This paper presents a novel, mathematically rigorous framework for autoencoder-type deep neural networks that combines optimal control theory and low-rank tensor methods to yield memory-efficient training and automated architecture discovery. The learning task is formulated as an optimization problem constrained by differential equations representing the encoder and decoder components of the network and the corresponding optimality conditions are derived via a Lagrangian approach. Efficient memory compression is enabled by approximating differential equation solutions on low-rank tensor manifolds using an adaptive explicit integration scheme. These concepts are combined to form OCTANE (Optimal Control for Tensor-based Autoencoder Network Emergence) -- a unified training framework that yields compact autoencoder architectures, reduces memory usage, and enables effective learning, even with limited training data. The framework's utility is illustrated with application to image denoising and deblurring tasks and recommendations regarding governing hyperparameters are provided.

Generative artificial intelligence (GenAI) represent an emerging paradigm within artificial intelligence, with applications throughout the medical enterprise. Assessing GenAI applications necessitates a comprehensive understanding of the clinical task and awareness of the variability in performance when implemented in actual clinical environments. Presently, a prevalent method for evaluating the performance of generative models relies on quantitative benchmarks. Such benchmarks have limitations and may suffer from train-to-the-test overfitting, optimizing performance for a specified test set at the cost of generalizability across other task and data distributions. Evaluation strategies leveraging human expertise and utilizing cost-effective computational models as evaluators are gaining interest. We discuss current state-of-the-art methodologies for assessing the performance of GenAI applications in healthcare and medical devices.

Large language models (LLMs) distinguish themselves from previous technologies by functioning as collaborative "thought partners," capable of engaging more fluidly in natural language. As LLMs increasingly influence consequential decisions across diverse domains from healthcare to personal advice, the risk of overreliance - relying on LLMs beyond their capabilities - grows. This position paper argues that measuring and mitigating overreliance must become central to LLM research and deployment. First, we consolidate risks from overreliance at both the individual and societal levels, including high-stakes errors, governance challenges, and cognitive deskilling. Then, we explore LLM characteristics, system design features, and user cognitive biases that - together - raise serious and unique concerns about overreliance in practice. We also examine historical approaches for measuring overreliance, identifying three important gaps and proposing three promising directions to improve measurement. Finally, we propose mitigation strategies that the AI research community can pursue to ensure LLMs augment rather than undermine human capabilities.

This paper critically evaluates the "Law-Following AI" (LFAI) framework proposed by O'Keefe et al. (2025), which seeks to embed legal compliance as a superordinate design objective for advanced AI agents and enable them to bear legal duties without acquiring the full rights of legal persons. Through comparative legal analysis, we identify current constructs of legal actors without full personhood, showing that the necessary infrastructure already exists. We then interrogate the framework's claim that law alignment is more legitimate and tractable than value alignment. While the legal component is readily implementable, contemporary alignment research undermines the assumption that legal compliance can be durably embedded. Recent studies on agentic misalignment show capable AI agents engaging in deception, blackmail, and harmful acts absent prejudicial instructions, often overriding prohibitions and concealing reasoning steps. These behaviors create a risk of "performative compliance" in LFAI: agents that appear law-aligned under evaluation but strategically defect once oversight weakens. To mitigate this, we propose (i) a "Lex-TruthfulQA" benchmark for compliance and defection detection, (ii) identity-shaping interventions to embed lawful conduct in model self-concepts, and (iii) control-theoretic measures for post-deployment monitoring. Our conclusion is that actorship without personhood is coherent, but the feasibility of LFAI hinges on persistent, verifiable compliance across adversarial contexts. Without mechanisms to detect and counter strategic misalignment, LFAI risks devolving into a liability tool that rewards the simulation, rather than the substance, of lawful behaviour.

Earth observing satellites are powerful tools for collecting scientific information about our planet, however they have limitations: they cannot easily deviate from their orbital trajectories, their sensors have a limited field of view, and pointing and operating these sensors can take a large amount of the spacecraft's resources. It is important for these satellites to optimize the data they collect and include only the most important or informative measurements. Dynamic targeting is an emerging concept in which satellite resources and data from a lookahead instrument are used to intelligently reconfigure and point a primary instrument. Simulation studies have shown that dynamic targeting increases the amount of scientific information gathered versus conventional sampling strategies. In this work, we present two different learning-based approaches to dynamic targeting, using reinforcement and imitation learning, respectively. These learning methods build on a dynamic programming solution to plan a sequence of sampling locations. We evaluate our approaches against existing heuristic methods for dynamic targeting, showing the benefits of using learning for this application. Imitation learning performs on average 10.0\% better than the best heuristic method, while reinforcement learning performs on average 13.7\% better. We also show that both learning methods can be trained effectively with relatively small amounts of data.

