Knowledge catalogue
arXiv:2101.01631v2 Announce Type: cross Abstract: We demonstrate that from an algorithm guaranteeing an approximation factor for the ratio of submodular (RS) optimization problem, we can build another
arXiv:2610.06627v1 Announce Type: cross Abstract: The information bottleneck (IB) seeks a representation U of a source X that retains as much information as possible about a target Y, subject to a con
arXiv:2610.03952v1 Announce Type: new Abstract: A one-step curvature probe evaluates an initial direction, whereas continual learners are judged after reaching comparable new-task fit. In an overparam
arXiv:2610.04454v1 Announce Type: new Abstract: Recently, Drifting Models and Wasserstein Gradient Flows have attracted substantial attention because they move iterative distributional refinement to t
arXiv:2610.04853v1 Announce Type: new Abstract: In weakly supervised Whole Slide Image (WSI) classification, feature extractors typically compress each image tile into a single global embedding. Conse
arXiv:2610.05810v1 Announce Type: cross Abstract: Machine learning (ML)-powered poisoning attack vectors are adversarial maneuvers whereby an attacker intentionally inserts, corrupts, or alters traini
arXiv:2408.00310v2 Announce Type: replace Abstract: We study Online Linear Programming (OLP) with batching. The planning horizon is cut into K batches, and decisions on orders can be delayed to the en
arXiv:2610.04552v1 Announce Type: new Abstract: Fusing radar, LiDAR, and camera enables robust perception in diverse and adverse conditions, but the fusion performance critically depends on accurate e
arXiv:2610.05017v1 Announce Type: new Abstract: Concept-based methods provide a semantic level for interpreting and manipulating learned representations, but existing editing approaches are typically
arXiv:2610.05038v1 Announce Type: cross Abstract: We present an oracle-optimal method for finite-sum monotone inclusions under mean-square Lipschitz continuity. Our switching regularization method fin
arXiv:2610.05191v1 Announce Type: new Abstract: DETR-style detectors use one-to-one bipartite matching during training to assign object queries to ground-truth objects, enabling end-to-end set predict
arXiv:2610.06002v1 Announce Type: cross Abstract: Diffusion models provide expressive priors over trajectories, but adapting these priors to test-time constraints requires maintaining feasibility and
arXiv:2610.04226v1 Announce Type: new Abstract: LoRA-based continual learning methods mitigate catastrophic forgetting through various mechanisms, yet nearly all complement these with small learning r
arXiv:2610.04652v1 Announce Type: new Abstract: Transferable adversarial attacks are arguably the most practical black-box threat model. Under the same perturbation budget, stronger transfer attacks a
arXiv:2610.05115v1 Announce Type: new Abstract: Small targets occupy few patches in a vision-language encoder, so spatial features often mix object appearance with surrounding content. We propose Prot
arXiv:2503.09368v2 Announce Type: replace Abstract: Despite recent progress in learned image compression, current image codecs still struggle to maintain realistic reconstructions at low bit-rates, of
arXiv:2610.05844v1 Announce Type: cross Abstract: Recovering complete phase sets from powder X-ray diffraction (PXRD) is challenging when weak-phase peaks overlap stronger signals. A natural strategy
arXiv:2509.22458v3 Announce Type: replace Abstract: Physics-informed graph neural networks (PIGNNs) have emerged as fast AC power-flow solvers that can replace the classic NewtonRaphson (NR) solvers,
arXiv:2603.09693v2 Announce Type: replace Abstract: Predicting interfacial degradation and microstructural evolution through phase-field modelling is computationally intensive, particularly for parame
arXiv:2610.04108v1 Announce Type: new Abstract: Accelerating the prediction of long-term behavior in chaotic systems is crucial in scientific computing. However, existing methods rely on numerical sol
arXiv:2610.06641v1 Announce Type: new Abstract: Human--robot co-transportation of deformable objects requires predicting object deformation during motion, since obstacle clearance depends on both the
arXiv:2604.05114v2 Announce Type: replace-cross Abstract: We study a QA curation pipeline for improving long-context complex reasoning in large language models (LLMs). Our approach, pi^2, constructs h
arXiv:2610.05699v1 Announce Type: new Abstract: The efficacy of traditional disease prediction is limited by spatial gaps and temporal lags, which impact the timing and targets of resource deployments
arXiv:2610.05570v1 Announce Type: new Abstract: Existing thermodynamically consistent neural operators impose the GENERIC degeneracy conditions by projecting the reversible operator onto the complemen
arXiv:2610.06032v1 Announce Type: cross Abstract: Physics simulators can support the discovery of quantum experiments by predicting the states generated by experimental configurations. When these simu
arXiv:2610.05102v1 Announce Type: new Abstract: We study differentially private learning problems in the realizable setting, where a hypothesis is specified by k components. A direct iteration of priv
arXiv:2610.06822v1 Announce Type: new Abstract: We study mistake bounds for differentially private online learning and online prediction under oblivious realisable adversaries. Online learning require
arXiv:2610.03972v1 Announce Type: new Abstract: Ising machines have emerged as promising hardware accelerators for intractable optimization and sampling problems, yet their practical impact increasing
arXiv:2610.03846v1 Announce Type: cross Abstract: Probability-flow ordinary differential equations (PF-ODEs) are widely used as deterministic samplers for score-based diffusion models. Their usual jus
arXiv:2511.05420v3 Announce Type: replace Abstract: Data-driven fault diagnosis models for smart grids are usually trained once on a fixed dataset, whereas in operation new fault types appear and moni
arXiv:2610.05807v1 Announce Type: cross Abstract: Protocol choices can change the conclusions drawn from log anomaly detection benchmarks even when detector settings are fixed. We present a joint empi
arXiv:2610.04519v1 Announce Type: new Abstract: Estimating treatment effects from observational data typically relies on the No Unmeasured Confounding Assumption (NUCA), which rarely holds in practice
arXiv:2610.04737v1 Announce Type: cross Abstract: Reasoning models can remain capable of solving a task while still defaulting to cheaper but misleading shortcuts. This creates a central oversight pro
arXiv:2610.06404v1 Announce Type: new Abstract: We consider the stability of multi-step reasoning processes, which have extensive applications in language models, including chain-of-thought and algori
arXiv:2610.06076v1 Announce Type: cross Abstract: Preparing quantum states from classical data can cost more than the computation they serve; most loaders tailor a circuit to each input. Here we show
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:2512.17703v3 Announce Type: replace-cross Abstract: The crystal structure of high-pressure solid hydrogen remains a fundamental open problem. Although the research frontier has mostly shifted to
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