Knowledge catalogue
arXiv:2610.04543v1 Announce Type: cross Abstract: Quantum key distribution proves its protocol secure and says nothing about the hardware beneath it, so military and government operators fielding it f
arXiv:2610.03888v1 Announce Type: cross Abstract: Reconstructing global sea surface pH from sparse observations is critical for monitoring ocean acidification and understanding marine carbon cycling.
arXiv:2610.06703v1 Announce Type: cross Abstract: Background music that matches the mood of a text has been shown to make readers feel more immersed and improve their reading experience, motivating re
arXiv:2610.06324v1 Announce Type: new Abstract: CLIP-like vision-language models remain a cornerstone of multimodal systems, yet their scores stay near chance on directed spatial relations, such as wh
arXiv:2610.04266v1 Announce Type: new Abstract: This paper presents a conformal-seeded hybrid strategy for solving inverse kinematics of offset, redundant 7-DoF robot arms of the humanoid class. Analy
arXiv:2610.03997v1 Announce Type: new Abstract: Robots can acquire bad habits from a few defective moments in otherwise useful teleoperation. In mixed-quality robot data, normal and problematic demons
arXiv:2610.04185v1 Announce Type: new Abstract: Referring multi-object tracking (RMOT) takes a video and a language expression as input and tracks all referred objects. Many tracking requirements invo
arXiv:2606.16496v2 Announce Type: replace-cross Abstract: Large multimodal language models (MLLMs) have emerged as powerful tools for guiding evolutionary search toward interpretable programmatic poli
arXiv:2512.22570v2 Announce Type: replace Abstract: Gliomas are among the most aggressive cancers, with complex diagnostic processes. Existing glioma segmentation methods often struggle with high vari
arXiv:2610.05718v1 Announce Type: new Abstract: We introduce Reduced-rank Mahalanobis Distance (ReMaD), a novel prototypical distance-based refinement to classification and out-of-distribution (OOD) d
arXiv:2610.05940v1 Announce Type: new Abstract: Despite their impressive performance on a wide range of video understanding tasks, current Vision Language Models (VLMs) are predominantly designed for
arXiv:2610.05417v1 Announce Type: new Abstract: Recent works augment Vision-Language Models with geometry features from pretrained 3D models, expecting that the geometric signal will boost spatial rea
arXiv:2610.04778v1 Announce Type: new Abstract: Patient world models are increasingly proposed for longitudinal prediction, intervention-aware reasoning, and clinical-trial simulation. Causal or clini
arXiv:2610.04098v1 Announce Type: cross Abstract: Language models exhibit strong reasoning capabilities, yet adapting them to structured domains remains challenging and can yield inconsistent outcomes
arXiv:2208.08697v2 Announce Type: replace-cross Abstract: The security of deep learning (DL) systems is an extremely important field of study as they are being deployed in several applications due to
arXiv:2610.04318v1 Announce Type: new Abstract: Efficient long-video understanding with vision-language models (VLMs) is often framed as selecting informative frames or visual tokens at a fixed native
arXiv:2610.05352v1 Announce Type: new Abstract: Tabular foundation models (TFMs) outperform established machine learning models on tabular benchmarks through in-context learning. Building on this succ
arXiv:2610.05717v1 Announce Type: cross Abstract: In-context retrieval (ICR) is a retrieval-based form of in-context learning (ICL) in which demonstrations are retrieved from a source database based o
arXiv:2610.03858v1 Announce Type: new Abstract: We investigate a general-purpose layer for deep learning that, instead of compressing arbitrary-size training data into fixed-size weight matrices, stor
arXiv:2610.05298v1 Announce Type: new Abstract: Motion-deblurring datasets are commonly synthesised by averaging N consecutive high-frame-rate frames and labelling the result with the central frame. W
arXiv:2610.04644v1 Announce Type: new Abstract: Neural approaches to Graph Edit Distance (GED) have achieved strong results under standard within-dataset evaluation, but much less is known about how w
arXiv:2503.21975v2 Announce Type: replace Abstract: Long-horizon manipulation under sparse rewards remains challenging for reinforcement learning due to delayed feedback and inefficient exploration. E
arXiv:2610.04091v1 Announce Type: new Abstract: Signal separation problems are common in science. A prominent example of this occurs during the use of diffraction or spectroscopy to identify the indiv
arXiv:2610.04979v1 Announce Type: new Abstract: Robust tensor completion recovers multidimensional data from partial observations corrupted by sparse gross errors. Existing convolutional low-rank mode
arXiv:2610.05131v1 Announce Type: new Abstract: Intermediate-layer features from multimodal large language models have shown strong potential for video anomaly detection (VAD), yet the origin of their
arXiv:2610.06847v1 Announce Type: new Abstract: Bidirectional video diffusion models denoise entire videos in parallel, yet when trained on effectively unlimited in-distribution data from procedural g
arXiv:2610.05066v1 Announce Type: new Abstract: Adapting step-distilled text-to-image (T2I) models through post-training incurs additional computational costs and affects native few-step generation be
arXiv:2610.04095v1 Announce Type: new Abstract: Visual grounding models can enhance radiology workflows by linking report findings to image regions. This is particularly valuable for 3D CT, where find
arXiv:2610.04905v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) are a central tool in mechanistic interpretability. However, existing SAEs are primarily trained per layer. The modeling subs
arXiv:2610.04810v1 Announce Type: new Abstract: Real-time agents are often evaluated with the world paused, or with decision time rounded to whole ticks (tick conversion). Tick conversion predicts alm
arXiv:2610.05325v1 Announce Type: new Abstract: In the field of deep learning, has seen significant advancements; however, shadow removal remains a persistent challenge owing to the variable sizes and
arXiv:2304.03056v1 Announce Type: cross Abstract: In this work, we derive sharp non-asymptotic deviation bounds for weighted sums of Dirichlet random variables. These bounds are based on a novel integ
arXiv:2610.06308v1 Announce Type: cross Abstract: We study deterministic and randomized midpoint discretizations of Langevin dynamics for a target pi propto e^{-V}, where 0 prec alpha Ipreceqnabla^2Vp
arXiv:2610.05679v1 Announce Type: new Abstract: We study the offline gap between deterministic calibration distance C and its fractional relaxation L for binary unit-weight sequences under total absol
arXiv:2610.04594v1 Announce Type: cross Abstract: Chain-of-Thought (CoT) faithfulness detectors are widely used to audit reasoning models, yet a detector is itself a predictor whose verdicts are treat
arXiv:2610.05290v1 Announce Type: new Abstract: In recent years, federated stochastic bilevel optimization has attracted increasing attention due to its wide range of applications in machine learning.
