LLM-based agents have demonstrated great potential in generating and managing code within complex codebases. In this paper, we introduce WebGen-Bench, a novel benchmark designed to measure an LLM-based agent's ability to create multifile website codebases from scratch. It contains diverse instructions for website generation, created through the combined efforts of human annotators and GPT4o. These instructions span three major categories and thirteen minor categories, encompassing nearly all important types of web applications. To assess the quality of the generated websites, we generate test cases targeting each functionality described in the instructions. These test cases are then manually filtered, refined, and organized to ensure accuracy, resulting in a total of 647 test cases. Each test case specifies an operation to be performed on the website and the expected outcome of the operation. To automate testing and improve reproducibility, we employ a powerful web-navigation agent to execute test cases on the generated websites and determine whether the observed responses align with the expected results. We evaluate three high-performance code-agent frameworks--Bolt.diy,

Learning from self-sampled data and sparse environmental feedback remains a fundamental challenge in training self-evolving agents.

Extremely memory-efficient model architectures are decisive to fit within an MCU's tiny memory budget e.g., 128kB of RAM. However, inference latency must remain small to fit real-time constraints. An approach to tackle this is patchbased fusion, which aims to optimize data flows across neural network layers. In this paper, we introduce msf-CNN, a novel technique that efficiently finds optimal fusion settings for convolutional neural networks (CNNs) by walking through the fusion solution space represented as a directed acyclic graph. Compared to previous work on CNN fusion for MCUs, msf-CNN identifies a wider set of solutions. We published an implementation of msf-CNN running on various microcontrollers (ARM Cortex-M, RISC-V, ESP32). We show that msf-CNN can achieve inference using 50% less RAM compared to the prior art (MCUNetV2 and StreamNet). We thus demonstrate how msf-CNN offers additional flexibility for system designers.

I n a small, crowded room in Taipei, Pan Chien-chin is trying to keep a drone hovering steadily. Imagining himself flying a plane, he gently nudges controller joysticks to guide the insect-like device as it hums through the air. Cheers break out as Pan, who has never flown a drone before, steers it around a rectangular course marked by traffic cones without crashing. Around him are about two dozen fellow trainees, all signed up for the same course: Taiwan's first civil defence drone training programme. "The war in Ukraine has really changed how drones are used," says Pan, 48, a food company worker. "It's like giving myself another skill, something I can use if it's ever needed one day," he adds.

Gradient boosting has become a cornerstone of machine learning, enabling base learners such as decision trees to achieve exceptional predictive performance. While existing algorithms primarily handle scalar or Euclidean outputs, increasingly prevalent complex-structured data, such as distributions, networks, and manifoldvalued outputs, present challenges for traditional methods. Such non-Euclidean data lack algebraic structures such as addition, subtraction, or scalar multiplication required by standard gradient boosting frameworks. To address these challenges, we introduce Fréchet geodesic boosting (FGBoost), a novel approach tailored for outputs residing in geodesic metric spaces. FGBoost leverages geodesics as proxies for residuals and constructs ensembles in a way that respects the intrinsic geometry of the output space. Through theoretical analysis, extensive simulations, and realworld applications, we demonstrate the strong performance and adaptability of FGBoost, showcasing its potential for modeling complex data.

Stability guarantees have emerged as a principled way to evaluate feature attributions, but existing certification methods rely on heavily smoothed classifiers and often produce conservative guarantees. To address these limitations, we introduce soft stability and propose a simple, model-agnostic, sample-efficient stability certification algorithm (SCA) that yields non-trivial and interpretable guarantees for any attribution method. Moreover, we show that mild smoothing achieves a more favorable trade-off between accuracy and stability, avoiding the aggressive compromises made in prior certification methods. To explain this behavior, we use Boolean function analysis to derive a novel characterization of stability under smoothing. We evaluate SCA on vision and language tasks and demonstrate the effectiveness of soft stability in measuring the robustness of explanation methods.

Trump turns on the charm after extended'alpha' handshake with Macron and kisses for Brigitte at Palace of Versailles Sensational REAL reason Jelly Roll is divorcing Bunnie XO: Insiders reveal'preacher's wife' bombshell that's the talk of Nashville... truth about legendary rocker cuckolding rumor... and G-string mishap LIZ JONES: The cracks in Harry and Meghan's perfect facade have started to show. It's so obvious he's tiring of her tone-deaf approach... and I predict there's serious trouble in store Taylor Swift's bottomless thirst for attention, her greed and sheer tackiness are now truly unbearable... this latest stunt has shown her true colors: MAUREEN CALLAHAN NBA star's fiancee breaks her silence after friend, 26, mysteriously dropped dead at her luxury bachelorette party in St Barts Luxury fashion tycoon beloved by the stars hangs her head in shame as she's indicted for allegedly exploiting her workers and stealing $50k from their wages Jeff Bezos mercilessly mocked for taking'fake phone calls' when out with wife Lauren Sanchez Anguished family members flee court over sick details of Gilgo Beach murderer's kill room: Live updates'She has not been transparent... the damage has been done': How influencer Elle Darby'betrayed' thousands of young female fans...as insiders tell MOLLY CLAYTON how she cashed in As a divorced mother-of-three, cocaine was my little treat while my fellow middle-class friends had a few wines. What happened next was every family's worst nightmare... this is my warning to mums who'dabble' Desperate search for mom-of-three who hasn't been seen in three days as husband pleads for her return The shocking betrayal behind Jelly Roll's divorce from Bunnie XO is so utterly cruel... but have you yet spotted her revenge: JACQUELYNN POWERS Devastating supply crunch forces Apple to raise prices on iPhones and other devices, calling the move'unavoidable' Jeff's Dream Team: Bezos recruits world's top architects to build most expensive mega mansion on Billionaire Bunker island The Ring star Daveigh Chase's friends searched for her on LA's Skid Row in months before her shock death at 35 Watch horrifying drone video that follows woman's plunge to death after bungee team threw her from bridge without rope Renowned scientist Neil deGrasse Tyson tells US government to'Show the alien!' after latest UFO disclosure Astrophysicist Neil deGrasse Tyson wants the US government to'Show the alien!' after the latest UFO disclosure from the Trump administration . Tyson, 67, joined The Fox News Rundown on Monday, explaining how the overwhelming amount of irrefutable evidence should finally be topped off with a picture of an extraterrestrial being. 'Is it too much to ask at this point for them to just show the alien?

Watermarking the outputs of generative models has emerged as a promising approach for tracking their provenance. Despite significant interest in autoregressive image generation models and their potential for misuse, no prior work has attempted the first such to watermark approach their by adapting outputs language at the tok model en level.

We propose Equivariance by Contrast (EbC) to learn equivariant embeddings from observation pairs (y,g y), where g is drawn from a finite group acting on the data. Our method jointly learns a latent space and a group representation in which group actions correspond to invertible linear maps--without relying on group-specific inductive biases. We validate our approach on the infinite dSprites dataset with structured transformations defined by the finite group G:= (Rm Zn Zn), combining discrete rotations and periodic translations. The resulting embeddings exhibit high-fidelity equivariance, with group operations faithfully reproduced in latent space.

Drawing a contrast with China, Koizumi has said that Japan would take a transparent approach to investing in new methods of warfare like drones and artificial intelligence.