Knowledge catalogue
arXiv:2605.10185v1 Announce Type: cross Abstract: Ghost imaging reconstructs spatial information from a single-pixel bucket detector by correlating structured illumination patterns with scalar intensi
arXiv:2605.09820v1 Announce Type: new Abstract: Diffusion language models (DLMs) have recently emerged as a promising alternative to autoregressive models, primarily due to their ability to enable par
arXiv:2605.10360v1 Announce Type: new Abstract: While novel view synthesis (NVS) for dynamic scenes has seen significant progress, reconstructing temporally consistent geometric surfaces remains a cha
arXiv:2605.10261v1 Announce Type: new Abstract: TCAV (Testing with Concept Activation Vectors) is an interpretability method that assesses the alignment between the internal representations of a train
arXiv:2505.05209v4 Announce Type: replace Abstract: Utilizing pre-trained Text-to-Image (T2I) diffusion models to guide Blind Super-Resolution (BSR) has become a predominant approach in the field. Whi
arXiv:2605.08723v1 Announce Type: new Abstract: Weakly supervised Audio-Visual Video Parsing (AVVP) aims to recognize and temporally localize audio, visual, and audio-visual events in videos using onl
arXiv:2605.09603v1 Announce Type: new Abstract: Masked diffusion language models enable parallel token generation and offer improved decoding efficiency over autoregressive models. However, their perf
arXiv:2602.05391v2 Announce Type: replace Abstract: Dataset distillation seeks to synthesize a highly compact dataset that achieves performance comparable to the original dataset on downstream tasks.
arXiv:2602.03783v2 Announce Type: replace-cross Abstract: Modern AI agents such as large language models are trained on diverse tasks -- translation, code generation, mathematical reasoning, and text
arXiv:2605.10251v1 Announce Type: new Abstract: We present GraphDepth, a monocular depth estimation architecture that synergistically integrates Graph Neural Networks (GNNs) within a convolutional enc
arXiv:2506.11578v4 Announce Type: replace Abstract: Recently, large language models (LLMs) have demonstrated strong performance, ranging from simple to complex tasks. However, while large models achie
arXiv:2605.08273v1 Announce Type: cross Abstract: Accurate traffic prediction is essential for optimizing transportation systems, enhancing resource allocation, and improving overall urban administrat
arXiv:2605.08606v1 Announce Type: new Abstract: Egocentric human mesh recovery (HMR) from monocular head-mounted cameras is increasingly important for AR/VR applications, but remains challenging due t
arXiv:2605.09874v1 Announce Type: cross Abstract: Next-generation visual assistants, such as smart glasses, embodied agents, and always-on life-logging systems, must reason over an entire day or more
arXiv:2509.21892v2 Announce Type: replace-cross Abstract: Mixture-of-Experts (MoE) models typically fix the number of activated experts k at both training and inference. However, real-world deployment
arXiv:2605.08799v1 Announce Type: new Abstract: Diffusion policies have demonstrated exceptional performance in embodied AI. However, their iterative denoising process results in high latency, and exi
arXiv:2605.10790v1 Announce Type: new Abstract: Diffusion models have achieved remarkable success, yet their training remains inefficient due to a severe optimization bottleneck, which we term Represe
arXiv:2605.10332v1 Announce Type: new Abstract: Embodied agents can benefit from skills that guide object search, action execution, and state changes across diverse environments. Since embodied enviro
arXiv:2601.22449v2 Announce Type: replace Abstract: Intrinsic Motivation (IM) aims to train agents without external rewards, enabling useful behavior to emerge from the agent's interaction with its en
arXiv:2605.09495v1 Announce Type: cross Abstract: Machine learning-based simulators offer the potential to model the dynamics of complex systems more efficiently than classical approaches, while retai
arXiv:2605.08910v1 Announce Type: cross Abstract: The new wave of adversarial attacks that utilize gradient-related vulnerabilities in neural network-based classifiers makes Network Intrusion Detectio
arXiv:2605.08572v1 Announce Type: new Abstract: Diffusion models for multi-agent trajectory prediction are limited by iterative denoising, which causes inference latency that hinders their use in time
arXiv:2605.09745v1 Announce Type: cross Abstract: Large language models (LLMs) achieve remarkable generative performance, yet their output quality is dependent on the decoding strategy. While sampling
arXiv:2605.09505v1 Announce Type: new Abstract: Epilepsy diagnosis and treatment require evidence-intensive reasoning across heterogeneous clinical knowledge, including biosignal patterns, genetic mec
arXiv:2605.09278v1 Announce Type: new Abstract: Multi-agent debate (MAD) systems increasingly rely on shared memory to support long-horizon reasoning, but this convenience opens a critical vulnerabili
arXiv:2605.09982v1 Announce Type: new Abstract: Recent advancements in Vision-Language Models (VLMs) enable large language models (LLMs) to process high-resolution images, significantly improving real
arXiv:2602.08617v2 Announce Type: replace Abstract: Scaling Federated Learning (FL) to billion-parameter models forces a challenging trade-off between privacy, scalability, and model utility. Existing
arXiv:2411.05516v3 Announce Type: replace Abstract: Autonomous Underwater Vehicles (AUVs) have advanced significantly in obstacle detection and path planning through sonar, cameras, and learning-based
arXiv:2509.18484v2 Announce Type: replace-cross Abstract: Estimating causal effects on networks is challenging because treatments may affect both treated units and their neighbors, while network homop
arXiv:2601.21484v2 Announce Type: replace Abstract: Reinforcement Learning (RL) post-training alignment for language models is effective, but also costly and unstable in practice, owing to its complic
