Knowledge catalogue
arXiv:2605.08862v1 Announce Type: cross Abstract: Reinforcement Learning (RL) has become a cornerstone for improving the performance of Large Language Models (LLMs). However, its rollout phase constit
arXiv:2605.10598v1 Announce Type: new Abstract: Large language models (LLMs) have emerged as powerful tools for automatic algorithm design (AAD). However, existing pipelines remain inefficient. They o
arXiv:2605.10241v1 Announce Type: new Abstract: Natural language understanding (NLU) is integral to task-oriented dialog systems, but demands a considerable amount of annotated training data to increa
arXiv:2605.08404v1 Announce Type: cross Abstract: This work investigates the use of large language models (LLMs) for tasks in smart cities. The core idea is to leverage remote sensing imagery to chara
arXiv:2605.10661v1 Announce Type: cross Abstract: Vision Transformers (ViTs) are built by stacking independently parameterized blocks, but it remains unclear how much of this depth requires layer spec
arXiv:2605.09611v1 Announce Type: new Abstract: This preprint presents an empirical analysis of byte-exact chunk-level deduplication in Retrieval-Augmented Generation (RAG) pipelines. We measure conte
arXiv:2605.10744v1 Announce Type: new Abstract: Safety-critical planning in complex environments, particularly at urban intersections, remains a fundamental challenge for autonomous driving. Existing
arXiv:2605.08653v1 Announce Type: new Abstract: Accurate state-of-charge (SOC) estimation is critical for the safe and efficient operation of lithium-ion batteries in battery management systems (BMS).
arXiv:2504.21228v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are susceptible to indirect prompt injection attacks, where the model inadvertently responds to instructions inje
arXiv:2605.10873v1 Announce Type: cross Abstract: Recovering editable CAD programs from images or 3D observations is central to AI-assisted design, but progress is difficult to measure because existin
arXiv:2605.09279v1 Announce Type: cross Abstract: Volumetric video (VV) streaming enables real-time, immersive access to remote 3D environments, powering telepresence, ecological monitoring, and robot
arXiv:2605.09823v1 Announce Type: cross Abstract: We introduce CalBench, a controlled evaluation environment for studying multi-agent coordination through calendar scheduling. In CalBench, N agents ea
arXiv:2605.09245v1 Announce Type: new Abstract: Multi-camera multi-object tracking (MCMOT) faces significant challenges in maintaining consistent object identities across varying camera perspectives,
arXiv:2605.09702v1 Announce Type: cross Abstract: Multi-judge evaluation is increasingly used to assess LLMs and reward models, and the prevailing heuristic is to curate: keep the most accurate judges
arXiv:2605.09737v1 Announce Type: new Abstract: Modern large language models (LLMs) rely on system prompts to establish behavioral constraints and safety rules. Standard causal self-attention treats p
arXiv:2605.08325v1 Announce Type: cross Abstract: Many vision datasets now provide segmentation masks in addition to annotated images to support a wide range of tasks. In this work, we propose Class A
arXiv:2605.10448v1 Announce Type: new Abstract: Interactive agent benchmarks map an agent run to a binary outcome through outcome checks. When these checks rely on surface level signals or fail to cap
arXiv:2601.12369v3 Announce Type: replace Abstract: Deep Research Agents increasingly automate survey generation, yet whether they match human experts at retrieving essential papers and organizing the
arXiv:2605.10419v1 Announce Type: cross Abstract: This paper investigates the effectiveness of large language models (LLMs) in answering questions over datasets. We examine their performance in two sc
arXiv:2605.08439v1 Announce Type: new Abstract: Accurately communicating the side effects of cancer treatments to cancer survivors is critical, particularly in settings such as informed consent, where
arXiv:2512.18880v2 Announce Type: replace-cross Abstract: Accurate estimation of item (question or task) difficulty is critical for educational assessment but suffers from the cold start problem. Whil
arXiv:2605.08255v1 Announce Type: cross Abstract: Can large language models predict physical and mechanical polymer properties simply by reading unstructured scientific prose? Polymer performance is r
arXiv:2605.08965v1 Announce Type: new Abstract: Despite strong performance of Multimodal Large Language Models (MLLMs) on multimodal tasks, predicting whether and why an image is persuasive remains ch
arXiv:2605.10468v1 Announce Type: new Abstract: Muon has emerged as an efficient alternative to Adam for pretraining, yet remains underused for fine-tuning. A key obstacle is that most open models are
arXiv:2605.08556v1 Announce Type: new Abstract: LLMs are increasingly used to make or support high-stakes decisions under uncertainty, where alignment depends not only on factual accuracy but on how m
arXiv:2605.06638v2 Announce Type: replace Abstract: Reinforcement learning (RL) has been applied to improve large language model (LLM) reasoning, yet the systematic study of how training scales with t
arXiv:2605.08938v1 Announce Type: new Abstract: Fourier Neural Operators (FNOs) can greatly accelerate PDE simulation, but they are often used without formal guarantees that they preserve basic physic
arXiv:2512.06949v3 Announce Type: replace Abstract: Histopathology image segmentation is essential for delineating tissue structures in skin cancer diagnostics, but modeling spatial context and inter-
arXiv:2605.09634v1 Announce Type: new Abstract: LLMs can estimate Hospital Anxiety and Depression Scale (HADS) scores from speech in a zero-shot manner, but clinical deployment requires reliability ac
arXiv:2605.10794v1 Announce Type: cross Abstract: Language models are deployed in settings that require compartmentalization: system prompts should not be disclosed, chain-of-thought reasoning is hidd
arXiv:2605.09795v1 Announce Type: new Abstract: This paper presents our systems and results for the Hope Speech Detection in Code-Mixed Tulu Language shared task at the Sixth Workshop on Speech, Visio
arXiv:2503.05066v5 Announce Type: replace-cross Abstract: The Mixture of Experts (MoE) is an effective architecture for scaling large language models by leveraging sparse expert activation to balance
arXiv:2605.08493v1 Announce Type: new Abstract: Wireless capsule endoscopy (WCE) enables non-invasive visual assessment of the small bowel, but its clinical utility is constrained by the large volume
arXiv:2605.10903v1 Announce Type: new Abstract: This paper proposes a novel approach to address the challenge that pretrained VLA models often fail to effectively improve performance and reduce adapta
arXiv:2512.04949v3 Announce Type: replace-cross Abstract: Agents capable of accomplishing complex tasks through multiple interactions with the environment have emerged as a popular research direction.
