Knowledge catalogue
arXiv:2605.12786v1 Announce Type: new Abstract: Emotion expression is central to human--robot interaction, yet little is known about how people interpret affect on robots with sparse, non-anthropomorp
arXiv:2511.17031v2 Announce Type: replace-cross Abstract: The rapidly growing computational demands of diffusion models for image generation have raised significant concerns about energy consumption a
arXiv:2605.13789v1 Announce Type: cross Abstract: Protein structure tokenizers (PSTs) are workhorses in protein language modeling, function prediction, and evolutionary analysis. However, existing PST
arXiv:2602.05000v2 Announce Type: replace-cross Abstract: Reward guidance, also known as posterior sampling, is a popular method for test-time adaptation and post-training in continuous diffusion mode
arXiv:2605.13063v1 Announce Type: new Abstract: Designing continuous trajectories whose time-averaged occupancy provably matches a prescribed spatial density (the ergodic coverage problem) is central
arXiv:2605.13131v1 Announce Type: new Abstract: Multi-Agent Proximal Policy Optimization (MAPPO) is a variant of the Proximal Policy Optimization (PPO) algorithm, specifically tailored for multi-agent
arXiv:2605.13841v1 Announce Type: cross Abstract: Voice agents, artificial intelligence systems that conduct spoken conversations to complete tasks, are increasingly deployed across enterprise applica
arXiv:2605.13152v1 Announce Type: cross Abstract: We introduce EvObj for unsupervised 3D instance segmentation that bridges the geometric domain gap between synthetic pretraining data and real-world p
arXiv:2605.13803v1 Announce Type: new Abstract: Video temporal grounding (VTG) takes an untrimmed video and a natural-language query as input and localizes the temporal moment that best matches the qu
arXiv:2502.02270v3 Announce Type: replace Abstract: We prove that transformers can exactly interpolate datasets of finite input sequences in R^d, dgeq 2, with corresponding output sequences of smaller
arXiv:2508.09320v3 Announce Type: replace-cross Abstract: Graph neural networks (GNNs) are increasingly often employed in high-stakes applications, such as fraud detection or healthcare, but are susce
arXiv:2505.18604v3 Announce Type: replace Abstract: State-Space Models (SSMs) excel at capturing long-range dependencies with structured recurrence, making them well-suited for sequence modeling. Howe
arXiv:2605.11679v2 Announce Type: replace Abstract: In the realm of multi-objective alignment for large language models, balancing disparate human preferences often manifests as a zero-sum conflict. S
arXiv:2605.12523v1 Announce Type: cross Abstract: Generative Artificial Intelligence (AI) introduces new considerations for English as a foreign language (EFL) writing pedagogy. This study explores ho
arXiv:2605.13380v1 Announce Type: new Abstract: This work compares three interaction modalities for human-robot collaboration: passive, reactive, and proactive. We studied 18 participants assembling a
arXiv:2602.22455v2 Announce Type: replace Abstract: We investigate the feasibility of using Multimodal Large Language Models (MLLMs) for real-time online episodic memory question answering. While clou
arXiv:2601.19979v2 Announce Type: replace-cross Abstract: We develop a reinforcement learning algorithm to study the holographic entropy cone. Given a target entropy vector, our algorithm searches for
arXiv:2602.04264v2 Announce Type: replace-cross Abstract: The choice of activation function fundamentally shapes the representational capacity and parameter efficiency of deep neural networks, yet mos
arXiv:2605.12995v1 Announce Type: new Abstract: Traditional retrieval pipelines optimize utility through stages of candidate retrieval and reranking, where ranking operates over a predefined candidate
arXiv:2605.13402v1 Announce Type: new Abstract: Computing a minimum s-t cut in a graph is a solution to a wide range of computer vision problems, and is often done using the Boykov-Kolmogorov (BK) alg
arXiv:2605.13759v1 Announce Type: new Abstract: Clustering is an unsupervised machine learning task that consists of identifying groups of similar objects. It has numerous applications and is increasi
arXiv:2605.13030v1 Announce Type: cross Abstract: Model merging combines task experts into one model and avoids joint training, retraining, or deploying many expert models, but the merged model often
arXiv:2512.21075v2 Announce Type: replace-cross Abstract: Deep neural networks have achieved remarkable success in practice, yet a mechanistic understanding of how features evolve during training rema
arXiv:2605.13475v1 Announce Type: new Abstract: Federated Learning (FL) enables collaborative training of distributed clients while protecting privacy. To enhance generalization capability in FL, prot
arXiv:2605.12704v1 Announce Type: cross Abstract: A fundamental challenge in symbolic regression (SR) is efficiently recovering complex mathematical expressions from observational data. Although this
arXiv:2409.02708v2 Announce Type: replace Abstract: Data scarcity poses a serious threat to modern machine learning and artificial intelligence, as their practical success typically relies on the avai
arXiv:2605.12790v1 Announce Type: new Abstract: Modeling concentric tube robots (CTRs) involves complex nonlinear continuum mechanics, and despite recent progress, physics-based models often lack an a
arXiv:2605.13193v1 Announce Type: new Abstract: Fine-grained recognition in everyday life is often not a closed-book classification problem: when encountering unfamiliar objects, humans actively searc
arXiv:2604.00001v2 Announce Type: replace-cross Abstract: Gradient-based data selection offers a principled framework for estimating sample utility in large language model (LLM) fine-tuning, but exist
arXiv:2605.13170v1 Announce Type: new Abstract: Multi-agent systems rely on communication for information sharing and action coordination, which exposes a vulnerability to attacks. We investigate sing
