Knowledge catalogue
arXiv:2609.07823v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) show strong progress on vision-language tasks, yet their reliability in safety-critical settings remains undere
arXiv:2609.06109v1 Announce Type: new Abstract: Port flood digital twins require analytics that warn operators before disruption, but official warning incidents are often few and adjacent observations
arXiv:2510.25183v2 Announce Type: replace-cross Abstract: Energy-efficient AI should be evaluated across the full application pipeline, not only by lowest error or shortest training time. We study thi
arXiv:2406.15070v3 Announce Type: replace-cross Abstract: We present Tempora-Fusion, the first homomorphic TLP scheme with efficient public verification of both individual puzzle solutions and homomor
arXiv:2609.05396v1 Announce Type: new Abstract: Wireless signals with position-related labels are pivotal for both performance evaluation and model training in the realm of wireless sensing. However,
arXiv:2609.04557v1 Announce Type: cross Abstract: Quantum error-correction decoders are typically benchmarked against synthetic circuit-level noise, under the assumption that a decoder's ranking under
arXiv:2609.04407v1 Announce Type: new Abstract: Deep neural operators learn mappings between input functions and complete PDE solution fields, enabling forward evaluations of new problem instances ord
arXiv:2609.04280v1 Announce Type: cross Abstract: Modern LLM-based agents operate through a harness of tools, reusable skills, and specialist agents that shapes what they observe and what they can do.
arXiv:2609.04720v1 Announce Type: cross Abstract: Vision-language models (VLMs) are expected to respond helpfully to appropriate requests while withholding compliance with requests that are incorrect,
arXiv:2609.04362v1 Announce Type: cross Abstract: Music audio-language models are evaluated almost entirely by accuracy on multiple-choice questions. This protocol forces the model to commit to an opt
arXiv:2609.05088v1 Announce Type: new Abstract: AI oversight methods rely on ground truth for validation, but what constitutes appropriate AI behavior is contested. This leaves evaluation of moral rea
arXiv:2609.05175v1 Announce Type: cross Abstract: Measuring the novelty of scientific papers is a central concern in research evaluation and scientometrics. From a recombination perspective, prior stu
arXiv:2609.04598v1 Announce Type: cross Abstract: We introduce PetQA, a Korean long-form question-answering (QA) benchmark for evaluating veterinary knowledge and clinical reasoning in large language
arXiv:2609.05141v1 Announce Type: new Abstract: Scientific papers require models to reason jointly over text, equations, figures, tables, code, and datasets while preserving the provenance of supporti
arXiv:2609.05125v1 Announce Type: new Abstract: Evaluating the calibration of Large Language Models (LLMs) is critical for their safe deployment as zero-shot classifiers. Yet, commercial API providers
arXiv:2609.04912v1 Announce Type: cross Abstract: Reliability evaluation of deep neural networks under hardware faults commonly relies on fault injection, but exhaustive campaigns are intractable for
arXiv:2609.04707v1 Announce Type: cross Abstract: Wireless foundation models (WFMs) have emerged as a promising approach for learning reusable representations from large-scale wireless data and adapti
arXiv:2609.03794v1 Announce Type: new Abstract: Mobile robotic platforms offer a flexible alternative to fixed manipulators for non-destructive evaluation (NDE) of large aerospace structures, but thei
arXiv:2609.03734v1 Announce Type: cross Abstract: BLEU-4 is the standard metric for evaluating sign language translation (SLT), but spoken-language metrics may not adequately reflect sign language pro
arXiv:2609.02901v1 Announce Type: new Abstract: Modern automatic speech recognition (ASR) scenarios require both spoken-form transcripts for faithful transcription and readable written-form transcript
arXiv:2609.04128v1 Announce Type: new Abstract: Scaling interactive and verifiable environments is critical for training terminal agents. As frontier models become more capable, environments synthesiz
arXiv:2609.03611v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) are increasingly used to evaluate robot manipulation outcomes, but existing benchmarks offer limited evidence of cross-d
arXiv:2609.03186v1 Announce Type: cross Abstract: Training deep learning-based medical image segmentation models is challenging with limited curated datasets. For AGITG TOPGEAR, a gastric cancer trial
arXiv:2609.03966v1 Announce Type: new Abstract: Agent evaluations report a tool-call rate read off the serving stack. That number can be zero while the model is emitting well-formed calls: the interfa
arXiv:2609.04013v1 Announce Type: new Abstract: Early screening of chronic kidney disease (CKD) is critical for timely intervention, yet most machine learning (ML) and deep learning (DL) approaches re
arXiv:2609.03507v1 Announce Type: cross Abstract: Tracking depression from multi-session counseling dialogues requires estimating both current symptom severity and how it changes across sessions. Yet
Moving Large Language Models (LLMs) from experimental prototypes into enterprise production exposes a critical truth: your infrastructure dictates both your performance ceilings and your unit economic
arXiv:2602.14048v2 Announce Type: replace-cross Abstract: Real-time embodied social interaction places two equally demanding requirements on an agent: continuously generating fluent multimodal interac
arXiv:2506.10226v3 Announce Type: replace-cross Abstract: Synthetic data generation is increasingly used in machine learning for training and data augmentation. Yet, current strategies often rely on e
arXiv:2609.03047v1 Announce Type: cross Abstract: Libraries and archives manage large collections with limited staff and computing budgets, yet common benchmarks do not systematically test their bibli
