Knowledge catalogue
arXiv:2506.18871v4 Announce Type: replace-cross Abstract: In this work, we introduce OmniGen2, a versatile and open-source generative model designed to provide a unified solution for diverse generatio
arXiv:2604.18827v1 Announce Type: cross Abstract: Scaling data and artificial neural networks has transformed AI, driving breakthroughs in language and vision. Whether similar principles apply to mode
arXiv:2502.16161v2 Announce Type: replace-cross Abstract: Visually-situated text parsing (VsTP) has recently seen notable advancements, driven by the growing demand for automated document understandin
arXiv:2604.00688v3 Announce Type: replace Abstract: We present OmniVoice, a massively multilingual zero-shot text-to-speech (TTS) model that scales to over 600 languages. At its core is a novel diffus
arXiv:2604.19022v1 Announce Type: new Abstract: A major challenge for niche scientific and technical domains in leveraging coding agents is the lack of access to up-to-date, domain- specific knowledge
arXiv:2604.18645v1 Announce Type: new Abstract: This paper addresses the Variable Gapped Longest Common Subsequence (VGLCS) problem, a generalization of the classical LCS problem involving flexible ga
arXiv:2601.13729v2 Announce Type: replace Abstract: In recent years, the non-deterministic properties of language models have garnered considerable attention and have shown a significant influence on
arXiv:2604.19312v1 Announce Type: new Abstract: Neural processes are meta-learning models that map context sets to predictive distributions. While inspired by stochastic processes, NPs do not generall
arXiv:2603.15471v2 Announce Type: replace Abstract: This note presents a concise mathematical formulation of tightly-coupled LiDAR-Inertial Odometry within an iterated error-state Kalman filter framew
arXiv:2409.09451v5 Announce Type: replace Abstract: Foundation models pre-trained using self-supervised learning have shown powerful transfer learning capabilities on various downstream tasks, includi
arXiv:2602.01651v2 Announce Type: replace-cross Abstract: Why do neural networks fail to generalize addition from 16-digit to 32-digit numbers, while a child who learns the rule can apply it to arbitr
arXiv:2604.19698v1 Announce Type: new Abstract: The standard Monte Carlo estimator widehat{I}_N^{MC} of int fdomega relies on independent samples from omega and has variance of order 1/N. Replacing th
arXiv:2602.05437v2 Announce Type: replace Abstract: Vision-language models (VLMs) can achieve high accuracy while still accepting culturally plausible but visually incorrect interpretations. Existing
arXiv:2601.18572v2 Announce Type: replace Abstract: Personalization of LLMs by sociodemographic subgroup often improves user experience, but can also introduce or amplify biases and unfair outcomes ac
arXiv:2604.18839v1 Announce Type: cross Abstract: Looped transformers scale computational depth without increasing parameter count by repeatedly applying a shared transformer block and can be used for
arXiv:2506.18186v3 Announce Type: replace Abstract: The restless multi-armed bandit (RMAB) framework is a popular approach to solving resource allocation problems in networked systems. In this paper,
arXiv:2604.00555v2 Announce Type: replace Abstract: Enterprise adoption of Large Language Models (LLMs) is constrained by hallucination, domain drift, and the inability to enforce regulatory complianc
arXiv:2412.09404v3 Announce Type: replace-cross Abstract: Nowadays, social media is the ground for political debate and exchange of opinions. There is a significant amount of research that suggests th
arXiv:2604.18800v1 Announce Type: cross Abstract: We study online learning for new products on a platform that makes capacity-constrained assortment decisions on which products to offer. For a newly l
arXiv:2604.19399v1 Announce Type: new Abstract: Federated learning (FL) is a key paradigm for distributed model learning across decentralized data sources. Communication in each FL round typically con
arXiv:2512.12448v2 Announce Type: replace Abstract: Efforts to improve Kolmogorov--Arnold networks (KANs) with architectural enhancements have been stymied by the complexity those enhancements bring,
arXiv:2509.15435v2 Announce Type: replace-cross Abstract: Large Vision-Language Models (LVLMs) exhibit strong multimodal capabilities but remain vulnerable to hallucinations from intrinsic errors and
arXiv:2604.18658v1 Announce Type: cross Abstract: Existing AI agent safety benchmarks focus on generic criminal harm (cybercrime, harassment, weapon synthesis), leaving a systematic blind spot for a d
arXiv:2604.19379v1 Announce Type: new Abstract: This paper presents the first study on Unsupervised Domain Adaptation (UDA) for multimodal 3D panoptic segmentation (mm-3DPS), aiming to improve general
arXiv:2604.19556v1 Announce Type: new Abstract: Current 3D mapping pipelines generally assume static environments, which limits their ability to accurately capture and reconstruct moving objects. To a
arXiv:2604.18864v1 Announce Type: new Abstract: Generalized Additive Models (GAMs) can be used to create non-linear glass-box (i.e. explicitly interpretable) models, where the predictive function is f
arXiv:2604.19656v1 Announce Type: new Abstract: Large language models have achieved remarkable progress on complex reasoning tasks. However, they often implicitly fabricate information when inputs are
arXiv:2604.19596v1 Announce Type: new Abstract: Point cloud registration involves aligning one point cloud with another or with a three-dimensional (3D) model, enabling the integration of multimodal d
arXiv:2604.18943v1 Announce Type: new Abstract: With the rise in capabilities of large language models (LLMs) and their deployment in real-world tasks, evaluating LLM alignment with human preferences
arXiv:2403.09905v5 Announce Type: replace-cross Abstract: Embodied navigation methods commonly operate in static environments with stationary objects. In this work, we present approaches for tackling