arXiv:2603.15854v2 Announce Type: replace-cross Abstract: Sampling from a categorical distribution is mathematically simple, but in large-vocabulary decoding, it often triggers extra memory traffic an
arXiv:2605.13108v1 Announce Type: new Abstract: Face presentation attack detection (FacePAD) remains challenging under diverse spoofing representation, including 2D print and replay, 3D mask-based spo
arXiv:2602.06138v2 Announce Type: replace Abstract: Generative policies based on diffusion models and flow matching have shown strong promise for offline reinforcement learning (RL), but their applica
arXiv:2602.10326v2 Announce Type: replace Abstract: Despite the remarkable success of sampling-based generative models such as flow matching, they can still produce samples of inconsistent or degraded
arXiv:2509.23056v2 Announce Type: replace Abstract: Remote sensing object detection is a critical technology for real-world applications such as natural resource monitoring, traffic management, and UA
arXiv:2512.07112v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated remarkable performance due to their large parameter counts and extensive training data. However
arXiv:2509.10796v4 Announce Type: replace Abstract: Robot person following (RPF) -- mobile robots that follow and assist a specific person -- has emerging applications in personal assistance, security
arXiv:2605.13788v1 Announce Type: new Abstract: Active learning for machine-learning interatomic potentials (MLIPs) must address several challenges to be practical: scaling to large candidate pools, l
arXiv:2605.13171v1 Announce Type: new Abstract: As automated reasoning systems advance rapidly, there is a growing need for research-level formal mathematical problems to accurately evaluate their cap
arXiv:2605.12826v1 Announce Type: cross Abstract: The proliferation of sophisticated image editing tools and generative artificial intelligence models has made verifying the authenticity of digital im
arXiv:2605.13757v1 Announce Type: new Abstract: Vision-Language-Action (VLA) policies are commonly trained from dense robot demonstration trajectories, often collected through teleoperation, by sampli
arXiv:2605.12997v1 Announce Type: new Abstract: Neural operators learn to map initial conditions to the terminal solution of partial differential equations (PDEs), providing a surrogate for the full o
arXiv:2603.05093v2 Announce Type: replace-cross Abstract: Feature attributions often hide a critical modeling choice: they explain a prediction along a counterfactual path from a reference state to an
arXiv:2605.12733v1 Announce Type: cross Abstract: Given a generalist model, learning a task-relevant specialist representation is fundamental for downstream applications. Identifiability, the asymptot
arXiv:2605.12788v1 Announce Type: new Abstract: Sustained effort is essential for realizing the benefits of intelligent tutoring systems (ITS), yet many learners disengage or underuse available practi
arXiv:2605.12944v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) data selection is commonly formulated as instance ranking: score each example and retain a top-k subset. However, effective
arXiv:2603.26839v2 Announce Type: replace-cross Abstract: How do multimodal models solve visual spatial tasks -- through genuine planning, or through brute-force search in token space? We introduce ex
arXiv:2603.27910v2 Announce Type: replace Abstract: AI agents that interact with users across multiple sessions require persistent long-term memory to maintain coherent, personalized behavior. Current
arXiv:2605.13217v1 Announce Type: cross Abstract: Reinforcement learning has become a powerful paradigm for post-training large language model agents, yet credit assignment in multi-turn environments
arXiv:2605.13266v1 Announce Type: new Abstract: Many Inertial Navigation Systems (INS) use Global Navigation Satellite System (GNSS) position as the primary measurement to drive filter performance and
arXiv:2605.13151v1 Announce Type: new Abstract: Category-agnostic pose estimation (CAPE) aims to localize keypoints on query images from arbitrary categories, using only a few annotated support exampl
arXiv:2605.13555v1 Announce Type: cross Abstract: Radiation therapy (RT) requires precise dose delivery over multiple fractions, with CT fundamental for treatment planning due to its electron density
arXiv:2406.13619v4 Announce Type: replace-cross Abstract: This paper develops a generative model by minimizing the second-order Wasserstein loss (the W_2 loss) through a distribution-dependent ordinar
arXiv:2512.10857v2 Announce Type: replace-cross Abstract: Transport-based methods have emerged as a leading paradigm for building generative models from large, clean datasets. However, in many scienti
arXiv:2605.13150v1 Announce Type: cross Abstract: Discrete automated processes in industrial and cyber-physical systems often exhibit a repetitive structure in which successive repetitions follow a co
arXiv:2605.12778v1 Announce Type: cross Abstract: Recent advances in generative models have yielded impressive progress on motion in-betweening, allowing for more complex, varied, and realistic motion
arXiv:2605.13755v1 Announce Type: new Abstract: In recent years, autonomous driving has significantly in creased the demand for high-quality data to train 2D and 3D perception models for safety-critic
arXiv:2605.13352v1 Announce Type: new Abstract: Standard dual-encoder vision-language models that map images and text to deterministic points on a shared unit hypersphere through ell_2 normalization t
arXiv:2509.19929v4 Announce Type: replace-cross Abstract: Uncertainty Quantification (UQ) is paramount for inference in engineering. A common inference task is to recover full-field information of phy
arXiv:2601.11942v3 Announce Type: replace Abstract: Variational quantum circuits are increasingly studied as continuous-function approximators, but quantum regression remains difficult to train when g