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Microsoft Research published a post about EvoLib, an approach that aims to convert accumulated experience into evolving knowledge by extracting reusable skills and insights to help LLMs learn and adapt across tasks after deployment. The post emphasizes that LLMs do not get smarter simply by remembering more, and positions EvoLib as a way to reuse experience for long-term adaptability.
The Trump administration released a 28-page AI Action Plan and an executive order titled “Preventing Woke AI in the Federal Government” that urges federal procurement to prioritize AI systems described as "truthful" and calls for reviewing Biden-era AI rules to remove references to misinformation, Diversity, Equity, and Inclusion, and climate change. Media commentary warns the directive could pressure companies to align model behavior with the administration’s political definitions of truth; so far major AI firms have not publicly objected, with some offering positive or neutral responses.
Researchers at Google (Bahare Fatemi and Bryan Perozzi) propose methods to translate graph-structured data into text that large language models can reason over, and introduce GraphQA, a benchmark of graph reasoning tasks and graph generators. They report that LLM performance varies with encoding method, task type, and graph structure, and that choosing the right encoding can improve graph-task performance by up to about 60%.
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