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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...
It is one thing to say AI will change the world. It is another to expect the class of 2026...
This year, there’s been a wave of notable energy companies going public via IPO in the US. The solar and...
Modern AI agents built on top of large language models (LLMs) are designed to run continuously.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in...
Modern AI agents built on top of large language models (LLMs) are designed to run continuously.
Reinforcement learning for language agents is growing more complex. Agents now manage multi-turn tool use, long-running contexts, and multi-agent orchestration....
arXiv:2605.26849v1 Announce Type: new Abstract: Sampling multiple responses improves language model reasoning, but uniform compute allocation is inefficient: easy questions...
Large language models become static after pretraining. Their knowledge does not update as the world changes. Retraining a full LLM...
Static vs. Dynamic vs. Continuous Batching in LLM Inference
Stateful vs. Stateless Agent Design: Tradeoffs for Scalable Agentic Systems
Start Making Sense(s): A Developmental Probe of Attention Specialization Using Lexical Ambiguity
STAR-Bench: Probing Deep Spatio-Temporal Reasoning as Audio 4D Intelligence
Stanford Researchers Introduce TRACE: A Capability-Targeted Agentic Training System That Turns Recurrent Agent Failures Into Synthetic RL Environment
