Knowledge catalogue
arXiv:2606.02300v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse domains, yet personalizing their outputs to individual users remai
arXiv:2606.01945v1 Announce Type: new Abstract: Visual Prompting (VP) has emerged as an efficient paradigm for adapting large-scale pre-trained vision models to downstream tasks by incorporating learn
arXiv:2507.15336v3 Announce Type: replace-cross Abstract: Designing high-performance neural networks for new tasks requires balancing optimization quality with search efficiency. Current methods fail
arXiv:2510.24078v2 Announce Type: replace Abstract: Text-to-image (T2I) models are increasingly used for synthetic dataset generation, but generating effective synthetic training data for classificati
arXiv:2606.02458v1 Announce Type: new Abstract: Organizations routinely run experiments for A/B testing, yet the data generated from one experiment is underutilized to inform subsequent intervention d
arXiv:2602.16794v2 Announce Type: replace-cross Abstract: Conformal prediction (CP) offers distribution-free uncertainty quantification for machine learning models, yet its interplay with fairness in
arXiv:2606.00737v1 Announce Type: new Abstract: This paper investigates the optimization mechanisms of non-convex Model Predictive Control (MPC) using the Maximum Entropy Differential Dynamic Programm
arXiv:2601.18340v2 Announce Type: replace Abstract: As video generation models are increasingly expected to manipulate physical dynamics, there is a growing need to move evaluation beyond appearance f
arXiv:2603.19453v2 Announce Type: replace Abstract: We study LLM policy synthesis: using a language model to iteratively generate programmatic agent policies for multi-agent environments. Rather than
arXiv:2508.10312v2 Announce Type: replace Abstract: Recommender systems in concert with Large Language Models (LLMs) present promising avenues for generating semantically-informed recommendations. How
arXiv:2606.01258v1 Announce Type: cross Abstract: Standard positional encodings for transformers - sinusoidal and rotary (RoPE) - treat every position as equally local: they encode where a token is, b
arXiv:2606.00452v1 Announce Type: new Abstract: Dynamic scene reconstruction via 3D Gaussian Splatting (3DGS) has emerged as a compelling approach for representing evolving environments, yet understan
arXiv:2603.18652v2 Announce Type: replace-cross Abstract: Reliably extracting tables from PDFs is essential for large-scale scientific data mining and knowledge base construction, yet existing evaluat
arXiv:2606.01007v1 Announce Type: cross Abstract: Sparsely activated Mixture-of-Experts (MoE) models scale capacity via conditional computation, but distributed inference suffers from cross-GPU expert
arXiv:2606.01095v1 Announce Type: cross Abstract: Vision-language-action (VLA) policies and World-Action Models (WAM) represent two increasingly important paradigms for robotic manipulation. However,
arXiv:2606.00065v1 Announce Type: cross Abstract: Automated extraction of materials composition-property data from scientific literature has advanced considerably with the development of large languag
arXiv:2606.00670v1 Announce Type: cross Abstract: Face-to-face speech comprehension is inherently multimodal, integrating acoustic signals with visible articulation, facial expression, head motion, an
arXiv:2606.01883v1 Announce Type: cross Abstract: Open-set recognition (OSR) requires a classifier to reject inputs from unseen classes which is essential in safety-critical settings such as medical i
arXiv:2606.00038v1 Announce Type: cross Abstract: Artificial intelligence (AI) literacy is increasingly recognized as a foundational competency for all university graduates. Yet students' engagement w
arXiv:2606.01482v1 Announce Type: new Abstract: Ensuring factuality and interpretability in RAG remains an open and urgent problem. We introduce Contrastive Evidence Rationale Attention (CERA), the fi
arXiv:2509.06948v3 Announce Type: replace Abstract: Supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR) are two widely used post-training paradigms for improving the
arXiv:2606.01287v1 Announce Type: cross Abstract: Recent latent visual reasoning methods achieve substantial gains by inserting continuous latent tokens into multimodal language models. These gains ar
arXiv:2606.00279v1 Announce Type: cross Abstract: Verifying claims about AI workloads is a pre- requisite for credible AI governance of covert adversaries (who comply with monitoring only when detecti
arXiv:2606.00079v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) large language models reduce per-token computation through sparse expert activation, but their deployment remains memory-inte
arXiv:2510.06048v4 Announce Type: replace Abstract: Effective data selection is essential for pretraining large language models (LLMs), enhancing efficiency and improving generalization to downstream
arXiv:2606.02241v1 Announce Type: new Abstract: Is the uniform-state diffusion framework a more powerful paradigm for discrete diffusion? Recent studies indicate that this may be the case. In combinat
arXiv:2411.09241v5 Announce Type: replace Abstract: We present the design, development, and experimental validation of BlueME, a compact magnetoelectric (ME) antenna array system for underwater robot-
arXiv:2606.01856v1 Announce Type: cross Abstract: Multimodal Federated Learning (MMFL) enables privacy-preserving collaborative learning across decentralized clients with heterogeneous data and modali
arXiv:2512.10414v2 Announce Type: replace Abstract: Recently, reinforcement learning (RL) has become a common choice in enhancing the reasoning capabilities of vision-language models (VLMs). Consideri
arXiv:2602.11641v2 Announce Type: replace Abstract: Text-attributed graphs (TAGs) associate nodes with textual attributes and graph structure, enabling GNNs to jointly model semantic and structural in
