Knowledge catalogue
arXiv:2609.36224v1 Announce Type: cross Abstract: Unified multimodal models (UMMs) combine image generation and visual understanding in a shared backbone. Since generation and understanding are invers
arXiv:2609.37113v1 Announce Type: new Abstract: Generalized reaction-diffusion systems encompass diverse transport mechanisms and coupled reaction kinetics. A central question for neural PDE solvers i
arXiv:2609.36620v1 Announce Type: new Abstract: Structural reasoning, the ability to recognize and make inferences over the relational structure between objects and concepts, is a hallmark of human co
arXiv:2609.36375v1 Announce Type: new Abstract: Continual learning is usually studied through mechanisms that preserve old knowledge. We develop Successional Learning Theory (SLT), a mesoscopic accoun
arXiv:2609.36878v1 Announce Type: new Abstract: LiDAR return intensity provides complementary surface-response cues for robotic perception and state estimation, yet many simulation pipelines omit it o
arXiv:2609.36655v1 Announce Type: new Abstract: Continual test-time adaptation (CTTA) adapts a source model to an unlabeled test stream whose distribution may change over time. Existing TTA methods of
arXiv:2609.37038v1 Announce Type: cross Abstract: Precipitation nowcasting demands accurate short-term forecasts under strong spatiotemporal variability. Diffusion models are well suited to modeling c
arXiv:2602.13540v2 Announce Type: replace-cross Abstract: Accurate confidence estimation is critical for reliable use of large language models (LLMs). Prior work on LLM calibration largely focuses on
arXiv:2602.04852v3 Announce Type: replace Abstract: Linear attention offers a computationally efficient yet expressive alternative to softmax attention. However, recent empirical results indicate that
arXiv:2609.37575v1 Announce Type: cross Abstract: We argue that dividing codec design into Coding for Machines (CfM) and Coding for Humans (CfH) is a misleading distinction for deciding what informati
arXiv:2609.36227v1 Announce Type: cross Abstract: Next-latent prediction fits a map from the current embedding to the next one. LeNEPA carries this objective to time series, replacing the stop-gradien
arXiv:2609.37861v1 Announce Type: new Abstract: In the Global South, the lower-income countries of Africa, Asia, and Latin America where most of the world's languages are spoken, a deployed text class
arXiv:2609.38073v1 Announce Type: cross Abstract: We study exact learning with membership queries for concept classes mathcal Csubseteq{0,1}^N, focusing on the relationships among their deterministic,
arXiv:2606.19302v2 Announce Type: replace-cross Abstract: As deep learning for physical systems continues to grow in popularity, efforts to improve generalizability have primarily focused on designing
arXiv:2405.07944v1 Announce Type: cross Abstract: Among the well-known methods to approximate derivatives of expectancies computed by Monte-Carlo simulations, averages of pathwise derivatives are ofte
arXiv:2609.37579v1 Announce Type: cross Abstract: Bresar and Mijatovic ite{bresar2025} show that Ornstein--Uhlenbeck diffusion is hard to beat in forward convergence under assumptions that exclude sup
arXiv:2609.37564v1 Announce Type: new Abstract: Merging pretrained models has emerged as an effective approach for consolidating diverse capabilities into a single unified model. However, prevailing m
arXiv:2609.36602v1 Announce Type: new Abstract: Transferring human motion to humanoid robots requires adapting the demonstrated motion to the robot morphology while preserving interactions with the en
arXiv:2609.36263v1 Announce Type: cross Abstract: Existing multimodal scaling laws fit multimodality terms empirically after testing and never vary how much data is multimodally paired at fixed data b
arXiv:2609.37323v1 Announce Type: new Abstract: Discrete diffusion models have emerged as a powerful paradigm for solving combinatorial optimization (CO) problems on graphs by learning to sample high-
arXiv:2609.37921v1 Announce Type: new Abstract: What are the inductive biases of a Transformer architecture? Existing theory on how the forward pass shapes representations either considers whether Tra
arXiv:2609.37350v1 Announce Type: new Abstract: The development of AI systems for tumor-specific applications is limited by the scarcity of labeled data. Synthetic tumor inpainting offers a promising
arXiv:2609.38023v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) embed governing equations into deep learning, but enforce them only through loss residuals, leaving highly osci
arXiv:2601.01207v2 Announce Type: replace Abstract: In stochastic message passing, an edge's sampled role changes the node states used to compute adaptive weights at later layers. Weight averaging the
arXiv:2609.36193v1 Announce Type: new Abstract: Learning from scientific measurements often requires aligning modalities with different spatial support and resolution. Subsurface characterization is a
arXiv:2609.36326v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) hands the user's query to whoever hosts the corpus. We propose PILLAR, a Privacy-Preserving RAG (PPRAG) system base
arXiv:2511.22435v2 Announce Type: replace Abstract: Invariant learning on graphs aims to build predictors that rely on causal substructures rather than on environment-specific shortcuts. Current metho
arXiv:2609.37907v1 Announce Type: new Abstract: Video games offer scalable environments for studying perception and control in embodied agents.Abundant online gameplay videos could supply demonstratio
arXiv:2609.37784v1 Announce Type: new Abstract: Satellite observations, precomputed embeddings, and map products describe the same evolving Earth, yet are stored as independent, petabyte-scale data pr
arXiv:2609.36543v1 Announce Type: new Abstract: Partial observability remains a fundamental challenge in robotic navigation, where limited sensor coverage and occlusions leave large portions of the en
