Knowledge catalogue
arXiv:2605.31027v1 Announce Type: new Abstract: We propose a novel neural network architecture, termed Multi-Scale Separable Fourier Neural Networks (MS-SFNN), for the accurate and efficient solution
arXiv:2605.31387v1 Announce Type: new Abstract: Robots operating in diverse environments rely on visual input to interpret objects and spatial layouts. In human-collaborative tasks, they are expected
arXiv:2605.30925v1 Announce Type: new Abstract: Text-to-motion generation has progressed rapidly in recent years, offering an expressive interface for animation and human-computer interaction. However
arXiv:2605.31136v1 Announce Type: new Abstract: In automated fact-checking (AFC), check-worthiness detection identifies claims requiring verification based on domain-specific criteria. On Wikipedia, t
arXiv:2410.15475v2 Announce Type: replace Abstract: Multimodal learning aims to preserve as much task-related information as possible from different inputs. However, current fusion designs often disto
arXiv:2511.16940v3 Announce Type: replace Abstract: Modern Vision-Language Models (VLMs) pose significant individual-level privacy risks by linking fragmented multimodal data to identifiable individua
arXiv:2605.31222v1 Announce Type: new Abstract: Distributional reinforcement learning (DRL) models the full return distribution rather than expectations, but extending it to multivariate settings rema
arXiv:2508.16687v2 Announce Type: replace Abstract: Traditional embeddings represent datapoints as vectors, which makes similarity easy to compute but limits how well they capture hierarchies and comp
arXiv:2605.31287v1 Announce Type: cross Abstract: Managers in manufacturing settings rely on digital interfaces to interpret operational data for decision-making, but growing data volume and complexit
arXiv:2601.21372v2 Announce Type: replace Abstract: We present NEMO, a system that translates Natural-language descriptions of decision problems into formal Executable Mathematical Optimization implem
arXiv:2505.17595v4 Announce Type: replace-cross Abstract: Large language models (LLMs) achieve impressive performance across domains but face significant challenges when deployed on consumer-grade GPU
arXiv:2509.00834v2 Announce Type: replace Abstract: This paper addresses the problem of suffix prediction in Business Process Management (BPM) by proposing a Neuro-Symbolic Predictive Process Monitori
arXiv:2605.31421v1 Announce Type: cross Abstract: In this paper, we show the possibility of a direct injection of algorithms into neural network architecture. We focus on a complex algorithm, that is,
arXiv:2605.30717v1 Announce Type: new Abstract: Language models (LMs) can produce gendered language and stereotypes even when given neutral prompts. Most prior work on gender bias in LMs primarily exa
arXiv:2602.15018v2 Announce Type: replace-cross Abstract: Neurosim is a fast, real-time, high-performance library for simulating sensors such as dynamic vision sensors, RGB cameras, depth sensors, and
arXiv:2603.05529v2 Announce Type: replace-cross Abstract: Data critical to real-world decision-making is increasingly found within organizations. Such data is heterogeneous, constantly evolving, and o
arXiv:2605.31309v1 Announce Type: new Abstract: We survey Lyapunov-based techniques for the finite-time analysis of stochastic iterative algorithms, also known as stochastic approximation (SA) algorit
arXiv:2605.30811v1 Announce Type: new Abstract: Differentiating between oyster species is important for developing new commercial oyster species suited to production systems and is critical for tracea
arXiv:2511.17380v2 Announce Type: replace Abstract: Deep learning (DL) models, despite their remarkable success, remain vulnerable to small input perturbations that can cause erroneous outputs, motiva
arXiv:2605.31126v1 Announce Type: cross Abstract: Can a language model improve from plain text sampled from itself, with no prompts, no teacher, no verifier, and no reward model? Yes, but only when th
arXiv:2602.21013v2 Announce Type: replace Abstract: Many dexterous manipulation tasks are non-markovian in nature, yet little attention has been paid to this fact in the recent upsurge of the vision-l
arXiv:2605.31116v1 Announce Type: new Abstract: Recent perception-free end-to-end (E2E) autonomous driving methods bypass explicit perception outputs by compressing dense image patch tokens into compa
arXiv:2605.30393v1 Announce Type: cross Abstract: Public numeric benchmarks appear in pretraining, so an evaluation that conditions on a date may be measuring memorized recall rather than out-of-sampl
arXiv:2605.31572v1 Announce Type: new Abstract: Reasoning is essential for autonomous driving (AD) in long-tail scenarios, where vehicles must apply commonsense knowledge, understand spatial relations
arXiv:2510.07651v2 Announce Type: replace-cross Abstract: Large language models (LLMs) with extended context windows enable powerful applications but impose significant memory overhead, as caching all
arXiv:2605.30778v1 Announce Type: new Abstract: Long-horizon planning for non-prehensile robot manipulation is challenging due to underactuated and discontinuous interactions. We propose a hierarchica
arXiv:2605.06235v2 Announce Type: replace-cross Abstract: Retrieval benchmarks are increasingly saturating, but we argue that efficient search is far from a solved problem. We identify a class of quer
arXiv:2507.05488v2 Announce Type: replace Abstract: We present OLG++, a semantic extension of the Obligation Logic Graph (OLG) for modeling regulatory and legal rules in municipal and interjurisdictio
arXiv:2605.30969v1 Announce Type: new Abstract: Text-based human motion editing aims to modify existing motion sequences according to natural language instructions while maintaining the consistency of
arXiv:2605.30519v1 Announce Type: new Abstract: Autoregressive (AR) video generation extends videos by producing latent chunks sequentially, but scaling to long videos requires repeated access to a gr