arXiv:2606.00957v1 Announce Type: new Abstract: We present a post-training quantization (PTQ) approach for Wan2.1-T2V-14B, a 14-billion-parameter text-to-video diffusion transformer, targeting the W8A
arXiv:2606.00193v1 Announce Type: new Abstract: The rapid spread of fake news on social media has become a major challenge, particularly in multilingual and under-resourced contexts such as North Afri
arXiv:2012.01494v2 Announce Type: replace Abstract: Braille is the only system to visually impaired people for reading and writing. However, general people cannot read Braille. So, teachers and relati
arXiv:2606.01237v1 Announce Type: new Abstract: Mild cognitive impairment (MCI) and subjective cognitive decline (SCD) are closely associated with the early Alzheimer's disease continuum, where accura
arXiv:2602.03719v2 Announce Type: replace Abstract: Agentic reinforcement learning enables large language models to perform multi-turn planning and tool use, but long-horizon training remains challeng
arXiv:2606.01166v1 Announce Type: cross Abstract: Computer-use agents extend language models from text generation to sustained interaction with files, terminals, browsers, and external tools. This shi
arXiv:2606.01783v1 Announce Type: cross Abstract: Digital platforms increasingly operate as isolated information silos, limiting their ability to construct comprehensive user representations across do
arXiv:2602.02470v2 Announce Type: replace Abstract: Autoregressive large language models (LLMs) have achieved remarkable success in many complex tasks, yet they can still fail in very simple logical r
arXiv:2606.00305v1 Announce Type: cross Abstract: On-Policy Distillation (OPD) improves large language model reasoning by training a student model on trajectories sampled from its own policy under tea
arXiv:2606.01385v1 Announce Type: cross Abstract: Software architecture design is a critical yet inherently complex and knowledge-intensive phase that requires balancing competing quality attributes a
arXiv:2606.00095v1 Announce Type: cross Abstract: Vision-Language Navigation (VLN) enables embodied agents to reach target locations in unseen environments by following language instructions. Despite
arXiv:2407.01374v2 Announce Type: replace Abstract: Malaysian English is a low resource creole language, where it carries the elements of Malay, Chinese, and Tamil languages, in addition to Standard E
arXiv:2509.23782v4 Announce Type: replace Abstract: While large language models (LLMs) perform strongly on diverse tasks, their trustworthiness is limited by erratic behavior that is unfaithful to the
arXiv:2606.02497v1 Announce Type: new Abstract: Time series forecasting has advanced rapidly, especially with the emergence of foundation models that show strong zero-shot performance on numerical ext
arXiv:2606.02434v1 Announce Type: new Abstract: Precise parametric control over circuit geometry is essential for semiconductor inspection, yet obtaining sufficient real training data remains costly.
arXiv:2606.00927v1 Announce Type: new Abstract: Accurate brain tumor classification from magnetic resonance imaging (MRI) is a key requirement for early diagnosis and clinical decision-making. Vision
arXiv:2606.01372v1 Announce Type: cross Abstract: Can neural networks learn abstract algebraic rules, or do they merely memorize training patterns? We investigate this using MNIST digits as states and
arXiv:2606.00144v1 Announce Type: cross Abstract: Speculative decoding speeds up autoregressive decoding by using a drafter to propose multiple tokens that a verifier validates in parallel. In resourc
arXiv:2606.00051v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in analytical workflows, but their suitability as exploratory data analysis (EDA) agents in busines
arXiv:2505.14411v4 Announce Type: replace Abstract: Existing time series tokenization methods predominantly encode a constant number of samples into individual tokens. This inflexible approach can gen
arXiv:2510.27249v2 Announce Type: replace Abstract: Deep neural networks (DNNs) have achieved remarkable success in computer vision tasks such as image classification, segmentation, and object detecti
arXiv:2211.14411v5 Announce Type: replace-cross Abstract: Hyperparameter optimization (HPO) is crucial for strong performance of deep learning algorithms and real-world applications often impose some
arXiv:2606.01182v1 Announce Type: cross Abstract: Large Language Models (LLMs) excel at static reasoning tasks, yet their performance often degrades in interactive scenarios where information must be
arXiv:2606.00548v1 Announce Type: cross Abstract: Concentrated Animal Feeding Operations (CAFOs) play an important role in agricultural production but are also associated with environmental, public he
arXiv:2512.12997v2 Announce Type: replace-cross Abstract: CLIP delivers strong zero-shot classification but remains highly vulnerable to adversarial attacks. Prior adversarial fine-tuning work primari
arXiv:2606.01013v1 Announce Type: new Abstract: Research is advancing faster than ever with artificial intelligence (AI); and so are the corresponding research papers. The exploding volume of AI-gener
arXiv:2606.01199v1 Announce Type: new Abstract: Large language agents are increasingly used for social simulation, yet it remains unclear whether they can sustain coherent behavior in structured organ
arXiv:2505.24069v4 Announce Type: replace-cross Abstract: Large language models (LLMs) are deployed on increasingly complex tasks that require multi-step decision-making. Understanding their algorithm
arXiv:2606.00089v1 Announce Type: cross Abstract: Predictive Physical AI systems output state rollouts, action chunks, and latent plans, yet a low root-mean-square error (RMSE) does not imply that a p
arXiv:2602.06033v2 Announce Type: replace Abstract: Pre-trained vision language models do not have good intuitions about the physical world. Recent work has shown that supervised fine-tuning can impro