Knowledge catalogue
arXiv:2604.20172v1 Announce Type: new Abstract: Consider betting against a sequence of data in [0,1], where one is allowed to make any bet that is fair if the data have a conditional mean m_0 in (0,1)
arXiv:2604.20763v1 Announce Type: cross Abstract: Retrieval quality is the primary bottleneck for accuracy and robustness in retrieval-augmented generation (RAG). Current evaluation relies on heuristi
arXiv:2604.19941v1 Announce Type: new Abstract: Reliable crack detection and segmentation are vital for structural health monitoring, yet the scarcity of well-annotated data constitutes a major challe
arXiv:2505.14984v2 Announce Type: replace Abstract: Open-Domain Table Question Answering (TQA) involves retrieving relevant tables from a large corpus to answer natural language queries. Traditional d
arXiv:2604.19926v1 Announce Type: new Abstract: Large language models can generate plausible game code, but turning this capability into iterative creative improvement remains difficult. In practice,
arXiv:2603.07474v2 Announce Type: replace-cross Abstract: What is the interplay between semantic representations learned by language models (LM) from surface form alone to those learned from more grou
arXiv:2505.04897v2 Announce Type: replace-cross Abstract: Interactive imitation learning makes an agent's control policy robust by stepwise supervisions from an expert. The recent algorithms mostly em
arXiv:2510.21464v2 Announce Type: replace Abstract: Deep learning models have achieved remarkable accuracy in chest X-ray diagnosis, yet their widespread clinical adoption remains limited by the black
arXiv:2604.20389v1 Announce Type: cross Abstract: The rapid evolution and use of Large Language Models (LLMs) in professional workflows require an evaluation of their domain-specific knowledge against
arXiv:2604.20771v1 Announce Type: cross Abstract: The Internet of Vehicles (IoV) is advancing modern transportation by improving safety, efficiency, and intelligence. However, the reliance on the Cont
arXiv:2604.20492v1 Announce Type: cross Abstract: In this paper, it is shown, for the first time, that centralized performance is achievable in decentralized learning without sharing the local dataset
arXiv:2604.20031v1 Announce Type: cross Abstract: We consider what we refer to as {Decision-Focused Federated Learning (DFFL)} framework, i.e., a predict-then-optimize approach employed by a collectio
arXiv:2604.20447v1 Announce Type: new Abstract: Named Entity Recognition (NER) is a key component in industrial information extraction pipelines, where systems must satisfy strict latency and throughp
arXiv:2604.19845v1 Announce Type: new Abstract: Self-modification is often taken as constitutive of artificial superintelligence (SI), yet modification is a relative action requiring a supplement outs
arXiv:2603.26692v3 Announce Type: replace-cross Abstract: We introduce a new notion, that of a contextuality profile of a system of random variables. Rather than characterizing a system's contextualit
arXiv:2604.19887v1 Announce Type: cross Abstract: Depression is one of the most prevalent and debilitating mental health conditions worldwide, frequently underdiagnosed and undertreated. The prolifera
arXiv:2604.19776v1 Announce Type: new Abstract: Tuberculosis (TB) is one of the world's deadliest infectious diseases, and in South Africa, it contributes a significant burden to the country's health
arXiv:2604.20841v1 Announce Type: new Abstract: Recent advances in video generative models enable the synthesis of realistic human-object interaction videos across a wide range of scenarios and object
arXiv:2601.00911v3 Announce Type: replace-cross Abstract: Automated negotiations in insurance and business-to-business (B2B) commerce encounter substantial challenges. Current systems force a trade-of
arXiv:2604.20811v1 Announce Type: new Abstract: As LLMs are increasingly integrated into agentic systems, they must adhere to dynamically defined, machine-interpretable interfaces. We evaluate LLMs as
arXiv:2604.19798v1 Announce Type: cross Abstract: Micro-scale street-level economic assessment is fundamental for precision spatial resource allocation. While Street View Imagery (SVI) advances urban
arXiv:2604.20443v1 Announce Type: cross Abstract: Large Language Models (LLMs) have been shown to possess Theory of Mind (ToM) abilities. However, it remains unclear whether this stems from robust rea
arXiv:2604.20098v1 Announce Type: new Abstract: Large Language Models (LLMs) frequently hallucinate, limiting their reliability in critical applications. Conformal Prediction (CP) addresses this by ca
arXiv:2604.20596v1 Announce Type: new Abstract: Federated learning (FL) enables training of a global model while keeping raw data on end-devices. Despite this, FL has shown to leak private user inform
arXiv:2511.17265v2 Announce Type: replace-cross Abstract: Nowadays, we are witnessing an Artificial Intelligence revolution that dominates the technology landscape in various application domains, such
arXiv:2604.19966v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly used in settings where sensitivity to low-level image degradations matters, including content moderatio
arXiv:2510.03013v3 Announce Type: replace Abstract: We propose a distributional framework for offline Inverse Reinforcement Learning (IRL) that jointly models uncertainty over reward functions and ful
arXiv:2604.20381v1 Announce Type: new Abstract: Quality-Diversity (QD) algorithms excel at discovering diverse repertoires of skills, but are hindered by poor sample efficiency and often require tens
arXiv:2604.19765v1 Announce Type: cross Abstract: Recent work identifies a sparse set of 'hallucination neurons' (H-neurons), less than 0.1% of feed-forward network neurons, that reliably predict when
arXiv:2604.19781v1 Announce Type: cross Abstract: Automated scoring of student work at scale requires balancing accuracy against cost and latency. In 'cascade' systems, small language models (LMs) han
