Knowledge catalogue
arXiv:2604.19786v1 Announce Type: new Abstract: Evaluating humor in large language models (LLMs) is an open challenge because existing approaches yield isolated, incomparable metrics rather than unifi
I have mixed feelings about it. All this great group of researchers, who are now leading the field, 'own' their success and name recognition to the openness (in code and publication) of the last decad
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This X post references Yann LeCun, a renowned AI researcher and Chief AI Scientist at Meta, likely discussing prominent researchers working in molecular dynamics (MD) or related computational fields.
arXiv:2604.20675v1 Announce Type: new Abstract: Linear models are widely used in computational neuroimaging to identify biomarkers associated with brain pathologies. However, interpreting the learned
arXiv:2508.18168v3 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) has become a widely recognized paradigm to combine parametric memory with non-parametric memories. An RAG model
arXiv:2604.20307v1 Announce Type: new Abstract: In this paper, a deep learning framework is proposed for automatic facial emotion based on deep convolutional networks. In order to increase the general
arXiv:2604.19806v1 Announce Type: cross Abstract: Accurate prediction of energy and forces for 3D molecular systems is one of fundamental challenges at the core of AI for Science applications. Many po
In the past 55 days, the president of the US has averaged about 20 social media posts a day, ranging from Bruce Lee videos to AI content of him beating up Canadians. To highlight this man's increasing
arXiv:2604.19760v1 Announce Type: new Abstract: We present a simulation-based evaluation of the Inference Headroom Ratio (IHR), a dimensionless diagnostic quantity for characterizing inference stabili
arXiv:2604.20050v1 Announce Type: cross Abstract: Can Large Language Models (AI agents) aggregate dispersed private information through trading and reason about the knowledge of others by observing pr
arXiv:2512.00281v2 Announce Type: replace Abstract: Early detection of malignant lung nodules remains limited by reliance on size- and growth-based screening criteria, which can delay diagnosis. We pr
arXiv:2604.20026v1 Announce Type: new Abstract: Automatic bacterial colony counting is a highly sought-after technology in modern biological laboratories because it eliminates manual counting effort.
arXiv:2604.19851v1 Announce Type: cross Abstract: In an election where n voters rank m candidates, a Condorcet winning set is a committee of k candidates such that for any outside candidate, a majorit
arXiv:2402.06266v2 Announce Type: replace Abstract: Multi-objective reinforcement learning (MORL) algorithms extend conventional reinforcement learning (RL) to the more general case of problems with m
arXiv:2511.15141v2 Announce Type: replace-cross Abstract: Recently, large language models (LLMs) have been widely used as recommender systems, owing to their reasoning capability and effectiveness in
arXiv:2604.19834v1 Announce Type: new Abstract: Functional fitness movements are widely used in training, competition, and health-oriented exercise programs, yet consistently enforcing repetition (rep
arXiv:2604.20686v1 Announce Type: new Abstract: In the design stage of robotic hands, it is not straightforward to quantitatively evaluate the effect of phalanx length ratios on dexterity without defi
arXiv:2604.20800v1 Announce Type: new Abstract: Reconstructing 3D Human-Object Interaction from an RGB image is essential for perceptive systems. Yet, this remains challenging as it requires capturing
arXiv:2604.20796v1 Announce Type: new Abstract: We present LLaDA2.0-Uni, a unified discrete diffusion large language model (dLLM) that supports multimodal understanding and generation within a nativel
arXiv:2604.20560v1 Announce Type: new Abstract: Automatically filling Case Report Forms (CRFs) from clinical notes is challenging due to noisy language, strict output contracts, and the high cost of f
arXiv:2603.04950v2 Announce Type: replace-cross Abstract: Differential medical VQA models compare multiple images to identify clinically meaningful changes and rely on vision encoders to capture fine-
arXiv:2604.19976v1 Announce Type: new Abstract: While the human eye can perceive an impressive twenty stops of dynamic range, smartphone camera sensors remain limited to about twelve stops despite dec
arXiv:2604.20143v1 Announce Type: cross Abstract: This is our fourth work in the series on machine learning (ML) moment closure models for the radiative transfer equation (RTE). In the first three pap
arXiv:2604.20221v1 Announce Type: new Abstract: This study applies symbolic time series analysis and Markov modeling to explore the phonological structure of Evgenij Onegin-as captured through a graph
arXiv:2509.08607v3 Announce Type: replace-cross Abstract: The geodesy of irregularly shaped small bodies presents fundamental challenges for gravitational field modeling, particularly as deep space ex
arXiv:2604.20074v1 Announce Type: new Abstract: A popular approach to apprenticeship learning (AL) is to formulate it as an inverse reinforcement learning (IRL) problem. The MaxEnt-IRL algorithm succe
arXiv:2604.20154v1 Announce Type: cross Abstract: Multi-look acquisition is a widely used strategy for reducing speckle noise in coherent imaging systems such as digital holography. By acquiring multi
arXiv:2604.19799v1 Announce Type: cross Abstract: Generative AI is rapidly transforming how organizations create value and evaluate talent. While large language models enhance baseline output quality,
arXiv:2601.02989v2 Announce Type: replace Abstract: Large language models (LLMs), despite strong performance on complex mathematical problems, exhibit systematic limitations in counting tasks. This is
