Knowledge catalogue
arXiv:2605.21157v1 Announce Type: new Abstract: In modern warfare, drones are becoming an essential part of intelligence gathering and carrying out precise attacks in different kinds of hostile enviro
arXiv:2605.20971v1 Announce Type: new Abstract: The growing sophistication of GAN-based image manipulation presents significant challenges for digital forensics. This study compares the performance of
arXiv:2602.02304v2 Announce Type: replace-cross Abstract: Large-scale foundation models exhibit behavioral shifts when subjected to interventions such as scaling, fine-tuning, reinforcement learning w
arXiv:2602.08686v2 Announce Type: replace Abstract: Prefill-only KV compression freezes a token subset at the end of prefill and decodes from it without further eviction. The retention decision is the
arXiv:2605.20555v1 Announce Type: new Abstract: We introduce a novel method that averages the logits of a frozen reference policy (e.g., SFT) and a trainable policy, and incorporate the method into Gr
arXiv:2605.21026v1 Announce Type: new Abstract: To accelerate automated remanufacturing, robotic disassembly must be considered during the product design phase. However, designers currently lack quant
arXiv:2605.20609v1 Announce Type: new Abstract: Compositional generalization is essential for reaching unseen goals under novel contextual variations in offline goal-conditioned reinforcement learning
arXiv:2407.08976v2 Announce Type: replace-cross Abstract: Recent years have seen a surge in methods for two-sample testing, among which the Maximum Mean Discrepancy (MMD) test has emerged as an effect
arXiv:2602.10455v2 Announce Type: replace-cross Abstract: Driven by scaling laws, recommender systems increasingly rely on larger-scale models to capture complex feature interactions and user behavior
arXiv:2605.20999v1 Announce Type: cross Abstract: We establish maximal concentration bounds for the iterates generated by stochastic approximation algorithms with general step sizes, where the noise h
arXiv:2605.20385v1 Announce Type: new Abstract: Recent progress in promptable segmentation has shifted visual perception from object-level localization toward concept-level understanding. However, the
arXiv:2605.20834v1 Announce Type: cross Abstract: Direct Preference Optimization (DPO) has emerged as a popular alternative to Reinforcement Learning from Human Feedback (RLHF), offering theoretical e
arXiv:2605.21041v1 Announce Type: cross Abstract: Gaussian processes (GPs) offer a principled probabilistic model over functions, but exact inference is restricted to the linear-Gaussian regime. We es
arXiv:2605.20566v1 Announce Type: new Abstract: Active perception in uncertain environments requires robots to navigate safely while acquiring informative observations to reduce map uncertainty. These
arXiv:2605.20758v1 Announce Type: cross Abstract: Inference-time guided sampling steers state-of-the-art diffusion and flow models without fine-tuning by interpreting the generation process as a contr
arXiv:2605.20270v1 Announce Type: new Abstract: A local specialist LLM, fine-tuned with reinforcement learning from verifiable rewards (RLVR) on operator-local data, is installed in a regulated organi
arXiv:2605.06395v2 Announce Type: replace Abstract: Modern deep learning architectures increasingly contend with sophisticated signals that are natively infinite-dimensional, such as time series, prob
arXiv:2605.20357v1 Announce Type: new Abstract: Knowledge distillation (KD) transfers knowledge from a high-capacity teacher to a compact student by matching their predictive distributions, with tempe
arXiv:2605.20538v1 Announce Type: new Abstract: Evolving data streams induce joint nonstationarity in continual semantic segmentation, where semantic classes, input distributions, and supervision avai
arXiv:2605.20434v1 Announce Type: cross Abstract: We study the contradiction graphs associated with binary concept classes. For a class H subseteq {0,1}^X, the order-m contradiction graph G_m(H) has a
arXiv:2506.20764v2 Announce Type: replace-cross Abstract: Although there is a substantial body of literature on control and optimization problems for parabolic and hyperbolic systems, the specific pro
arXiv:2503.15105v4 Announce Type: replace-cross Abstract: We study the fundamental computational problem of approximating optimal transport (OT) equations using neural differential equations (Neural O
arXiv:2605.20345v1 Announce Type: cross Abstract: Latent Gaussian models (LGMs) are a popular class of Bayesian hierarchical models that include Gaussian processes, as well as certain spatial models a
arXiv:2605.20756v1 Announce Type: new Abstract: Preconditioned optimizers are central to language model training, but their stochastic update rules are usually treated as direct approximations to popu
arXiv:2605.20247v1 Announce Type: cross Abstract: Catastrophic forgetting remains a major obstacle to continual learning in large language models (LLMs) and vision--language models (VLMs). Although Mi
arXiv:2605.21317v1 Announce Type: new Abstract: The aggregation of conflicting client updates remains a fundamental bottleneck in federated learning (FL) under heterogeneous data distributions. Naive
arXiv:2603.20420v2 Announce Type: replace-cross Abstract: Nanopore sequencing can read substantially longer sequences of nucleic acid molecules, called reads, than other sequencing methods, which has
arXiv:2605.21049v1 Announce Type: new Abstract: Large language models (LLMs) reliably predict neural activity during language comprehension and transformer depth has been interpreted as mirroring hier
arXiv:2505.17354v3 Announce Type: replace Abstract: In many real-world settings--e.g., single-cell RNA sequencing, mobility sensing, and environmental monitoring--data are observed only as temporally
arXiv:2605.20771v1 Announce Type: new Abstract: Spurious correlations in real-world datasets cause machine learning models to rely on irrelevant patterns, undermining reliability, generalization, and
