Knowledge catalogue
arXiv:2603.23547v2 Announce Type: replace-cross Abstract: Independent component analysis is a core framework within blind source separation for recovering latent source signals from observed mixtures
arXiv:2511.02777v2 Announce Type: replace Abstract: We present PercHead, a model for single-image 3D head reconstruction and disentangled 3D editing - two tasks that are inherently challenging due to
arXiv:2604.21706v1 Announce Type: new Abstract: We previously introduced a training-free method for dysarthria severity assessment based on d-prime separability of phonological feature subspaces in fr
arXiv:2604.20910v1 Announce Type: cross Abstract: The surface and subsurface of worlds beyond Mars remain largely unexplored. Yet these worlds hold keys to fundamental questions in planetary science -
arXiv:2604.21253v1 Announce Type: cross Abstract: While LLMs demonstrate remarkable fluency in narrative generation, existing methods struggle to maintain global narrative coherence, contextual logica
arXiv:2604.21313v1 Announce Type: new Abstract: Accurate quantification of the physical exposure area of beach litter, rather than simple item counts, is essential for credible ecological risk assessm
arXiv:2604.21216v1 Announce Type: cross Abstract: The First Fundamental Theorem of Welfare Economics assumes that welfare-bearing agents are autonomous and implicitly relies on a binary distinction be
arXiv:2505.11702v2 Announce Type: replace Abstract: This work develops a framework for post-training augmentation invariance, in which our goal is to add invariance properties to a pretrained network
arXiv:2508.21720v2 Announce Type: replace Abstract: Automating scientific poster generation requires hierarchical document understanding and coherent content-layout planning. Existing methods often re
arXiv:2604.21422v1 Announce Type: new Abstract: This paper deals with the case of using nonlinear diffusion filters to obtain piecewise constant images as a previous process for segmentation technique
arXiv:2604.21536v1 Announce Type: cross Abstract: Sequential recommender systems have achieved significant success in modeling temporal user behavior but remain limited in capturing rich user semantic
arXiv:2604.21100v1 Announce Type: new Abstract: To address the increasing long-context compute limitations of softmax attention, several subquadratic recurrent operators have been developed. This work
arXiv:2604.20899v1 Announce Type: cross Abstract: Scalable synthesis remains the gate between MOF discovery and industrial deployment, as scale-up know-how is fragmented across disparate reports. We i
arXiv:2604.21481v1 Announce Type: new Abstract: Crowdsourced pairwise evaluation has emerged as a scalable approach for assessing foundation models. However, applying it to Text to Speech(TTS) introdu
arXiv:2604.21191v1 Announce Type: new Abstract: Prefix parsing asks whether an input prefix can be extended to a complete string generated by a given grammar. In the weighted setting, it also provides
arXiv:2604.20867v1 Announce Type: cross Abstract: Recent events surrounding the relationship between frontier AI suppliers and national-security customers have made a structural problem newly visible:
arXiv:2406.11354v3 Announce Type: replace-cross Abstract: Humans can retain old knowledge while learning new information, but Large Language Models (LLMs) often suffer from catastrophic forgetting whe
arXiv:2604.21104v1 Announce Type: new Abstract: New geospatial foundation models introduce a new model architecture and pretraining dataset, often sampled using different notions of data diversity. Pe
arXiv:2604.21337v1 Announce Type: new Abstract: In this paper, we aim to extend the traditional point-mass-like robot representation in swarm robotics and instead study a swarm of long Heavy Articulat
arXiv:2106.01254v3 Announce Type: replace Abstract: In many classification tasks, there is no definitive ground truth, only human judgments that may disagree. We address two challenges that arise in s
arXiv:2604.21765v1 Announce Type: new Abstract: Data is a central resource for modern enterprises, and data validation is essential for ensuring the reliability of downstream applications. However, ex
arXiv:2604.21556v1 Announce Type: new Abstract: The problem of probabilistic verification of a neural network investigates the probability of satisfying the safe constraints in the output space when t
arXiv:2604.21811v1 Announce Type: cross Abstract: A primary goal of online deliberation platforms is to identify ideas that are broadly agreeable to a community of users through their expressed prefer
arXiv:2604.21611v1 Announce Type: cross Abstract: Inference-time scaling for LLM reasoning has focused on three axes: chain depth, sample breadth, and learned step-scorers (PRMs). We introduce a fourt
arXiv:2506.03374v2 Announce Type: replace Abstract: The use of machine learning (ML) techniques has allowed rapid advancements in many scientific and engineering fields. One of these problems is that
arXiv:2604.21041v1 Announce Type: new Abstract: Machine unlearning for text-to-image diffusion models aims to selectively remove undesirable concepts from pre-trained models without costly retraining.
