Knowledge catalogue
arXiv:2604.17433v1 Announce Type: new Abstract: Self-consistency (SC) is a popular technique for improving the reasoning accuracy of large language models by aggregating multiple sampled outputs, but
arXiv:2506.10137v3 Announce Type: replace Abstract: While goal-conditioned behavior cloning (GCBC) methods can perform well on in-distribution training tasks, they do not necessarily generalize zero-s
arXiv:2604.17652v1 Announce Type: new Abstract: Sentinel-5P (S5P) plays a critical role in atmospheric monitoring; however, its spatial resolution limits fine-scale analysis. Existing super-resolution
arXiv:2604.18237v1 Announce Type: new Abstract: In large-scale distributed scenarios, increasingly complex tasks demand more intelligent collaboration across networks, requiring the joint extraction o
arXiv:2604.17659v1 Announce Type: new Abstract: We introduce the Semantic Density Effect (SDE): the empirical finding that prompts carrying higher semantic information per token consistently produce m
Sen. Warren: 'Did Donald Trump lose the 2020 election?' Handsome Kevin: 'ummm... errr... ' His response raises real questions about whether Warsh is independent of the President and if he has the cour
arXiv:2505.13353v4 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed for understanding large codebases, but whether they understand operational semantics of long
arXiv:2604.02908v2 Announce Type: replace Abstract: We present SentiAvatar, a framework for building expressive interactive 3D digital humans, and use it to create SuSu, a virtual character that speak
arXiv:2604.18580v1 Announce Type: cross Abstract: Modern sequence models are dominated by Transformers, where self-attention mixes information from the visible context in an input-dependent way. Howev
arXiv:2604.16842v1 Announce Type: cross Abstract: This thesis develops numerical and theoretical approaches for understanding and analyzing singularity formation in Partial Differential Equations (PDE
arXiv:2604.16517v1 Announce Type: cross Abstract: Large vision-language models (VLMs) achieve strong performance on multimodal tasks but often suffer from hallucination and poor grounding in knowledge
arXiv:2505.13660v2 Announce Type: replace-cross Abstract: This paper introduces a new constraint-free concave dual formulation for the Wasserstein barycenter. Tailoring the vanilla dual gradient ascen
arXiv:2406.06543v2 Announce Type: replace-cross Abstract: Deep learning has driven significant technological advancements, but its high energy consumption limits its use on battery-operated edge devic
arXiv:2506.18141v3 Announce Type: replace Abstract: We identify semantically coherent, context-consistent network components in large language models (LLMs) using coactivation of sparse autoencoder (S
arXiv:2604.17385v1 Announce Type: new Abstract: Spatial intelligence, which refers to the ability to reason about geometric and physical structure from visual observations, remains a core challenge fo
arXiv:2604.17287v1 Announce Type: new Abstract: Copy-move forgery, where a region within an image is duplicated to hide or fabricate content, remains a persistent threat to visual media integrity. We
arXiv:2604.17397v1 Announce Type: new Abstract: Autoregressive video diffusion is emerging as a promising paradigm for streaming video synthesis, with step distillation serving as the primary means of
arXiv:2512.21204v2 Announce Type: replace Abstract: Human infants, with only a few hundred hours of speech exposure, acquire basic units of new languages, highlighting a striking efficiency gap compar
arXiv:2511.05152v2 Announce Type: replace Abstract: Deformable Gaussian Splatting (GS) accomplishes photorealistic dynamic 3-D reconstruction from dense multi-view video (MVV) by learning to deform a
arXiv:2511.11391v3 Announce Type: replace Abstract: Phase-time arrays, which integrate phase shifters (PSs) and true-time delays (TTDs), have emerged as a cost-effective architecture for generating fr
arXiv:2604.16607v1 Announce Type: new Abstract: With the rise of generative language models, machine-generated text detection has become a critical challenge. A wide variety of models is available, bu
arXiv:2604.17068v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) enable parallel text generation by iteratively denoising a fully masked sequence, unmasking a subset of masked t
arXiv:2601.04052v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have demonstrated impressive capabilities in generalized robotic control; however, they remain notoriously
arXiv:2506.08013v2 Announce Type: replace Abstract: Multi-task learning for dense prediction is limited by the need for extensive annotation for every task, though recent works have explored training
arXiv:2604.16774v1 Announce Type: new Abstract: Long-horizon language model systems increasingly rely on persistent memory, yet many current designs still treat memory primarily as a static store: wri
arXiv:2604.17611v1 Announce Type: new Abstract: Parkinson's disease (PD) is a progressive disorder in which symptom burden and functional impairment evolve over time, making severity staging essential
arXiv:2602.11528v2 Announce Type: replace-cross Abstract: Recent studies have shown that large language models (LLMs) can infer private user attributes (e.g., age, location, gender) from user-generate
arXiv:2604.17381v1 Announce Type: cross Abstract: This paper proposes StrEBM, a structured latent energy-based model for source-wise structured representation learning. The framework is motivated by a
This paper presents a prompting technique called 'String Seed of Thought' that enables large language models to generate outputs that are both faithful to underlying data distributions and maintain di
arXiv:2604.16947v1 Announce Type: cross Abstract: This work introduces Structured 3D-SVD as a practical framework for the reconstruction, compression, and analysis of biological volumetric data. Inspi
