Knowledge catalogue
arXiv:2606.22166v1 Announce Type: cross Abstract: Author rebuttals are the main post-submission window in peer review, but their effect on reviewer scores remains hard to measure because score updates
arXiv:2606.23617v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are commonly fine-tuned through passive imitation learning, where additional demonstrations are collected for task
arXiv:2606.21238v1 Announce Type: cross Abstract: Key-value (KV) caching is a powerful technique for accelerating large language model inference and generation. Inference workloads are large and diver
arXiv:2602.06937v2 Announce Type: replace-cross Abstract: Realistic sound propagation is essential for immersion in a virtual scene, yet physically accurate wave-based simulations remain computational
arXiv:2603.19925v2 Announce Type: replace-cross Abstract: Whole slide image (WSI) analysis heavily relies on multiple instance learning (MIL). While recent methods benefit from large-scale foundation
arXiv:2606.21289v1 Announce Type: new Abstract: Nondestructive detection methods, based on vibrational spectroscopy, are vitally important in a wide range of applications including industrial chemistr
arXiv:2606.22363v1 Announce Type: cross Abstract: We introduce reference-free measures for evaluating the physical consistency of generated videos, combining relative and absolute approaches to assess
arXiv:2606.21086v1 Announce Type: new Abstract: We present Reflow-regularized Flow Matching Policy Gradients (ReFPO), a simple online RL method that adds explicit Reflow regularization to FPO for effi
arXiv:2512.21988v3 Announce Type: replace-cross Abstract: Background: Smartphone-based dermatology requires inter-device colorimetric reliability that holds across calibration regimes, yet quantitativ
arXiv:2606.22108v1 Announce Type: cross Abstract: Urban traffic congestion remains a persistent challenge in car-dependent cities, imposing significant economic and societal costs. Traffic signal syst
arXiv:2406.07884v2 Announce Type: replace-cross Abstract: Using partial knowledge of a quantum state to control multiqubit entanglement is a largely unexplored paradigm in the emerging field of quantu
arXiv:2606.22402v1 Announce Type: cross Abstract: Large language model (LLM)-based approaches for automated code compliance (ACC) of building regulations are prone to generating incorrect and hallucin
arXiv:2606.21179v1 Announce Type: new Abstract: Soil properties relevant to agricultural and environmental applications are conventionally measured using elaborate laboratory methods involving physica
arXiv:2606.20736v1 Announce Type: new Abstract: Static visual question answering (VQA) benchmarks age quickly: Once the items leak into training corpora, scores can reflect memorization rather than ge
arXiv:2606.20832v1 Announce Type: cross Abstract: Astrophysical simulations frequently address multi-scale, multi-physics problems through subsystem decomposition, problem-tailored integration schemes
arXiv:2606.22351v1 Announce Type: cross Abstract: Test-time adaptation (TTA) can mitigate domain shift without source data, but it is highly brittle under adversarially contaminated test streams, wher
arXiv:2601.03357v2 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) has become a standard approach to reconstruct and render photorealistic 3D head avatars. A major challenge is to religh
arXiv:2606.21188v1 Announce Type: new Abstract: Long horizon, contact-rich manipulation is inherently partially observable. This is as a single visual observation rarely captures a robot's full action
arXiv:2505.17338v2 Announce Type: replace Abstract: Photorealistic volumetric rendering of CT scans greatly benefits clinical workflows, yet neural approaches such as Neural Radiance Fields (NeRF) and
arXiv:2606.23104v1 Announce Type: new Abstract: On-policy distillation (OPD) improves LLM reasoning by training a student model on its own generated outputs, but standard OPD treats all student-genera
arXiv:2606.20737v1 Announce Type: cross Abstract: We study factual edit propagation in a controlled synthetic knowledge-graph QA setting, comparing four architectures that cross loop recurrence with s
arXiv:2606.22181v1 Announce Type: new Abstract: Deep allergenicity classifiers are increasingly used in safety screening of novel foods, and recent protein language models have substantially improved
arXiv:2606.22195v1 Announce Type: new Abstract: Orthogonal frequency division multiplexing (OFDM)-based integrated sensing and communication (ISAC) systems commonly extract target parameters by peak-s
arXiv:2606.23361v1 Announce Type: new Abstract: Backdoor attacks on molecular graph neural networks (GNNs) are typically evaluated as abstract graph edits, but real molecular learning pipelines do not
arXiv:2606.23436v1 Announce Type: new Abstract: Unsupervised video object tracking aims to decompose dynamic scenes into persistent, object-centric entities without manual annotations. Many recent app
arXiv:2606.23069v1 Announce Type: new Abstract: Few-shot object detection aims to detect novel object categories from only a few labeled examples, avoiding costly large-scale annotation. Recent protot
arXiv:2606.21913v1 Announce Type: new Abstract: Cell segmentation is critical for computational pathology and biomedical discovery. While recent Vision Foundation Models (VFMs) have demonstrated remar
arXiv:2603.08305v2 Announce Type: replace Abstract: Text-conditioned generative models for volumetric medical imaging provide semantic control but lack explicit anatomical guidance, often resulting in
arXiv:2606.22823v1 Announce Type: new Abstract: Enzyme substrate interaction (ESI) prediction is a fundamental computational task for biocatalyst discovery and reaction screening in large biochemical
arXiv:2409.05755v3 Announce Type: replace Abstract: Over the past decade, Graph Neural Networks (GNNs) have achieved great success on machine learning tasks with relational data. However, recent studi