arXiv:2604.21629v1 Announce Type: cross Abstract: We compare lightweight automata-based models (n-grams) with neural architectures (LSTM, Transformer) for next-activity prediction in streaming event l
arXiv:2604.21098v1 Announce Type: new Abstract: Motivated by loss of control risks from misaligned AI systems, we develop and apply methods for measuring language models' propensity for unsanctioned b
arXiv:2604.21360v1 Announce Type: new Abstract: Test-time adaptation (TTA) has emerged as a promising paradigm for vision-language models (VLMs) to bridge the distribution gap between pre-training and
arXiv:2604.21809v1 Announce Type: cross Abstract: Diffusion-based generative models have reformed generative AI, and have enabled new capabilities in the science domain, for example, generating 3D str
arXiv:2603.27112v2 Announce Type: replace Abstract: As Automatic Train Operation (ATO) advances toward GoA4 and beyond, it increasingly depends on efficient, reliable cab-view visual perception and de
arXiv:2604.21728v1 Announce Type: new Abstract: Pretrained vision-language models such as CLIP exhibit strong zero-shot generalization but remain sensitive to distribution shifts. Test-time adaptation
arXiv:2604.21357v1 Announce Type: new Abstract: This paper proposes ReaGeo, an end-to-end geocoding framework based on large language models, designed to overcome the limitations of traditional multi-
arXiv:2604.20860v1 Announce Type: cross Abstract: Despite the success of Retrieval-Augmented Generation (RAG) in grounding LLMs with external knowledge, its application over heterogeneous sources (e.g
arXiv:2604.21249v1 Announce Type: new Abstract: While Vision-Language Models (VLMs) enable high-level semantic reasoning for end-to-end autonomous driving, particularly in unstructured environments, e
arXiv:2604.21454v1 Announce Type: cross Abstract: Reasoning in large language models is often treated as a monolithic capability, but its observed gains may arise from more basic operations. We study
arXiv:2604.21232v1 Announce Type: new Abstract: Vision-Language-Action systems follow instructions to execute multi-step tasks in multimodal environments. Recent VLA approaches typically rely on post-
arXiv:2604.05673v2 Announce Type: replace-cross Abstract: Visual navigation is a core challenge in Embodied AI, requiring autonomous agents to translate high-dimensional sensory observations into cont
arXiv:2509.25868v3 Announce Type: replace Abstract: The mechanisms underlying scientific confabulation in Large Language Models (LLMs) remain poorly understood. We introduce ReFACT (Reddit False And C
arXiv:2604.21203v1 Announce Type: cross Abstract: We study online inference and asymptotic covariance estimation for the stochastic gradient descent (SGD) algorithm. While classical methods (such as p
arXiv:2310.02635v5 Announce Type: replace-cross Abstract: Reinforcement learning (RL) is a promising approach for solving robotic manipulation tasks. However, it is challenging to apply the RL algorit
arXiv:2604.21160v1 Announce Type: new Abstract: Point-Vision-Language Models promise to empower embodied agents with executable spatial reasoning, yet they frequently succumb to geometric hallucinatio
arXiv:2604.20904v1 Announce Type: cross Abstract: Information handling practices of LLM agents are broadly misaligned with the contextual privacy expectations of their users. Contextual Integrity (CI)
arXiv:2604.21393v1 Announce Type: new Abstract: Relocation of compact sets in an n-dimensional manifold by self-diffeomorphism is of its own interest as well as significant potential applications to d
arXiv:2510.20505v4 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) remains brittle on multi-step questions and heterogeneous evidence sources, trading accuracy against late
arXiv:2604.21863v1 Announce Type: cross Abstract: Deep reinforcement learning (RL) for quantum circuit optimization faces three fundamental bottlenecks: replay buffers that ignore the reliability of t
arXiv:2604.21776v1 Announce Type: new Abstract: Precise camera control for reshooting dynamic videos is bottlenecked by the severe scarcity of paired multi-view data for non-rigid scenes. We overcome
arXiv:2604.21478v1 Announce Type: new Abstract: Nowadays, visual data forgery detection plays an increasingly important role in social and economic security with the rapid development of generative mo
arXiv:2511.11439v2 Announce Type: replace-cross Abstract: Binary security has increasingly relied on deep learning to reason about malware behavior and program semantics. However, the performance ofte
arXiv:2604.21893v1 Announce Type: cross Abstract: Geographic context is often consider relevant to motor insurance risk, yet public actuarial datasets provide limited location identifiers, constrainin
arXiv:2602.03875v4 Announce Type: replace-cross Abstract: We introduce a reversible deep learning model for 13C NMR that uses a single conditional invertible neural network for both directions between
arXiv:2604.20847v1 Announce Type: cross Abstract: Music Recommendation Systems (MRSs) are a cornerstone of modern streaming platforms. Existing recommendation models, spanning both recall and ranking
arXiv:2604.21882v1 Announce Type: new Abstract: Understanding what kinds of factual knowledge large language models (LLMs) memorize is essential for evaluating their reliability and limitations. Entit
arXiv:2506.01937v2 Announce Type: replace Abstract: Reward models are used throughout the post-training of language models to capture nuanced signals from preference data and provide a training target
arXiv:2601.09253v2 Announce Type: replace-cross Abstract: While Supervised Fine-Tuning (SFT) and Rejection Sampling Fine-Tuning (RFT) are standard for LLM alignment, they either rely on costly expert
arXiv:2604.20851v1 Announce Type: cross Abstract: Modern video-text retrieval (VTR) models excel on in-distribution benchmarks but are highly vulnerable to real-world query shifts, where the distribut
arXiv:2604.21256v1 Announce Type: new Abstract: Policies for Partially Observable Markov Decision Processes (POMDPs) are often designed using a nominal system model. In practice, this model can deviat
arXiv:2604.21355v1 Announce Type: new Abstract: Humanoid robots have demonstrated impressive motor skills in a wide range of tasks, yet whole-body control for humanlike long-time, dynamic fighting rem
arXiv:2510.10971v2 Announce Type: replace-cross Abstract: Hate speech remains prevalent in human society and continues to evolve in its forms and expressions. Modern advancements in internet and onlin
arXiv:2604.21409v1 Announce Type: new Abstract: We present S1-VL, a multimodal reasoning model for scientific domains that natively supports two complementary reasoning paradigms: Scientific Reasoning