Inside Dewar's cavernous whisky warehouses, man's best mechanical friend--a Boston Dynamics robot dog with an ethanol sensor for a nose--is on the hunt for leaky barrels. Wooden barrels are what make the magic happen in your favorite bottle of whisky . At Bacardi Limited, the world's largest privately held spirits company, barrel leakage is a massive headache. Consider the company's Dewar's blended Scotch whisky brand (just one of the dozens it owns). Most of the time, Dewar's will have over 100 warehouses full of aging barrels of whisky, 25,000 casks in each one.

Big hitter Peter F. Hamilton has a new sci-fi novel out this month - and Booker winner George Saunders ventures into speculative fiction with his latest book, Vigil Is it an asteroid or an alien in Van Jensen's Godfall? Welcome to January, a month when many of us are keen to escape from the world into the pages of a book. Thankfully, science fiction is here to help, whether that's with a story set on a generation ship where things aren't as they seem, courtesy of Peter F. Hamilton, or journeying to an alternate version of this world where the Roman Empire is still in charge, in Solitaire Townsend's . Add to the mix a time-loop murder, a UFO romance and some eco-horror, and there's plenty of choice for sci-fi fans this month. A generation ship is in search of a new home in Peter F. Hamilton's latest sci-fi novel Big hitter Peter F. Hamilton sets his latest outing on a generation ship in search of a new world, where people are only allowed to live for 65 years so they don't deplete the ship's resources. When a teenager Hazel's brother has an accident that means he is no longer productive, he is set to be killed off.

This paper is concerned with the problem of representing and learning a linear transformation using a linear neural network.

Although Large Language Models (LLMs) show exceptional fluency, efforts persist to extract stronger reasoning capabilities from them. Drawing on search-based interpretations of LLM computation, this paper advances a systematic framework for understanding LLM reasoning and optimization. Namely, that enhancing reasoning is best achieved by structuring a multi-agent pipeline to ensure a traversal of the search space in a gradual, incremental, and sequential (GIS) manner. Stated succinctly, high-quality reasoning is a controlled, incremental search. To test this framework, we investigate the efficacy of recursive refinement (RR)--an iterative process of self-criticism, adversarial stress-testing, and integrating critical feedback--as a practical method for implementing GIS search. We designed an experiment comparing a simple, linear pipeline against a complex, explicitly structured pipeline leveraging a recursive refinement layer. The multi-agent models were constructed to reflect the historical personas of three US Founding Fathers (Hamilton, Jefferson, and Madison) using RAG-powered corpora and were prompted to generate responses to three contemporary political issues. Model performance was evaluated using a two-tiered approach: a quantitative score from an LLM arbiter agent and qualitative human judgment. Our results revealed that the complex model consistently outperformed the simple model across all nine test cases with an average arbiter-outputted score of 88.3 versus 71.7. The complex model's arguments were superior in analytical depth, structural nuance, and strategic framing. We conclude that recursive refinement is a robust architectural feature for enhancing LLM reasoning via GIS search.

Could an AI scientist help researchers come up with breakthroughs by analysing data and searching the existing scientific literature? That's the claim of the inventors of Kosmos, but not everyone is convinced Artificial intelligence can process large amounts of data, but can it do science? An AI scientist can work independently for hours while doing research that would take humans months to complete, and has made several "novel contributions" to science, its creators claim - but others are more doubtful. The system, called Kosmos, is actually a collection of AI agents that are specialised in analysing data and searching through the existing scientific literature, in an effort to make new scientific breakthroughs. "We've been working on building an AI scientist for about two years now," says Sam Rodriques at Edison Scientific, the US-based firm behind Kosmos.

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For a long time, the authorship of the Federalist Papers had been a subject of inquiry and debate, not only by linguists and historians but also by statisticians. In what was arguably the first Bayesian case study, Mosteller and Wallace (1963) provided the first statistical evidence for attributing all disputed papers to Madison. Our paper revisits this historical dataset but from a lens of modern language models, both small and large. We review some of the more popular Large Language Model (LLM) tools and examine them from a statistical point of view in the context of text classification. We investigate whether, without any attempt to fine-tune, the general embedding constructs can be useful for stylometry and attribution. We explain differences between various word/phrase embeddings and discuss how to aggregate them in a document. Contrary to our expectations, we exemplify that dimension expansion with word embeddings may not always be beneficial for attribution relative to dimension reduction with topic embeddings. Our experiments demonstrate that default LLM embeddings (even after manual fine-tuning) may not consistently improve authorship attribution accuracy. Instead, Bayesian analysis with topic embeddings trained on ``function words" yields superior out-of-sample classification performance. This suggests that traditional (small) statistical language models, with their interpretability and solid theoretical foundation, can offer significant advantages in authorship attribution tasks. The code used in this analysis is available at github.com/sowonjeong/slm-to-llm

Crowds gather as Lewis Hamilton drives a Ferrari Formula 1 car for the first time at the team's Fiorano test track.