Wephrase the challenge ofsolving SMT formulas asatree search problemwhere ateach step atransformation is applied to the input formula until the formula is solved.

Whether your chip is running a vintage computer game or the latest DeepSeek model, it'll reward you for speaking its native language. But if you took a look beneath the pixels--the rickety rides, the crowds of hungry, thirsty, barfing people (and the janitors mopping in their wake)--deep down at the level of the code, you saw craftsmanship so obsessive that it bordered on insane. Chris Sawyer, the game's sole developer, wrote the whole thing in assembly. Because if/when the machines take over, we should at least speak their language. Certain programming languages, like Python or Go or C++, are called "high-level" because they work sort of like human language, written in commands and idioms that might fit in at a poetry slam.

Adaptive robust optimization (ARO) extends static robust optimization by allowing decisions to depend on the realized uncertainty - weakly dominating static solutions within the modeled uncertainty set. However, ARO makes previous constraints that were independent of uncertainty now dependent, making it vulnerable to additional infeasibilities when realizations fall outside the uncertainty set. This phenomenon of adaptive policies being brittle is analogous to overfitting in machine learning. To mitigate against this, we propose assigning constraint-specific uncertainty set sizes, with harder constraints given stronger probabilistic guarantees. Interpreted through the overfitting lens, this acts as regularization: tighter guarantees shrink adaptive coefficients to ensure stability, while looser ones preserve useful flexibility. This view motivates a principled approach to designing uncertainty sets that balances robustness and adaptivity.

LLM chatbot interfaces allow students to get instant, interactive assistance with homework, but doing so carelessly may not advance educational objectives. In this study, an interactive homework help system based on DeepSeek R1 is developed and first implemented for students enrolled in a large computer science beginning programming course. In addition to an assist button in a well-known code editor, our assistant also has a feedback option in our command-line automatic evaluator. It wraps student work in a personalized prompt that advances our educational objectives without offering answers straight away. We have discovered that our assistant can recognize students' conceptual difficulties and provide ideas, plans, and template code in pedagogically appropriate ways. However, among other mistakes, it occasionally incorrectly labels the correct student code as incorrect or encourages students to use correct-but-lesson-inappropriate approaches, which can lead to long and frustrating journeys for the students. After discussing many development and deployment issues, we provide our conclusions and future actions.

Recent advancements in software engineering (SWE) agents have largely followed a $\textit{tool-based paradigm}$, where agents interact with hand-engineered tool APIs to perform specific tasks. While effective for specialized tasks, these methods fundamentally lack generalization, as they require predefined tools for each task and do not scale across programming languages and domains. We introduce $\texttt{Programming with Pixels}$ (PwP), an agent environment that unifies software development tasks by enabling $\textit{computer-use agents}$-agents that operate directly within an IDE through visual perception, typing, and clicking, rather than relying on predefined tool APIs. To systematically evaluate these agents, we propose $\texttt{PwP-Bench}$, a benchmark that unifies existing SWE benchmarks spanning tasks across multiple programming languages, modalities, and domains under a task-agnostic state and action space. Our experiments demonstrate that general-purpose computer-use agents can approach or even surpass specialized tool-based agents on a variety of SWE tasks without the need for hand-engineered tools. However, our analysis shows that current models suffer from limited visual grounding and fail to exploit many IDE tools that could simplify their tasks. When agents can directly access IDE tools, without visual interaction, they show significant performance improvements, highlighting the untapped potential of leveraging built-in IDE capabilities. Our results establish PwP as a scalable testbed for building and evaluating the next wave of software engineering agents. We release code and data at https://programmingwithpixels.com