arXiv:2605.09429v1 Announce Type: cross Abstract: Are low-attention visual tokens truly redundant in vision-language reasoning? Existing pruning methods often assume so, ranking visual tokens by shall
arXiv:2605.08685v1 Announce Type: cross Abstract: We propose a new class of waveform foundation models that departs from conventional sequence based representations by modeling physiological time seri
arXiv:2603.09465v3 Announce Type: replace-cross Abstract: Vision-Language-Action models have shown great promise for autonomous driving, yet they suffer from degraded perception after unfreezing the v
arXiv:2605.09924v1 Announce Type: new Abstract: Recent advancements in Neural Machine Translation (NMT) have significantly improved translation quality. However, the increasing size and complexity of
arXiv:2605.10663v1 Announce Type: new Abstract: Experience-driven self-evolving agents aim to overcome the static nature of large language models by distilling reusable experience from past interactio
arXiv:2605.08769v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems have shown strong potential on complex tasks through agent specialization, tool use, and collaborat
arXiv:2605.10343v1 Announce Type: cross Abstract: Streaming video understanding demands more than watching longer videos: assistants must decide when to speak in real time, balancing responsiveness ag
arXiv:2605.10680v1 Announce Type: new Abstract: This paper proposes a paradigm shift linking machine unlearning directly to the structure of the data distributions rather than a mere update of the neu
arXiv:2605.08195v1 Announce Type: new Abstract: Local execution of AI on edge devices is important for low latency and offline operation. However, deploying models on diverse hardware remains fragment
arXiv:2601.18061v3 Announce Type: replace Abstract: Learning from human feedback~(LHF) assumes that expert judgments, appropriately aggregated, yield valid ground truth for training and evaluating AI
arXiv:2605.09369v1 Announce Type: new Abstract: Knowledge Tracing (KT) models students' knowledge states based on learning interactions to predict performance. While deep learning-based KT models have
arXiv:2605.10054v1 Announce Type: new Abstract: Deep neural networks for medical image diagnosis often achieve high predictive accuracy while relying on spurious or clinically irrelevant visual cues,
arXiv:2605.09944v1 Announce Type: new Abstract: Robust humanoid stair climbing remains challenging due to geometric discontinuities, sensitivity to step height variations, and perception uncertainty i
arXiv:2605.09853v1 Announce Type: new Abstract: Post-training techniques combined with inference-time scaling significantly enhance the reasoning and alignment capabilities of large language models (L
arXiv:2605.10318v1 Announce Type: new Abstract: Large language models (LLMs) allow users to query databases using natural language by translating questions into executable queries. Despite strong prog
arXiv:2605.08434v1 Announce Type: new Abstract: Vision-language-action (VLA) models provide a promising paradigm for scalable robotic manipulation, yet their reliance on success-only behavioral clonin
arXiv:2510.23074v2 Announce Type: replace-cross Abstract: We propose Fast-MIA (https://github.com/Nikkei/fast-mia), a Python library for efficiently evaluating membership inference attacks (MIA) again
arXiv:2603.16231v2 Announce Type: replace-cross Abstract: Numerical optimal control has long been split between globally structured but dimensionally intractable Hamilton--Jacobi--Bellman (HJB) method
arXiv:2605.08760v1 Announce Type: new Abstract: Federated Learning (FL) facilitates collaborative model training across decentralized clients while preserving data privacy by avoiding raw data exchang
arXiv:2605.10082v1 Announce Type: new Abstract: Large language models (LLMs) exhibit strong reasoning capabilities when guided by high-quality demonstrations, yet such data is often distributed across
arXiv:2605.09089v1 Announce Type: cross Abstract: Digital identity verification systems used in remote onboarding rely on document images to authenticate users, making them vulnerable to localized man
arXiv:2605.10439v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) has become a widely used mechanism for customizing diffusion models, enabling users to inject new visual concepts or styles t
arXiv:2605.09218v1 Announce Type: cross Abstract: 3D scene understanding spans reasoning about free space, object grounding, hypothetical object insertions, complex geometric relationships, and integr
arXiv:2605.09430v1 Announce Type: new Abstract: Large-scale autoregressive models have demonstrated remarkable capabilities in image generation. However, their sequential raster-scan decoding relies o
arXiv:2605.09003v1 Announce Type: new Abstract: Recently, diffusion-based object removal models have achieved impressive results in eliminating objects and their associated visual effects. However, th
arXiv:2605.08520v1 Announce Type: new Abstract: LLM-based evolution has emerged as a promising way to improve agents by refining non-parametric artifacts, but its wall-clock cost remains a major bottl
arXiv:2605.08640v1 Announce Type: new Abstract: Plug-and-play (PnP) methods for solving inverse problems have recently achieved strong performance by leveraging denoising priors based on powerful gene
arXiv:2605.08648v1 Announce Type: new Abstract: Many biological systems evolve through continuous local dynamics while switching between latent regimes defined by learning, stimulus context, internal
arXiv:2605.09932v1 Announce Type: new Abstract: Large language models can now process increasingly long inputs, yet their ability to effectively use information spread across long contexts remains lim
arXiv:2605.10230v1 Announce Type: new Abstract: Molecular optimization seeks to improve a molecule through small structural edits while preserving similarity to the starting compound. Recent language-