Silencio leveraged Vultr's full-stack infrastructure platform to optimize their operations, likely achieving improvements in performance, scalability, or cost efficiency through Vultr's cloud computin
arXiv:2605.08173v1 Announce Type: new Abstract: The generalization performance (GP) of deep learning-based arbitrary-scale image super-resolution (ASISR) methods is subject to limited training dataset
arXiv:2605.08663v1 Announce Type: new Abstract: We propose CAST, a dual-stream architecture that utilizes channel-aware spatial transfer learning for isolated sign language recognition addressing the
arXiv:2605.09016v1 Announce Type: new Abstract: Neural operators have emerged as powerful data-driven solvers for PDEs, offering substantial acceleration over classical numerical methods. However, exi
arXiv:2605.09223v1 Announce Type: new Abstract: Understanding long videos with multimodal large language models (MLLMs) requires selecting a small subset of informative frames under strict computation
arXiv:2605.08740v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) decompose transformer residual streams into interpretable feature dictionaries, yet the relationship between SAE width and
arXiv:2507.03310v2 Announce Type: replace-cross Abstract: This paper studies causal discovery in irregularly sampled time series-a key challenge in risk-sensitive domains like finance, healthcare, and
arXiv:2603.02678v3 Announce Type: replace Abstract: This paper argues for recognizing an emerging paradigm of causal learning by wisdom of the crowd. Recent developments in government, industry, and r
arXiv:2605.10396v1 Announce Type: new Abstract: Neural networks have proved an effective means of learning control policies for autonomous systems, but these learned policies are difficult to understa
arXiv:2605.09663v1 Announce Type: cross Abstract: Machine learning classifiers in dynamic environments face concept drift -- changes in the data-generating process that degrade performance. Convention
arXiv:2605.08590v1 Announce Type: cross Abstract: LLMs are increasingly used to explain personal sensing data, translating traces of activity and mood into natural-language accounts of why an anomalou
arXiv:2605.10586v1 Announce Type: new Abstract: Learning a physical model from video data that can comprehend physical laws and predict the future trajectories of objects is a formidable challenge in
arXiv:2605.09079v1 Announce Type: new Abstract: Despite surpassing human performance across mathematics, coding, and other knowledge-intensive tasks, large language models (LLMs) continue to struggle
arXiv:2605.10473v1 Announce Type: cross Abstract: We introduce a cavity-enhanced optical architecture for collective quantum processing in which logical qubits are encoded in the polarization subspace
arXiv:2605.08333v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) is sensitive to the vast hyperparameters of the retriever and generator, yet optimizing them using given queries
arXiv:2605.10362v1 Announce Type: new Abstract: Training AI models for computational pathology currently requires access to expensive whole-slide-image datasets, GPU infrastructure, deep expertise in
arXiv:2605.08417v1 Announce Type: new Abstract: Designing model-free algorithms for distributionally robust reinforcement learning (DRRL) poses fundamental challenges. The robust Bellman operator is n
arXiv:2605.08174v1 Announce Type: cross Abstract: To mitigate the memory constraints associated with fine-tuning large pre-trained models, existing parameter-efficient fine-tuning (PEFT) methods, such
arXiv:2605.10111v1 Announce Type: cross Abstract: Motor imagery electroencephalography (MI-EEG) decoding offers a non-invasive route for post-stroke rehabilitation, but cross-patient use remains diffi
arXiv:2605.09350v1 Announce Type: new Abstract: Smart-contract exploits have caused billions of USD in cumulative losses, yet audits remain expensive and slow. Automated tools have emerged to close th
arXiv:2603.25777v2 Announce Type: replace-cross Abstract: There is great potential for the application of AI tools in fusion research, and substantial worldwide benefit if fusion power is realised. Ho
arXiv:2605.08383v1 Announce Type: new Abstract: Philosophical accounts of persuasion often assume that shared evidence and rational argumentation should lead to a convergence of views between peers, y
arXiv:2605.09676v1 Announce Type: cross Abstract: Spatio-temporal graph neural networks (STGNNs) are widely used for short-term forecasting in dynamic physical systems such as traffic and weather. How
arXiv:2605.09092v1 Announce Type: new Abstract: This study addresses automatic transliteration from Tajik (Cyrillic script) to Persian (Perso-Arabic script). We present a curated, lexicographically ve
arXiv:2605.10290v1 Announce Type: cross Abstract: This paper aims at analyzing the regularization effect that data augmentation induces on supervised regression methods in the proportional regime, whe