Search: “benchmark”

arXiv:2604.17090v1 Announce Type: new Abstract: Human action recognition and motion generation are two active research problems in human-centric computer vision, both aiming to align motion with textu

arXiv:2604.18152v1 Announce Type: cross Abstract: Deep learning (DL) has become a cornerstone of modern machine learning (ML) praxis. We introduce the R package mlr3torch, which is an extensible DL fr

arXiv:2603.21298v2 Announce Type: replace Abstract: Combating hate speech on social media is critical for securing cyberspace, yet relies heavily on the efficacy of automated detection systems. As con

arXiv:2604.18203v1 Announce Type: new Abstract: Multimodal LLMs can accurately perceive numerical content across modalities yet fail to perform exact multi-digit multiplication when the identical unde

arXiv:2604.16937v1 Announce Type: new Abstract: Translation-based prompting is widely used in multilingual LLMs, yet its effectiveness varies across languages and tasks. We evaluate prompting strategi

arXiv:2604.16576v1 Announce Type: cross Abstract: Decoder-only large language models (LLMs) are increasingly replacing BERT-style architectures as the backbone for dense retrieval, achieving substanti

arXiv:2604.16813v1 Announce Type: cross Abstract: Agentic AI systems are rapidly advancing toward real-world applications, yet their readiness in complex and personalized environments remains insuffic

arXiv:2604.17163v1 Announce Type: new Abstract: We propose PPEDCRF, a calibrated selective perturbation framework that protects background-based location privacy in released video frames against galle

arXiv:2506.13674v3 Announce Type: replace Abstract: Parameter-Efficient Fine-Tuning (PEFT) methods have become crucial for rapidly adapting large language models (LLMs) to downstream tasks. Prefix-Tun

arXiv:2508.05132v2 Announce Type: replace Abstract: As medical LLMs transition to clinical deployment, assessing their ethical reasoning capability becomes critical. While achieving high accuracy on k

arXiv:2604.16909v1 Announce Type: new Abstract: As large language models (LLMs) evolve from conversational assistants into agents capable of handling complex tasks, they are increasingly deployed in h

arXiv:2604.18459v1 Announce Type: new Abstract: Visual agents operating in the wild must respond to queries precisely when sufficient evidence first appears in a video stream, a critical capability th

arXiv:2604.18575v1 Announce Type: new Abstract: Story Visualization aims to generate a sequence of images that faithfully depicts a textual narrative that preserve character identity, spatial configur

arXiv:2604.17800v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have gained much attention from the research community thanks to their strength in translating multimodal observat

arXiv:2505.19897v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have extended their impact beyond Natural Language Processing, substantially fostering the development of interdi

arXiv:2604.17166v1 Announce Type: cross Abstract: Sparsity or complexity? In modern high-dimensional asset pricing, these are often viewed as competing principles: richer feature spaces appear to favo

arXiv:2603.23885v3 Announce Type: replace Abstract: Document parsing has recently advanced with multimodal large language models (MLLMs) that directly map document images to structured outputs. Tradit

arXiv:2601.08841v2 Announce Type: replace Abstract: The increasing volume and complexity of scientific literature demand robust methods for organizing and understanding research documents. In this stu

arXiv:2602.09130v3 Announce Type: replace Abstract: Model compression is increasingly essential for deploying large language models (LLMs), yet existing comparative studies largely focus on pruning an

arXiv:2510.21804v2 Announce Type: replace Abstract: Autoregressive neural surrogates offer computational acceleration for fluid dynamics but inherently suffer from error accumulation and non-physical

arXiv:2602.00114v4 Announce Type: replace-cross Abstract: Few-shot learning (FSL) challenges model generalization to novel classes based on just a few shots of labeled examples, a testbed where tradit

arXiv:2604.15390v1 Announce Type: cross Abstract: Code deobfuscation is the task of recovering a readable version of a program while preserving its original behavior. In practice, this often requires

arXiv:2503.13304v2 Announce Type: replace Abstract: Feature selection (FS) is a fundamental challenge in machine learning, particularly for high-dimensional tabular data, where interpretability and co