arXiv:2605.30544v1 Announce Type: new Abstract: Visual monitoring systems that rely on cloud-based AI inference expose raw image data to external services, creating fundamental tensions with the data-
arXiv:2605.31460v1 Announce Type: new Abstract: Reasoning-based robotic policies using large language and vision-language models achieve strong semantic planning capabilities but mostly suffer from a
arXiv:2605.31500v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) are bottlenecked by sparse, irregular memory access. Popular frameworks such as DGL and PyTorch Geometric support general
arXiv:2605.31090v1 Announce Type: cross Abstract: Mislabeled samples in training datasets severely degrade the performance of deep networks, as overparameterized models tend to memorize erroneous labe
arXiv:2501.12020v2 Announce Type: replace Abstract: Face recognition systems (FRS) exhibit significant accuracy differences based on the user's gender. Since such a gender gap reduces the trustworthin
arXiv:2605.30790v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) supplements a language model's input with retrieved documents, yet most RAG pipelines inherit retrieval component
arXiv:2602.18333v2 Announce Type: replace-cross Abstract: Despite the remarkable practical success of transformer-based language models, recent work has raised concerns about their ability to perform
arXiv:1709.08894v3 Announce Type: replace-cross Abstract: Since their invention, generative adversarial networks (GANs) have become a popular approach for learning to model a distribution of real (unl
arXiv:2605.31518v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) decompose neural network activations into interpretable features, but many learned features never activate, a problem called
arXiv:2605.31142v1 Announce Type: cross Abstract: Large-scale multilingual text embedding models play crucial role in both research and industry, yet their behavior in language-specific, multi-task se
arXiv:2605.30792v1 Announce Type: cross Abstract: Speech translation systems increasingly span speech-to-text translation (S2TT), speech-to-speech translation (S2ST), offline translation, and streamin
arXiv:2512.14366v2 Announce Type: replace Abstract: Implicit Neural Representations (INRs) based on vanilla Multi-Layer Perceptrons (MLPs) are widely believed to be incapable of representing high-freq
arXiv:2605.30736v1 Announce Type: cross Abstract: The rapid development of large language models, each with distinct capabilities and inference costs, raises a practical deployment question: given an
arXiv:2506.12060v2 Announce Type: replace-cross Abstract: Cybersecurity organizations are adapting to GenAI integration through modified frameworks and hybrid operational processes, with success influ
arXiv:2512.00919v2 Announce Type: replace-cross Abstract: We address the problem of causal effect estimation in the presence of hidden confounders using nonparametric instrumental variable (IV) regres
arXiv:2510.10544v3 Announce Type: replace-cross Abstract: We derive a novel PAC-Bayesian generalization bound for reinforcement learning that explicitly accounts for Markov dependencies in the data, t
arXiv:2605.30758v1 Announce Type: new Abstract: Pairwise preference data is widely used in language-model evaluation and alignment, often for model ranking, reward modeling, or preference optimization
arXiv:2601.22296v2 Announce Type: replace-cross Abstract: Reservoir Computing (RC) has established itself as an efficient paradigm for temporal processing. However, its scalability remains severely co
arXiv:2605.30991v1 Announce Type: cross Abstract: Inference-time reward alignment steers pretrained diffusion and flow-based generative models to satisfy user-specified rewards without retraining. Rec
arXiv:2602.06902v2 Announce Type: replace Abstract: In this paper, we study dynamic regret in unconstrained online convex optimization (OCO) with movement costs. Specifically, we generalize the standa
arXiv:2602.07721v3 Announce Type: replace-cross Abstract: KV-cache retrieval is essential for long-context LLM inference, yet existing methods struggle with distribution drift and high latency at scal
arXiv:2601.11702v3 Announce Type: replace-cross Abstract: AI compliance is becoming increasingly critical as AI systems grow more powerful and pervasive. Yet the rapid expansion of AI policies creates
arXiv:2605.30880v1 Announce Type: cross Abstract: Text-agent environments are typically modeled as partially observable Markov decision processes (POMDPs), assuming that the simulator's latent state a
arXiv:2605.31029v1 Announce Type: new Abstract: Video-language models can process only a limited number of frames, making frame selection a key bottleneck for efficient video captioning. Most captioni
arXiv:2601.13704v3 Announce Type: replace-cross Abstract: In speech machine learning, neural network models are typically designed by choosing an architecture with fixed layer sizes and structure. The
arXiv:2605.31513v1 Announce Type: new Abstract: Large vision-language models (LVLMs) have demonstrated strong general multimodal capability and are increasingly deployed in downstream systems. This tr
arXiv:2605.31275v1 Announce Type: cross Abstract: Artificial Intelligence (AI) agents personalize their responses by tailoring explanations to users' backgrounds, interests, and prior interactions, re
arXiv:2605.30512v1 Announce Type: new Abstract: Generating physics diagrams from text requires strict adherence to physical laws. While current generative models produce visually plausible outputs, th
arXiv:2605.30542v1 Announce Type: new Abstract: World models for embodied AI must be physically viable: constructed to answer intervention queries by representing the physical structure governing acti
arXiv:2512.05976v3 Announce Type: replace-cross Abstract: Designing materials with controlled heat flow at the nano-scale is central to advances in microelectronics, thermoelectrics, and energy-conver