Knowledge catalogue
arXiv:2608.29519v1 Announce Type: new Abstract: We introduce Function-Room Generation, a new indoor 3D scene generation setting that creates rooms supporting explicit functional goals rather than mere
arXiv:2407.20371v3 Announce Type: replace-cross Abstract: Artificial intelligence (AI) hiring tools have revolutionized resume screening, and large language models (LLMs) have the potential to do the
arXiv:2311.13892v4 Announce Type: replace-cross Abstract: The social biases and unwelcome stereotypes revealed by pretrained language models are becoming obstacles to their application. Compared to nu
arXiv:2608.28872v1 Announce Type: cross Abstract: The ability to leverage images from co-available modalities to inform target-domain reconstruction is highly desirable in imaging algorithms. In this
arXiv:2608.29959v1 Announce Type: new Abstract: Language is humanity's most consequential technology, yet for over a billion speakers across India's twenty-two constitutionally recognised languages, i
arXiv:2608.30712v1 Announce Type: new Abstract: Large multimodal models follow instructions about what to generate, but not necessarily about what evidence to rely on. Hence, models may continue to de
arXiv:2502.00857v2 Announce Type: replace Abstract: Large Language Models (LLMs) increasingly provide direct answers to user questions, raising concerns about reduced engagement in critical thinking a
arXiv:2608.30841v1 Announce Type: new Abstract: Large language models can often generate plausible mathematical reasoning traces, but reliably identifying the correct solution among multiple candidate
arXiv:2608.30475v1 Announce Type: cross Abstract: We present an overview of the ImageEval 2026 shared task on culturally grounded Arabic multimodal evaluation. It includes two tasks: (i) AynVQA, cover
arXiv:2608.31133v1 Announce Type: cross Abstract: Neural network mixed-effects models (NMMs) have gained traction by combining the strong representation and predictive power of artificial neural netwo
arXiv:2608.29942v1 Announce Type: cross Abstract: The key limitation of current state-of-the-art influence-based guardrails is that they do not reliably distinguish a legitimate, user-authorized actio
arXiv:2608.29535v1 Announce Type: cross Abstract: Infectious disease transmission is shaped by patterns of human interaction, which adapt as epidemic conditions change. Capturing these context-depende
arXiv:2608.28612v1 Announce Type: new Abstract: Generating professional scholarly content, such as peer reviews and rebuttals, requires an intricate synergy between domain reasoning and factual ground
arXiv:2608.29901v1 Announce Type: cross Abstract: Electronic Health Record (EHR) prediction models in the intensive care unit must learn from sparse and irregular measurements while preserving the cli
arXiv:2608.28991v1 Announce Type: cross Abstract: Causal representation learning (CRL) aims to recover latent causal variables and their structural relations from high-dimensional observations. Existi
arXiv:2608.29168v1 Announce Type: new Abstract: The LLM-as-a-Judge paradigm has emerged as a scalable alternative to human evaluation. However, single-model judges are limited by their inherent model
arXiv:2508.19485v2 Announce Type: replace Abstract: Gas leaks pose severe risks to human health and industrial safety. However, accurate and timely monitoring of gas leaks remains a major challenge. E
arXiv:2601.07794v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly evaluated on their ability to perform multi-hop reasoning, i.e., to combine multiple pieces of i
arXiv:2508.13073v3 Announce Type: replace-cross Abstract: Robotic manipulation, a key frontier in robotics and embodied AI, requires precise motor control and multimodal understanding, yet traditional
arXiv:2607.10851v2 Announce Type: replace Abstract: Medical image classification models are ideally expected to identify diagnostically relevant regions while making predictions, yet standard classifi
arXiv:2608.30510v1 Announce Type: cross Abstract: For provenance research and art market studies, auction catalogs are an essential resource to trace specific objects over time and space. While histor
arXiv:2509.04183v3 Announce Type: replace-cross Abstract: The growing demand for scalable psychological counseling highlights the need for high-quality, privacy-compliant data, yet such data remains s
arXiv:2608.30114v1 Announce Type: new Abstract: Brazilian Portuguese remains under-served by open language models, and the few that exist are difficult to reproduce and are often compared without meas
arXiv:2608.28671v1 Announce Type: cross Abstract: AI-generated art has sparked debates around potential plagiarism, as these images may closely resemble existing artworks. This research quantifies the
arXiv:2608.28605v1 Announce Type: new Abstract: Recent advancements in multimodal learning for medical time series (MedTS) classification highlight the benefits of integrating complementary modalities
arXiv:2603.03677v2 Announce Type: replace-cross Abstract: Psychiatric consultation requires agents to elicit discriminative evidence, map uncertain narratives to diagnostic criteria, and decide when e
arXiv:2608.28913v1 Announce Type: new Abstract: High-resolution 3D radar data is scarce. Commodity mmWave sensors use small antenna arrays that limit angular resolution to several degrees, and existin
arXiv:2608.30563v1 Announce Type: new Abstract: Multimodal Emotion Recognition (MER) systems often suffer from missing modalities in real-world scenarios. Existing methods usually generate, align, or
arXiv:2608.29278v1 Announce Type: new Abstract: Omni-modal large language models are increasingly evaluated on clean text--vision--audio inputs, where every channel is present, synchronized, and readi
arXiv:2608.28674v1 Announce Type: new Abstract: Multi-exposure is an efficient way to capture real-world high-dynamic-range (HDR) scenes. However, HDR imaging suffers from severe ghosting artifacts in
