Knowledge catalogue
arXiv:2607.29237v1 Announce Type: new Abstract: LiDAR scene flow estimation has settled into a monoculture: nearly all recent methods share the same feed-forward architecture and the same family of se
arXiv:2607.29355v1 Announce Type: cross Abstract: Cross-lingual transfer is central to low-resource machine translation, but its behavior within closely related language families remains insufficientl
arXiv:2607.29365v1 Announce Type: new Abstract: Graph Domain Adaptation (GDA) transfers predictive knowledge from labeled source graphs to unlabeled target graphs under distribution shift. Existing me
arXiv:2607.29009v1 Announce Type: new Abstract: Multi-robot systems, particularly heterogeneous robot swarms, can improve the efficiency of complex task execution through parallel collaboration and co
arXiv:2607.28707v1 Announce Type: new Abstract: Entropy-based pruning has been proposed as an effective method for compressing Chain-of-Thought (CoT) reasoning with negligible accuracy loss. We test t
arXiv:2607.29394v1 Announce Type: cross Abstract: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is essential for breast cancer management, but reliance on gadolinium-based contrast ag
arXiv:2607.29657v1 Announce Type: new Abstract: Fault detection and diagnosis (FDD) technology is essential for improving HVAC system reliability, energy efficiency, and maintenance effectiveness. How
arXiv:2607.29687v1 Announce Type: new Abstract: Sequential robot manipulation requires policies to execute novel combinations of familiar instruction components. However, collecting demonstrations for
arXiv:2606.13303v2 Announce Type: replace Abstract: Recent diffusion editors perform diverse instruction-based edits while conditioning on the source image at every denoising step. Yet persistent sour
arXiv:2511.02570v3 Announce Type: replace Abstract: Bayesian optimization (BO) is a widely used approach to hyperparameter optimization (HPO). However, most existing HPO methods only incorporate exper
arXiv:2607.28959v1 Announce Type: cross Abstract: Adversarial training is one of the most effective defenses against adversarial attacks, yet the computational cost remains prohibitive at modern scale
arXiv:2512.17303v2 Announce Type: replace Abstract: In diffusion and flow-matching generative models, guidance techniques are widely used to improve sample quality and consistency. Classifier-free gui
arXiv:2607.28659v1 Announce Type: new Abstract: Cross-domain sequential recommendation (CDSR) aims to model users' dynamic interest transitions and sequential patterns across multiple domains. Recentl
arXiv:2602.15751v2 Announce Type: replace-cross Abstract: This paper presents an end-to-end demonstration of a viable, ultra-fast, radiation-hard machine learning (ML) application on FPGAs, which coul
arXiv:2510.12953v4 Announce Type: replace-cross Abstract: Recent medical vision-language models have shown promise on tasks such as VQA, report generation, and anomaly detection. However, most are ada
arXiv:2603.24917v3 Announce Type: replace Abstract: Recent work shows that standard greedy-decoding extraction methods for quantifying memorization in LLMs miss how extraction risk varies across seque
arXiv:2607.29058v1 Announce Type: new Abstract: Professional-document review is a constraint-checking problem in which decisions depend on relations among text, geometry, pages, and document revisions
arXiv:2607.29484v1 Announce Type: new Abstract: Interventional data is widely regarded as the gold standard for teaching models causal reasoning. We test this assumption in a fully controlled syntheti
arXiv:2607.29642v1 Announce Type: new Abstract: The iterated learning model was introduced to investigate language evolution: the way in which the characteristic properties of human languages have bee
arXiv:2607.29581v1 Announce Type: new Abstract: A marketplace review photograph is a document: platforms approve refunds on it, and generative models drove the cost of forging one to zero. We study th
arXiv:2607.29419v1 Announce Type: cross Abstract: In reinforcement learning, exploration with sparse and delayed rewards presents a significant challenge due to the limited feedback available for guid
arXiv:2607.29383v1 Announce Type: new Abstract: In recent years, with the development of big data technology, increasingly more companies use HDFS for data processing and storage. As a result, the mai
arXiv:2607.28762v1 Announce Type: new Abstract: This work embeds feature interaction modules derived from factorization machines (FMs) into physics-informed neural networks (PINNs) and neural operator
arXiv:2607.29284v1 Announce Type: new Abstract: 4D Gaussian Splatting (4DGS) can render dynamic scenes photorealistically. However, with limited viewpoint coverage, some spatiotemporal regions remain
arXiv:2601.22146v3 Announce Type: replace Abstract: Due to limited supervised training data, large language models (LLMs) are typically pre-trained via a self-supervised 'predict the next word' object
arXiv:2607.28834v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) is evolving from one-time reconstruction into deliverable, inspectable, and maintainable visual assets. Existing workflows
arXiv:2607.29106v1 Announce Type: new Abstract: Reconstructing animatable 3D human avatars from a single image remains particularly challenging for loose garments, whose geometry and motion cannot be
