Deterministic We shows Lemma Given >0, ther I and PoF (G,I,0) 1 .

Quantum annealing offers a promising paradigm for solving NP-hard combinatorial optimization problems, but its practical application is severely hindered by two challenges: the complex, manual process of translating problem descriptions into the requisite Quadratic Unconstrained Binary Optimization (QUBO) format and the scalability limitations of current quantum hardware. To address these obstacles, we propose a novel end-to-end framework, LLM-QUBO, that automates this entire formulation-to-solution pipeline. Our system leverages a Large Language Model (LLM) to parse natural language, automatically generating a structured mathematical representation. To overcome hardware limitations, we integrate a hybrid quantum-classical Benders' decomposition method. This approach partitions the problem, compiling the combinatorial complex master problem into a compact QUBO format, while delegating linearly structured sub-problems to classical solvers. The correctness of the generated QUBO and the scalability of the hybrid approach are validated using classical solvers, establishing a robust performance baseline and demonstrating the framework's readiness for quantum hardware. Our primary contribution is a synergistic computing paradigm that bridges classical AI and quantum computing, addressing key challenges in the practical application of optimization problem. This automated workflow significantly reduces the barrier to entry, providing a viable pathway to transform quantum devices into accessible accelerators for large-scale, real-world optimization challenges.

We write a lot about robots here at New Scientist – the latest cutting-edge developments, the newest technology. But we also have a great deal of fondness for them in fiction, whether that's the super cute likes of WALL-E and BB-8, or the darker side of the robotic family, from the Terminator to Ava from Ex Machina. Last month, Sierra Greer's novel about the rebellion of a robot designed for intimacy, Annie Bot, won this year's Arthur C Clarke award, the UK's top prize for science fiction. It was described by judges as "a tightly-focused first person account of a robot designed to be the perfect companion who struggles to become free". Greer's win felt like the right moment to ask New Scientist staff to nominate their own favourite fictional robotic beings, from page or screen. After a bit of quibbling about what constitutes a robot, and a lot of people plumping for various Star Wars droids and Futurama creations, here, in no particular order, they are.

The novelist Ewan Morrison was alarmed, though amused, to discover he had written a book called Nine Inches Pleases a Lady. Intrigued by the limits of generative artificial intelligence (AI), he had asked ChatGPT to give him the names of the 12 novels he had written. "I've only written nine," he says. "Always eager to please, it decided to invent three." The "nine inches" from the fake title it hallucinated was stolen from a filthy Robert Burns poem.

AI chatbots from tech companies such as OpenAI and Google have been getting so-called reasoning upgrades over the past months – ideally to make them better at giving us answers we can trust, but recent testing suggests they are sometimes doing worse than previous models. The errors made by chatbots, known as "hallucinations", have been a problem from the start, and it is becoming clear we may never get rid of them. Hallucination is a blanket term for certain kinds of mistakes made by the large language models (LLMs) that power systems like OpenAI's ChatGPT or Google's Gemini. It is best known as a description of the way they sometimes present false information as true. But it can also refer to an AI-generated answer that is factually accurate, but not actually relevant to the question it was asked, or fails to follow instructions in some other way.

In at least one crucial way, AI has already won its campaign for global dominance. An unbelievable volume of synthetic prose is published every moment of every day--heaping piles of machine-written news articles, text messages, emails, search results, customer-service chats, even scientific research. Chatbots learned from human writing. Now the influence may run in the other direction. Some people have hypothesized that the proliferation of generative-AI tools such as ChatGPT will seep into human communication, that the terse language we use when prompting a chatbot may lead us to dispose of any niceties or writerly flourishes when corresponding with friends and colleagues.

Many Bayesian statistical inference problems come down to computing a maximum a-posteriori (MAP) assignment of latent variables. Yet, standard methods for estimating the MAP assignment do not have a finite time guarantee that the algorithm has converged to a fixed point. Previous research has found that MAP inference can be represented in dual form as a linear programming problem with a non-polynomial number of constraints. A Lagrangian relaxation of the dual yields a statistical inference algorithm as a linear programming problem. However, the decision as to which constraints to remove in the relaxation is often heuristic. We present a method for maximum a-posteriori inference in general Bayesian factor models that sequentially adds constraints to the fully relaxed dual problem using Benders' decomposition. Our method enables the incorporation of expressive integer and logical constraints in clustering problems such as must-link, cannot-link, and a minimum number of whole samples allocated to each cluster. Using this approach, we derive MAP estimation algorithms for the Bayesian Gaussian mixture model and latent Dirichlet allocation. Empirical results show that our method produces a higher optimal posterior value compared to Gibbs sampling and variational Bayes methods for standard data sets and provides certificate of convergence.

An agent providing an information retrieval service may work with a corpus of text documents. The documents in the corpus may contain annotations such as Subjective Content Descriptions (SCD) -- additional data associated with different sentences of the documents. Each SCD is associated with multiple sentences of the corpus and has relations among each other. The agent uses the SCDs to create its answers in response to queries supplied by users. However, the SCD the agent uses might reflect the subjective perspective of another user. Hence, answers may be considered faulty by an agent's user, because the SCDs may not exactly match the perceptions of an agent's user. A naive and very costly approach would be to ask each user to completely create all the SCD themselves. To use existing knowledge, this paper presents ReFrESH, an approach for Relation-preserving Feedback-reliant Enhancement of SCDs by Humans. An agent's user can give feedback about faulty answers to the agent. This feedback is then used by ReFrESH to update the SCDs incrementally. However, human feedback is not always unambiguous. Therefore, this paper additionally presents an approach to decide how to incorporate the feedback and when to update the SCDs. Altogether, SCDs can be updated with human feedback, allowing users to create even more specific SCDs for their needs.

Benders decomposition is widely used to solve large mixed-integer problems. This paper takes advantage of machine learning and proposes enhanced variants of Benders decomposition for solving two-stage stochastic security-constrained unit commitment (SCUC). The problem is decomposed into a master problem and subproblems corresponding to a load scenario. The goal is to reduce the computational costs and memory usage of Benders decomposition by creating tighter cuts and reducing the size of the master problem. Three approaches are proposed, namely regression Benders, classification Benders, and regression-classification Benders. A regressor reads load profile scenarios and predicts subproblem objective function proxy variables to form tighter cuts for the master problem. A criterion is defined to measure the level of usefulness of cuts with respect to their contribution to lower bound improvement. Useful cuts that contain the necessary information to form the feasible region are identified with and without a classification learner. Useful cuts are iteratively added to the master problem, and non-useful cuts are discarded to reduce the computational burden of each Benders iteration. Simulation studies on multiple test systems show the effectiveness of the proposed learning-aided Benders decomposition for solving two-stage SCUC as compared to conventional multi-cut Benders decomposition.

On Tuesday morning, the merchants of artificial intelligence warned once again about the existential might of their products. Hundreds of AI executives, researchers, and other tech and business figures, including OpenAI CEO Sam Altman and Bill Gates, signed a one-sentence statement written by the Center for AI Safety declaring that "mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war." Those 22 words were released following a multi-week tour in which executives from OpenAI, Microsoft, Google, and other tech companies called for limited regulation of AI. They spoke before Congress, in the European Union, and elsewhere about the need for industry and governments to collaborate to curb their product's harms--even as their companies continue to invest billions in the technology. Several prominent AI researchers and critics told me that they're skeptical of the rhetoric, and that Big Tech's proposed regulations appear defanged and self-serving.