arXiv:2609.35854v1 Announce Type: cross Abstract: In the field of Machine Learning, many papers contain empirical results supporting claimed statements or illustrating the performance of a proposed me
arXiv:2609.38054v1 Announce Type: cross Abstract: We present Pow3R-SLAM, a real-time RGB-D simultaneous localization and mapping (SLAM) system that uses Pow3R for tracking and mapping. Inspired by MAS
arXiv:2609.36216v1 Announce Type: new Abstract: Neural operators are typically trained in a supervised fashion, which requires a dataset to be generated with a classical solver. Training them physics-
arXiv:2503.03791v1 Announce Type: cross Abstract: Understanding how individual traits influence team performance is valuable, but these traits are not always directly observable. Prior research has in
arXiv:2609.37717v1 Announce Type: cross Abstract: Decoder-only transformers are trained only through a terminal next-token prediction loss, yet this loss constrains every intermediate hidden state thr
arXiv:2609.37789v1 Announce Type: cross Abstract: Self-supervised learning (SSL) by predicting in latent space, without generating the input data itself, learns highly abstract, useful representations
arXiv:2609.37529v1 Announce Type: new Abstract: Pretrained denoisers provide a powerful way to incorporate image priors into restoration algorithms. Plug-and-Play and RED approaches exploit fixed-nois
arXiv:2609.35937v1 Announce Type: cross Abstract: While prior work has documented privacy failures in LLM agents, it remains unclear how the presentation of privacy guidance influences their choice of
arXiv:2609.37202v1 Announce Type: cross Abstract: Droplet collision governs droplet population dynamics in many chemical engineering processes, such as spray drying, spray cooling, agricultural sprayi
arXiv:2609.36279v1 Announce Type: cross Abstract: The shallow embedding of higher-order modal logic in classical higher-order logic, used in Benzmuller and Scott's Notes on Godel's and Scott's variant
arXiv:2603.15106v2 Announce Type: cross Abstract: Enabling efficient deep neural network (DNN) inference on edge devices with different hardware constraints is a challenging task that typically requir
arXiv:2609.36315v1 Announce Type: cross Abstract: Wildfires are an increasing hazard to ecosystems, air quality, and human systems, creating a growing need for datasets that support systematic develop
arXiv:2609.36376v1 Announce Type: cross Abstract: Dense retrieval, the key component of Retrieval Augmented Generation (RAG), retrieves the most relevant documents by comparing dense vector representa
arXiv:2609.36479v1 Announce Type: cross Abstract: Quantum computing has already been explored in several network-security applications. However, how quantum computing may contribute to network-securit
arXiv:2609.36702v1 Announce Type: cross Abstract: Quantum Generative Adversarial Networks (QGANs) have emerged as representative generative models in the Noisy Intermediate-Scale Quantum (NISQ) era an
arXiv:2609.37650v1 Announce Type: new Abstract: Counterfactual explanations of graph neural networks identify edge deletions that flip a prediction. On heterogeneous graphs, however, existing methods
arXiv:2609.36902v1 Announce Type: new Abstract: Existing multimodal fake news detection methods often introduce external information to assist detection. However, most of them rely on entity-level ret
arXiv:2603.17815v2 Announce Type: replace Abstract: Understanding and evaluating multi-step reasoning in LLMs at the level of individual steps remains a key challenge. Process reward models (PRMs) pro
arXiv:2609.37688v1 Announce Type: new Abstract: Reading behaviour varies not only with linguistic input, but also with reader proficiency. In this study, we investigate whether the layer-wise relation
arXiv:2609.36984v1 Announce Type: new Abstract: Complex questions often require multi-hop reasoning that connects facts distributed across sources or distant regions of a long context through intermed
arXiv:2609.36126v1 Announce Type: cross Abstract: Modern AI architectures used to solve visual reasoning tasks typically rely heavily on global connectivity and synchronization. As biological systems
arXiv:2609.38112v1 Announce Type: cross Abstract: Many applications of black-box predictive models require controlling task-relevant error rates, such as missed lesion pixels in segmentation or missed
arXiv:2609.37734v1 Announce Type: new Abstract: Research in modular robotics has produced capable approaches allowing a robot's morphology to change online, with recent efforts also developing approac
arXiv:2603.11210v2 Announce Type: replace Abstract: Machine unlearning aims to remove the influence of specific training data from a model while preserving its general utility. In vision, many approxi
arXiv:2609.35824v1 Announce Type: new Abstract: Large language models (LLMs) can give reliable labels under one setup yet change those labels when researchers make other reasonable design choices. We
arXiv:2609.36452v1 Announce Type: cross Abstract: Masked diffusion language models (MDLMs) can generate text efficiently by predicting multiple masked tokens in parallel, but predictions from the same
arXiv:2609.37587v1 Announce Type: cross Abstract: Longitudinal electronic health record (EHR) modeling requires integrating new visits with an expanding patient history. Yet the continual accumulation
arXiv:2609.35867v1 Announce Type: new Abstract: We propose ReLOBGen, a method for generating limit order book (LOB) messages that are replayable by construction. Replayability is required for closed-l
arXiv:2609.36121v1 Announce Type: cross Abstract: Large language models are vulnerable to prompt injection attacks, where third-party adversarial content can hijack the model's behavior. In this paper
arXiv:2609.36974v1 Announce Type: new Abstract: Text-to-speech models loop, truncate and lose count on text that repeats a phrase many times. We show that repetition itself is what breaks them, not th