Knowledge catalogue
arXiv:2606.12350v1 Announce Type: new Abstract: The rapid proliferation of large language models (LLMs) raises critical questions about human creativity and individual expression in an era of AI-assis
arXiv:2606.11505v1 Announce Type: cross Abstract: Biometric systems are increasingly deployed in security applications; however, they remain vulnerable to spoofing attacks, in which attackers exploit
arXiv:2606.11202v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in applications for global multilingual users, yet safety training remains concentrated in domina
arXiv:2606.11680v1 Announce Type: new Abstract: Large language model (LLM) agents struggle with long-horizon tasks due to their inherent statelessness, requiring all task-relevant information to be en
arXiv:2606.12282v1 Announce Type: cross Abstract: Expressive performance rendering (EPR) aims to generate realistic performances constrained on sequences of notes. However, flow matching audio editing
arXiv:2602.23545v2 Announce Type: replace Abstract: In the real world, planning is often challenged by distribution shifts. As such, a model of the environment obtained under one set of conditions may
arXiv:2606.11245v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks, raising expectations for Artificial General Intelligence (A
arXiv:2606.11339v1 Announce Type: cross Abstract: We study distributed optimization with stochastic gradients and finite-bit communication modeled by random (unbiased) quantization. We propose q-PDGD,
arXiv:2606.11391v1 Announce Type: new Abstract: Tensor Product Representations provide the structural fidelity required for symbolic reasoning in models but suffer from exponential dimensionality grow
arXiv:2606.11522v1 Announce Type: new Abstract: Autoresearch agents now propose, evaluate, and select scientific candidates against a metric, and that metric is usually an aggregate reduced over a het
arXiv:2511.05203v3 Announce Type: replace Abstract: Today's autonomous agents, largely driven by foundation models (FMs), can understand natural language instructions and solve long-horizon tasks with
arXiv:2606.11543v1 Announce Type: new Abstract: Agent Skills augment large language model (LLM) agents with procedural knowledge at inference time, but current benchmarks rarely distinguish what a Ski
arXiv:2606.11691v1 Announce Type: new Abstract: Latent diffusion and flow matching have emerged as leading approaches for synthetic turbulence generation, yet they systematically under-represent dissi
arXiv:2606.12033v1 Announce Type: new Abstract: Video understanding is a crucial part of computer vision, with numerous application scenarios. With the increasing popularity of mobile devices, an incr
arXiv:2606.11743v1 Announce Type: cross Abstract: Vision-language-action (VLA) models provide strong visual, language, and action priors for robot manipulation, but visual observations alone often mis
arXiv:2606.11853v1 Announce Type: cross Abstract: Multi-modal large language models (MLLMs) depend on in-context learning (ICL) for rapid task adaptation, but their scalability is severely limited by
arXiv:2606.11844v1 Announce Type: new Abstract: Continual anomaly detection in tabular data is challenging and remains largely underexplored, particularly in settings with heterogeneous feature schema
arXiv:2602.10392v2 Announce Type: replace Abstract: When designing new materials, it is often necessary to tailor the material design to have some desired properties. As the set of design parameters g
arXiv:2606.11371v1 Announce Type: cross Abstract: Spoken language, whether produced by humans or large language models (LLM), unfolds over time with varying semantic content. However, we still lack si
arXiv:2606.11215v1 Announce Type: cross Abstract: Large Language Model (LLM) usage in recent years has become increasingly widespread in the Artificial Intelligence in Education (AIED) community. Whil
arXiv:2603.12261v2 Announce Type: replace-cross Abstract: Text-to-image generation models have advanced rapidly, yet achieving fine-grained control over generated images remains difficult, largely due
arXiv:2506.17137v3 Announce Type: replace Abstract: Object counting models often degrade under cross-domain deployment because density composition varies across domains and is itself task-relevant. St
arXiv:2606.11269v1 Announce Type: new Abstract: Personality assessment aims to infer stable personality traits from dynamic behaviors across language, voice, and facial cues. Since different personali
arXiv:2510.02660v2 Announce Type: replace-cross Abstract: When researchers claim AI systems possess ToM or mental models, they are fundamentally discussing behavioral predictions and bias corrections
arXiv:2606.12199v1 Announce Type: cross Abstract: Spoken dialogue models typically start from text LLM backbones, yet reasoning often degrades when conditioning on speech instead of text. We attribute
1. Open the Profiles page from the sidebar 2. Click the Build button in the page header (next to the quick Create button) 3. That takes you to the /profiles/new builder flow: Identity → Model → Skills
arXiv:2606.10782v1 Announce Type: cross Abstract: The adoption and integration of heterogeneous stacks in most of today's open-source based networks brings clear benefits like interoperability and ava
arXiv:2606.11012v1 Announce Type: new Abstract: Background and purpose: oART enables daily plan adaptation to interfraction anatomical variations, but cumulative dose estimation remains limited by DIR
arXiv:2510.08622v2 Announce Type: replace Abstract: Software requirements are derived from a variety of elicitation techniques, many of which have a conversational nature, like interviews. However, ev
arXiv:2606.09876v1 Announce Type: new Abstract: Large language models often express high confidence in answers that are wrong. Standard calibration remedies typically act globally or at the score leve