arXiv:2604.01965v2 Announce Type: replace-cross Abstract: Scientific knowledge discovery increasingly relies on large language models, yet many existing scholarly assistants depend on proprietary syst
arXiv:2604.19859v1 Announce Type: cross Abstract: Edge-scale deep research agents based on small language models are attractive for real-world deployment due to their advantages in cost, latency, and
arXiv:2603.00696v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used as reasoning engines in autonomous driving, yet their decision-making remains opaque. We propose
arXiv:2604.20306v1 Announce Type: cross Abstract: Medical Visual Question Answering (MedVQA) aims to generate clinically reliable answers conditioned on complex medical images and questions. However,
arXiv:2604.20183v1 Announce Type: new Abstract: Large Language Models (LLMs) often struggle with structural ambiguity in optimization problems, where a single problem admits multiple related but confl
arXiv:2604.20168v1 Announce Type: new Abstract: This paper presents the Duluth approach to SemEval-2026 Task 6 on CLARITY: Unmasking Political Question Evasions. We address Task 1 (clarity-level class
arXiv:2604.20470v1 Announce Type: new Abstract: Leveraging the natural spatiotemporal energy decay in video diffusion offers a path to efficiency, yet relying solely on rigid static masks risks losing
arXiv:2604.20523v1 Announce Type: cross Abstract: We study whether Large Language Models (LLMs) can perform feature model analysis operations (AOs) directly on semi-formal textual blueprints, i.e., co
arXiv:2604.20531v1 Announce Type: new Abstract: This paper investigates Multilingual Medical Question Answering across high-resource (English, Spanish, French, Italian) and low-resource (Basque, Kazak
arXiv:2604.20291v1 Announce Type: new Abstract: Efficient single-image super-resolution (SISR) requires balancing reconstruction fidelity, model compactness, and robustness under low-bit deployment, w
arXiv:2604.20777v1 Announce Type: new Abstract: In streaming platforms churn is extremely costly, yet A/B tests are typically evaluated using outcomes observed within a limited experimental horizon. E
arXiv:2604.19980v1 Announce Type: new Abstract: This paper presents a model-based reinforcement learning (RL) framework for optimal closed-loop control of nonlinear robotic systems. The proposed appro
arXiv:2604.20516v1 Announce Type: cross Abstract: Determining identifiability of causal effects from observational data under latent confounding is a central challenge in causal inference. For linear
arXiv:2604.20500v1 Announce Type: new Abstract: Self-consistency boosts inference-time performance by sampling multiple reasoning traces in parallel and voting. However, in constrained domains like ma
arXiv:2511.06209v4 Announce Type: replace Abstract: LLMs can solve complex tasks by generating long, multi-step reasoning chains. Test-time scaling (TTS) can further improve LLM performance by samplin
arXiv:2501.07399v2 Announce Type: replace Abstract: Consistent maps are key for most autonomous mobile robots, and they often use SLAM approaches to build such maps. Loop closures via place recognitio
arXiv:2604.20012v1 Announce Type: cross Abstract: Vision-Language-Action Models (VLAs) inherit their visual and linguistic capabilities from Vision-Language Models (VLMs), yet most VLAs are built from
arXiv:2604.19816v1 Announce Type: new Abstract: The Transformer, a breakthrough architecture in artificial intelligence, owes its success to the attention mechanism, which utilizes long-range interact
arXiv:2604.20083v1 Announce Type: cross Abstract: Active learning (AL) has emerged as a crucial methodology for minimizing labeling costs in deep learning by selecting the most valuable samples from a
arXiv:2604.19797v1 Announce Type: cross Abstract: Automatic Speech Recognition (ASR) for low-resource Dravidian languages like Telugu and Kannada faces significant challenges in specialized medical do
arXiv:2604.20548v1 Announce Type: cross Abstract: Scientific progress depends on the continual generation of innovative re-search ideas. However, the rapid growth of scientific literature has greatly
arXiv:2604.20229v1 Announce Type: cross Abstract: Speaker verification is a task of confirming an individual's identity through the analysis of their voice. Whispered speech differs from phonated spee
arXiv:2604.19839v1 Announce Type: cross Abstract: Vision-language models (VLMs) have shown strong perception and reasoning abilities for instruction-following embodied agents. However, despite these a
arXiv:2601.14295v3 Announce Type: replace Abstract: Large language models increasingly function as artificial reasoners: they evaluate arguments, assign credibility, and express confidence. Yet their
arXiv:2601.09871v2 Announce Type: replace Abstract: Human-AI complementarity is the claim that a human supported by an AI system can outperform either alone in a decision-making process. Since its int
arXiv:2604.19779v1 Announce Type: new Abstract: Environmental, Social, and Governance (ESG) reports are central to investment decision-making, yet their length, heterogeneous content, and lack of stan
arXiv:2604.20295v1 Announce Type: new Abstract: Tactile sensors are increasingly integrated into dexterous robotic manipulators to enhance contact perception. However, learning manipulation policies t
arXiv:2604.20577v1 Announce Type: cross Abstract: An assurance case is a structured argument document that justifies claims about a system's requirements or properties, which are supported by evidence
arXiv:2603.15867v2 Announce Type: replace Abstract: The growing use of Machine Learning (ML) tools comes with critical challenges, such as limited model explainability. We propose a global explainabil
arXiv:2508.17761v3 Announce Type: replace Abstract: In safety-critical applications data-driven models must not only be accurate but also provide reliable uncertainty estimates. This property, commonl