The Frank-Wolfe (FW) optimization algorithm has lately re-gained popularity thanks in particular to its ability to nicely handle the structured constraints appearing in machine learning applications. However, its convergence rate is known to be slow (sublinear) when the solution lies at the boundary. A simple lessknown fix is to add the possibility to take'away steps' during optimization, an operation that importantly does not require a feasibility oracle. In this paper, we highlight and clarify several variants of the Frank-Wolfe optimization algorithm that have been successfully applied in practice: away-steps FW, pairwise FW, fully-corrective FW and Wolfe's minimum norm point algorithm, and prove for the first time that they all enjoy global linear convergence, under a weaker condition than strong convexity of the objective. The constant in the convergence rate has an elegant interpretation as the product of the (classical) condition number of the function with a novel geometric quantity that plays the role of a'condition number' of the constraint set. We provide pointers to where these algorithms have made a difference in practice, in particular with the flow polytope, the marginal polytope and the base polytope for submodular optimization. The Frank-Wolfe algorithm [9] (also known as conditional gradient) is one of the earliest existing methods for constrained convex optimization, and has seen an impressive revival recently due to its nice properties compared to projected or proximal gradient methods, in particular for sparse optimization and machine learning applications. On the other hand, the classical projected gradient and proximal methods have been known to exhibit a very nice adaptive acceleration property, namely that the the convergence rate becomes linear for strongly convex objective, i.e. that the optimization error of the same algorithm after t iterations will decrease geometrically with O((1 ρ)

Language is commonly understood to be the "stuff" of thought. People "talk it out" and "speak their mind," follow "trains of thought" or "streams of consciousness." Some of the pinnacles of human creation--music, geometry, computer programming--are framed as metaphorical languages. The underlying assumption is that the brain processes the world and our experience of it through a progression of words. And this supposed link between language and thinking is a large part of what makes ChatGPT and similar programs so uncanny: The ability of AI to answer any prompt with human-sounding language can suggest that the machine has some sort of intent, even sentience.

Will AI art eventually permanently replace human artists? Never, with the statistical data predictive ML/DL/AI, as image generators or art programs; for it is brainless and mindless to its depth, mimicking artistic intelligence by its not very intelligent developers. Some might insist that today's ML/LLM models could do the art, as painting and composing, like human artists, thus meeting the Creativity Turing Test (TT), as AI-Da, who is sold as'the world's first ultra-realistic robot artist and whose first exhibition of self-portraits was on display at the Design Museum. Again, the deepfake AI art engines like DALL-E 2, Midjourney and Imagen can take an arbitrary text description and create original artwork IF ONLY PROPERLY PROMPTED. Their SENSELESS images range from simple to dizzyingly complex, from concrete to abstract, from cartoonish to photorealistic, as Jason Allen's A.I.-generated work, "Théâtre D'opéra Spatial," which took first place in the digital category at the Colorado State Fair.

Could an important step towards #AGI be to link words in a language model to concrete, pictorial descriptions of the words for concrete words, then abstract words would in turn be based on foundation of concrete words? Abstracta/Quality and Concreta/Quantity are interdependent, interconnected and interrelated, as everything else in the world. There is no principal difference between concrete entities and abstract entities, as (1) existence inside or outside space-time, (2) having causes and effects or not, (3) having contingent or necessary existence, (4) being particular or universal, (5) belonging to either the physical or the mental realm. The issue is how these two world are related and interconnected. Some believe in emergency: "Emergence is when quantitative changes in a system result in qualitative changes in behavior". Specifically, we define emergent abilities of large language models as abilities that are not present in smaller-scale models but are present in large-scale models; thus they cannot be predicted by simply extrapolating the performance improvements on smaller-scale models.

AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner Intelligence in an artificial intelligence (AI) system can be measured in mainly 4 ways: Breadth: Most systems we know to be intelligent such as the human brain have broad capabilities. A child learns a lot of tasks such as walking, talking and many more things. An AI system that should be considered intelligent should also have such similar broad capabilities. The so-called strong AI system should be able to learn any task without any modification directly to its source code by human engineers. But we all know about the no free lunch theorem which states that an algorithm that is good at a particular set of tasks pays for that by performing poorly on the other remaining set of tasks.