Knowledge catalogue
arXiv:2606.00910v1 Announce Type: new Abstract: Composed Video Retrieval (CoVR) seeks the target video that results from applying a free-form textual modification to a reference video. We address the
arXiv:2606.02450v1 Announce Type: new Abstract: CoVR-R studies reason-aware composed video retrieval: given a reference video and an edit instruction, the system must retrieve the target video that sa
arXiv:2603.01471v2 Announce Type: replace-cross Abstract: Multimodal embedding models, rooted in multimodal large language models (MLLMs), have yielded significant performance improvements across dive
arXiv:2606.02252v1 Announce Type: new Abstract: Model merging offers a training-free way to combine multiple post-trained expert models, but merging experts obtained through reinforcement learning (RL
arXiv:2606.00542v1 Announce Type: new Abstract: Shampoo-style optimizers approximate gradient covariance matrices using Kronecker-factored structures. Recent work~ite{lin2026understanding} showed that
arXiv:2606.02134v1 Announce Type: cross Abstract: Deep neural networks achieve strong performance on many supervised learning tasks but remain vulnerable to adversarial perturbations. Neural network v
arXiv:2606.00306v1 Announce Type: cross Abstract: Reverse Kullback-Leibler (RKL) divergence is widely favored over forward KL (FKL) in large language models (LLM) distillation, yet this preference is
arXiv:2606.02479v1 Announce Type: new Abstract: Maintaining long-term geometric consistency remains challenging for long-horizon autoregressive video generation. Memory-augmented generative models add
arXiv:2606.02328v1 Announce Type: new Abstract: We explore Riemannian optimization techniques for rank-factored matrix parameters, targeting contemporary deep learning applications. We examine ten poi
arXiv:2606.00413v1 Announce Type: cross Abstract: Sufficient dimension reduction (SDR) makes high-dimensional regression tractable by projecting the covariates onto a low-dimensional subspace that pre
arXiv:2602.19126v2 Announce Type: replace Abstract: We propose a robust Bayesian formulation of random feature (RF) regression that accounts explicitly for prior and likelihood misspecification via Hu
arXiv:2606.01566v1 Announce Type: new Abstract: Small-to-medium scientific datasets place machine learning pipelines under two compounding pressures. Single-run feature selection produces feature sets
arXiv:2606.00449v1 Announce Type: new Abstract: Orientation-aware manipulation is essential in post-harvest agricultural processing, where produce must be grasped and placed in consistent configuratio
arXiv:2602.20807v2 Announce Type: replace Abstract: Combining 3D Gaussian splatting with Simultaneous Localization and Mapping (SLAM) has gained popularity as it enables continuous 3D environment reco
arXiv:2606.01332v1 Announce Type: new Abstract: We study the problem of scaling dynamic loco-manipulation from a single free-rolling sphere to multiple spheres transported simultaneously on the back o
arXiv:2606.02151v1 Announce Type: new Abstract: Effective scheduling in the energy sector is essential to ensure the reliable operation of electrical grids and their connected assets by, for instance,
arXiv:2512.02342v3 Announce Type: replace-cross Abstract: The stochastic Polyak step size (SPS) has proven to be a promising choice for stochastic gradient descent (SGD), delivering competitive perfor
arXiv:2606.00460v1 Announce Type: new Abstract: Speech-aware large language models often generalize poorly to out-of-domain settings. We propose SALSA (Speech-Aware LLM Adaptation via Learned Steering
arXiv:2606.01340v1 Announce Type: new Abstract: We ask: when do Bayesian model averaging (BMA) weights over decision trees carry sufficient epistemic information to justify committed exploitation of t
arXiv:2601.03615v2 Announce Type: replace Abstract: Audio Language Models (ALMs) offer a promising shift towards explainable audio deepfake detections (ADD), moving beyond extit{black-box} classifiers
arXiv:2606.01539v1 Announce Type: cross Abstract: Estimating the causal effect of time-varying treatments on survival outcomes in large observational studies is computationally demanding, particularly
arXiv:2511.04981v2 Announce Type: replace Abstract: Model depth is a double-edged sword in deep learning: deeper models achieve higher accuracy but require higher computational cost. To efficiently tr
arXiv:2502.07617v2 Announce Type: replace Abstract: We provide an empirical investigation of the potential of pre-training vision-language models on an unprecedented scale: 100 billion examples. We fi
arXiv:2606.00307v1 Announce Type: new Abstract: Recent works have explored unifying SLAM with geometric foundation models (GFMs). However, directly using GFM predictions for tracking is highly sensiti
arXiv:2606.00739v1 Announce Type: new Abstract: Large language models hallucinate even when the answer lies within their parameters. While inference-time scaling can surface this latent knowledge, the
arXiv:2510.08825v2 Announce Type: replace Abstract: Large language models (LLMs) augmented with knowledge graphs (KGs) offer a promising approach for knowledge-intensive reasoning. Central to this app
arXiv:2510.24870v2 Announce Type: replace Abstract: We introduce MiRAGE, an evaluation framework for retrieval-augmented generation (RAG) from multimodal sources. As audiovisual media becomes a preval
arXiv:2606.00098v1 Announce Type: new Abstract: We introduce segmentation-guided spatial indexing for generalizable and explainable deepfake detection. The key idea reverses the standard design order:
arXiv:2606.01542v1 Announce Type: cross Abstract: Chunked-document retrieval is a common component of retrieval-augmented generation (RAG) systems. Documents are split into overlapping chunks, embedde
arXiv:2606.01612v1 Announce Type: new Abstract: Can internal attention patterns in Large Vision Language Models (LVLMs) identify reliable small-object boxes without fine-tuning? In this work, we provi
