Knowledge catalogue
arXiv:2601.06972v2 Announce Type: replace Abstract: In speech language modeling, two architectures dominate the frontier: the Transformer and the Conformer. However, it remains unknown whether their c
arXiv:2606.28946v1 Announce Type: new Abstract: This paper presents a robust Automatic Number Plate Recognition (ANPR) system tailored for Nepali license plates written in Devanagari script. In this p
arXiv:2510.09278v2 Announce Type: replace-cross Abstract: Training expert LLMs in domains with scarce data is difficult, often relying on multiple-choice questions (MCQs). However, standard outcome-ba
arXiv:2606.28516v1 Announce Type: new Abstract: We present CLEAR-MoE, a four-phase post-training pipeline that converts a frozen pretrained Vision Transformer (ViT) into a sparse Mixture-of-Experts (M
arXiv:2606.30082v1 Announce Type: new Abstract: Developing a multi-level grading model for coronary artery stenosis holds great clinical significance for the diagnosis of coronary artery disease. Howe
arXiv:2606.28574v1 Announce Type: cross Abstract: When a large language model (LLM) codes a construct in text as a human annotator would, that agreement makes the LLM a reliable coder. Yet reliability
arXiv:2606.28929v1 Announce Type: cross Abstract: Cybersecurity is a real-life test-bed for many machine learning problems at once, especially when considering modern strides in using Large Language M
arXiv:2601.12282v2 Announce Type: replace-cross Abstract: The functions of different regions of the human brain are closely linked to their distinct cytoarchitecture, which is defined by the spatial a
arXiv:2606.29314v1 Announce Type: new Abstract: With the growing demand for immersive visual experiences, high-quality omnidirectional images (ODIs) have become increasingly important. However, limita
arXiv:2606.29280v1 Announce Type: cross Abstract: We identify intervention bias as a previously unquantified failure mode of zero-shot large-language-model (LLM) educational advisory agents: without t
arXiv:2601.13534v3 Announce Type: replace-cross Abstract: Time series generation (TSG) is widely used across domains, yet most existing methods assume regular sampling and fixed output resolutions. Th
arXiv:2606.29198v1 Announce Type: new Abstract: As the most perceptually powerful Face Video Super-Resolution (FVSR) method, existing works in Generative FVSR (GFVSR) mainly exploit the generative pri
arXiv:2509.12159v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models have demonstrated exceptional performance in UI2Code tasks, significantly enhancing website development effic
arXiv:2606.29267v1 Announce Type: new Abstract: Visual grounding aims to associate free-form textual queries with specific regions in an image. While recent Multimodal Large Language Models (MLLMs) ha
arXiv:2512.21545v2 Announce Type: replace Abstract: Object removal must prevent the masked target from reappearing and reconstruct the occluded background with structural and contextual fidelity, rath
arXiv:2606.30152v1 Announce Type: cross Abstract: Contextual language models conflate grammatical gender and social semantic bias in gendered languages such as Spanish. Existing gender debiasing appro
arXiv:2504.14969v2 Announce Type: replace Abstract: This paper proposes a framework for evaluating large language models (LLMs) on Chinese topic constructions, focusing on their sensitivity to island
arXiv:2606.28409v1 Announce Type: cross Abstract: Software-compilable C programs routinely fail to complete the four-stage pipeline of a high-level synthesis (HLS) toolchain -- compilation, C simulati
arXiv:2606.29182v1 Announce Type: new Abstract: Open-ended scientific discovery with large language models (LLMs) increasingly operates as a long-horizon loop of hypothesis search and verification, wh
arXiv:2606.29859v1 Announce Type: cross Abstract: With the rise of data-intensive science, algorithms have become central to scientific research. In academic papers, algorithms are mentioned for diffe
arXiv:2606.28882v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown the potential to generate code explanations that surpass those of peers in quality, offering promising opportu
arXiv:2606.29064v1 Announce Type: cross Abstract: The unfairness of recommender systems has become a topic of concern due to its significant social and ethical implications. Although existing works ha
arXiv:2601.17146v2 Announce Type: replace-cross Abstract: Empirical investigations into unintended model behavior often show that the algorithm is predicting another outcome than what was intended. Th
arXiv:2606.28607v1 Announce Type: cross Abstract: This work tackles the challenge of enhancing low-resolution LiDAR sensors for SLAM applications through a novel Deep Unrolling-based Super-Resolution
arXiv:2606.30376v1 Announce Type: cross Abstract: Aligning generative flow models on continuous spaces via online reinforcement learning is constrained by intractable trajectory likelihoods. Existing
arXiv:2606.30196v1 Announce Type: cross Abstract: This paper offers an in-depth analysis of non-sequential multimodal sentence-level embeddings, with a particular focus on the SONAR model. We demonstr
arXiv:2509.00123v2 Announce Type: replace-cross Abstract: A fundamental challenge in microbial ecology is determining whether bacteria compete or cooperate in different environmental conditions. With
arXiv:2406.13944v2 Announce Type: replace-cross Abstract: This paper establishes the generalization error of pooled min-ell_2-norm interpolation in transfer learning, where data from diverse distribut
arXiv:2606.29161v1 Announce Type: new Abstract: Predicting tandem mass spectra (MS/MS) from molecular structures represents a central task in analytical chemistry with direct relevance to clinical met
arXiv:2606.29148v1 Announce Type: cross Abstract: Developing controllers capable of completing a wide range of tasks in a natural and life-like manner is a key challenge in enabling practical applicat
arXiv:2304.11171v5 Announce Type: replace-cross Abstract: To overcome the limitations of point-based inputs, overly fine computation and limited adaptability in existing artificial intelligence method
arXiv:2606.28334v1 Announce Type: cross Abstract: Recent advances in artificial intelligence (AI) and social media data have led to growing optimism about the ability to detect suicide risk at scale.
