping near full-model performance, and still works as a strong general-purpose model when all experts are used […]
Read more