Knowledge catalogue
arXiv:2606.10607v1 Announce Type: cross Abstract: Causal discovery aims to uncover causal structures from observational data, which is crucial for real-world decision-making. However, different causal
arXiv:2511.02603v2 Announce Type: replace Abstract: Large language models (LLMs) are often queried multiple times at test time, with predictions aggregated by majority vote. While effective, this self
arXiv:2510.04514v3 Announce Type: replace Abstract: Recent multimodal LLMs have shown promise in chart-based visual question answering, but their performance declines sharply on unannotated charts-tho
arXiv:2606.10640v1 Announce Type: new Abstract: In this report, we present our champion solution for the DataMFM Challenge Track 2: Chart Understanding. This track requires models to recover structure
arXiv:2606.11063v1 Announce Type: new Abstract: AI control protocols oversee untrusted models by monitoring their actions and modifying potentially unsafe steps, often using a trusted model. This part
arXiv:2503.24007v4 Announce Type: replace-cross Abstract: In time series forecasting, covariates represent external factors that influence target variables. Some covariates are observable only in the
arXiv:2606.10798v1 Announce Type: new Abstract: Pretrained time series foundation models (TSFMs) have enabled zero-shot forecasting on unseen target series. However, existing TSFMs often incur high co
arXiv:2505.11034v2 Announce Type: replace-cross Abstract: Robust machine learning depends on clean data, yet current image data cleaning benchmarks rely on synthetic noise or narrow human studies, lim
arXiv:2606.10372v1 Announce Type: new Abstract: In abdominal CT imaging, developing a low-dose, no-reference image quality assessment (No-reference IQA) model that mimics doctors' reading habits for e
arXiv:2606.10789v1 Announce Type: new Abstract: Zero-shot learning (ZSL) for inertial measurement unit (IMU)-based human activity recognition (HAR) faces a central challenge: bridging the gap between
arXiv:2606.10935v1 Announce Type: cross Abstract: Large language model inference is bottlenecked by autoregressive decoding, where each token requires a full forward pass. Multi-token prediction (MTP)
arXiv:2606.10673v1 Announce Type: cross Abstract: Although some very common test beds exist for assessing the performance of clustering methods, large scale benchmarking is typically limited to relati
arXiv:2606.09919v1 Announce Type: cross Abstract: Perceptual uncertainty is a central challenge for heterogeneous robot teams operating in unstructured outdoor environments, where no single viewpoint
arXiv:2606.10401v1 Announce Type: new Abstract: Spatial intelligence is a key frontier for multimodal large language models (MLLMs), enabling them to reason about the physical world from visual experi
arXiv:2606.10087v1 Announce Type: new Abstract: Pre-training on raw code teaches syntax but provides sparse signal for diverse real-world task formats. While synthetic data has proven transformative f
arXiv:2606.11162v1 Announce Type: new Abstract: In this work, we present COGENT, a continuous graph emulator with Neural Ordinary Differential Equations for long-term physical forecasting on irregular
arXiv:2606.09833v1 Announce Type: cross Abstract: AI agents are reshaping the workspace, leading to drastic change of how humans work. Despite the considerable potential of human-agent collaboration b
arXiv:2606.10479v1 Announce Type: new Abstract: Combinatorics is central to Olympiad-level mathematical problem solving, requiring deep discrete reasoning, creative constructions, and rigorous structu
arXiv:2601.18026v2 Announce Type: replace Abstract: Language identification (LID) is a fundamental step in curating multilingual corpora. However, LID models still perform poorly for many languages, e
arXiv:2606.09930v1 Announce Type: cross Abstract: The boundary between program execution and gradient-based optimization has long limited the use of code itself as a learnable scientific model. We pre
arXiv:2606.10059v1 Announce Type: cross Abstract: Finite-state transducers (FSTs) are essential for modeling string rewriting in computational linguistics and natural language processing (NLP), partic
arXiv:2606.10153v1 Announce Type: new Abstract: Learning the compositional nature of the physical world requires joint observation of interacting factors. However, because practical data is often dece
arXiv:2411.02817v2 Announce Type: replace-cross Abstract: Generative models guided by text prompts are widely evaluated for fidelity and prompt alignment, yet their ability to produce outputs remains
arXiv:2606.09923v1 Announce Type: cross Abstract: Neural operators such as the Fourier Neural Operator (FNO) have emerged as powerful surrogates for solving partial differential equations (PDEs), achi
arXiv:2606.09860v1 Announce Type: cross Abstract: Non-alcoholic fatty liver disease (NAFLD) affects roughly 25% of global adults, posing substantial hepatic and cardiovascular risks. Yet, population-l
arXiv:2606.10913v1 Announce Type: new Abstract: We explore whether intrinsic symmetries of the training data lead to conserved quantities during gradient-flow training of neural networks. Under the as
arXiv:2510.04195v2 Announce Type: replace Abstract: Given a map description through global traversal navigation instructions, an LLM can often infer the implicit spatial layout and answer user queries
arXiv:2606.10328v1 Announce Type: cross Abstract: The integration of spatial and spectral information is beneficial to the improvement of change detection performance. However, existing methods cannot
arXiv:2606.10722v1 Announce Type: new Abstract: We study dense-to-sparse continual training as a way to construct channel-sparse large language models from dense checkpoints. Starting from a Qwen2.5-8
arXiv:2606.10050v1 Announce Type: cross Abstract: Traditional UV unwrapping relies on direct optimization of geometric distortion energies and can fail through invalid initialization, local minima, or