arXiv:2606.28862v1 Announce Type: new Abstract: Many visual requests -- ``the object to open this bottle'', ``the person not wearing a helmet'' -- require reasoning, not just category matching. Pure o
arXiv:2606.29744v1 Announce Type: new Abstract: Accurate coronary Digital Subtraction Angiography (DSA) vessel segmentation is essential for computer-aided diagnosis and treatment planning of coronary
In this post, you will learn five practical patterns for building resilient generative AI applications on AWS, progressing from native Amazon Bedrock features to multi-model orchestration using an LLM
arXiv:2606.28728v1 Announce Type: cross Abstract: Leveraging large-scale weakly supervised datasets is crucial to train robust end-to-end automatic speech recognition (ASR) models. However, such datas
arXiv:2512.17504v2 Announce Type: replace-cross Abstract: Recent advances in diffusion models have enabled impressive video editing capabilities, yet production-grade Video Object Insertion (VOI) rema
Muon optimizer shows faster training speeds compared to traditional optimizers, but analysis reveals it sacrifices the simplicity bias that older optimizers maintain, potentially impacting model gener
arXiv:2606.16776v2 Announce Type: replace Abstract: Generalist robot policies require trustworthy evaluation and robot-usable training data, but both are difficult to scale with physical robots alone.
arXiv:2601.21864v2 Announce Type: replace Abstract: Large language models (LLMs) exhibit social biases that reinforce harmful stereotypes, limiting their safe deployment. Most existing debiasing metho
arXiv:2606.29374v1 Announce Type: new Abstract: High-fidelity reconstruction of dynamic urban environments is a cornerstone of autonomous driving simulation and large-scale world modeling. While 3D Ga
arXiv:2606.30544v1 Announce Type: new Abstract: Latent Action Models (LAMs) learn action-like proxies from observation transitions. However, in multi-object or distractor-rich scenes, these visual eff
arXiv:2509.23292v4 Announce Type: replace Abstract: Tool-integrated reasoning (TIR) has become a key approach for improving large reasoning models (LRMs) on complex problems. Prior work has mainly stu
arXiv:2606.29400v1 Announce Type: cross Abstract: In computer graphics, visual content is continuously warped, zoomed and resampled. This occurs when engines upscale frames, users zoom into 3D scenes,
arXiv:2606.29498v1 Announce Type: new Abstract: Weakly supervised video anomaly detection (WSVAD) has predominantly focused on temporal localization, identifying when anomalies occur while largely neg
arXiv:2606.30555v1 Announce Type: new Abstract: The rapid integration of Large Language Models (LLMs) has driven the evolution of Multi-Agent Systems (MAS), where specialized agents collaborate to exe
arXiv:2606.28450v1 Announce Type: cross Abstract: Large language model (LLM) agents are rapidly being integrated into real-world systems. Their autonomy and tool-use capabilities generate substantial
arXiv:2606.28688v1 Announce Type: new Abstract: Current anomaly detection methods primarily focus on structural anomalies, while paying insufficient attention to anomalies that violate logical constra
arXiv:2606.28911v1 Announce Type: new Abstract: Machine-learned (ML) operator models can be trained to predict density functional theory (DFT) Hamiltonian/density matrices at significantly reduced com
arXiv:2606.29066v1 Announce Type: new Abstract: Masked diffusion language models (MDLMs) generate text by iteratively unmasking tokens, but their standard decoder reduces each step to a binary action:
arXiv:2606.14202v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have advanced Automatic Heuristic Design (AHD) by enabling heuristic generation through reasoning and code synthe
arXiv:2606.29030v1 Announce Type: new Abstract: AI agents extend conventional large language model (LLM) applications by integrating language understanding with task execution, external tool use, and
arXiv:2606.29038v1 Announce Type: cross Abstract: Metric aggregation divergence (MAD) is the silent inconsistency that arises when distinct pipeline stages in an agent-based model coupled with a multi
arXiv:2606.29462v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) inherit rich relational priors from their language backbones, yet often fail when asked to apply these relation
arXiv:2606.29447v1 Announce Type: new Abstract: Over the past decade, interest in applying machine learning (ML) to automate forest monitoring has grown significantly. However, existing training datas
arXiv:2606.29425v1 Announce Type: new Abstract: Existing multi-agent debate frameworks suffer from two critical limitations: they rely on static architectures where agent roles and coordination patter
arXiv:2606.29395v1 Announce Type: new Abstract: Recently, Large Language Models (LLMs) have emerged as promising layout agents for 3D scene generation. Existing layout agents still suffer from implaus
arXiv:2606.29340v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) trains a reasoning model on rollouts sampled from its own policy by matching a privileged teacher that also sees veri
arXiv:2601.12033v2 Announce Type: replace Abstract: Quantization is widely adopted to reduce the computational cost of large language models (LLMs); however, its implications for fairness and safety,
arXiv:2606.29026v1 Announce Type: new Abstract: Multi-agent AI systems can improve answer selection by allowing different language models to exchange reasoning traces, revise initial predictions, and