Parameter-efficient Quantum Multi-task Learning
arXiv:2604.13560v1 Announce Type: new Abstract: Multi-task learning (MTL) improves generalization and data efficiency by jointly learning related tasks through shared representations. In the widely us
Knowledge catalogue
arXiv:2604.13560v1 Announce Type: new Abstract: Multi-task learning (MTL) improves generalization and data efficiency by jointly learning related tasks through shared representations. In the widely us
arXiv:2604.07662v2 Announce Type: replace-cross Abstract: Monotone variational inequalities (VIs) provide a unifying framework for convex minimization, equilibrium computation, and convex-concave sadd
arXiv:2604.14010v1 Announce Type: cross Abstract: Supervised Fine-Tuning (SFT) of large language models often suffers from task interference and catastrophic forgetting. Recent approaches alleviate th
arXiv:2604.13175v1 Announce Type: new Abstract: Large language models can be aligned with human preferences through offline reinforcement learning (RL) on small labeled datasets. While single-objectiv
arXiv:2511.01619v2 Announce Type: replace Abstract: ParlaSpeech is a collection of spoken parliamentary corpora currently spanning four Slavic languages - Croatian, Czech, Polish and Serbian - all tog
arXiv:2604.13918v1 Announce Type: new Abstract: We present PartNerFace, a part-based neural radiance fields approach, for reconstructing animatable facial avatar from monocular RGB videos. Existing so
arXiv:2604.13294v1 Announce Type: new Abstract: Existing video coding for machines is often trained for a specific downstream task and model. As a result, the compressed representation becomes tightly
arXiv:2604.13153v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has recently enabled highly photorealistic 3D reconstruction from casually captured multi-view images. However, this access
arXiv:2604.13791v1 Announce Type: new Abstract: Accurate lesion segmentation in ultrasound images is essential for preventive screening and clinical diagnosis, yet remains challenging due to low contr
arXiv:2604.13356v1 Announce Type: new Abstract: Mechanisms for continued self-improvement of language models without external supervision remain an open challenge. We propose Peer-Predictive Self-Trai
arXiv:2510.17043v2 Announce Type: replace Abstract: Advanced feature extraction methods have significantly contributed to enhancing the task of person re-identification. In addition, modifications to
arXiv:2604.13074v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) serve as daily assistants for millions. However, their ability to generate responses aligned with individual pr
arXiv:2604.13419v1 Announce Type: new Abstract: Noncontact exfiltration of electronic screen content poses a security challenge, with side-channel incursions as the principal vector. We introduce an o
arXiv:2604.13992v1 Announce Type: new Abstract: Accurate methane sorption prediction across heterogeneous coal ranks requires models that combine thermodynamic consistency, efficient knowledge transfe
arXiv:2604.13723v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) recast PDE solving as an optimisation problem in function space by minimising a residual-based objective, yet m
arXiv:2604.13291v1 Announce Type: new Abstract: Accurate characterization of subsurface heterogeneity is challenging but essential for applications such as reservoir pressure management, geothermal en
arXiv:2604.14054v1 Announce Type: cross Abstract: Deep search agents have emerged as a promising paradigm for addressing complex information-seeking tasks, but their training remains challenging due t
arXiv:2604.14029v1 Announce Type: new Abstract: While Large Multimodal Models (LMMs) demonstrate impressive visual perception, they remain epistemically constrained by their static parametric knowledg
arXiv:2604.13863v1 Announce Type: new Abstract: Image generation technology can synthesize condition-specific images to supplement real-world industrial anomaly data and enhance anomaly detection mode
arXiv:2510.04995v3 Announce Type: replace Abstract: Power transforms are popular parametric methods for making data more Gaussian-like, and are widely used as preprocessing steps in statistical analys
arXiv:2601.03173v2 Announce Type: replace Abstract: Time pressure critically influences risky maneuvers and crash proneness among powered two-wheeler riders, yet its prediction remains underexplored i
arXiv:2604.13986v1 Announce Type: new Abstract: Predicting the effects of perturbations in-silico on cell state can identify drivers of cell behavior at scale and accelerate drug discovery. However, m
arXiv:2604.13966v1 Announce Type: new Abstract: We study value adaptation in offline-to-online reinforcement learning under general function approximation. Starting from an imperfect offline pretraine
arXiv:2604.13395v1 Announce Type: cross Abstract: Large Reasoning Models (LRMs) have recently demonstrated significant improvements in complex reasoning. While quantifying generation uncertainty in LR
arXiv:2604.13786v1 Announce Type: new Abstract: As large language models become standard backends for content generation, practical provenance increasingly requires multi-bit watermarking. In provider
arXiv:2604.13951v1 Announce Type: new Abstract: This study evaluates colorectal risk factors and compares classical models against Quantum Neural Networks (QNNs) for anastomotic leak prediction. Analy
arXiv:2509.20490v4 Announce Type: replace-cross Abstract: Agentic systems offer a potential path to solve complex clinical tasks through collaboration among specialized agents, augmented by tool use a
arXiv:2604.13571v1 Announce Type: new Abstract: The challenge of 3D multi-object tracking (3D MOT) is achieving robustness in real-world applications, for example under adverse conditions and maintain
arXiv:2604.13492v1 Announce Type: cross Abstract: Radar is more resilient to adverse weather and lighting conditions than visual and Lidar simultaneous localization and mapping (SLAM). However, most r