arXiv:2605.21001v1 Announce Type: new Abstract: Existing 3D clothed avatar reconstruction methods achieve high visual fidelity but ignore geometric structure and physical plausibility. They either mod
arXiv:2605.20680v1 Announce Type: new Abstract: Human Action Recognition (HAR) is a fundamental computer vision task with diverse real-world applications. Practical deployments often involve low-light
arXiv:2605.20936v1 Announce Type: cross Abstract: Hybrid attention architectures are becoming an increasingly important paradigm for improving LLM inference efficiency while preserving model quality,
arXiv:2605.21348v1 Announce Type: new Abstract: Solving partial differential equations with neural operators significantly reduces computational costs but remains bottlenecked by high training data re
arXiv:2605.20196v1 Announce Type: new Abstract: We investigate the hypothesis that real-data scaling laws are governed by progressive coverage of a latent predictive contribution spectrum rather than
arXiv:2601.22932v2 Announce Type: replace Abstract: We study a sampling problem whose target distribution is pi propto exp(-f-r) where the data fidelity term f is Lipschitz smooth while the regularize
arXiv:2605.20716v1 Announce Type: new Abstract: Random forests aggregate tree votes by simple majority, treating all trees as equally informative. We observe that the topological pattern along each tr
arXiv:2605.20402v1 Announce Type: new Abstract: MXFP4 arithmetic can dramatically accelerate reinforcement learning (RL) post-training of large language models (LLMs), yet the quantization error intro
arXiv:2605.20807v1 Announce Type: new Abstract: Subject-driven text-to-image generation still struggles to preserve high-frequency identity details such as logos, patterns, and text. Existing methods
arXiv:2605.21311v1 Announce Type: new Abstract: Modern vision systems can detect, track, and forecast urban actors at scale, yet translating perception outputs to urban design remains limited. We intr
arXiv:2605.21085v1 Announce Type: cross Abstract: Communication enables coordination in multi-agent reinforcement learning (MARL), but many real-world applications, e.g., search-and-rescue with drone
arXiv:2605.20732v1 Announce Type: new Abstract: Convolutional Neural Networks (CNNs) often exploit spurious correlations in datasets, learning superficially predictive yet causally irrelevant features
arXiv:2605.20580v1 Announce Type: new Abstract: This work explores a dynamics-informed Temporal Fusion Transformer (TFT) as a data-driven surrogate for computationally intensive Earth system simulatio
arXiv:2601.00473v3 Announce Type: replace Abstract: We revisit the analogy between feed-forward deep neural networks (DNNs) and discrete dynamical systems derived from neural integral equations and th
arXiv:2603.14184v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) often suffer from perceptual impairments under extended reasoning modes, particularly in visual question an
arXiv:2605.21308v1 Announce Type: new Abstract: State Space Models (SSMs) have emerged as a powerful and efficient alternative to Transformers, demonstrating linear-time complexity and exceptional seq
arXiv:2605.20369v1 Announce Type: new Abstract: Number prediction stands as a fundamental capability of large language models (LLMs) in mathematical problem-solving and code generation. The widely ado
arXiv:2605.21467v1 Announce Type: cross Abstract: Reinforcement learning from verifiable rewards (RLVR) has emerged as a central technique for improving the reasoning capabilities of large language mo
arXiv:2605.20576v1 Announce Type: new Abstract: Inferring rigid-body physical states and properties from monocular videos is a fundamental step toward physics-based perception and simulation. Existing
arXiv:2605.20811v1 Announce Type: new Abstract: Robotic imitation learning is often treated as reproducing demonstrated actions, but actions are inherently embodiment-specific. When demonstrations com
arXiv:2602.03209v2 Announce Type: replace Abstract: Autonomous field robots operating in unstructured environments require robust perception to ensure safe and reliable operations. Recent advances in
arXiv:2605.20644v1 Announce Type: new Abstract: Design for manufacturing plays a critical role in advanced aeroengine development, where complex components necessitate careful consideration of manufac
arXiv:2605.20982v1 Announce Type: cross Abstract: AlltoAll dispatch is the dominant bottleneck of MoE expert parallelism, and the interconnect community has responded with four families of mitigations
arXiv:2411.01141v2 Announce Type: replace Abstract: There are two shortages in the current Large Language Models (LLMs) era. The first is short of multilingual models, where most LLMs are English-cent
arXiv:2605.20766v1 Announce Type: new Abstract: Point supervision has become a scalable solution to address dense annotation for infrared small target detection, but its performance is limited by two
arXiv:2603.13419v2 Announce Type: replace Abstract: Diffusion models generalize well in practice. However, an optimal diffusion model fully memorizes the training data and therefore fails to generaliz
arXiv:2605.12960v2 Announce Type: replace Abstract: Towards more general and human-like intelligence, large language models should seamlessly integrate both multilingual and multimodal capabilities; h
arXiv:2605.20588v1 Announce Type: new Abstract: The field of sign language translation has witnessed significant progress in the translation between sign and spoken languages, but the translation betw
arXiv:2605.20856v1 Announce Type: cross Abstract: Language-conditioned manipulation policies typically process instructions and observations through shared network parameters. This task-state entangle
arXiv:2508.04999v2 Announce Type: replace Abstract: Multimodal sentiment analysis (MSA) aims to understand human emotions by integrating information from multiple modalities, such as text, audio, and