arXiv:2411.06837v2 Announce Type: replace Abstract: The rapid rise of Large Language Models (LLMs) has created new disruptive possibilities for persuasive communication, enabling fully-automated, pers
arXiv:2602.09370v2 Announce Type: replace Abstract: Skateboards offer a compact and efficient means of transportation as a type of personal mobility device. However, controlling them with legged robot
arXiv:2604.19738v1 Announce Type: cross Abstract: We establish central and non-central limit theorems for sequences of functionals of the Gaussian output of an infinitely-wide random neural network on
arXiv:2512.00993v2 Announce Type: replace Abstract: Composition matters during the photo-taking process, yet many casual users struggle to frame well-composed images. To provide composition guidance,
arXiv:2511.08418v2 Announce Type: replace Abstract: Accurately simulating systems governed by PDEs, such as voltage fields in cardiac electrophysiology (EP) modelling, remains a significant modelling
arXiv:2602.20323v5 Announce Type: replace-cross Abstract: Reliable object manipulation requires understanding physical properties that vary across objects and environments. Vision-language model (VLM)
arXiv:2511.12606v3 Announce Type: replace Abstract: Group Activity Recognition (GAR) is well studied on the video modality for surveillance and indoor team sports (e.g., volleyball, basketball). Yet,
arXiv:2604.19324v1 Announce Type: cross Abstract: We introduce PLaMo 2.1-VL, a lightweight Vision Language Model (VLM) for autonomous devices, available in 8B and 2B variants and designed for local an
arXiv:2604.19695v1 Announce Type: new Abstract: We propose SmoothCruiser, a new planning algorithm for estimating the value function in entropy-regularized Markov decision processes and two-player gam
arXiv:2604.19036v1 Announce Type: new Abstract: Defeasible statements are statements that are likely, or probable, or usually true, but may occasionally be false. Plausible reasoning makes conclusions
arXiv:2603.29078v2 Announce Type: replace Abstract: We present PolarQuant, a post-training weight quantization method for large language models (LLMs) that exploits the distributional structure of neu
arXiv:2604.19024v1 Announce Type: new Abstract: Safe Reinforcement Learning from Human Feedback (Safe RLHF) has recently achieved empirical success in developing helpful and harmless large language mo
arXiv:2604.19129v1 Announce Type: new Abstract: Existing facial reenactment methods struggle with a trade-off between expressiveness and fine-grained controllability. Holistic facial reenactment model
arXiv:2510.18333v2 Announce Type: replace-cross Abstract: Despite progress in watermarking algorithms for large language models (LLMs), real-world deployment remains limited. We argue that this gap st
arXiv:2604.18649v1 Announce Type: cross Abstract: As generative AI faces intensifying legal challenges, the machine learning community has increasingly relied on post-hoc mitigation -- especially mach
arXiv:2604.19684v1 Announce Type: new Abstract: Explainable artificial intelligence (XAI) has predominantly focused on generating model-centric explanations that approximate the behavior of black-box
arXiv:2601.15349v2 Announce Type: replace Abstract: In narrow, unstructured underwater environments such as environmental monitoring and minimally invasive medical procedures, micro soft robots exhibi
arXiv:2604.18801v1 Announce Type: new Abstract: Lossy compression is widely used to reduce storage and I/O costs for large-scale particle datasets in scientific applications such as cosmology, molecul
arXiv:2510.13763v2 Announce Type: replace-cross Abstract: Amortized simulator-based inference offers a powerful framework for tackling Bayesian inference in computational fields such as engineering or
arXiv:2604.18892v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) improves multimodal reasoning by rewarding verifiable final answers. Yet answer-correct trajectori
arXiv:2604.18712v1 Announce Type: new Abstract: Probing has shown that language model representations encode rich linguistic information, but it remains unclear whether they also capture cognitive sig
arXiv:2604.19069v1 Announce Type: cross Abstract: Neural NLI models overfit dataset artifacts instead of truly reasoning. A hypothesis-only model gets 57.7% in SNLI, showing strong spurious correlatio
arXiv:2604.19083v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable success in cross-modal understanding and generation, yet their deployment is threate
arXiv:2508.01371v3 Announce Type: replace-cross Abstract: Smart contracts are important for digital finance, yet they are hard to patch once deployed. Prior work has mainly explored LLMs for smart con
arXiv:2604.18919v1 Announce Type: new Abstract: Analyzing topics extracted from text data in relation to external outcomes is important across fields such as computational social science and organizat
arXiv:2512.06380v3 Announce Type: replace-cross Abstract: Audio Large language models (LLMs) are increasingly deployed in the real world, where they inevitably capture speech from unintended nearby by
arXiv:2506.06211v2 Announce Type: replace-cross Abstract: Puzzlehunts are a genre of complex, multi-step puzzles lacking well-defined problem definitions. In contrast to conventional reasoning benchma
arXiv:2604.00161v2 Announce Type: replace Abstract: Optical Character Recognition (OCR) is increasingly regarded as a foundational capability for modern vision-language models (VLMs), enabling them no
arXiv:2601.00679v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have been emerging as prominent AI models for solving many natural language tasks due to their high performance (
arXiv:2508.20467v2 Announce Type: replace-cross Abstract: In the highly volatile and uncertain global financial markets, traditional quantitative trading models relying on statistical modeling or empi