arXiv:2601.19210v2 Announce Type: replace Abstract: Vision-language models (VLMs) such as CLIP have demonstrated remarkable zero-shot generalization, yet remain highly vulnerable to adversarial exampl
arXiv:2606.10580v1 Announce Type: cross Abstract: The asymptotic behaviour of Monte Carlo optimistic policy iteration (MC-O-PI) is a long-standing open question. When the model of the environment is u
arXiv:2606.10119v1 Announce Type: cross Abstract: Current-status data arise when an event time is observed only through an indicator of whether it occurred before an examination time. This paper studi
arXiv:2606.10842v1 Announce Type: new Abstract: We describe ConvMemory v2, an opt-in token-evidence reranker that sits after the lightweight ConvMemory v1 reranker and reorders only v1's protected top
arXiv:2606.10770v1 Announce Type: cross Abstract: Variable importance produced by Random Forests (RF) is used widely in statistical data analysis, and has played an important role in a variety of task
arXiv:2506.14753v3 Announce Type: replace Abstract: Diffusion models are well known for their ability to generate a high-fidelity image for an input prompt through an iterative denoising process. Unfo
arXiv:2504.02323v4 Announce Type: replace Abstract: Large language models (LLMs) have created new opportunities to assist teachers and support student learning. While researchers have explored various
arXiv:2606.10504v1 Announce Type: new Abstract: Cross-modal knowledge distillation (CMKD) studies how a (large) teacher model trained on one type of data (e.g., images) can guide a (smaller) student m
arXiv:2606.09041v1 Announce Type: cross Abstract: Research on artificial intelligence in education (AIED) is rapidly expanding, yet technical progress often lacks human-centered grounding and adequate
arXiv:2507.22017v4 Announce Type: replace-cross Abstract: Pancreatic cancer is projected to be the second-deadliest cancer by 2030, making early detection critical. Intraductal papillary mucinous neop
arXiv:2602.06411v2 Announce Type: replace Abstract: EEG-based emotion recognition supports affective brain-computer interfaces and mental health monitoring yet remains challenged by signal complexity,
arXiv:2606.11140v1 Announce Type: cross Abstract: Data assimilation (DA) in subsurface flow entails calibrating model parameters to match observed data, typically at wells, while preserving geological
arXiv:2606.09957v1 Announce Type: cross Abstract: Semantic faults specific to the use of machine learning models are a common problem for machine learning developers, causing suboptimal predictions, h
arXiv:2606.11118v1 Announce Type: new Abstract: We study a dynamic assortment problem on a two-sided service platform with incomplete information and heterogeneous customers in a discrete-time setting
arXiv:2606.11017v1 Announce Type: new Abstract: Airport surface operations increasingly constrain performance at high-throughput hubs. This study examines arrival taxi-in decisions at Hartsfield-Jacks
arXiv:2606.11176v1 Announce Type: cross Abstract: Data tells stories that shape society; the data journalist's job is to turn raw information into stories non-experts can trust. A high-quality news fe
arXiv:2606.10142v1 Announce Type: new Abstract: Recent advances in 3D generation have led to substantial improvements in realism, controllability, and efficiency, yet the evaluation of 3D assets remai
arXiv:2606.10756v1 Announce Type: new Abstract: Accelerated acquisition of fMRI enables enhanced detection of neurovascular (BOLD) activity in the brain, but image reconstruction becomes challenging w
arXiv:2606.10662v1 Announce Type: cross Abstract: Multi-agent systems (MAS) can scale large language model reasoning at test time by decomposing complex problems into parallel subtasks. However, most
arXiv:2606.10187v1 Announce Type: cross Abstract: We develop a decision-calibrated conformal framework for pacing decisions in streaming advertising. Pacing depends on uncertain future inventory, dema
arXiv:2606.10092v1 Announce Type: new Abstract: Decision-making under risk is typically studied through single-shot lottery choices. Yet many real decisions involve combinatorial risk, where risk aris
arXiv:2606.10475v1 Announce Type: cross Abstract: Multi-agent debate frameworks have been shown to improve large language model performance in convergent tasks, but they are currently optimized in a w
arXiv:2606.09466v2 Announce Type: replace Abstract: Classification tasks require annotated data, which can often be expensive, time-consuming, or even unfeasible to collect. This is the case of the me
arXiv:2510.06473v3 Announce Type: replace-cross Abstract: Understanding and modeling human mobility is central to challenges in transport planning, sustainable urban design, and public health. Despite
arXiv:2606.10811v1 Announce Type: new Abstract: There is no doubt that over the last decade, techniques from the field of machine learning have revolutionized how we process and interpret data, especi
arXiv:2606.09953v1 Announce Type: cross Abstract: Head computed tomography (CT) typically uses sub-millimeter in-plane resolution but 2-5 mm through-plane spacing, creating substantial anisotropy that
arXiv:2402.00152v5 Announce Type: replace Abstract: Constructing the architecture of a neural network is a challenging pursuit for the machine learning community, and the dilemma of whether to go deep
arXiv:2606.10940v1 Announce Type: cross Abstract: Camera traps have become a cornerstone of biodiversity monitoring, but the artificial intelligence that turns vast quantities of images into usable ec
arXiv:2606.10932v1 Announce Type: new Abstract: We present Density Field State Space Models (DF-SSM), a framework for compressing SSMs to a 1-bit scaffold with int8 low-rank correction. Applied to Mam
arXiv:2606.10198v1 Announce Type: cross Abstract: Hallucination detection in large language and vision-language models is increasingly framed as selective prediction, where a detector assigns a confid