arXiv:2604.05096v2 Announce Type: replace Abstract: Large language models (LLMs) acquire most of their knowledge during pretraining, which ties them to a fixed snapshot of the world and makes adaptati
arXiv:2508.05663v4 Announce Type: replace-cross Abstract: Random walk (RW)-based algorithms have long been popular in distributed systems due to low overheads and scalability, with recent growing appl
arXiv:2604.13830v1 Announce Type: cross Abstract: Integro-differential equations arise in a wide range of applications, including transport, kinetic theory, radiative transfer, and multiphysics modeli
arXiv:2505.19054v2 Announce Type: replace Abstract: Modern learning-based locomotion controllers typically rely on fully trainable deep neural networks with a large number of parameters. This paper st
arXiv:2604.13213v1 Announce Type: cross Abstract: Rare events such as conformational changes in biomolecules, phase transitions, and chemical reactions are central to the behavior of many physical sys
arXiv:2604.13735v1 Announce Type: cross Abstract: This work identifies a necessary condition for any variational quantum approach to reach the exact ground state. Briefly, the norms of the projections
arXiv:2604.13549v1 Announce Type: new Abstract: The conversion of 2D freehand sketches into 3D models remains a pivotal challenge in computer vision, bridging the gap between human creativity and digi
arXiv:2604.13730v1 Announce Type: new Abstract: Continual learning enables models to acquire new knowledge over time while retaining previously learned capabilities. However, its application to text-t
arXiv:2604.13064v1 Announce Type: new Abstract: Skill ecosystems have emerged as an increasingly important layer in Large Language Model (LLM) agent systems, enabling reusable task packaging, public d
arXiv:2601.03027v3 Announce Type: replace Abstract: Preference alignment methods such as RLHF and Direct Preference Optimization (DPO) improve instruction following, but they can also reinforce halluc
arXiv:2512.10696v2 Announce Type: replace-cross Abstract: Procedural memory enables large language model (LLM) agents to internalize 'how-to' knowledge, theoretically reducing redundant trial-and-erro
arXiv:2604.13994v1 Announce Type: new Abstract: Generative diffusion priors have recently achieved state-of-the-art performance in natural image super-resolution, demonstrating a powerful capability t
arXiv:2604.13517v1 Announce Type: new Abstract: Temporal credit assignment in reinforcement learning has long been a central challenge. Inspired by the multi-timescale encoding of the dopamine system
arXiv:2603.28942v3 Announce Type: replace Abstract: The pervasive deployment of deep learning models across critical domains has concurrently intensified privacy concerns due to their inherent propens
arXiv:2604.04101v2 Announce Type: replace Abstract: This paper investigates the Restless Multi-Armed Bandit (RMAB) framework under individual penalty constraints to address resource allocation challen
arXiv:2604.13905v1 Announce Type: new Abstract: We present SparseGen, a novel framework for efficient image-to-3D generation, which exhibits low input-view bias while being significantly faster. Unlik
arXiv:2604.13262v1 Announce Type: new Abstract: In medical image segmentation, uncertainty estimates are often reported but rarely used to guide decisions. We study the missing step: how uncertainty m
arXiv:2604.13993v1 Announce Type: cross Abstract: Physical reasoning over visual inputs demands tight integration of visual perception, domain knowledge, and multi-step symbolic inference. Yet even st
arXiv:2604.13602v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) and related alignment paradigms have become central to steering large language models (LLMs) and multi
arXiv:2604.14128v1 Announce Type: new Abstract: Rhetorical questions are asked not to seek information but to persuade or signal stance. How large language models internally represent them remains unc
arXiv:2604.13326v1 Announce Type: new Abstract: The robustness of machine learning models can be compromised by spurious correlations between non-causal features in the input data and target labels. A
arXiv:2604.13531v1 Announce Type: cross Abstract: Graphical User Interface (GUI) agents show strong capabilities for automating web tasks, but existing interactive benchmarks primarily target benign,
arXiv:2508.00222v5 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Reward (RLVR) has significantly advanced the complex reasoning abilities of Large Language Models (LLMs
arXiv:2603.22126v3 Announce Type: replace Abstract: Deploying learned robot manipulation policies in industrial settings requires rigorous pre-deployment validation, yet exhaustive testing across high
arXiv:2511.07717v2 Announce Type: replace-cross Abstract: Estimating robot pose from a monocular RGB image is a challenge in robotics and computer vision. Existing methods typically build networks on
arXiv:2604.13476v1 Announce Type: cross Abstract: Surround-view perception is increasingly important for robotic navigation and loco-manipulation, especially in human-in-the-loop settings such as tele
arXiv:2604.13441v1 Announce Type: new Abstract: Ensuring energy feasibility under wind uncertainty is critical for the safety and reliability of UAV delivery missions. In realistic truck-drone logisti
arXiv:2604.13525v1 Announce Type: cross Abstract: The robust low-rank tensor completion problem addresses the challenge of recovering corrupted high-dimensional tensor data with missing entries, outli
arXiv:2604.13833v1 Announce Type: new Abstract: Reward models are central to aligning large language models, yet they often overfit to spurious cues such as response length and overly agreeable tone.
arXiv:2604.13806v1 Announce Type: new Abstract: Large Language Models (LLMs) are widely used across many domains, but their scale makes deployment challenging. Post-Training Quantization (PTQ) reduces
arXiv:2511.14755v2 Announce Type: replace-cross Abstract: As perception-based controllers for autonomous systems become increasingly popular in the real world, it is important that we can formally ver