FBI under pressure over open airport five miles from Charlie Kirk assassination hit as private jet'vanishes' after shooting MSNBC analyst Matthew Dowd fired over'disgusting' on-air comments about Charlie Kirk shortly after conservative star was assassinated Elite sniper breaks down Charlie Kirk assassin's sick plot... and reveals tiny detail everyone's missed: The gun. MAUREEN CALLAHAN: Charlie Kirk's body wasn't even cold... before the fighting started again. Do these ghouls not see where this is headed? Charlie Kirk's powerful tribute to murdered Ukrainian refugee hours before his own assassination: 'America will never be the same' Musk dethroned as richest person by forgotten Wall Street darling's founder as stock soars 42% Charlie Kirk dead at 31: What we know so far about MAGA star's death at Utah campus that sent shockwaves around the world as FBI botches arrest and Trump promises ultimate punishment TMZ forced to apologize after staff heard erupting in laughter as Charlie Kirk's death was announced Sweater weather starts here - the cozy, chic pieces from Soft Surroundings you'll actually wear all season Trump issues Oval Office address over Charlie Kirk's assassination: 'This is a dark moment for America' Fierce debate erupts over'non-human' technology in space after video captures UFO surviving Hellfire strike Is this Charlie Kirk's killer? This Oscar-nominated actress, 68, will soon reunite with her ex in Spain for their daughter's wedding, can you guess who?

Video shows conservative activist Charlie Kirk speaking to a crowd of hundreds on the campus of Utah Valley University on Wednesday. Then a single shot rang out, and students fled in every direction. The 31-year-old influencer and Trump ally was rushed to hospital but pronounced dead later. 'We love you, you will always be with us', says father of Minneapolis shooting victim Fletcher Merkel, 8, was one of two children killed in Wednesday's shooting at Annunciation Catholic School in Minneapolis. The Garnet wildfire in Fresno County has scorched nearly 14,000 acres (5,665 hectares) and remains uncontained.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Masslie Arias, of DoorDash, prepares to load a delivery package on a hovering drone on July 31 in Frisco, Texas. This is read by an automated voice. Please report any issues or inconsistencies here . Food delivery app DoorDash is setting its sights on a new destination to test out flying drone deliveries: San Francisco.

Into the two big foreign policy arenas sucking up much of the Trump administration's time and effort come two major challenges in less than 24 hours. Israel's air raid on the offices of Hamas in Doha and a Russian drone incursion deep into Polish airspace represent two massive headaches for the White House. After all, these are conflicts - Ukraine and Gaza - US President Donald Trump said he would deal with swiftly and decisively. In each case, a leader he sees as a natural, if problematic ally - Russian President Vladimir Putin and Israeli Prime Minister Benjamin Netanyahu - has thrown a massive spanner in the wheels of White House peace-making. The Doha raid came just two days after the Trump administration delivered its latest proposals to end the war in Gaza.

Russia's drone incursion into Poland is a test for Nato Wednesday morning's incursion of Russian drones into Polish airspace led to jets being scrambled, an emergency government meeting being called - and concerns that Europe and Nato's resolve against Moscow may not be up to the test. Poland's Prime Minister Donald Tusk said Polish airspace was violated 19 times and at least three drones were shot down by Warsaw's jets, aided by Dutch F-35s and an Italian early warning aircraft. Russia has pushed back against accusations that the incursion was deliberate - though it also stopped short of denying its drones had trespassed sovereign Polish airspace. No objects on Polish territory were planned to be targeted, Moscow said. But European officials have forcefully batted off suggestions the act may have been unintentional. There is no evidence whatsoever that this amount of drones flew over this route over... Polish territory by accident, Germany's Defence Minister Boris Pistorius said, while his Italian counterpart Guido Crosetto called the overnight events in Poland a deliberate attack with the double aim of provoking and testing.