Knowledge catalogue
arXiv:2609.06232v1 Announce Type: new Abstract: Ground-truth defect masks in industrial inspection datasets are typically reserved for evaluation. This paper repurposes them as spatial supervision sig
arXiv:2609.07718v1 Announce Type: cross Abstract: Most automated brain parcellation tools are developed and validated on T1-weighted (T1w) MRI. Yet, some clinical workflows for which parcellation is r
arXiv:2609.07064v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) have achieved strong performance on diverse visual tasks, yet their ability to reconstruct and reason about the 3D
arXiv:2603.05581v2 Announce Type: replace Abstract: Urban traffic flow is governed by the complex, nonlinear interaction between land use configuration and spatiotemporally heterogeneous mobility dema
arXiv:2609.06186v1 Announce Type: new Abstract: Prioritized Sweeping (PS) accelerates model-based reinforcement learning by selecting backups according to Bellman residual magnitude. In nonstationary
arXiv:2609.08219v1 Announce Type: cross Abstract: Neural networks acquire internal representations through learning. In this work, we formulate stochastic gradient descent (SGD) as a Markovian stochas
arXiv:2609.07571v1 Announce Type: new Abstract: 360{eg} salient object detection (SOD) aims to accurately segment salient regions across a full field of view. However, equirectangular projection (ERP)
arXiv:2609.09012v1 Announce Type: new Abstract: Spherical observations provide global visual context for 3D scene understanding. However, visual information is encoded in an angular domain, whereas th
arXiv:2609.07933v1 Announce Type: new Abstract: High-quality demonstration data is becoming a central bottleneck for training general-purpose humanoid robots. While recent humanoid teleoperation syste
arXiv:2510.08999v2 Announce Type: replace Abstract: Compressing large-scale neural networks is essential for deploying models on resource-constrained devices. Most existing methods adopt weight prunin
arXiv:2507.18073v2 Announce Type: replace Abstract: Deploying large language models (LLMs) is challenging due to their massive parameters and high computational costs. Ultra low-bit quantization can s
arXiv:2601.10324v3 Announce Type: replace Abstract: Synthetic aperture radar (SAR) imagery exhibits intrinsic information sparsity due to its unique electromagnetic scattering mechanism. Despite the w
arXiv:2609.05661v1 Announce Type: new Abstract: We present Srijika, a system for producing installable OpenType fonts for nine Brahmic scripts: Devanagari, Tamil, Bengali, Telugu, Kannada, Malayalam,
arXiv:2609.06430v1 Announce Type: new Abstract: We study the identity straight-through estimator (STE) for training a two-layer binary-activation network with hinge loss from the perspective of Statis
arXiv:2609.07148v1 Announce Type: cross Abstract: While Reinforcement Learning (RL) effectively incentivizes reasoning in Large Language Models, current pipelines are hindered by training instability
arXiv:2609.05916v1 Announce Type: new Abstract: Large vision-language models (LVLMs) achieve strong multimodal understanding, but the hundreds to thousands of visual tokens they process impose substan
arXiv:2609.07165v1 Announce Type: new Abstract: Learning-by-Demonstration (LbD) enables intuitive robot programming by capturing expert skills, which is crucial for agility in high-mix, low- volume ma
arXiv:2609.07512v1 Announce Type: new Abstract: Statistical post-processing improves ensemble weather forecasts, but generating calibrated predictions at locations without observations remains challen
arXiv:2609.06289v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly deployed in alignment-sensitive contexts, activation steering has emerged as a lightweight, inference
arXiv:2609.06951v1 Announce Type: new Abstract: Activation steering promises modular control of language model behavior: a behavior such as politeness corresponds to a direction in a model's activatio
arXiv:2609.06473v1 Announce Type: new Abstract: Inference-time activation steering enables behavioral control of large language models without parameter modification, while post-training quantization
arXiv:2609.06580v1 Announce Type: cross Abstract: We investigate stochastic simple bilevel optimization with smooth and possibly nonconvex upper- and lower-level objectives. Existing stochastic extens
arXiv:2609.08412v1 Announce Type: new Abstract: Machine-learning weather models (MLWMs) now match or outperform operational numerical weather prediction (NWP) at global medium-range forecasting, at fa
arXiv:2609.05956v1 Announce Type: new Abstract: Spatial transcriptomics (ST) provides unprecedented insights into tumor heterogeneity by capturing spatially resolved gene expression, yet its high expe
arXiv:2601.14862v2 Announce Type: replace Abstract: We introduce Strategic Doctrine Language Models (sdLM), a learning-system framework for multi-document strategic reasoning with doctrinal consistenc
arXiv:2609.07956v1 Announce Type: new Abstract: Tabular Foundation Models (TFMs) have recently demonstrated strong predictive performance through in-context learning, but their deployment in high-thro
arXiv:2609.06499v1 Announce Type: new Abstract: One-shot federated graph learning (FGL) requires the server to estimate client contributions from highly compressed information, yet conventional volume
arXiv:2609.05911v1 Announce Type: cross Abstract: Long-horizon LLM agents interact with untrusted content, persistent memory, external state, and sensitive tools. Existing analyses often characterize
arXiv:2609.07952v1 Announce Type: new Abstract: We study the local errors of classical machine-learning surrogate models, which approximate the time evolution of the one-dimensional viscous Burgers eq
arXiv:2609.08543v1 Announce Type: new Abstract: Despite recent advances in surgical vision-language models (VLMs), temporal reasoning remains limited because existing supervision is largely frame-cent
