Knowledge catalogue
arXiv:2604.20835v1 Announce Type: new Abstract: Modern language models demonstrate impressive coding capabilities in common programming languages (PLs), such as C++ and Python, but their performance i
arXiv:2604.20816v1 Announce Type: cross Abstract: Reinforcement Learning (RL) post-training has become the standard for aligning generative models with human preferences, yet most methods rely on a si
arXiv:2604.20711v1 Announce Type: new Abstract: Artificial intelligence is increasingly deployed to synthesize large-scale public input in policy consultations and participatory processes. Yet no form
arXiv:2604.20557v1 Announce Type: new Abstract: Shared Control methods often use impedance control to track target poses in a robotic manipulator. The guidance behavior of such controllers is shaped b
arXiv:2604.20047v1 Announce Type: new Abstract: Vision Transformers (ViTs) have achieved remarkable success across vision tasks, yet recent studies show they remain vulnerable to backdoor attacks. Exi
arXiv:2604.19784v1 Announce Type: cross Abstract: Recently, it has been found that frontier AI models can resist their own shutdown, a behavior known as self-preservation. We extend this concept to th
arXiv:2604.20764v1 Announce Type: cross Abstract: This paper presents a personalized Battery Electric Vehicle (BEV) energy consumption estimation framework that integrates map-based contextual feature
arXiv:2602.20537v3 Announce Type: replace Abstract: Spatiotemporal predictive learning (STPL) aims to forecast future frames from past observations and is essential across a wide range of applications
arXiv:2604.19774v1 Announce Type: cross Abstract: Writing discharge summaries to transfer medical information is an important but time-consuming process that can be assisted by Large Language Models (
arXiv:2604.20783v1 Announce Type: new Abstract: Internal ice layers imaged by radar provide key evidence of snow accumulation and ice dynamics, but radar-derived layer boundary observations are often
arXiv:2604.20175v1 Announce Type: cross Abstract: Accurate prediction of thermal runaway in lithium-ion batteries is essential for ensuring the safety, efficiency, and reliability of modern energy sto
arXiv:2604.19930v1 Announce Type: new Abstract: Neural surrogates for stiff differential-algebraic equations (DAEs) face two key challenges: soft-constraint methods leave algebraic residuals that stif
arXiv:2602.19470v2 Announce Type: replace Abstract: 3D imaging of specular surfaces remains challenging in real-world scenarios, such as in-line inspection or hand-held scanning, requiring fast and ac
arXiv:2604.20594v1 Announce Type: new Abstract: Retinal laser speckle contrast imaging (LSCI) is a noninvasive optical modality for monitoring retinal blood flow dynamics. However, conventional tempor
arXiv:2602.07044v2 Announce Type: replace-cross Abstract: Pipeline integrity is critical to industrial safety and environmental protection, with Magnetic Flux Leakage (MFL) detection being a primary n
arXiv:2603.21373v2 Announce Type: replace-cross Abstract: In-context learning (ICL) adapts large language models by conditioning on a small set of ICL examples, avoiding costly parameter updates. Amon
arXiv:2604.20834v1 Announce Type: new Abstract: Recent advances in Vision-Language-Action (VLA) models have opened new avenues for robot manipulation, yet existing methods exhibit limited efficiency a
arXiv:2604.19773v1 Announce Type: cross Abstract: The construction of CAD models has traditionally relied on labor-intensive manual operations and specialized expertise. Recent advances in large langu
arXiv:2604.20145v1 Announce Type: cross Abstract: Cloud data warehouses bill compute based on slot-time consumed. In shared multi-tenant environments, query cost is highly variable and hard to estimat
arXiv:2604.19795v1 Announce Type: new Abstract: We introduce prism{} (extbf{P}robabilistic extbf{R}etrieval with extbf{I}nformation-extbf{S}tratified extbf{M}emory), an evolutionary memory substrate f
arXiv:2604.20486v1 Announce Type: new Abstract: Training multimodal agents via reinforcement learning for knowledge-intensive visual reasoning is fundamentally hindered by the extreme sparsity of outc
arXiv:2604.20301v1 Announce Type: cross Abstract: We consider the problem of sampling from a probability distribution pi. It is well known that this can be written as an optimisation problem over the
arXiv:2501.00112v2 Announce Type: replace Abstract: This work proposes QuadPiPS, a perception-informed framework for quadrupedal foothold planning in the perception space. QuadPiPS employs a novel ego
arXiv:2604.20706v1 Announce Type: cross Abstract: With the growing synergy between deep learning and quantum computing, Quantum Neural Networks (QNNs) have emerged as a promising paradigm by leveragin
arXiv:2602.07085v2 Announce Type: replace-cross Abstract: Financial markets are noisy and non-stationary, making alpha mining highly sensitive to noise in backtesting results and sudden market regime
arXiv:2504.05336v3 Announce Type: replace-cross Abstract: Integrating quantum computing into deep learning architectures is a promising but poorly understood endeavor: when does a quantum layer actual
arXiv:2202.07980v4 Announce Type: replace-cross Abstract: We investigate practical algorithms for inconsistency-tolerant query answering over prioritized knowledge bases, which consist of a logical th
arXiv:2604.20696v1 Announce Type: new Abstract: Large vision-language models (LVLMs) have demonstrated impressive performance in various multimodal understanding and reasoning tasks. However, they sti
arXiv:2604.20316v1 Announce Type: new Abstract: Function calling empowers large language models (LLMs) to interface with external tools, yet existing RL-based approaches suffer from misalignment betwe
arXiv:2604.19823v1 Announce Type: cross Abstract: Rabies remains a major public health concern across many African and Asian countries, where accurate diagnosis is critical for effective epidemiologic