arXiv:2604.20467v1 Announce Type: cross Abstract: Artificial Intelligence (AI) weather models are improving rapidly, and their forecasts are already competitive with long-established traditional Numer
arXiv:2604.19902v1 Announce Type: cross Abstract: We present MMCORE, a unified framework designed for multimodal image generation and editing. MMCORE leverages a pre-trained Vision-Language Model (VLM
arXiv:2506.02132v5 Announce Type: replace Abstract: Large transformer-based language models dominate modern NLP, yet our understanding of how they encode linguistic information relies primarily on stu
arXiv:2604.20468v1 Announce Type: cross Abstract: Industrial robot applications require increasingly flexible systems that non-expert users can easily adapt for varying tasks and environments. However
arXiv:2604.20283v1 Announce Type: new Abstract: Multimodal Entity Linking (MEL) is a fundamental task in data management that maps ambiguous mentions with diverse modalities to the multimodal entities
arXiv:2511.14311v2 Announce Type: replace-cross Abstract: Model Predictive Control (MPC) has established itself as the primary methodology for constrained control, enabling autonomy across diverse app
arXiv:2604.19846v1 Announce Type: cross Abstract: IceCube is a cubic-kilometer-scale neutrino detector located at the geographic South Pole. A precise directional reconstruction of IceCube neutrinos i
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arXiv:2604.20041v1 Announce Type: cross Abstract: Normalizing Flows (NFs) are a classical family of likelihood-based methods that have received revived attention. Recent efforts such as TARFlow have s
arXiv:2604.20454v1 Announce Type: new Abstract: Metaphors are powerful framing devices, yet their source domains alone do not fully explain the specific associations they evoke. We argue that the inte
arXiv:2604.20361v1 Announce Type: new Abstract: Object Referring-guided Scanpath Prediction (ORSP) aims to predict the human attention scanpath when they search for a specific target object in a visua
arXiv:2508.08822v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI) models are currently driven by a significant upscaling of their complexity, with massive matrix-multiplication wo
arXiv:2505.13510v3 Announce Type: replace Abstract: Though neural networks trained on large datasets have been successfully used to describe and predict many physical phenomena, there is a sense among
arXiv:2506.18739v2 Announce Type: replace-cross Abstract: Previous work on the learnability of transformers extemdash focused primarily on examining their ability to approximate specific algorithmic p
arXiv:2604.20585v1 Announce Type: new Abstract: This study investigates the impact of face image background correction through segmentation on face recognition and morphing attack detection performanc
arXiv:2604.20079v1 Announce Type: cross Abstract: Auto-regressive Large Language Models (LLMs) achieve strong performance on coding tasks, but incur high memory and inference costs. Diffusion-based la
arXiv:2604.20115v1 Announce Type: cross Abstract: Bilevel optimization and bilevel minimax optimization have recently emerged as unifying frameworks for a range of machine-learning tasks, including hy
arXiv:2604.19989v1 Announce Type: new Abstract: With modern defense applications increasingly relying on inexpensive, small Unmanned Aerial Vehicles (UAVs), a major challenge lies in designing intelli
arXiv:2604.20296v1 Announce Type: cross Abstract: Survival analysis is a widely used statistical framework for modeling time-to-event data under censoring. Classical methods, such as the Cox proportio
arXiv:2604.20401v1 Announce Type: cross Abstract: Approximate nearest neighbor (ANN) search in AI systems increasingly handles sensitive data on third-party infrastructure. Trusted execution environme
arXiv:2604.20268v1 Announce Type: new Abstract: Background: Osteoporosis and osteopenia are often undiagnosed until fragility fractures occur. Dual-energy X-ray absorptiometry (DXA) is the reference s
arXiv:2604.19999v1 Announce Type: new Abstract: Visual detection of Unmanned Aerial Vehicles (UAVs) is a critical task in surveillance systems due to their small physical size and environmental challe
arXiv:2601.12078v2 Announce Type: replace Abstract: Large language models (LLMs) excel at general-purpose tasks, yet adapting their responses to individual users remains challenging. Retrieval augment
arXiv:2604.19766v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) expands the knowledge of Large Language Models (LLMs), yet current static retrieval methods struggle with complex
arXiv:2603.27518v2 Announce Type: replace Abstract: Aligned language models that are trained to refuse harmful requests also exhibit over-refusal: they decline safe instructions that seemingly resembl
arXiv:2604.20130v1 Announce Type: cross Abstract: Mode collapse remains a fundamental challenge in training generative adversarial networks (GANs). While existing works have primarily focused on inter
Recurrent Neural Networks (RNNs) are naturally suited to efficient inference, requiring far less memory and compute than attention-based architectures, but the sequential nature of their computation h
arXiv:2604.20816v1 Announce Type: cross Abstract: Reinforcement Learning (RL) post-training has become the standard for aligning generative models with human preferences, yet most methods rely on a si
arXiv:2604.20557v1 Announce Type: new Abstract: Shared Control methods often use impedance control to track target poses in a robotic manipulator. The guidance behavior of such controllers is shaped b
arXiv:2604.20047v1 Announce Type: new Abstract: Vision Transformers (ViTs) have achieved remarkable success across vision tasks, yet recent studies show they remain vulnerable to backdoor attacks. Exi