Knowledge catalogue
arXiv:2610.07277v1 Announce Type: new Abstract: Scenario-based MPC is an attractive strategy for chance-constrained motion planning that approximates uncertainty via a finite set of sampled scenarios.
arXiv:2610.07207v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) transformers scale capacity by activating only a few experts per token, but this sparsity creates a hidden reliability proble
arXiv:2610.06960v1 Announce Type: new Abstract: We present DistScene, a framework for single-image compositional 3D scene generation by jointly modeling the environment and individual objects. Unlike
arXiv:2610.07911v1 Announce Type: cross Abstract: Despite recent advances in video generation, motion customization remains challenging due to content leakage, where appearance attributes from the ref
arXiv:2610.07659v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive generation in large language models. In each drafting stage, a lightweight draft model proposes tokens t
arXiv:2610.07981v1 Announce Type: new Abstract: Higher-order models (e.g., hypergraph neural networks) often outperform lower-order baselines on hypergraph learning benchmarks, and their advantages ar
arXiv:2610.07497v1 Announce Type: new Abstract: Muon combines current and past gradients into matrix momentum. For M=USigma V^op, the idealized polar update Q=UV^op gives every singular direction the
arXiv:2509.04603v4 Announce Type: replace-cross Abstract: When faced with new data, we often conduct a cluster analysis to obtain a better understanding of the data's structure and the archetypical sa
arXiv:2610.08674v1 Announce Type: new Abstract: Current large video-language models (LVLMs) still face challenges when dealing with long videos, mainly because frames are often processed independently
arXiv:2610.07795v1 Announce Type: new Abstract: This paper presents a novel effective Gaussian Splatting (GS) sequence Compression method that utilizes the Video codec (GSCV). Existing video-based GS
arXiv:2610.07681v1 Announce Type: cross Abstract: Dexterous manipulation poses a challenging high-dimensional optimization problem, as useful behaviors require coordinated motion across many hand join
arXiv:2610.07603v1 Announce Type: cross Abstract: This article proposes Selective Affective Layer Fine-Tuning (SALFT), an efficient adaptation framework for Video Vision Transformers in player arousal
arXiv:2610.07206v1 Announce Type: new Abstract: This paper introduces an energy-adaptive noise scheduling and whitening strategy for transform-domain diffusion models. Existing spectral diffusion meth
arXiv:2610.05370v2 Announce Type: replace Abstract: Generative reward models (GRMs) are important for LLM optimization. Unlike scalar reward models, GRMs generate natural-language critiques alongside
arXiv:2409.19979v4 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated prominent reasoning capabilities in recommendation tasks by transforming them into text-generat
arXiv:2610.06986v1 Announce Type: new Abstract: AI research agents are increasingly used to search over programs, mathematical constructions, and proofs. However, existing systems typically optimize e
arXiv:2610.08286v1 Announce Type: new Abstract: This paper addresses the challenge of automatic, frame-accurate event detection in table tennis videos. Current methods for estimating 3d ball trajector
arXiv:2610.08367v1 Announce Type: new Abstract: Several discrete design tasks, such as molecular discovery, require diverse collections of useful candidates at low computational cost. High validity al
arXiv:2610.07721v1 Announce Type: cross Abstract: We study ridge regression from exactly s distinct rows of a fixed design. Responses are fixed, and only the subset is random. The determinant law and
arXiv:2610.08090v1 Announce Type: cross Abstract: IPv6 extension headers (EHs), such as fragmentation, segment routing, and in-situ telemetry, are operationally important yetwidely dropped in transit,
arXiv:2610.07623v1 Announce Type: cross Abstract: Probability forecasts are calibrated when predicted probabilities match empirical outcome frequencies: among events assigned a probability p, we'd hop
arXiv:2610.08107v1 Announce Type: cross Abstract: Attacker ASV systems for voice anonymization have been studied primarily in English, leaving their behavior in multilingual settings largely unexplore
arXiv:2601.20571v3 Announce Type: replace-cross Abstract: Asynchronous primal-dual methods for decentralized non-smooth convex optimization often require each node to maintain O(d) auxiliary variables
arXiv:2311.18029v2 Announce Type: replace-cross Abstract: The current trend in the literature on Time Series Classification is to develop increasingly accurate algorithms by combining multiple models
arXiv:2610.07966v1 Announce Type: cross Abstract: Neural audio synthesis models like the Realtime Audio Variational autoEncoder (RAVE) achieve impressive genera tion quality, yet how their internal re
arXiv:2610.08626v1 Announce Type: cross Abstract: Diffusion models generate data through a continuum of denoising problems, and are widely observed to reveal coarse structure before fine detail. Yet,
arXiv:2610.08574v1 Announce Type: cross Abstract: Skin cancer is a major global health concern, and early detection and accurate lesion delineation are important for effective diagnosis and treatment
arXiv:2610.07079v1 Announce Type: new Abstract: Accurate bioactivity prediction is a central challenge in early-stage drug discovery, as individual assays often contain too few measurements to train r
arXiv:2610.08200v1 Announce Type: new Abstract: We ask whether specific attention heads, and more finely specific neurons inside those heads, are responsible for recognizing that a language model's co
arXiv:2610.07209v1 Announce Type: cross Abstract: Limited review teams must identify which emerging claims are likely to keep growing before their eventual reach is known. We center early misinformati