arXiv:2604.18251v1 Announce Type: new Abstract: In this paper, we present three neural network architectures designed for real-time classification of weather conditions (sunny, rain, snow, fog) from i
arXiv:2510.02025v4 Announce Type: replace Abstract: We introduce a constraint-selection-based experiment design for measuring narrative preferences of Large Language Models (LLMs). This design offers
arXiv:2604.17551v1 Announce Type: new Abstract: Standard approaches to goal-conditioned reinforcement learning (GCRL) that rely on temporal-difference learning can be unstable and sample-inefficient d
arXiv:2604.17830v1 Announce Type: new Abstract: Traditional Task and Motion Planning (TAMP) systems depend on physics models for motion planning and discrete symbolic models for task planning. Althoug
arXiv:2604.18310v1 Announce Type: cross Abstract: Variational inference (VI) is a central tool in modern machine learning, used to approximate an intractable target density by optimising over a tracta
arXiv:2603.18361v2 Announce Type: replace Abstract: Conversational agents are required to respond to their users not only with high quality (i.e. commonsense bearing) responses, but also considering m
arXiv:2508.11184v2 Announce Type: replace Abstract: Distractors-incorrect yet plausible answer choices in multiple-choice questions (MCQs)-are vital in educational assessments, as they help identify s
arXiv:2604.17566v1 Announce Type: cross Abstract: Machine learning is becoming increasingly important for nonlinear system identification, including dynamical systems with spatially distributed output
arXiv:2604.18107v1 Announce Type: new Abstract: Vision-Language-Action models (VLAs) achieve remarkable performance in sequential decision-making but remain fragile to subtle environmental shifts, suc
arXiv:2510.07761v2 Announce Type: replace Abstract: Large language models (LLMs) now give reasoning before answering, excelling in tasks like multiple-choice question answering (MCQA). Yet, a concern
arXiv:2504.18269v2 Announce Type: replace Abstract: When generating images from prompts that include specific entities, the model must retain as much entity-specific knowledge as possible. However, th
arXiv:2509.07024v2 Announce Type: replace-cross Abstract: The Trapped Gyro-Landau Fluid (TGLF) model provides fast, accurate predictions of turbulent transport in tokamaks, but whole device simulation
arXiv:2604.18096v1 Announce Type: cross Abstract: LLMs are increasingly presented as collaborators in programming, design, writing, and analysis. Yet the practical experience of working with them ofte
arXiv:2604.16704v1 Announce Type: new Abstract: This study examines the role of artificial intelligence in translation, focusing on ChatGPT, specifically ChatGPT-4, and the extent to which human poste
Single-use plastics are a persistent source of environmental pollution, and the need to house a growing global population puts increasing pressure on resources such as timber. MIT engineers have an id
arXiv:2510.09378v2 Announce Type: replace Abstract: Recent efforts to accelerate LLM pretraining have focused on computationally-efficient approximations that exploit second-order structure. This rais
arXiv:2510.00546v5 Announce Type: replace Abstract: Large Reasoning Models (LRMs) allocate substantial inference-time compute to Chain-of-Thought (CoT) reasoning, improving performance on mathematics,
arXiv:2604.18352v1 Announce Type: cross Abstract: State-of-the-art Differentially Private (DP) synthetic data generators such as MST and AIM are widely used, yet tightly auditing their privacy guarant
arXiv:2502.15315v3 Announce Type: replace Abstract: Sparse Mixture-of-Experts (MoE) architectures have emerged as a promising approach to decoupling model capacity from computational cost. At the core
arXiv:2410.15368v2 Announce Type: replace-cross Abstract: We revisit FedExProx - a recently proposed distributed optimization method designed to enhance convergence properties of parallel proximal alg
arXiv:2604.16887v1 Announce Type: new Abstract: Robotic manipulators for aerospace applications require a delicate balance between lightweight construction and fault-tolerant operation to satisfy stri
arXiv:2509.18060v2 Announce Type: replace Abstract: Tibetan is a low-resource language with limited parallel speech corpora spanning its three major dialects (U-Tsang, Amdo, and Kham), limiting progre
arXiv:2603.28178v2 Announce Type: replace Abstract: 3D Scene Graph (3DSG) generation plays a pivotal role in spatial understanding and affordance perception. To mitigate generalization issues from dat
arXiv:2510.19410v2 Announce Type: replace Abstract: Identifying which text spans refer to entities - mention detection - is both foundational for information extraction and a known performance bottlen
arXiv:2509.11612v3 Announce Type: replace Abstract: Reservoir is an efficient network for time series processing. It is well known that network structure is one of the determinants of its performance.
arXiv:2509.15651v2 Announce Type: replace Abstract: Assessing the impact the training data on machine learning models is crucial for understanding the behavior of the model, enhancing the transparency
arXiv:2604.16834v1 Announce Type: cross Abstract: Privacy-preserving machine learning (PPML) has become increasingly important in applications where sensitive data must remain confidential. Homomorphi
arXiv:2604.18239v1 Announce Type: new Abstract: Preference optimization is widely used to align large language models (LLMs) with human preferences. However, many margin-based objectives suppress the
arXiv:2604.18089v1 Announce Type: new Abstract: Bayesian Deep Ensembles (BDEs) represent a powerful approach for uncertainty quantification in deep learning, combining the robustness of Deep Ensembles
arXiv:2604.00505v2 Announce Type: replace Abstract: Overparameterized neural networks often show a benign overfitting property in the sense of achieving excellent generalization behavior despite the n