arXiv:2609.03480v1 Announce Type: cross Abstract: We map tree species across Denmark using National Forest Inventory plots and EO data, while evaluating the potential of foundation models for large-sc
arXiv:2510.20721v4 Announce Type: replace-cross Abstract: Large language models (LLMs) are rapidly being adopted for tasks like drafting emails, summarizing meetings, and answering health questions. I
arXiv:2609.03467v1 Announce Type: cross Abstract: Large language models (LLMs) are increas- ingly deployed as long-horizon conversational agents, motivating growing interest in mem- ory systems. Howev
arXiv:2609.02110v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) commonly evaluate the spatial derivatives appearing in partial differential equation residuals using automatic
arXiv:2609.02277v1 Announce Type: cross Abstract: Perceptual illusions have long served as crucial probes into human cognition, revealing biases and limitations of perception. In the auditory domain,
arXiv:2609.01861v1 Announce Type: new Abstract: The performance of an LLM agent depends on the scaffold around a frozen model. A common way to improve that scaffold is to use a coding agent as an opti
arXiv:2609.01982v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as assistants for statistical and data science work, yet existing evaluations largely assume the anal
arXiv:2609.02796v1 Announce Type: new Abstract: Sign language processing systems have traditionally operated at the sentence level, ignoring critical discourse phenomena fundamental to sign language c
arXiv:2609.02111v1 Announce Type: cross Abstract: Dermatology artificial intelligence (AI) models are predominantly trained on light-skinned, cancer-focused image collections, yet they are increasingl
arXiv:2609.02059v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have achieved strong performance on structured visual understanding tasks such as chart and document question a
arXiv:2609.01888v1 Announce Type: new Abstract: Recent inference-time hallucination mitigation methods for large vision-language models (LVLMs) report strong gains on hallucination benchmarks. However
arXiv:2609.01742v1 Announce Type: new Abstract: Anti-UAV systems increasingly fuse multiple sensors, yet their detection heads provide no per-modality reliability signal. This study evaluates whether
arXiv:2609.02679v1 Announce Type: cross Abstract: When LLMs support public-facing or high-stakes workflows, missed fabrications can harm users and institutions, while false alarms consume limited huma
arXiv:2609.02246v1 Announce Type: new Abstract: Self-improving agent pipelines have a problem at their center. An optimizer rewrites prompts to score higher, and the score comes from a judge that is i
arXiv:2607.10103v2 Announce Type: replace-cross Abstract: As large language models (LLMs) become widely deployed, their outputs can be copied, transformed, and redistributed at scale without reliable
arXiv:2609.02649v1 Announce Type: new Abstract: Aggregating noisy, conflicting textual hypotheses into a reliable consensus is a fundamental challenge when deploying NLP systems in real-world industri
arXiv:2609.01612v1 Announce Type: cross Abstract: We introduce MESSY STREETS, a benchmark for evaluating geocoders on verbatim web addresses, with existence verification and controlled measurement of
arXiv:2609.02379v1 Announce Type: cross Abstract: While existing work on LLM authorship attribution (AA) has made progress, available benchmarks remain limited, often focusing on English, controlled s
arXiv:2609.02849v1 Announce Type: cross Abstract: Competitive programming has become a key test of large language model reasoning, with international competitions such as IOI and ICPC representing its
arXiv:2609.01622v1 Announce Type: cross Abstract: The rise of agentic AI has catalyzed a shift toward self-iterating systems, opening new frontiers for the autonomous optimization of production recomm
arXiv:2609.02036v1 Announce Type: new Abstract: Few-step diffusion models substantially compress temporal computation, making the spatial cost of each model evaluation an increasingly dominant source
arXiv:2512.15662v4 Announce Type: replace Abstract: Human beings solve complex problems through critical thinking, where reasoning and evaluation are intertwined to converge toward correct solutions.
arXiv:2609.01810v1 Announce Type: new Abstract: Farsi, spoken by more than 120 million people, lacks a comprehensive benchmark for dialogue generation and understanding. We introduce TALKFA, a unified
arXiv:2609.02462v1 Announce Type: new Abstract: End-to-end autonomous driving in urban environments requires robust decision-making under partial observability and complex multi-agent interactions. Se
arXiv:2609.02651v1 Announce Type: new Abstract: While English language models have been widely examined for anti-queer bias, Dutch models remain understudied. To address this gap, we developed a cultu
arXiv:2609.01608v1 Announce Type: cross Abstract: Black-box optimization problems remain challenging because of large, weakly structured, and high-dimensional search spaces. Existing methods often suf
arXiv:2609.00763v1 Announce Type: new Abstract: Hierarchical Knowledge graph (KG)-based retrieval augmented generation (RAG) has emerged as a powerful approach for supporting large language models wit
arXiv:2609.00949v1 Announce Type: cross Abstract: Multi-turn tool calling is a core evaluation scenario for large language model (LLM) agents. On public tool-calling benchmarks, open-weight models now
arXiv:2609.01535v1 Announce Type: cross Abstract: This paper explores whether large language models (LLMs) can design video coding tools, a highly challenging task due to the intricate algorithmic cou
arXiv:2609.00709v1 Announce Type: cross Abstract: Large Vision-Language Models produce fluent image descriptions but offer limited semantic control: users cannot reliably specify whether captions shou