Andrew Jackson's White House once hosted a cheese feeding frenzy The seventh president's farewell party featured 1,400 pounds of cheddar. In 1835, a New York dairy farmer sent President Andrew Jackson a 1,400-pound cheddar cheese to celebrate the president's second inauguration. Two years later, it was finally eaten. Breakthroughs, discoveries, and DIY tips sent every weekday. It's February 1837, and the White House is about to bear witness to one of the greatest feeding frenzies in this nation's proud history of competitive consumption.

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.

The performance of a constraint model can often be improved by converting a subproblem into a single table constraint (referred to as tabulation). Finding subproblems to tabulate is traditionally a manual and time-intensive process, even for expert modellers. This paper presents TabID, an entirely automated method to identify promising subproblems for tabulation in constraint programming. We introduce a diverse set of heuristics designed to identify promising candidates for tabulation, aiming to improve solver performance. These heuristics are intended to encapsulate various factors that contribute to useful tabulation. We also present additional checks to limit the potential drawbacks of suboptimal tabulation. We comprehensively evaluate our approach using benchmark problems from existing literature that previously relied on manual identification by constraint programming experts of constraints to tabulate. We demonstrate that our automated identification and tabulation process achieves comparable, and in some cases improved results. We empirically evaluate the efficacy of our approach on a variety of solvers, including standard CP (Minion and Gecode), clause-learning CP (Chuffed and OR-Tools) and SAT solvers (Kissat). Our findings highlight the substantial potential of fully automated tabulation, suggesting its integration into automated model reformulation tools.

Conversational AI agents use Retrieval Augmented Generation (RAG) to provide verifiable document-grounded responses to user inquiries. However, many natural questions do not have good answers: about 25\% contain false assumptions~\cite{Yu2023:CREPE}, and over 50\% are ambiguous~\cite{DBLP:conf/emnlp/MinMHZ20}. RAG agents need high-quality data to improve their responses to confusing questions. This paper presents a novel guided hallucination-based method to efficiently generate a diverse set of borderline out-of-scope confusing questions for a given document corpus. We conduct an empirical comparative evaluation of several large language models as RAG agents to measure the accuracy of confusion detection and appropriate response generation. We contribute a benchmark dataset to the public domain.

In the wake of large language models, there has been a resurgence of claims and questions about the Turing test and its value for AI, which are reminiscent of decades of practical "Turing" tests. If AI were quantum physics, by now several "Schr\"odinger's" cats could have been killed. Better late than never, it is time for a historical reconstruction of Turing's beautiful thought experiment. In this paper I present a wealth of evidence, including new archival sources, give original answers to several open questions about Turing's 1950 paper, and address the core question of the value of Turing's test.

This week, the panel is joined by Slate writer and senior editor Sam Adams to dissect Poor Things, director Yorgos Lanthimos horny, steampunk Frankenstein tale about Bella Baxter (played by Emma Stone), a pregnant woman who commits suicide then is brought back to life by a brilliant scientist (Willem Dafoe), with an eccentric caveat: She now has the brain of her unborn fetus. Then, the three remember Norman Lear, the late television pioneer and American icon who died at the age of 101 and who was responsible for ushering in a new era of character-driven, comedic, topical, and morally serious TV with hit sitcoms like All in the Family, The Jeffersons, Maude, and One Day at a Time. Finally, they are joined by Slate's books and culture columnist, Laura Miller, who shares her top ten books of the year, and along with Dana, discusses the joys and challenges of year-end listmaking. In the exclusive Slate Plus segment, the panel reunites with Sam Adams to spoil Poor Things, detailing what is arguably the film's weakest portion: the final ten minutes. The deadline to submit is Wednesday, December 13.

We study a planning problem based on Plotting, a tile-matching puzzle video game published by Taito in 1989. The objective of this game is to remove a target number of coloured blocks from a grid by sequentially shooting blocks into the grid. Plotting features complex transitions after every shot: various blocks are affected directly, while others can be indirectly affected by gravity. We highlight the challenges of modelling Plotting with PDDL and of solving it with a grounding-based state-of-the-art planner.

Strive Asset Management founder Vivek Ramaswamy responds to the federal response to a derailed train in Ohio releasing toxic chemicals and discusses the growing list of potential GOP candidates as he weighs his own presidential bid. George Orwell envisioned the dangers of monolithic government armed with artificial intelligence in his famous novel of a future dystopia, "1984," published in 1949. The Party, led by Big Brother, uses omnipresent technology to monitor constantly and to propagandize to the docile citizens of Oceania. The terrifying tandem of technology and the human intoxicant of power is used in Oceania to rewrite history, control society, crush the human spirit and keep the Party entrenched forever. Protagonist Winston Smith works for the ironically named Ministry of Truth, a job he hates.

On June 25, 1949, the British neurologist Geoffrey Jefferson gave a lecture to the Royal College of Surgeons of England entitled The Mind of Mechanical Man. It may be surprising that machine intelligence was the subject of much debate in Jefferson's time, with some describing the 1904s as the period in which artificial intelligence was born following the development of cybernetics. Jefferson's ideas about the intersection of human and machine were ahead of their time and even impressed the great Alan Turing with their prescience and clarity. "[N]ot until a machine can write a sonnet or a concerto because of thoughts and emotions felt, and not by the chance fall of symbols, could we agree that machine equals brain -- that is, not only write it but know that it had written it," Jefferson said in his lecture. "No mechanism could feel (and not merely artificially signal, an easy contrivance) pleasure at its successes, grief when its valves fuse, be warmed by flattery, be made miserable by its mistakes, be charmed by sex, be angry or miserable when it cannot get what it wants." Whether they know it or not, critics of artificial intelligence's application in the art world -- and by extension, the world of NFTs -- employ a version of Jefferson's argument when they decry that the technology takes something away from the creative "soul" of artists and their work.

In Tom Hanks' latest movie, the beloved actor plays a man living in isolation from the rest of humanity and making his way through a sometimes-harsh environment, giving the star the opportunity to act opposite an untraditional and unemotive screen partner. The movie is Finch, a sci-fi drama now on Apple TV, but on paper it sounds a lot like Cast Away, the blockbuster survival drama that netted Hanks a Best Actor nomination in 2001. But while Finch is probably the closest Hanks has ever come to reviving that particular and distinctive mid-career triumph, it's not exactly an anomaly in Hanks' filmography, especially when you look at the last two decades. In this late stage, Hanks' all-American everyman has increasingly found himself going it alone, replacing the team efforts of Apollo 13 and Saving Private Ryan, and the romantic devotion of Forrest Gump and You've Got Mail and with stories that deal more explicitly with solitude. At the time of its release, Cast Away felt like a novelty act, albeit one executed with great skill.