arXiv:2609.09143v1 Announce Type: new Abstract: Image tokenizers define the ``visual language'' of unified multimodal models, yet are commonly studied through isolated metrics or generation-/understan
arXiv:2609.08634v1 Announce Type: new Abstract: Integrating robust safety guardrails into Large Language Models (LLMs) is essential for delivering helpful yet harmless responses. While proprietary sys
arXiv:2609.07726v1 Announce Type: cross Abstract: Automatic modulation classification (AMC) models are frequently trained and validated on synthetic or channel-cabled data, leaving open the question o
arXiv:2609.06929v1 Announce Type: new Abstract: Inter-frame affine misalignment caused by platform jitter and attitude adjustments poses a fundamental challenge for multi-frame analysis of point targe
arXiv:2609.05550v1 Announce Type: new Abstract: Near-infrared (NIR) video is a promising modality for contactless sleep monitoring, but recent video-based sleep staging methods often use it as a route
arXiv:2603.28678v2 Announce Type: replace Abstract: We introduce PACE, a backpropagation-free continual test-time adaptation system that directly optimizes the affine parameters of normalization layer
arXiv:2609.08642v1 Announce Type: new Abstract: Exploration in reinforcement learning (RL) remains a fundamental challenge. Recent goal-conditioned RL strategies (which select goals to encourage broad
arXiv:2609.08317v1 Announce Type: new Abstract: Objective assessment of Freezing of Gait (FoG) in Parkinson's disease (PD) relies predominantly on wearable Inertial Measurement Units (IMUs). While IMU
arXiv:2609.07081v1 Announce Type: new Abstract: Text spotting requires both accurate text recognition and precise spatial localization. Current specialised spotters excel at predicting tight bounding
arXiv:2609.07954v1 Announce Type: cross Abstract: Sparse Sinkhorn layers use a fixed support graph to restrict transport between tokens. How does this graph control gradient propagation through the sc
Surveillance plagiarism - Hosted AI company pumps their stock price by training upon researchers' AI sessions, so that their internal model can solve problems with seemingly less human guidance, but r
arXiv:2510.25183v2 Announce Type: replace-cross Abstract: Energy-efficient AI should be evaluated across the full application pipeline, not only by lowest error or shortest training time. We study thi
arXiv:2601.13713v2 Announce Type: replace-cross Abstract: Software testing is crucial for ensuring the correctness and reliability of software systems. Automated generation of issue reproduction tests
arXiv:2609.06651v1 Announce Type: new Abstract: Diffusion models have general generative abilities but struggle to align with specific objectives. Fine-tuning can improve alignment, yet its training c
arXiv:2609.09155v1 Announce Type: new Abstract: World models are increasingly used as policy-in-the-loop imagination environments, where reliable rollouts require fine-grained controllability with res
arXiv:2609.08268v1 Announce Type: new Abstract: Urban region embeddings have shown promising results in diverse urban sensing tasks such as crime, income, and service-call prediction. Recent methods i
arXiv:2510.19251v2 Announce Type: replace-cross Abstract: Predicting which hypothetical inorganic crystals can be experimentally realized remains a central challenge in accelerating materials discover
arXiv:2609.08108v1 Announce Type: new Abstract: Accurate estimation of clinically meaningful gait parameters from monocular video is important for scalable mobility assessment, yet progress is limited
arXiv:2609.08627v1 Announce Type: new Abstract: In proton therapy, plans are typically optimized on a single planning CT, making robustness evaluation essential under anatomical changes. However, curr
arXiv:2609.07441v1 Announce Type: new Abstract: Biomedical tables often combine thousands of measured variables with only tens or hundreds of labelled samples, a regime that is poorly represented in g
arXiv:2609.08214v1 Announce Type: new Abstract: TacClip is a minimally encumbering wearable device for recording fingertip deformation caused by contact forces and vibrations. It can be combined with
arXiv:2609.07286v1 Announce Type: cross Abstract: Large audio-language models (LALMs) can hallucinate audio objects, answering 'yes' to absent sound events, thus undermining reliability in audio quest
arXiv:2609.09158v1 Announce Type: new Abstract: We study the problem of navigating cluttered indoor environments with a humanoid robot. Unlike conventional methods that model navigation as a 2D path p
arXiv:2609.08618v1 Announce Type: new Abstract: Benchmark scores describe what a checkpoint can do now, but they do not determine how it will respond to the next training episode. We measure this miss
arXiv:2609.08512v1 Announce Type: new Abstract: Autonomous exploration on uneven terrain requires ground robots to balance exploration efficiency, coverage completeness, and terrain safety. Detailed t
arXiv:2609.09025v1 Announce Type: new Abstract: State-of-the-art vision models process images in their entirety, lacking the ability to selectively zoom in on relevant regions. This limitation is part
arXiv:2609.06242v1 Announce Type: cross Abstract: Connected and automated vehicle platoons require reliable dissemination of task-relevant hazard information to enable appropriate downstream responses
arXiv:2609.08011v1 Announce Type: new Abstract: Image restoration is commonly applied before object detection under adverse conditions, yet a visually improved image need not improve the downstream ta
arXiv:2609.07444v1 Announce Type: new Abstract: The rise of privacy-preserving artificial intelligence (AI) has shifted the focus of model adaptation and personalization towards on-device learning, wh
arXiv:2609.06144v1 Announce Type: new Abstract: Visual dubbing must synchronize mouth motion with replacement speech while preserving identity, appearance, and temporal consistency. Although X-Dub pro