arXiv:2610.06229v1 Announce Type: new Abstract: Spherical data arise in many scientific applications. Often spherical transformers disregard the geometry of the underlying spherical domain, causing di
arXiv:2610.06513v1 Announce Type: cross Abstract: Evaluating text generation requires measuring how well the generated distribution matches the data distribution. For autoregressive models, this is do
arXiv:2603.07865v2 Announce Type: replace-cross Abstract: Text-to-audio (T2A) diffusion models generate high-quality audio but require tens of neural function evaluations (NFEs), resulting in substant
arXiv:2610.05642v1 Announce Type: cross Abstract: Emerging mission classes such as on-orbit servicing, satellite inspection, and active debris removal require trajectory design methods that are adapta
arXiv:2610.03931v1 Announce Type: new Abstract: This paper presents sparse calibration-based personalization for kernel-based gait phase and walking speed co-estimation from wearable inertial measurem
arXiv:2610.04203v1 Announce Type: new Abstract: Surface reconstruction under sparse-view settings remains challenging due to limited geometric cues. Volume rendering methods based on signed distance f
arXiv:2610.05891v1 Announce Type: new Abstract: Post-hoc class activation maps (CAMs) are a standard tool for inspecting the evidence behind an image classifier's predictions, yet nothing in ordinary
arXiv:2610.04875v1 Announce Type: cross Abstract: Diffusion large language models (DLLMs) generate text through iterative block denoising, and multi-branch speculative decoding accelerates this proces
arXiv:2510.14217v3 Announce Type: replace Abstract: The spectral properties of feature embeddings offer critical insights into model generalization and representation quality. While deep learning mode
arXiv:2610.06012v1 Announce Type: new Abstract: In this work, we study transformer attention through the lens of spectral geometry and operator theory. We view each attention head as a functional map
arXiv:2610.06334v1 Announce Type: new Abstract: Diffusion models have greatly advanced instruction-guided image editing, while also raising concerns about unauthorized image manipulation. Image immuni
Spontaneous morning chat with Juergen Schmidhuber and Jake Newfield philosophizing about AI. Sry my mic somehow disconnected but Juergen had the more interesting points anyways! Our CEO @Jake_Newfield
arXiv:2610.05688v1 Announce Type: new Abstract: We study adversarial multiplayer bandits with K arms and 2le m<K labeled players, without collision information, shared randomness, or an external commu
arXiv:2601.19730v2 Announce Type: replace Abstract: The empirical evidence indicates that stochastic optimization with heavy-tailed gradient noise is more appropriate to characterize the training of m
arXiv:2610.06344v1 Announce Type: new Abstract: In deep graph neural networks, increasing depth enlarges the receptive field but often leads to over-smoothing, where node representations tend to align
arXiv:2610.05576v1 Announce Type: new Abstract: Stochastic order-independent transparency enables efficient and elegant rendering of primitive-based radiance fields like 3D Gaussian Splatting models,
arXiv:2610.06383v1 Announce Type: new Abstract: Inference-time steering offers cheap, fine-grained control over a language model's outputs by estimating a concept's representation in activation space
arXiv:2610.03743v1 Announce Type: cross Abstract: Accurate modeling of the daily evolution of sea ice concentration (SIC) is central to improving the credibility and operational forecasting capability
arXiv:2505.24033v3 Announce Type: replace-cross Abstract: We investigate whether hidden states from Structured State Space Models (SSMs) can be merged post hoc to support downstream reasoning. Inspire
arXiv:2610.03947v1 Announce Type: new Abstract: Finding a compact formula that fits a set of input-output pairs and predicts outputs on unseen inputs is a fundamental problem in science. Symbolic regr
arXiv:2601.02799v3 Announce Type: replace Abstract: Uniform-noise discrete diffusion and flow models generate sequences non-autoregressively through iterative, context-dependent token replacements. Ho
arXiv:2610.05731v1 Announce Type: new Abstract: Earth observation (EO) data provide rich temporal supervision, yet existing remote sensing foundation models mainly exploit sequential observations thro
arXiv:2610.05583v1 Announce Type: new Abstract: Passive leader arms transmit an operator's motion without returning remote contact or load cues. TacGELLO uses the existing servos of a 3D-printed leade
arXiv:2610.04896v1 Announce Type: new Abstract: Robotic controllers increasingly rely on analytical models, simulators, cost-query interfaces, and learned world models. However, physical deployment ca