Knowledge catalogue
arXiv:2507.07986v3 Announce Type: replace-cross Abstract: We study the problem of training and fine-tuning expressive policies with online reinforcement learning (RL) given an offline dataset. Trainin
arXiv:2604.28036v1 Announce Type: new Abstract: Exponential families encompass the distributions central to modern machine learning -- softmax, Gaussians, and Boltzmann distributions -- and underlie t
arXiv:2604.27669v1 Announce Type: new Abstract: The incorporation of fairness into the distribution network (DN) planning and operation has become a key goal of recent studies. The cost of implementin
arXiv:2604.27590v1 Announce Type: new Abstract: Recent advances in 3D reconstruction and neural rendering,particularly 3D Gaussian Splatting, make it feasible and simple to edit 3D scenes and re-rende
arXiv:2604.28016v1 Announce Type: new Abstract: 3D Gaussian Splatting has emerged as a powerful scene representation for real-time novel-view synthesis. However, its standard adaptive density control
arXiv:2501.04066v2 Announce Type: replace Abstract: As a special type of multimedia data, Lithography Hotspot Detection (LHD) training often requires stronger privacy protection than conventional mult
arXiv:2604.28024v1 Announce Type: new Abstract: Federated Multi-Label Learning is a distributed paradigm where multiple clients possess heterogeneous multi-label data and perform collaborative learnin
arXiv:2604.27014v1 Announce Type: new Abstract: The scarcity of high-quality annotated medical data, particularly in mental health, poses a significant bottleneck for training robust machine learning
arXiv:2604.28102v1 Announce Type: new Abstract: Solving practical multi-depot vehicle routing problems (MDVRP) is a challenging optimization task central to modern logistics, increasingly driven by e-
arXiv:2601.06992v2 Announce Type: replace-cross Abstract: Financial question answering (QA) over long corporate filings requires evidence to satisfy strict constraints on entities, financial metrics,
arXiv:2506.02515v4 Announce Type: replace-cross Abstract: Multi-step symbolic reasoning is essential for robust financial analysis; yet, current benchmarks largely overlook this capability. Existing d
arXiv:2604.27974v1 Announce Type: new Abstract: Despite the rapid progress of large vision-language models (LVLMs), fine-grained, state-conditioned GUI interaction remains challenging. Current evaluat
arXiv:2604.26976v1 Announce Type: cross Abstract: We study the problem of fitting a description logic (DL) ontology to a given set of positive and negative examples that take the form of an ABox and a
arXiv:2506.07180v3 Announce Type: replace-cross Abstract: As video large language models (Video-LLMs) become increasingly integrated into real-world applications that demand grounded multimodal reason
arXiv:2604.28156v1 Announce Type: cross Abstract: We present FlexiTac, a low-cost, open-source, and scalable piezoresistive tactile sensing solution designed for robotic end-effectors. FlexiTac is a p
arXiv:2604.02715v2 Announce Type: replace Abstract: Mixture-of-Experts (MoE) models have become a dominant paradigm for scaling large language models, but their rapidly growing parameter sizes introdu
arXiv:2604.27935v1 Announce Type: new Abstract: This paper presents an expert-guided active-inference-inspired framework for adaptive UAV swarm trajectory planning. The proposed method converts multi-
arXiv:2604.27510v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, yet its performance deteriorates und
arXiv:2510.20933v2 Announce Type: replace-cross Abstract: Medical image segmentation is essential for clinical applications such as disease diagnosis, treatment planning, and disease development monit
arXiv:2604.27807v1 Announce Type: new Abstract: The design of embedded safety-critical systems such as those used in next-generation automotive and autonomous platforms, is increasingly challenged by
arXiv:2503.04956v2 Announce Type: replace-cross Abstract: Time series forecasts are widely used to inform decisions. Human decision-makers interpret these forecasts, incorporate prior experience and u
arXiv:2306.10407v3 Announce Type: replace-cross Abstract: Inverse reinforcement learning (IRL) is a powerful paradigm for uncovering the incentive structure that drives agent behavior, by inferring an
arXiv:2604.28057v1 Announce Type: new Abstract: As autonomous vehicles slowly deploy into urban roads for limited use cases with significant edge case issues, closed facilities like marshaling yards p
arXiv:2604.28115v1 Announce Type: cross Abstract: Existing learning-based occupancy prediction methods rely on large-scale 3D annotations and generalize poorly across environments. We present FreeOcc,
arXiv:2505.22701v3 Announce Type: replace Abstract: A major challenge in rare animal image classification is the scarcity of data, as many species usually have only a small number of labeled samples.
