Knowledge catalogue
arXiv:2610.14368v1 Announce Type: new Abstract: We study learned one-step predictive control of a synthetic population of 64 leaky integrate-and-fire neurons whose light sensitivity is scaled by a hid
arXiv:2610.15715v1 Announce Type: cross Abstract: Linear attention promises the efficiency needed for long-context language modelling, yet its fixed-size recurrent state must simultaneously support on
arXiv:2610.14971v1 Announce Type: new Abstract: WavePhaseNet introduced frequency-resolved semantic representations, QuantumPhaseNet introduced gauge-covariant phase connections, and ModularPhaseNet m
arXiv:2603.21862v2 Announce Type: replace Abstract: Scaling laws govern macroscopic resource allocation for LLMs, yet precise architectural configurations for Mixture-of-Experts (MoE) models remain gu
arXiv:2610.17030v1 Announce Type: cross Abstract: A two-dimensional electron gas in a uniform neutralizing background forms a triangular Wigner crystal at low density. In a strongly mass-asymmetric el
arXiv:2610.18504v1 Announce Type: cross Abstract: Recently, Morris et al. introduced TinyLoRA, a strategy for LLM fine-tuning which introduces arbitrarily few parameters, and demonstrated accuracy imp
arXiv:2610.15830v1 Announce Type: new Abstract: When several predictions use the same internal representation, information about some targets can remain easier to recover than information about others
arXiv:2610.16051v1 Announce Type: cross Abstract: Topology optimization places material so that a structure resists deformation under given loads. Machine-learning methods often report large speed-ups
arXiv:2610.14707v1 Announce Type: cross Abstract: Expert-aligned 'taste' benchmarks should reward craft judgment, not the ability to game the test. We introduce TasteBench, a pairwise good-vs-bad benc
arXiv:2511.18606v2 Announce Type: replace-cross Abstract: Latent safety filters extend Hamilton-Jacobi (HJ) reachability to operate on latent state representations and dynamics learned directly from h
arXiv:2610.13992v1 Announce Type: new Abstract: Positional embeddings encode the spatial position of each image patch in a Vision Transformer (ViT), allowing the model to distinguish different spatial
arXiv:2610.18133v1 Announce Type: new Abstract: Dual-student learning enriches predictive diversity in SSMIS by introducing a strongly augmented view alongside a weakly augmented view. Insufficient st
arXiv:2606.19370v2 Announce Type: replace Abstract: Self-play reinforcement learning has recently emerged as a way to train driving policies without any human data. It uses cheap, large-scale simulati
arXiv:2610.16855v1 Announce Type: new Abstract: Real-time bimanual humanoid teleoperation requires accurate wrist tracking while keeping arm configurations predictable. Because a 7-DoF arm admits mult
arXiv:2610.14093v1 Announce Type: new Abstract: Despite advancements in sub-pixel decoding, modern Human Pose Estimation (HPE) architectures remain constrained by deterministic coordinate extraction f
arXiv:2602.13850v4 Announce Type: replace Abstract: We introduce Humanoid Hanoi, a challenge problem for long-horizon humanoid skill composition that requires a robot to repeatedly sequence pickup, lo
arXiv:2610.14679v1 Announce Type: new Abstract: We introduce HumanPhys, a human-centric benchmark for real-world physical perception, and a training corpus emitted by the same pipeline. Each question
arXiv:2610.16589v1 Announce Type: new Abstract: Electroencephalography (EEG) representation learning is challenged by noisy signals, inter-subject variability, and heterogeneous electrode montages in
arXiv:2610.16047v1 Announce Type: cross Abstract: Automatic extraction and anatomical numbering (R01-R12, L01-L12) of all 24 rib centerlines in thoracic CT is essential for unfolded rib visualization,
arXiv:2610.17779v1 Announce Type: new Abstract: The Kolmogorov-Arnold Network (KAN) challenges traditional MLPs by placing learnable activation functions on network edges. However, scaling KANs to spa
arXiv:2610.18861v1 Announce Type: cross Abstract: How can we build an agent that thinks beyond human language, yet still acts through it in the external environment? Modern LLM agents still reason and
arXiv:2610.17251v1 Announce Type: new Abstract: Separating Alzheimer's Disease (AD), Parkinson's Disease (PD), and Healthy Controls (HC) using structural MRI remains challenging because disease signat
arXiv:2603.07815v2 Announce Type: replace Abstract: Iterative diffusion generation requires different amounts of model capacity at different token positions. Prior model combinations allocate capacity
arXiv:2603.15228v3 Announce Type: replace Abstract: Unified Multimodal Models struggle to bridge the fundamental gap between the abstract representations needed for visual understanding and the detail
arXiv:2610.15760v1 Announce Type: new Abstract: Multiscale structure is central to time series forecasting: temporal signals contain slowly varying trends, intermediate seasonal patterns, and high-fre
arXiv:2610.18307v1 Announce Type: new Abstract: Service and delivery robots often need to complete long-horizon navigation missions spanning indoor and outdoor environments. Existing embodied navigati
arXiv:2610.17748v1 Announce Type: new Abstract: Policies trained offline from teleoperated demonstrations can degrade outside their training distribution. This gap is particularly acute in long-horizo
arXiv:2610.14520v1 Announce Type: cross Abstract: Determinantal point processes (DPPs) provide tractable distributions for nite random sets, but the usual symmetric-kernel specication imposes nonposit
arXiv:2603.29602v2 Announce Type: replace-cross Abstract: Existing multi-turn image editing paradigms are often confined to isolated single-step execution. Due to a lack of context-awareness and close
arXiv:2610.14254v1 Announce Type: new Abstract: Text-to-motion generation typically relies on dataset-specific skeletal representations, with models trained separately for benchmarks with different jo
