Repeated multi-unit auctions, where a seller allocates multiple identical items over many rounds, are common mechanisms in electricity markets and treasury auctions. We compare the two predominant formats: uniform-price and discriminatory auctions, focusing on the perspective of a single bidder learning to bid against stochastic adversaries. We characterize the learning difficulty in each format, showing that the regret scales similarly for both auction formats under both fullinformation and bandit feedback, as Θ( T)and Θ(T2/3), respectively. However, analysis beyond worst-case regret reveals structural differences: uniform-price auctions may admit faster learning rates, with regret scaling as Θ( T)in settings where discriminatory auctions remain at Θ(T2/3). Finally, we provide a specific analysis for auctions in which the other participants are symmetric and have unitdemand, and show that in these instances, a similar regret rate separation appears.

Nowadays deep models are required to be versatile due to the increasing realistic needs. Multi-task learning (MTL) offers an efficient way for this purpose to learn multiple tasks simultaneously with a single model. However, prior MTL solutions often focus on resolving conflicts and imbalances during optimization, which may not outperform simple linear scalarization strategies [Xin et al., 2022]. Instead of altering the optimization trajectory, this paper leverages mode connectivity to efficiently approach the Pareto front and identify the desired trade-off point. Unlike Pareto Front Learning (PFL), which aims to align with the entire Pareto front, we focus on effectively and efficiently exploring optimal trade-offs. However, three challenges persist: (1) the low-loss path can neither fully traverse trade-offs nor align with user preference due to its randomness, (2) commonly adopted Bézier curves in mode connectivity are ill-suited to navigating the complex loss landscapes of deep models, and (3) poor scalability to large-scale task scenarios. To address these challenges, we adopt non-uniform rational B-Splines (NURBS) to model mode connectivity, allowing for more flexible and precise curve optimization. Additionally, we introduce an order-aware objective to explore task loss tradeoffs and employ a task grouping strategy to enhance scalability under massive task scenarios. Extensive experiments on key MTL datasets demonstrate that our proposed method, EXTRA(EXplore TRAde-offs), effectively identifies the desired point on the Pareto front and achieves state-of-the-art performance.

AI makes Pompeii victim's final moments look shockingly real The archaeologists behind the video believe the man covered his head with a bowl to protect himself from volcanic debris. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. This victim of the 79 CE eruption of Mount Vesuvius was discovered in the Pompeii archaeological area near Naples in southern Italy. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

When you purchase through links in our articles, we may earn a small commission. Innocn's massive 49-inch curved ultrawide is down to $529.98 from $750, a $220 savings on a screen that genuinely does the work of two monitors. It's also the lowest price we've seen for this model. The monitor features a 3840x1080p resolution and a speedy 144Hz refresh rate. We'd certainly prefer a crispier resolution, but that typically costs more.

The company's latest recall of 3,871 vehicles follows incidents of its autonomous cars "prioritizing other hazards" or failing to recognize closed construction zones altogether. Waymo has filed its fourth safety recall since February 2024, after its driverless cars were caught entering closed freeway-construction zones. The recall, filed with the National Highway Traffic Safety Administration (NHTSA) on June 17, appears to affect Waymo's entire US fleet, covering 3,871 vehicles running Waymo's 5th Generation automated driving system (ADS). NHTSA estimates 100 precent of the affected units carry the defect, which is outlined in the filed safety recall report as "under certain circumstances, the AV may enter and drive at speed in freeway-construction zones due to inappropriately prioritizing the avoidance of other freeway hazards and/or failing to recognize the construction zone." Waymo started offering highway rides in late 2025, and the underlying problem appears to be a failure of priority logic.

Understanding the behavior of deep reinforcement learning (DRL) agents-- particularly as task and agent sophistication increase--requires more than simple comparison of reward curves, yet standard methods for behavioral analysis remain underdeveloped in DRL. We apply tools from neuroscience and ethology to study DRL agents in a novel, complex, partially observable environment, ForageWorld, designed to capture key aspects of real-world animal foraging--including sparse, depleting resource patches, predator threats, and spatially extended arenas. We use this environment as a platform for applying joint behavioral and neural analysis to agents, revealing detailed, quantitatively grounded insights into agent strategies, memory, and planning. Contrary to common assumptions, we find that modelfree RNN-based DRL agents can exhibit structured, planning-like behavior purely through emergent dynamics--without requiring explicit memory modules or world models. Our results show that studying DRL agents like animals--analyzing them with neuroethology-inspired tools that reveal structure in both behavior and neural dynamics--uncovers rich structure in their learning dynamics that would otherwise remain invisible. We distill these tools into a general analysis framework linking core behavioral and representational features to diagnostic methods, which can be reused for a wide range of tasks and agents. As agents grow more complex and autonomous, bridging neuroscience, cognitive science, and AI will be essential--not just for understanding their behavior, but for ensuring safe alignment and maximizing desirable behaviors that are hard to measure via reward. We show how this can be done by drawing on lessons from how biological intelligence is studied.

Semantic Scene Completion (SSC) aims to reconstruct the 3D geometry and semantics of the surrounding environment. With dense voxel labels, prior works typically formulate SSC as a dense segmentation task, independently classifying each voxel.

We study the regret in stochastic Multi-Armed Bandits (MAB) with multiple agents that communicate over an arbitrary connected communication graph. We analyzed a variant of Cooperative Successive Elimination algorithm, Coop-SE, and show an individual regret bound of O(R/m+A2 +A logT) and a nearly matching lower bound. Here Ais the number of actions, T the time horizon, mthe number of agents, and R= P i>0 log(T)/ i is the optimal single agent regret, where i is the sub-optimality gap of action i. Our work is the first to show an individual regret bound in cooperative stochastic MAB that is independent of the graph's diameter. When considering communication networks there are additional considerations beyond regret, such as message size and number of communication rounds. First, we show that our regret bound holds even if we restrict the messages to be of logarithmic size. Second, for logarithmic number of communication rounds, we obtain a regret bound of O(R/m+AlogT).

Encountering shifted data at test time is a ubiquitous challenge when deploying predictive models. Test-time adaptation (TTA) methods address this issue by continuously adapting a deployed model using only unlabeled test data. While TTA can extend the model's lifespan, it is only a temporary solution. Eventually the model might degrade to the point that it must be taken offline and retrained. To detect such points of ultimate failure, we propose pairing TTA with risk monitoring frameworks that track predictive performance and raise alerts when predefined performance criteria are violated. Specifically, we extend existing monitoring tools based on sequential testing with confidence sequences to accommodate scenarios in which the model is updated at test time and no test labels are available to estimate the performance metrics of interest. Our extensions unlock the application of rigorous statistical risk monitoring to TTA, and we demonstrate the effectiveness of our proposed TTA monitoring framework across a representative set of datasets, distribution shift types, and TTA methods.

Select all applicable actions that occur and output None if not observedQwen2.5-VL: