Search: “benchmark”

arXiv:2604.26577v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly considered for deployment as the control component of robotic health attendants, yet their safety in this

arXiv:2604.25072v1 Announce Type: new Abstract: Unified Multimodal Models (uMMs) aim to support both visual understanding and visual generation within a shared representation. However, existing evalua

arXiv:2604.25700v1 Announce Type: cross Abstract: Software quality assurance remains a major challenge in industrial environments, where large-scale and long-lived systems inevitably accumulate defect

arXiv:2604.23114v1 Announce Type: new Abstract: In limited-data settings, a single endpoint mean of an evaluation metric such as the Continuous Ranked Probability Score (CRPS) is itself a random varia

arXiv:2604.24665v1 Announce Type: cross Abstract: This paper investigates whether source trustworthiness shapes Turkish evidential morphology and whether large language models (LLMs) track this sensit

arXiv:2604.23091v1 Announce Type: new Abstract: Scaling EEG foundation models requires pooling data across heterogeneous electrode montages, a prerequisite both for larger pretraining corpora and for

arXiv:2604.24361v1 Announce Type: new Abstract: Large language models (LLMs) have achieved strong performance in general machine translation, yet their ability in culture-aware scenarios remains poorl

arXiv:2604.23341v1 Announce Type: cross Abstract: The deployment of Large Language Models (LLMs) as assistants in electric grid operations promises to streamline compliance and decision-making but exp

arXiv:2604.23743v1 Announce Type: cross Abstract: Deploying quantum machine learning on NISQ devices requires architectures where training overhead does not negate computational advantages. We systema

arXiv:2511.07410v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) and Vision Language Models (VLMs) have become popular tools for embodied high-level planning. However, their depl

arXiv:2509.22166v4 Announce Type: replace-cross Abstract: The demand for efficient large language model (LLM) inference has intensified the focus on sparsification techniques. While semi-structured (N

arXiv:2604.21953v1 Announce Type: new Abstract: Anti-doping programs rely on biological testing to detect performance-enhancing drugs, but such testing costs over $800 per sample and is limited by sho

arXiv:2604.22662v1 Announce Type: cross Abstract: Shapley values are a cornerstone of explainable AI, yet their proliferation into competing formulations has created a fragmented landscape with little

arXiv:2604.21276v1 Announce Type: cross Abstract: As pretrained large language models replace task-specific decoders in speech recognition, a critical question arises: do their text-derived priors mak

arXiv:2604.20157v1 Announce Type: new Abstract: Recent advances in model architectures, compute, and data scale have driven rapid progress in video generation, producing increasingly realistic content

arXiv:2604.20543v1 Announce Type: new Abstract: Referring detection refers to locate the target referred by natural languages, which has recently attracted growing research interests. However, existin

arXiv:2509.12483v4 Announce Type: replace Abstract: This study compares the Boundary Element Method (BEM) and Physics-Informed Neural Networks (PINNs) for solving the two-dimensional Helmholtz equatio

arXiv:2601.09926v3 Announce Type: replace Abstract: Current approaches to proactive assistance move beyond the ask-and-respond paradigm by anticipating user needs. In practice, they either burden user

arXiv:2604.16775v1 Announce Type: new Abstract: Every prediction from a generative medical event model is bounded by how clinical events are tokenized, yet input representation is rarely isolated from

arXiv:2604.17306v1 Announce Type: new Abstract: This paper provides a review of the NTIRE 2026 challenge on mobile real-world image super-resolution, highlighting the proposed solutions and the result

arXiv:2604.16022v1 Announce Type: new Abstract: As Large Language Models (LLMs) transition from text processors to autonomous agents, evaluating their social reasoning in embodied multi-agent settings

arXiv:2604.15702v1 Announce Type: new Abstract: We introduce a cross-domain behavioural assay of monitoring-control coupling in LLMs, grounded in the Nelson and Narens (1990) metacognitive framework a

arXiv:2601.07338v2 Announce Type: replace Abstract: Large Language Models (LLMs) have significantly advanced Machine Translation (MT), applying them to linguistically complex domains-such as Social Ne