arXiv:2610.16759v1 Announce Type: new Abstract: Latent diffusion models generate images as codes of an image tokenizer, whose decoder turns them into the pixel image. Recent tokenizers improve how wel
arXiv:2610.13941v2 Announce Type: replace Abstract: Multimodal diffusion transformers (MMDiTs) allow the evolving image to update internal text representations through joint attention. These represent
arXiv:2610.16326v1 Announce Type: new Abstract: Multimodal model predictive control (MM-MPC) avoids local minima among moving obstacles by solving one optimal control problem (OCP) per combination of
arXiv:2610.15639v1 Announce Type: new Abstract: Long-horizon robotic manipulation is inherently composed of semantically meaningful sub-tasks. While Movement Primitives (MPs) can model these component
arXiv:2610.15281v1 Announce Type: new Abstract: Verifier-guided inference-time scaling uses additional computation to score intermediate states and steer generation toward better outputs. However, imp
arXiv:2610.17151v1 Announce Type: cross Abstract: Variational inference is a flexible tool for modeling experimental data and has proven useful in crystallography. Pathwise gradient estimators provide
arXiv:2511.10448v2 Announce Type: replace Abstract: Bolting operations are critical in industrial assembly and in the maintenance of scientific facilities, requiring high precision and robustness to f
arXiv:2610.17994v1 Announce Type: new Abstract: Symbolic regression is the problem of finding an expression that fits a dataset. Solving it requires searching a combinatorial space of candidate expres
arXiv:2610.17298v1 Announce Type: new Abstract: In this work, we explore the idea of expert intervention delays in human-supervised robotic systems for imitation learning system. Previous works in imi
arXiv:2610.16008v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems are increasingly used for biomedical Question Answering (QA). However, evaluating their effectiveness rem
arXiv:2610.17061v1 Announce Type: cross Abstract: We study in-context binary classification in a simple attention model trained to minimise a logistic loss. Each prompt has a latent logistic teacher,
arXiv:2610.12942v2 Announce Type: replace Abstract: Vision-language models (VLMs) learn strong statistical regularities during training, which can make them fail to properly respond to visual evidence
arXiv:2610.14577v1 Announce Type: new Abstract: During human-robot interaction, sight plays an important role. When the human sees a robot, they can observe the robot's actions and better predict the
arXiv:2610.15032v1 Announce Type: new Abstract: Industrial anomaly detection benchmarks have advanced rapidly, yet they remain largely static and therefore underrepresent deployment settings in which
arXiv:2610.16652v1 Announce Type: new Abstract: We introduce IND-Eval, a public benchmark that organizes industrial time series across domains into source-specific forecasting tasks. Relative to estab
arXiv:2610.15789v1 Announce Type: cross Abstract: In this paper, we develop an asymptotically normal estimator for the value of policy improvement (VPI) under adaptively collected data. This value, wh
arXiv:2610.17873v1 Announce Type: new Abstract: Nuclear magnetic resonance (NMR) magnetic tracking offers a promising alternative to magnetic resonance imaging (MRI)-based localization for medical rob
arXiv:2610.15043v1 Announce Type: new Abstract: Repetitive, degenerate generation is a persistent failure of LLM decoding, and the standard remedies (temperature, nucleus, and top-k sampling) are stat
arXiv:2610.16784v1 Announce Type: new Abstract: Generating and editing presentation slides requires models to combine accurate text rendering, instruction following, visual grounding, aesthetic design
arXiv:2610.14544v1 Announce Type: new Abstract: Roadside 4D occupancy forecasting predicts future semantic occupancy in a fixed infrastructure coordinate system. Roadside scenes are globally structure
arXiv:2610.18051v1 Announce Type: new Abstract: This work investigates the application of the Membership Inference Test (MINT) to large language models (LLMs) fine-tuned with LoRA, aiming to determine
arXiv:2610.15954v1 Announce Type: new Abstract: We study online multiclass multicalibration over T rounds, and give an algorithm with beyond-worst-case guarantees. With n possible outcomes, forecasts
arXiv:2511.14208v3 Announce Type: replace Abstract: Video inverse problems are fundamental to streaming, telepresence, and AR/VR, where high perceptual quality must coexist with tight latency constrai
arXiv:2610.17671v1 Announce Type: cross Abstract: The design of ergonomic products and workspaces requires a deep understanding of human anatomy and posture. In particular, the head-neck-shoulder regi
arXiv:2610.14723v1 Announce Type: new Abstract: A method integrating policy gradient with fuzzy inference has been proposed. This method, hereafter referred to as the base model, has high flexibility
arXiv:2610.16627v1 Announce Type: cross Abstract: Existing human-centred XAI research predominantly evaluates explanations after they have been generated. However, this fails to provide actionable ins
arXiv:2610.18386v1 Announce Type: new Abstract: Manipulation tasks such as unscrewing a cap or sliding a part in contact with a surface require imposing force along a high-friction direction: the robo
arXiv:2610.17895v1 Announce Type: new Abstract: Coding agents are tasked with producing sophisticated interactive applications such as websites, desktop applications, and games. While humans assess th
arXiv:2610.18855v1 Announce Type: new Abstract: Contact-rich manipulation requires robots to coordinate not only where to move, but also how contact and force should evolve throughout an interaction.
arXiv:2610.16167v1 Announce Type: cross Abstract: We propose the task of Interactive 4D Volumetric Liquid Forecasting: predicting the spatiotemporal evolution of a future dense 3D liquid velocity fiel