Elon Musk on the rapid advancement of AI and robotics 'I'm confident that if AI and robotics continue to advance which they are advancing very rapidly working will be optional, and people will have an

arXiv:2604.15415v1 Announce Type: cross Abstract: Large language models (LLMs) have evolved into autonomous agents that rely on open skill ecosystems (e.g., ClawHub and Skills.Rest), hosting numerous

arXiv:2604.02880v2 Announce Type: replace Abstract: Table structure recognition (TSR) holds widespread practical importance by parsing tabular images into structured representations, yet encounters si

arXiv:2406.15809v5 Announce Type: replace Abstract: Citizen reporting platforms help the public and authorities stay informed about sexual harassment incidents. However, the high volume of data shared

arXiv:2604.15979v1 Announce Type: new Abstract: Gait recognition has emerged as a powerful biometric technique for identifying individuals at a distance without requiring user cooperation. Most existi

arXiv:2603.27312v2 Announce Type: replace Abstract: Maximum entropy (MaxEnt) modelling provides a principled framework for generating synthetic populations from aggregate census data, without access t

arXiv:2604.15579v1 Announce Type: cross Abstract: AI agents that interact with their environments through tools enable powerful applications, but in high-stakes business settings, unintended actions c

arXiv:2604.16123v1 Announce Type: new Abstract: Accurate molecular property prediction is central to drug discovery, catalysis, and process design, yet real-world applications are often limited by sma

arXiv:2604.15409v1 Announce Type: cross Abstract: KV caching is a ubiquitous optimization in autoregressive transformer inference, long presumed to be numerically equivalent to cache-free computation.

The Jensen + @dwarkesh_sp podcast was fantastic. Jensen is someone who understood how ecosystems work and someone who understands real-world trade, policy and controls work. And in some deeper sense h

arXiv:2509.13397v3 Announce Type: replace-cross Abstract: Social scientists are now using large language models to create 'silicon samples': synthetic datasets intended to stand in for human responden

arXiv:2411.12502v4 Announce Type: replace-cross Abstract: Neural Processes (NPs) are a rapidly evolving class of models designed to directly model the posterior predictive distribution of stochastic p

arXiv:2502.15972v2 Announce Type: replace-cross Abstract: Text-to-image generation models have achieved strong performance in culturally homogeneous settings, yet their ability to generate multicultur

arXiv:2510.17210v3 Announce Type: replace Abstract: The increase in computing power and the necessity of AI-assisted decision-making boost the growing application of large language models (LLMs). Alon

This post explains the technical details behind common terms and tools encountered on Hugging Face for running large language models locally, including quantization formats (GGUF, Q_4_M, IQ_4XL), opti

A downside with using VLMs to parse PDFs is guaranteeing that the output text is *correct* and output in the correct reading order. 1️⃣ Text correctness: making sure that digits, words, sentences are

> grok4.20-beta1 is a much smaller model than opus but is #1 ranked in medicine and healthcare > 4.3 and 4.4 will be much larger models, and likely will have a significant boost in performance on comp

arXiv:2604.14655v1 Announce Type: cross Abstract: We present AgentGA, a framework that evolves autonomous code-generation runs by optimizing the agent seed: the task prompt plus optional parent archiv

arXiv:2604.14472v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) are often selected by a single scalar loss even when the quantity of interest is more specific. We study a hybr

arXiv:2604.14892v1 Announce Type: new Abstract: Evaluating medical AI systems using expert clinician panels is costly and slow, motivating the use of large language models (LLMs) as alternative adjudi

arXiv:2604.14210v1 Announce Type: new Abstract: A claim has been circulating on social media and practitioner forums that Chinese prompts are more token-efficient than English for LLM coding tasks, po

arXiv:2603.20340v3 Announce Type: replace-cross Abstract: Self-generated skills for web agents are often unstable and can even hurt performance relative to direct acting. We argue that the key bottlen

arXiv:2604.15086v1 Announce Type: cross Abstract: Recent advances in video-to-audio (V2A) generation enable high-quality audio synthesis from visual content, yet achieving robust and fine-grained cont