arXiv:2606.01645v1 Announce Type: cross Abstract: Diffusion models have emerged as a leading framework for deep generative modeling. While the standard Gaussian formulation is theoretically convenient
arXiv:2411.17790v3 Announce Type: replace-cross Abstract: Accurate 3D mapping in endoscopy enables quantitative, holistic lesion characterization within the gastrointestinal (GI) tract, requiring reli
arXiv:2601.11460v2 Announce Type: replace-cross Abstract: Learning structured task representations from human demonstrations is essential for bimanual manipulation, where action ordering, object invol
arXiv:2606.00220v1 Announce Type: cross Abstract: Formal methods provide rigorous accounts of program behavior, but practical software engineering often works through executable libraries, tests, and
arXiv:2606.00512v1 Announce Type: new Abstract: In many modern machine learning pipelines, abundant pretrained representations serve as noisy proxy covariates, while task-specific labels remain scarce
arXiv:2606.00558v1 Announce Type: new Abstract: Transfer learning aims to facilitate the learning of a target domain by transferring knowledge from a source domain. The source domain typically contain
arXiv:2512.00088v2 Announce Type: replace Abstract: We propose SemImage, a novel method for representing a text document as a two-dimensional semantic image to be processed by convolutional neural net
arXiv:2606.01746v1 Announce Type: new Abstract: Modern neural networks are highly susceptible to adversarial perturbations. In this work, we identify that part of this vulnerability stems from the sen
arXiv:2606.00201v1 Announce Type: new Abstract: Designing robots for high-torque, high-fidelity haptic interaction is challenging. Parallel Elastic Actuators (PEAs) use elastic elements in parallel to
arXiv:2502.08884v3 Announce Type: replace-cross Abstract: We present ShapeLib, the first method that uses the priors of Large Language Models (LLMs) to design libraries of programmatic 3D shape abstra
arXiv:2606.02247v1 Announce Type: cross Abstract: Shapley values are a principled attribution measure widely used in interpretable machine learning, but their exact computation scales exponentially wi
arXiv:2606.02544v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) have recently emerged as a promising alternative to autoregressive (AR) LLMs, offering faster inference throug
arXiv:2505.17648v5 Announce Type: replace-cross Abstract: We introduce a framework for simulating macroeconomic expectations in survey experiments using LLM-based economic agents (LLM Agents). We cons
arXiv:2606.01910v1 Announce Type: cross Abstract: Line drawings are a highly expressive art form that requires the artist to abstract and distill the essence of their subject. We present the first sem
arXiv:2606.01246v1 Announce Type: new Abstract: Text-to-SQL on complex schemas is unreliable on a single pass, so recent systems generate multiple SQL candidates and let voting filter out errors. Yet
arXiv:2606.02548v1 Announce Type: new Abstract: Word Error Rate (WER) is the dominant metric for automatic speech recognition (ASR), but it can overestimate errors when references and hypotheses encod
arXiv:2606.00397v1 Announce Type: new Abstract: Soft wearable robotic systems have emerged as a promising solution for assisting individuals with reduced hand function. This paper presents SoFiE, a mo
arXiv:2606.01101v1 Announce Type: cross Abstract: The quadratic complexity of self-attention remains a bottleneck for Large Language Models (LLMs) processing ultra-long contexts. The Naive Bayes Cogni
arXiv:2606.01479v1 Announce Type: new Abstract: Integrating large language models (LLMs) into text-to-speech (TTS) systems has improved speech expressiveness, yet interpretable emotional control remai
arXiv:2606.01526v1 Announce Type: new Abstract: In multi-robot systems, maintaining persistent communication graph connectivity is often overly restrictive, especially when robots have limited communi
arXiv:2606.02455v1 Announce Type: new Abstract: Molecular dynamics (MD) is a key tool for simulating the dynamical behavior of atomic systems. However, MD is inherently serial, which makes it difficul
arXiv:2605.25144v2 Announce Type: replace Abstract: Deformable medical image registration aligns anatomical structures across images but remains computationally dense at 3D resolution. Spiking neural
arXiv:2606.01493v1 Announce Type: new Abstract: Reconstructing a photorealistic 3D face avatar from a single unconstrained photograph is challenging: feed-forward 3D Gaussian Splatting (3DGS) models d
arXiv:2606.01126v1 Announce Type: cross Abstract: Pruning is a process designed to reduce the number of weights in a large neural network. This can substantially speed up inference but might cause a c
arXiv:2602.03970v3 Announce Type: replace-cross Abstract: We study the statistical behavior of reasoning probes in a stylized model of iterative computation inspired by neural algorithmic reasoning. T
arXiv:2606.00758v1 Announce Type: cross Abstract: In recent years, graph signal processing has emerged as a powerful framework at the intersection of signal processing and graph theory, providing tool
arXiv:2603.03031v2 Announce Type: replace Abstract: Large Language Models (LLMs) have achieved strong complex reasoning capabilities through Chain-of-Thought (CoT) reasoning. However, their reasoning
arXiv:2606.01787v1 Announce Type: new Abstract: A new class of asynchronous adaptive first-order optimization methods is introduced, comprising asynchronous variants of several popular algorithms. Ver
arXiv:2606.00312v1 Announce Type: cross Abstract: Over the past half-dozen years, stochastic rounding (SR) has regained significant attention as a quantization scheme for low-precision floating-point
arXiv:2606.01302v1 Announce Type: new Abstract: Modern large-scale deep learning exhibits two striking empirical phenomena: behavioural scaling laws (predictable performance gains with increasing scal