arXiv:2608.31065v1 Announce Type: new Abstract: Surgical phase recognition is key to context-aware computer-assisted feedback in vitreoretinal procedures, yet the scarcity of synchronized multimodal i
arXiv:2603.02482v2 Announce Type: replace-cross Abstract: Safety evaluation of multimodal large language models requires tracking not only whether an attack succeeds, but also how the interaction unfo
arXiv:2509.12458v3 Announce Type: replace-cross Abstract: Miniaturized Uncrewed Aerial Vehicles (UAVs) can access indoor and hard-to-reach spaces, but severe constraints on payload and autonomy have l
arXiv:2608.29045v1 Announce Type: new Abstract: Biclustering, or co-clustering, aims to discover coherent submatrices by grouping rows and columns of a data matrix simultaneously. This local two-dimen
arXiv:2608.29513v1 Announce Type: cross Abstract: Machine unlearning enables deep neural networks to selectively remove the influence of specific data in response to privacy and regulatory requirement
arXiv:2608.28663v1 Announce Type: new Abstract: Reliable individual cattle identification supports disease surveillance, vaccination records, breeding management, and livestock insurance. Although the
arXiv:2608.30179v1 Announce Type: cross Abstract: Open-source autonomous driving systems provide an inspectable software foundation for intelligent vehicle research. Under real-vehicle deployment cond
arXiv:2608.28704v1 Announce Type: new Abstract: AI agents increasingly perform long-term reasoning, planning, tool use, memory integration, and autonomous decision making, yet erroneous intermediate s
arXiv:2603.17356v3 Announce Type: replace Abstract: Drug recommendation requires a deep understanding of individual patient context, especially for complex conditions like Parkinson's disease. While L
arXiv:2608.28633v1 Announce Type: cross Abstract: Generative AI systems increasingly mediate cultural adaptation, but their cultural decisions are often hidden inside prompts, transient model plans, o
arXiv:2603.10009v2 Announce Type: replace-cross Abstract: Despite their sophisticated general-purpose capabilities, Large Language Models (LLMs) often fail to align with diverse individual preferences
arXiv:2505.17537v2 Announce Type: replace Abstract: Large language models (LLMs) often produce incorrect answers with high confidence, yet the factors associated with such overconfidence remain insuff
arXiv:2608.30449v1 Announce Type: new Abstract: Click-through rate (CTR) models vary in feature-interaction design, yet their top networks usually remain a single multilayer perceptron shared by all e
arXiv:2509.10882v2 Announce Type: replace Abstract: In high-stakes domains such as healthcare, privacy concerns severely limit the use of real-world training data. Differentially private (DP) syntheti
arXiv:2608.28433v2 Announce Type: replace Abstract: Proof assistants such as Lean 4 promise the paradigm of formally verified mathematics, but large-scale formalization projects have faced major barri
arXiv:2608.29798v1 Announce Type: cross Abstract: The same Persona behavior can be beneficial in one context but harmful in another, causing static Persona elicitation to perform inconsistently across
arXiv:2512.18718v3 Announce Type: replace Abstract: Image correction and rectangling are valuable tasks in practical photography systems such as smartphones. Recent remarkable advancements in deep lea
arXiv:2608.25920v2 Announce Type: replace Abstract: As large language model (LLM)-based multi-agent systems (MASs) are increasingly applied to long-horizon complex tasks, their reliability has emerged
arXiv:2608.30946v1 Announce Type: new Abstract: Closed-loop human-AI systems generate high-dimensional behavioural trajectories whose collective dynamics remain obscure. Using 297,915 learners' adapti
arXiv:2602.14275v2 Announce Type: replace-cross Abstract: Artificial intelligence/machine learning (AI/ML) systems and emerging quantum computing software present unprecedented testing challenges char
arXiv:2608.28934v1 Announce Type: new Abstract: Differential privacy (DP) has traditionally been used to provide theoretical upper bounds on an algorithm's stability to changing its training data. In
arXiv:2608.28639v1 Announce Type: new Abstract: Formal theorem proving with large language models remains challenging due to the difficulty of navigating large proof search spaces efficiently. Existin
arXiv:2603.10807v2 Announce Type: replace-cross Abstract: Existing LLM safety evaluations rely on binary attack-success rates and domain-agnostic taxonomies, leaving regulated Banking, Financial Servi
arXiv:2608.30889v1 Announce Type: cross Abstract: Deploying a new control policy for voltage control in active distribution grids requires evidence that physical limits will be satisfied before the po
arXiv:2608.30165v1 Announce Type: cross Abstract: Scientific progress depends not only on finding solutions, but on learning the rules that explain why they work and using that understanding to design
arXiv:2608.29475v1 Announce Type: new Abstract: Surface material recognition from incomplete visual observations remains a challenging problem in robotic perception and environmental understanding. Th
arXiv:2608.30456v1 Announce Type: new Abstract: We compare six self-supervised pretext tasks for infant cry analysis under a fixed budget, meaning the same compact encoder of 1.17M parameters, the sam
arXiv:2512.15052v4 Announce Type: replace-cross Abstract: Disclaimer: Samples in this paper may be harmful and cause discomfort. Multimodal large language models (MLLMs) enable multimodal understandin
arXiv:2608.28600v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong performance on mathematical reasoning benchmarks, yet the mathematically meaningful skills underlying their
arXiv:2608.29800v1 Announce Type: new Abstract: Tool-using agents are being proposed for medical imaging, and their behaviour when a tool returns a false finding is largely unmeasured. We audit whethe