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Train-to-Test scaling explained: How to optimize your end-to-end AI compute budget for inference
The standard guidelines for building large language models (LLMs) optimize only for training costs and ignore inference costs. This poses a challenge for real-world applications that use inference-tim [...]
New memory framework builds AI agents that can handle the real world's unpredictability
Researchers at the University of Illinois Urbana-Champaign and Google Cloud AI Research have developed a framework that enables large language model (LLM) agents to organize their experiences into a m [...]
Phi-4 proves that a 'data-first' SFT methodology is the new differentiator
AI engineers often chase performance by scaling up LLM parameters and data, but the trend toward smaller, more efficient, and better-focused models has accelerated. The Phi-4 fine-tuning methodology [...]
Google’s new framework helps AI agents spend their compute and tool budget more wisely
In a new paper that studies tool-use in large language model (LLM) agents, researchers at Google and UC Santa Barbara have developed a framework that enables agents to make more efficient use of tool [...]
Moonshot’s Kimi K2.5 is 'open,' 595GB, and built for agent swarms — Reddit wants a smaller one
Two days after releasing what analysts call the most powerful open-source AI model ever created, researchers from China's Moonshot AI logged onto Reddit to face a restless audience. The Beijing-b [...]
Thinking Machines challenges OpenAI's AI scaling strategy: 'First superintelligence will be a superhuman learner'
While the world's leading artificial intelligence companies race to build ever-larger models, betting billions that scale alone will unlock artificial general intelligence, a researcher at one of [...]