arXiv:2604.15267v1 Announce Type: cross Abstract: It is increasingly important that LLM agents interact effectively and safely with other goal-pursuing agents, yet, recent works report the opposite tr

arXiv:2602.18502v2 Announce Type: replace Abstract: Although deep learning models in medical imaging often achieve excellent classification performance, they can rely on shortcut learning, exploiting

arXiv:2604.11762v1 Announce Type: new Abstract: Deep learning underpins a wide range of applications in MRI, including reconstruction, artifact removal, and segmentation. However, progress has been dr

arXiv:2512.00336v2 Announce Type: replace Abstract: The rapid advancement of AI-generated multimodal video-audio content has raised significant concerns regarding information security and content auth

arXiv:2604.09982v1 Announce Type: cross Abstract: Reproducibility must validate architectural robustness, not just numerical accuracy. We evaluate ColBERT-v2 and ConstBERT across five dimensions, find

arXiv:2604.08757v1 Announce Type: cross Abstract: Humor is one of the most culturally embedded and socially significant dimensions of human communication, yet it remains largely unexplored as a dimens

arXiv:2604.07720v2 Announce Type: replace Abstract: Deep Research (DR) requires LLM agents to autonomously perform multi-step information seeking, processing, and reasoning to generate comprehensive r

arXiv:2601.10198v3 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning and generation, serving as the foundation for advanced persona s

arXiv:2503.08292v4 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly applied to outpatient referral tasks across healthcare systems. However, there is a lack of stan

arXiv:2604.07759v1 Announce Type: new Abstract: Ship detection for navigation is a fundamental perception task in intelligent waterway transportation systems. However, existing public ship detection d

arXiv:2605.07492v1 Announce Type: new Abstract: The past year has seen over 20 open-source document parsing models, yet thefield still benchmarks almost exclusively on OmniDocBench, a 1,355-pagemanual

arXiv:2609.18366v1 Announce Type: new Abstract: Reliable agent evaluation is complicated by automatic harness optimization, which repeatedly uses a released benchmark B_{rel} to guide a Proposer that

arXiv:2610.18535v1 Announce Type: new Abstract: Video understanding benchmarks are the primary yardstick for measuring progress in video-language modeling, and a growing body of work relies on them to

arXiv:2610.13461v1 Announce Type: new Abstract: AI systems are making rapid progress in mathematics, but this progress is hard to measure. Benchmarks of solved problems saturate, and in benchmarks of

arXiv:2505.00759v3 Announce Type: replace-cross Abstract: The steady improvements of text-to-image (T2I) generative models lead to slow deprecation of automatic evaluation benchmarks that rely on stat

arXiv:2608.28884v1 Announce Type: new Abstract: We introduce MineCEraft (Minecraft Construction Engineering Benchmark, pronounced mine-see-ee-raft), an easy-to-use, open-source benchmark designed to s

arXiv:2607.02032v1 Announce Type: new Abstract: Evaluating LLM agents on benchmarks like SWE-Bench and GAIA can be expensive, time-consuming, and requires complex infrastructure. A single evaluation c

arXiv:2603.13966v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models are increasingly evaluated across multiple simulation benchmarks, yet adding each benchmark to an evaluation pip

arXiv:2609.06535v1 Announce Type: new Abstract: We introduce 3DHarnessBench, a benchmark that evaluates the agentic ability of frontier vision-language models (VLMs) to recover 3D geometry as Blender

arXiv:2609.03261v1 Announce Type: cross Abstract: A benchmark score credits final answers, but not the route by which an item can be answered. In medical multimodal multiple-choice questions (MCQs), t

arXiv:2608.05086v1 Announce Type: new Abstract: Language models differ in how safely they behave and these differences are measured by safety benchmarks. But aggregated benchmark scores are hard to tr

arXiv:2601.15307v2 Announce Type: replace-cross Abstract: The rapid development of automated survey generation technology has made it increasingly important to establish a comprehensive benchmark to e

arXiv:2610.17841v1 Announce Type: cross Abstract: Evaluating fashion retrieval systems remains challenging due to the limited availability of benchmarks that capture the fine-grained, open-ended queri