arXiv:2604.19962v1 Announce Type: new Abstract: Rotating FMCW radar odometry methods often assume flat ground conditions. While this assumption is sufficient in many scenarios, including urban environ
arXiv:2604.20256v1 Announce Type: new Abstract: A common strategy in transfer learning is few shot fine-tuning, but its success is highly dependent on the quality of samples selected as training examp
arXiv:2604.20474v1 Announce Type: new Abstract: The points on the point clouds that can entirely outline the shape of the model are of critical importance, as they serve as the foundation for numerous
arXiv:2604.20000v1 Announce Type: new Abstract: Automated wildlife monitoring from aerial imagery is vital for conservation but remains limited by two persistent challenges: the difficulty of detectin
arXiv:2602.09520v2 Announce Type: replace Abstract: The Rashomon set captures the collection of models that achieve near-identical empirical performance yet may differ substantially in their decision
arXiv:2506.15518v2 Announce Type: replace Abstract: This paper presents a framework for the real-time initialization of unknown Ultra-Wideband (UWB) anchors in UWB-aided navigation systems. The method
arXiv:2508.07050v3 Announce Type: replace-cross Abstract: Large Language Model (LLM) based listwise ranking has shown superior performance in many passage ranking tasks. With the development of Large
arXiv:2502.06151v2 Announce Type: replace-cross Abstract: Recency bias is a useful inductive prior for sequential modeling: it emphasizes nearby observations and can still allow longer-range dependenc
arXiv:2604.20543v1 Announce Type: new Abstract: Referring detection refers to locate the target referred by natural languages, which has recently attracted growing research interests. However, existin
arXiv:2604.20775v1 Announce Type: new Abstract: Trajectory Inference (TI) seeks to recover latent dynamical processes from snapshot data, where only independent samples from time-indexed marginals are
arXiv:2604.20805v1 Announce Type: cross Abstract: The value alignment problem for artificial intelligence (AI) is often framed as a purely technical or normative challenge, sometimes focused on hypoth
arXiv:2604.20730v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have shown promising capabilities in generating Scalable Vector Graphics (SVG) via direct code synthesis. Howev
arXiv:2604.20024v1 Announce Type: new Abstract: We study replicable algorithms for stochastic multi-armed bandits (MAB) and linear bandits with UCB (Upper Confidence Bound) based exploration. A bandit
arXiv:2510.01706v2 Announce Type: replace-cross Abstract: Standard representational similarity methods align each layer of a network to its best match in another independently, producing asymmetric re
arXiv:2604.19838v1 Announce Type: new Abstract: Understanding how road users resolve space-sharing conflicts is important both for traffic safety and the safe deployment of autonomous vehicles. While
arXiv:2604.20738v1 Announce Type: new Abstract: We introduce a benchmark dataset for question answering and translation in bilingual Latin and English settings, containing about 7,800 question-answer
arXiv:2604.20276v1 Announce Type: new Abstract: The analysis of neural representation has become an integral part of research aiming to better understand the inner workings of neural networks. While t
arXiv:2604.19857v1 Announce Type: cross Abstract: Reinforcement fine-tuning with verifiable rewards (RLVR) has emerged as a powerful paradigm for equipping large vision-language models (LVLMs) with ag
arXiv:2604.20258v1 Announce Type: new Abstract: Instruction-based image editing (IIE) aims to modify images according to textual instructions while preserving irrelevant content. Despite recent advanc
arXiv:2602.08580v2 Announce Type: replace-cross Abstract: Automatic extraction of retinal vascular biomarkers from color fundus images (CFI) is crucial for large-scale studies of the retinal vasculatu
arXiv:2603.07076v2 Announce Type: replace Abstract: Underwater images often suffer from severe degradation caused by light absorption and scattering, leading to color distortion, low contrast and redu
arXiv:2510.14274v2 Announce Type: replace Abstract: Training effective multilingual embedding models presents unique challenges due to the diversity of languages and task objectives. Although small mu
arXiv:2601.08558v2 Announce Type: replace Abstract: Incomplete point clouds captured by 3D sensors often result in the loss of both geometric and semantic information. Most existing point cloud comple
arXiv:2506.22598v3 Announce Type: replace Abstract: Agents based on Large Language Models (LLMs) have shown promise for performing sophisticated software engineering tasks autonomously. In addition, t
arXiv:2604.20147v1 Announce Type: cross Abstract: Data-driven decision-making under uncertainty typically presumes the collection of historical data from an unknown target probability distribution. Ho
arXiv:2603.25132v2 Announce Type: replace Abstract: Robust principal component analysis (RPCA) seeks a low-rank component and a sparse component from their summation. Yet, in many applications of inte
arXiv:2604.20403v1 Announce Type: new Abstract: Fault location in distribution grids is critical for reliability and minimizing outage durations. Yet, it remains challenging due to partial observabili
arXiv:2506.02618v2 Announce Type: replace-cross Abstract: Understanding and predicting articulated actions is important in robot learning. However, common architectures such as MLPs and Transformers l
arXiv:2604.20623v1 Announce Type: cross Abstract: Traditional change detection identifies where changes occur, but does not explain what changed in natural language. Existing remote sensing change cap
arXiv:2604.20146v1 Announce Type: cross Abstract: Grounded Multimodal Named Entity Recognition (GMNER) aims to extract named entities and localize their visual regions within image-text pairs, serving