arXiv:2610.07753v1 Announce Type: cross Abstract: Tool-using agents make consequential changes to external state, yet correct outcomes do not guarantee that their actions were supported by evidence es
arXiv:2610.07896v1 Announce Type: new Abstract: This paper presents the design and control strategy for MetaMorpher, a metamorphic Unmanned Aerial Vehicle (UAV), capable of both spinning-wing hover an
arXiv:2610.07310v1 Announce Type: cross Abstract: Security operations centres and national incident-response teams defending critical infrastructure collect abundant threat data yet struggle to turn i
arXiv:2610.07938v1 Announce Type: new Abstract: Implicit-process priors specify distributions over functions through sample-forward mechanisms such as Bayesian neural networks and stochastic simulator
arXiv:2610.08601v1 Announce Type: new Abstract: Emergency messaging such as extreme-weather reports and earthquake instructions can involve high stakes, to the extent that translation errors can lead
arXiv:2603.16273v3 Announce Type: replace Abstract: For field robotic missions such as inspection, search-and-rescue, and exploration, light detection and ranging (LiDAR)-inertial odometry (LIO) can s
arXiv:2610.07555v1 Announce Type: new Abstract: Optimal-transport couplings have been shown to reduce training variance in unconditional flow models, but their role in conditional generation remains u
Google DeepMind, Meta, and AI drug discovery startup Isomorphic Labs are jointly investing 300 million into Biohub, the nonprofit biomedical research organization founded by Mark Zuckerberg and his wi
arXiv:2610.06978v1 Announce Type: new Abstract: Gaussian Belief Propagation (GBP) is a distributed inference algorithm that passes messages in graphical models, making it attractive for scalable spati
arXiv:2610.08345v1 Announce Type: cross Abstract: Using a replica-symmetric high-dimensional characterization, we develop an inferential framework for sparse support vector machines when the sample si
arXiv:2511.11500v3 Announce Type: replace Abstract: Modern language models fail a fundamental requirement of trustworthy intelligence: knowing when not to answer. Despite achieving impressive accuracy
arXiv:2610.08544v1 Announce Type: cross Abstract: Probes are the workhorse of interpretability. If a model's hidden states predict a variable, the model is said to represent it. But a probe score has
arXiv:2610.08227v1 Announce Type: cross Abstract: Machine learning classifiers for remote sensing imagery are typically evaluated as though every pixel were an independent sample. Spatial autocorrelat
arXiv:2610.08350v1 Announce Type: cross Abstract: Visual world models enable goal-directed control through decision-time action search, but their deployment efficiency is often limited by conservative
arXiv:2610.07610v1 Announce Type: new Abstract: Semi-supervised learning typically assumes that labeled and unlabeled data share an identical class distribution and label space. However, this setting
arXiv:2610.08433v1 Announce Type: new Abstract: Modern action recognition models rely on video transformers pretrained on massive collections of web-crawled videos, such as Kinetics-700. However, the
arXiv:2610.08642v1 Announce Type: cross Abstract: Contact with contaminated objects can spread hazards through a household robot's grippers, tools, and shared surfaces, while new contacts can make an
arXiv:2312.11018v3 Announce Type: replace-cross Abstract: Bundle recommendation ranks sets of related items rather than isolated items. Its central challenge is to connect user preferences, item inter
arXiv:2410.21917v3 Announce Type: replace-cross Abstract: The identifiability analysis of linear Ordinary Differential Equation (ODE) systems is a necessary prerequisite for making reliable causal inf
arXiv:2610.07791v1 Announce Type: new Abstract: Illusory pattern perception is a well-documented human cognitive tendency to infer meaningful relationships in data that is actually random. Such a tend
arXiv:2610.07788v1 Announce Type: new Abstract: Consumer-grade cameras in flat-port housings are widely used for underwater exploration and mapping of coral reefs and seafloor habitats due to their lo
arXiv:2610.06729v2 Announce Type: replace Abstract: Large language models (LLMs) can generate accurate responses, but these are void of diversity. We attempt to address this for the task of creative s
arXiv:2610.08585v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly relied upon to support ambient documentation and clinical reasoning. Here we examine the impact of a failu
arXiv:2610.07389v1 Announce Type: cross Abstract: Sparse autoencoders are widely used to uncover interpretable features in neural networks, yet reliable recovery remains difficult when features overla
arXiv:2610.08495v1 Announce Type: cross Abstract: Machine learning can accelerate molecular discovery by designing molecules and planning experiments. However, many scientific challenges demand molecu
arXiv:2610.08604v1 Announce Type: new Abstract: Automatic Speech Recognition (ASR) systems often show uneven performance across demographic groups, and errors can be especially difficult to address fo
arXiv:2610.07032v1 Announce Type: cross Abstract: Large-scale pretrained transformer models have achieved state-of-the-art performance across diverse machine translation tasks, including multilingual
arXiv:2610.07906v1 Announce Type: new Abstract: We study sequential content sufficiency by investigating whether a representation retains the ordered target information available in its input. An info
arXiv:2610.06995v1 Announce Type: new Abstract: Whether more accurate traffic forecasts or higher uncertainty coverage improve route decisions is unclear. We evaluate this question with a frozen proto
arXiv:2610.08663v1 Announce Type: new Abstract: Video gaze prediction is led by gaze-trained models, yet gaze-free priors carry signal those models have not absorbed, if one knows when to trust them.