arXiv:2511.02603v2 Announce Type: replace Abstract: Large language models (LLMs) are often queried multiple times at test time, with predictions aggregated by majority vote. While effective, this self
arXiv:2606.10372v1 Announce Type: new Abstract: In abdominal CT imaging, developing a low-dose, no-reference image quality assessment (No-reference IQA) model that mimics doctors' reading habits for e
arXiv:2606.11162v1 Announce Type: new Abstract: In this work, we present COGENT, a continuous graph emulator with Neural Ordinary Differential Equations for long-term physical forecasting on irregular
arXiv:2606.10059v1 Announce Type: cross Abstract: Finite-state transducers (FSTs) are essential for modeling string rewriting in computational linguistics and natural language processing (NLP), partic
arXiv:2606.09923v1 Announce Type: cross Abstract: Neural operators such as the Fourier Neural Operator (FNO) have emerged as powerful surrogates for solving partial differential equations (PDEs), achi
arXiv:2606.10580v1 Announce Type: cross Abstract: The asymptotic behaviour of Monte Carlo optimistic policy iteration (MC-O-PI) is a long-standing open question. When the model of the environment is u
arXiv:2606.10475v1 Announce Type: cross Abstract: Multi-agent debate frameworks have been shown to improve large language model performance in convergent tasks, but they are currently optimized in a w
arXiv:2606.09466v2 Announce Type: replace Abstract: Classification tasks require annotated data, which can often be expensive, time-consuming, or even unfeasible to collect. This is the case of the me
Row-Bot is a local-first desktop AI assistant that orchestrates tools and models to handle reasoning and workflows while keeping data local. The demo likely showcases how Row-Bot's integrated tools, k
arXiv:2606.10198v1 Announce Type: cross Abstract: Hallucination detection in large language and vision-language models is increasingly framed as selective prediction, where a detector assigns a confid
arXiv:2606.10184v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) relies on the diversity of K rollouts within each group; otherwise, the group-mean advantage A^{(k)} = r^{(k
arXiv:2606.11138v1 Announce Type: new Abstract: We introduce First-Order Trajectory Matching (FTM), a surrogate-modeling method that learns the first-order local transport of probability mass from tra
arXiv:2606.10896v1 Announce Type: new Abstract: We present extbf{Flash-GMM}, a fused Triton kernel for efficient computation of Gaussian Mixture Models (GMMs) over large-scale data in a single GPU pas
arXiv:2606.11057v1 Announce Type: new Abstract: Despite its importance to applications in protein design, predicting protein properties like binding affinity and thermostability from sparse experiment
arXiv:2606.11023v1 Announce Type: cross Abstract: Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior. However, the limited quality of i
arXiv:2601.18765v2 Announce Type: replace Abstract: Autonomous robotic systems are widely deployed in smart factories and operate in dynamic, uncertain, and human-involved environments that require lo
arXiv:2606.09951v1 Announce Type: new Abstract: During the training of large Transformer models, attention masks regulate the scope and direction of information flow across a sequence. Numerous mask v
arXiv:2606.09960v1 Announce Type: cross Abstract: We present HydraCIL, a decoupled continual learning model based on prototype-guided multi-head classifiers, targeting sustainable deployment in embedd
arXiv:2606.11096v1 Announce Type: new Abstract: Built on pretrained vision foundation models (VFMs), representation autoencoders (RAEs) have recently emerged as a promising approach for constructing s
arXiv:2606.10068v1 Announce Type: cross Abstract: Hyperparameter Optimization (HPO) is essential for building high-performing ML/DL models, yet conventional optimizers often struggle in high-dimension
arXiv:2602.19393v2 Announce Type: replace Abstract: Steck, Ekanadham, and Kallus [arXiv:2403.05440] demonstrate that cosine similarity of learned embeddings from matrix factorization models can be ren
In film, 'we'll fix it in post' is what you say when something went wrong on set and you don't want to redo it. AI research has made it our entire methodology: train the model, then patch whatever com
arXiv:2510.12071v2 Announce Type: replace Abstract: Current training data attribution (TDA) methods treat the influence one sample has on another as static, but neural networks learn in distinct stage
arXiv:2601.22763v3 Announce Type: replace Abstract: Current state-of-the-art multi-class unsupervised anomaly detection (MUAD) methods rely on training encoder--decoder models to reconstruct anomaly-f
arXiv:2606.11156v1 Announce Type: cross Abstract: Recent one-step generative models accelerate sampling by learning deterministic flow maps of the underlying dynamics. These methods rely on learning f
arXiv:2509.24710v2 Announce Type: replace-cross Abstract: Score-based diffusion models are a highly effective method for generating samples from a distribution of images. We consider scenarios where t
arXiv:2602.03164v2 Announce Type: replace-cross Abstract: Time series forecasting (TSF) plays a critical role in decision-making for many real-world applications. Recently, large language model (LLM)-
arXiv:2606.09855v1 Announce Type: cross Abstract: Korean folk painting (minhwa) is built from a small vocabulary of auspicious symbols, a tiger for protection, a pair of birds for marital harmony, a p
arXiv:2606.10237v1 Announce Type: new Abstract: Genetic programming (GP) is based on two important insights. First, that any learning task can fundamentally be posed as a program induction problem, wh
arXiv:2606.10250v1 Announce Type: cross Abstract: Class imbalance is a common problem in deep learning that severely degrades performance. In federated learning (FL), it is a critical factor contribut