arXiv:2603.18985v2 Announce Type: replace-cross Abstract: Deep learning models have become the dominant approach for multivariate time series anomaly detection (MTSAD), often reporting substantial per
arXiv:2511.07272v2 Announce Type: replace Abstract: Overparameterized fully-connected neural networks have been shown to behave like kernel models when trained with gradient descent, assuming standard
arXiv:2606.21271v1 Announce Type: new Abstract: Sparse rewards pose a central challenge in reinforcement learning, since agents receive no informative signal until they reach their goal. Intrinsic-rew
arXiv:2606.20959v1 Announce Type: new Abstract: Large language models can store both outdated facts and newer superseding facts in their parameters, but standard prompting may still elicit the outdate
arXiv:2606.20638v1 Announce Type: cross Abstract: Large language models are increasingly deployed as long-lived agents that must adapt across users, tasks, domains, modalities, and feedback regimes wi
arXiv:2606.22840v1 Announce Type: new Abstract: We present RLM-Cascade, a proxy-layer system that applies speculative decoding at the response level to reduce LLM API costs without requiring model arc
arXiv:2606.22142v1 Announce Type: new Abstract: We present RoboLineage, an agent-native data lifecycle governance system for robot policy iteration. Modern robot policies improve through repeated data
arXiv:2606.21572v1 Announce Type: new Abstract: Large vision-language models contain several priors about the world and object interactions, making them useful critics during inference to steer robot
arXiv:2606.21406v1 Announce Type: cross Abstract: A central question in robot learning is how to acquire skills from the kinds of data that humans learn from: passive observation, embodied practice, a
arXiv:2511.23221v2 Announce Type: replace Abstract: In this paper, we challenge the conventional notion in 3DGS-SLAM that rendering quality is the primary determinant of tracking accuracy. We argue th
arXiv:2507.15232v2 Announce Type: replace-cross Abstract: Recent advances have sparked significant interest in the development of privacy-preserving Principal Component Analysis (PCA). However, many e
arXiv:2606.20666v1 Announce Type: cross Abstract: Associative memory models play a fundamental role in pattern retrieval, but their performance often degrades under adversarial perturbations and sever
arXiv:2606.22521v1 Announce Type: cross Abstract: We show that replacing the standard MSE denoising loss in diffusion models with a nonlinear transformation induced by an f-divergence yields a simple
arXiv:2606.20703v1 Announce Type: new Abstract: Label-free, image-based cellular mechanophenotyping in microfluidic devices provides a high-throughput method for single-cell profiling. However, while
arXiv:2602.03531v2 Announce Type: replace-cross Abstract: Masked Autoencoders (MAEs) achieve impressive performance in image classification tasks, yet the internal representations they learn remain le
arXiv:2603.02742v2 Announce Type: replace Abstract: Autonomous drone racing (ADR) demands state estimation that is simultaneously computationally efficient and resilient to the perceptual degradation
arXiv:2606.20734v1 Announce Type: new Abstract: Open Vocabulary Action Recognition (OVAR) enables the recognition of novel actions by leveraging vision-language representations, overcoming the limitat
arXiv:2606.21488v1 Announce Type: new Abstract: The vulnerability of ML models to adversarial examples has recently emerged as a major concern. While adversarial training is one of the most effective
arXiv:2606.20980v1 Announce Type: new Abstract: As Self-Driving Cars continue to expand internationally and use multi-modal systems such as VLMs as a cognitive backbone for their Action models; how we
arXiv:2606.21786v1 Announce Type: new Abstract: We introduce RocketPFN, a training-free pipeline for time series classification that combines random convolutional feature extraction (Rocket) with in-c
arXiv:2606.21264v1 Announce Type: new Abstract: Physically localizing unauthorized wireless devices remains a critical bottleneck in cyber-physical security operations, where rogue access points can p
arXiv:2606.21788v1 Announce Type: cross Abstract: Point-cloud goals provide a direct way to specify dexterous in-hand reorientation: instead of defining an object-specific pose frame or estimating 6D
arXiv:2606.18774v2 Announce Type: replace Abstract: We present RouteJudge, an online pairwise preference evaluation framework for LLM routing systems, with a public platform available at https://route
arXiv:2606.21755v1 Announce Type: new Abstract: Pre-Phase-A design of lunar micro-rovers is dominated by tightly coupled mobility, power, thermal, and mass trades, yet conceptual-design tooling for th
arXiv:2606.23221v1 Announce Type: new Abstract: Recent years have witnessed remarkable progress in image generation and editing, particularly regarding instruction following and visual fidelity. Howev
arXiv:2606.23344v1 Announce Type: new Abstract: Accurate document layout analysis remains a critical bottleneck for document parsing systems, due to the intricate coupling among heterogeneous document
arXiv:2511.20651v2 Announce Type: replace Abstract: Reinforcement learning (RL) has recently emerged as a promising approach for aligning text-to-image generative models with human preferences. A key
arXiv:2508.08048v2 Announce Type: replace Abstract: While video generation models excel at producing high-quality monocular videos, generating 3D stereoscopic and spatial videos for immersive applicat
arXiv:2606.23267v1 Announce Type: new Abstract: Flow matching has recently emerged as a strong paradigm for state-of-the-art text-to-image (T2I) generation, enabling high-quality generation with a sma
arXiv:2603.05497v3 Announce Type: replace Abstract: Traditional safety-critical control methods, such as control barrier functions, suffer from semantic blindness, exhibiting the same behavior around