Unlike reasoning, which often entails a deep sequence of deductive steps, complex real-world planning is characterized by the need to synthesize a broad spectrum of parallel and potentially conflicting information and constraints. For example, in travel planning scenarios, it requires the integration of diverse real-world information and user preferences. While LLMs show promise, existing methods with long-horizon thinking struggle with handling multifaceted constraints, leading to suboptimal solutions. Motivated by the challenges of real-world travel planning, this paper introduces the Multiple Aspects of Planning (MAoP), empowering LLMs with "wide-horizon thinking" to solve planning problems with multifaceted constraints. Instead of direct planning, MAoP leverages the strategist to conduct pre-planning from various aspects and provide the planning blueprint for planners, enabling strong inference-time scalability by scaling aspects to consider various constraints. In addition, existing benchmarks for multi-constraint planning are flawed because they assess constraints in isolation, ignoring causal dependencies within the constraints, e.g, travel planning, where past activities dictate future itinerary. To address this, we propose Travel-Sim, an agent-based benchmark assessing plans via real-world simulation, thereby inherently resolving these causal dependencies. This paper advances LLM capabilities in complex planning and offers novel insights for evaluating sophisticated scenarios through simulation.

Jessica used to come for tea on Tuesdays, and all she wanted to do was cut grass. Because she was a couple of years younger than me, she couldn't encounter a ChuChu or a Bokoblin without dying, so instead she'd spend hours slicing at virtual greenery. At the time, I found it a little annoying. In hindsight, I understand that Jessica was simply following in the footsteps of our ancestors. Grass-cutting has been a mainstay of video games for decades.

Recent work has demonstrated a remarkable ability to customize text-to-image diffusion models to multiple, fine-grained concepts in a sequential (i.e., continual) manner while only providing a few example images for each concept. This setting is known as continual diffusion. Here, we ask the question: Can we scale these methods to longer concept sequences without forgetting? Although prior work mitigates the forgetting of previously learned concepts, we show that its capacity to learn new tasks reaches saturation over longer sequences. We address this challenge by introducing a novel method, STack-And-Mask INcremental Adapters (STAMINA), which is composed of low-ranked attention-masked adapters and customized MLP tokens. STAMINA is designed to enhance the robust fine-tuning properties of LoRA for sequential concept learning via learnable hard-attention masks parameterized with low rank MLPs, enabling precise, scalable learning via sparse adaptation. Notably, all introduced trainable parameters can be folded back into the model after training, inducing no additional inference parameter costs. We show that STAMINA outperforms the prior SOTA for the setting of text-to-image continual customization on a 50-concept benchmark composed of landmarks and human faces, with no stored replay data. Additionally, we extended our method to the setting of continual learning for image classification, demonstrating that our gains also translate to state-of-the-art performance in this standard benchmark.

You're familiar with the phrase, "A picture is worth 1,000 words." Well, Microsoft and Intel are applying this philosophy to malware detection--using deep learning and a neural network to turn malware into images for analysis at scale. Project STAMINA--an acronym for STAtic Malware-as-Image Network Analysis--converts malware samples into two-dimensional grayscale images that can be analyzed based on their unique criteria. Researchers from the two companies have worked together to develop this interesting approach to malware detection. STAMINA uses deep learning--a type of machine learning designed to create an intelligent system capable of learning on its own from unstructured and unlabeled input data.

President Trump joins Fox News medical contributor Dr. Marc Siegel for an exclusive interview on'Tucker Carlson Tonight.' In an interview Wednesday with Fox News medical contributor Marc Siegel, President Trump boasted about his cognitive test performance and said presumptive Democratic presidential candidate Joe Biden should take the test. "In a way he has an obligation to," Trump said, adding that the presidency requires "stamina" and "mental health." Trump said he took the test to prove to the media that he was fit to serve in the presidency after reports supposedly questioning his cognitive ability. Trump has used the argument that Biden is too old to run for president as a cornerstone strategy in his presidential campaign against the former vice president.

A branch of artificial intelligence called machine learning is all around us. It's employed by Facebook to help curate content (and target us with ads), Google uses it to filter millions of spam messages each day, and it's part of what enabled the OpenAI bot to beat the reigning Dota 2 champions last year in two out of three matches. There are seemingly endless uses. Adding one more to the pile, Microsoft and Intel have come up with a clever machine learning framework that is surprisingly accurate at detecting malware through a grayscale image conversion process. Microsoft detailed the technology in a blog post (via ZDNet), which it calls static malware-as-image network analysis, or STAMINA.

Microsoft and Intel have partnered up in an effort to develop a new kind of malware detection. The project, called Static Malware-as-Image Network Analysis (STAMINA), is a joint effort by the tech giants to develop a software that sniffs out malicious code by converting it into greyscale images that can be assessed by utilizing deep-learning. Specifically, STAMINA converts one-dimensional malware bits into two-dimensional greyscale images and then'looks' at the images for patterns that may indicate specific types of malicious code using computer vision software designed to analyze images. One the image is assembled, STAMINA then resizes it into a smaller dimension to make it easier to view. This compressions, according to researchers helps avoid needing the software to assess billions of pixels - which would likely slow the process - and does not negatively affect its ability to identify malware.

Microsoft and Intel have a novel approach to classifying malware: visualizing it. They're collaborating on STAMINA (Static Malware-as-Image Network Analysis), a project that turns rogue code into grayscale images so that a deep learning system can study them. The approach converts the binary form of an input file into a simple stream of pixels, and turns that into a picture with dimensions that vary depending on aspects like file size. A trained neural network then determines what (if anything) has infected the file. ZDNet noted that the AI is trained on the huge amount of data Microsoft has collected from Windows Defenders installations. The technology doesn't need full-size, pixel-by-pixel recreations of viruses, which makes sense when large malware could easily translate to gigantic pictures.

Fighting Fantasy is a popular recreational fantasy gaming system worldwide. Combat in this system progresses through a stochastic game involving a series of rounds, each of which may be won or lost. Each round, a limited resource (`luck') may be spent on a gamble to amplify the benefit from a win or mitigate the deficit from a loss. However, the success of this gamble depends on the amount of remaining resource, and if the gamble is unsuccessful, benefits are reduced and deficits increased. Players thus dynamically choose to expend resource to attempt to influence the stochastic dynamics of the game, with diminishing probability of positive return. The identification of the optimal strategy for victory is a Markov decision problem that has not yet been solved. Here, we combine stochastic analysis and simulation with dynamic programming to characterise the dynamical behaviour of the system in the absence and presence of gambling policy. We derive a simple expression for the victory probability without luck-based strategy. We use a backward induction approach to solve the Bellman equation for the system and identify the optimal strategy for any given state during the game. The optimal control strategies can dramatically enhance success probabilities, but take detailed forms; we use stochastic simulation to approximate these optimal strategies with simple heuristics that can be practically employed. Our findings provide a roadmap to improving success in the games that millions of people play worldwide, and inform a class of resource allocation problems with diminishing returns in stochastic games.

Turns out humans and crustaceans aren't so different after all, as new research has revealed that female crabs prefer mates who can'go the distance'. Fiddler crabs with the energy to keep upping the pace of their courtship displays are the most likely to attract a mate - though they still only last five minutes. Females pick males with an unrelenting dance because they have more stamina, meaning they are better fighters with bigger burrows, experts said. Scientists at Anglia Ruskin University in Cambridge made the discovery after introducing female fiddler crabs to specially designed waving robot claws. Scientists have found that female fiddler craps prefer mates with stamina.