arXiv:2604.27875v1 Announce Type: new Abstract: AI-generated images are becoming increasingly realistic and diverse, posing significant challenges for generalizable detection. While Vision Foundation
arXiv:2502.07645v3 Announce Type: replace Abstract: Behavior cloning (BC) optimizes policies by treating human demonstrations as pointwise action labels. While effective with accurate action labels, t
arXiv:2604.27453v1 Announce Type: new Abstract: Large language models have achieved remarkable progress in text generation but still struggle with generative writing tasks. In terms of evaluation, exi
arXiv:2602.04572v2 Announce Type: replace Abstract: While Generative AI (GenAI) systems draw users away from (Q&A) forums, they also depend on the very data those forums produce to improve their perfo
arXiv:2604.27660v1 Announce Type: new Abstract: Many real-world tasks require language models (LMs) to reason over complex contexts that exceed their parametric knowledge. This calls for context learn
arXiv:2604.27972v1 Announce Type: new Abstract: Since the dawn of Trading Card Games, the genre has grown into a multi-billion-dollar industry engaging millions of analog and digital players worldwide
arXiv:2604.27969v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) are increasingly used to translate visual artifacts into code, from UI mockups into HTML to scientific plots
arXiv:2604.27267v1 Announce Type: cross Abstract: As large language models are integrated into autonomous robotic systems for task planning and control, compromised inputs or unsafe model outputs can
arXiv:2510.05192v2 Announce Type: replace-cross Abstract: When AI agents operating with access to sensitive information encounter a conflict between completing an assigned task and following rules or
arXiv:2505.17056v2 Announce Type: replace-cross Abstract: As large language models (LLMs) are increasingly integrated into educational tools, current evaluations on standardized tests predominantly fo
arXiv:2604.27906v1 Announce Type: new Abstract: Persistent AI memory is often reduced to a retrieval problem: store prior interactions as text, embed them, and ask the model to recover relevant contex
arXiv:2604.27410v1 Announce Type: cross Abstract: Entity search, i.e., finding the most similar entities to a query entity, faces unique challenges in e-commerce, where product similarity varies acros
arXiv:2604.27653v1 Announce Type: new Abstract: Conventional push-broom hyperspectral imaging suffers from slow acquisition speeds, precluding real-time object detection; in contrast, snapshot spectra
arXiv:2604.27557v1 Announce Type: new Abstract: Despite advances in dexterous hand manipulation, robotic hand design is still largely decoupled from task-driven evaluation and control, limiting system
arXiv:2604.27353v1 Announce Type: new Abstract: Gait recognition has emerged as a compelling biometric modality for surveillance and security applications, offering inherent advantages such as non-int
arXiv:2601.19768v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly paired with activation-based monitoring to detect and prevent harmful behaviors that may not be appare
arXiv:2502.14698v2 Announce Type: replace-cross Abstract: Decision makers may suffer from uncertainty induced by limited data. This may be mitigated by accounting for epistemic uncertainty, which is h
arXiv:2604.28193v1 Announce Type: new Abstract: Reconstructing 3D scenes from sparse, unposed images remains challenging under real-world conditions with varying illumination and transient occlusions.
arXiv:2604.27155v1 Announce Type: new Abstract: Model merging aims to combine multiple models into one without additional training. Naive parameter-space averaging can be fragile under architectural s
arXiv:2604.27918v1 Announce Type: new Abstract: Existing talking avatar methods typically adopt an image-to-video pipeline conditioned on a static reference image within the same scene as the target g
arXiv:2503.01448v5 Announce Type: replace Abstract: Realistic human geometry generation is an important yet challenging task, requiring both the preservation of fine clothing details and the accurate
arXiv:2604.27636v1 Announce Type: new Abstract: Predicting stable and metastable structures is central to molecular and materials discovery, but remains limited by the cost of searching high-dimension
arXiv:2604.27914v1 Announce Type: new Abstract: When language models lack relevant knowledge for a given query, they frequently generate plausible responses that can be hallucinations, rather than adm
arXiv:2604.28144v1 Announce Type: new Abstract: Efficient exploration is a central problem in reinforcement learning and is often formalized as maximizing the entropy of the state-action occupancy mea
arXiv:2604.27175v1 Announce Type: new Abstract: Contact-rich manipulation is challenging due to its high dimensionality, the requirement for long time horizons, and the presence of hybrid contact dyna
arXiv:2603.25385v2 Announce Type: replace-cross Abstract: Quantization techniques such as BitsAndBytes, AWQ, and GPTQ are widely used as a standard method in deploying large language models but often
arXiv:2604.27764v1 Announce Type: new Abstract: Mango cultivation is crucial in the agricultural sector, significantly contributing to economic development and food security. However, diseases affecti
arXiv:2503.01817v2 Announce Type: replace Abstract: A fundamental challenge in neurosymbolic systems is applying continuous gradient-based optimization to discrete logical domains. While fuzzy relaxat
arXiv:2411.18796v2 Announce Type: replace Abstract: Early diagnosis and discovery of therapeutic drug targets are crucial objectives for effective management of Alzheimer's Disease (AD). Current appro
arXiv:2604.27895v1 Announce Type: new Abstract: As one of the mainstream models of artificial intelligence, world models allow agents to learn the representation of the environment for efficient predi
arXiv:2509.16248v3 Announce Type: replace-cross Abstract: This paper presents GRAPHMEND, a high-level compiler technique that eliminates FX graph breaks in PyTorch 2 programs. Although PyTorch 2 intro
arXiv:2602.11084v2 Announce Type: replace-cross Abstract: Feature selection remains a major challenge in medical prediction, where existing approaches such as LASSO often lack robustness and interpret
arXiv:2604.27638v1 Announce Type: new Abstract: Machine learning continues to emerge as an important tool to be utilised within structural engineering and structural health monitoring, due to its abil
arXiv:2601.10702v2 Announce Type: replace-cross Abstract: Deploying large language models in long-horizon, goal-oriented interactions remains challenging because similar entities and facts recur under
arXiv:2604.28111v1 Announce Type: new Abstract: End-to-end (E2E) autonomous driving presents a promising approach for translating perceptual inputs directly into driving actions. However, prohibitive