arXiv:2607.28671v1 Announce Type: cross Abstract: Accurate fracture risk prediction is important for osteoporosis management, but commonly used clinical tools may not fully use information available i
arXiv:2607.29665v1 Announce Type: new Abstract: PDE discovery from sparse observations requires reconstructing a continuous field and selecting the correct differential terms. Our analysis of optimiza
arXiv:2607.29019v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) enhances large language models by incorporating external knowledge, but existing pipelines typically operate on p
arXiv:2504.19683v4 Announce Type: replace Abstract: Fine-grained robotic manipulation often fails when inaccurate initial grasps propagate errors and necessitate complex pose correction. We propose Gr
arXiv:2607.28667v1 Announce Type: cross Abstract: Recent work has shown that classifying large language models (LLMs)' responses can be distinguished by modeling token embeddings as trajectories of a
arXiv:2607.29144v1 Announce Type: cross Abstract: Synthetic face datasets are increasingly used to reduce privacy exposure and data access constraints in biometric recognition. Yet the generators that
MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what’s coming next. You can read more from the series here. When two OpenAI model
arXiv:2607.28670v1 Announce Type: new Abstract: A stochastic Gumbel-Top-K router defines, for every token of a mixture-of-experts (MoE) model, a routing law: a distribution over ordered expert lists a
arXiv:2603.23413v2 Announce Type: replace Abstract: Despite remarkable progress in video generation, maintaining long-term scene consistency upon revisiting previously explored areas remains challengi
arXiv:2607.28635v1 Announce Type: new Abstract: In Natural Language Processing (NLP), dealing with underrepresented topics is challenging, especially in unsupervised tasks where clustering might not a
arXiv:2607.29043v1 Announce Type: cross Abstract: Single-cell RNA sequencing (scRNA-seq) has become an essential tool in modern cellular biology, and generating accurate synthetic scRNA-seq data is be
arXiv:2309.02332v3 Announce Type: replace-cross Abstract: In the mammalian central nervous system, neurons are organized into populations communicating by spike trains propagating along axonal bundles
Introducing Sakana Namazu: An LLM API with Japanese-vibes! Today we’re rolling out an upgraded version of our LLM, Namazu, now available as an API. Try Sakana Namazu API: https://sakana.ai/namazu 🐟 🐟
arXiv:2607.29211v1 Announce Type: new Abstract: Large language models generate computationally expensive yet semantically void reasoning on beyond-capability tasks, creating risks where plausible-soun
arXiv:2601.15037v2 Announce Type: replace-cross Abstract: Open-domain Relational Triplet Extraction (ORTE) aims to mine structured knowledge without predefined relation schemas. Large Language Models
arXiv:2607.28939v1 Announce Type: new Abstract: Structure-preserving neural networks are essential for the long-term prediction of Hamiltonian systems from data. Many important Hamiltonian systems in
arXiv:2607.28672v1 Announce Type: cross Abstract: When a neural network predicts a physical system accurately, has it learned the governing law as formal, structured knowledge, and if so, does the net
arXiv:2607.29059v1 Announce Type: new Abstract: Despite significant advances in image segmentation, even state-of-the-art models produce masks with imperfect boundaries, semantic inconsistencies, and
arXiv:2607.29051v1 Announce Type: cross Abstract: State-of-the-art neural network verifiers use the branch-and-bound procedure as their core solving mechanism. We introduce an inprocessing framework f
arXiv:2607.28876v1 Announce Type: cross Abstract: Music is often used as a medium for communicating emotion, with performers shaping perceived affect through interpretation. This study addresses the c
arXiv:2607.28970v1 Announce Type: new Abstract: Hyperspectral image classification is complicated by mixed pixels, spectral ambiguity, class imbalance, and limited annotations. Most current classifier
arXiv:2607.29509v1 Announce Type: new Abstract: Effective multi-organ segmentation in surgical data requires learning the intricate anatomical features and alleviating the challenge of class imbalance
arXiv:2607.28684v1 Announce Type: new Abstract: Existing benchmarks for scientific equation discovery are largely composed of well-known equations available in the public domain, making it difficult t
arXiv:2607.29167v1 Announce Type: cross Abstract: Long-term memory lets large language model(LLM) agents reuse prior preferences and work flows, but it also turns untrusted observations into persisten
arXiv:2607.28675v1 Announce Type: cross Abstract: Polygonal meshes are the standard surface representation of modern 3D pipelines, and generating high-quality meshes with artist-style topology is esse
arXiv:2607.28683v1 Announce Type: cross Abstract: Large language models benefit from elements in natural language, such as metaphors and analogies in training data and inference input to achieve gener
arXiv:2607.29083v1 Announce Type: new Abstract: Gait recognition is shaped by its input representation. Silhouettes encode projected body shape, skeletons encode sparse joint coordinates, and 3D meshe
arXiv:2607.28954v1 Announce Type: new Abstract: Branch and Bound (BaB) aims to achieve complete verification of neural networks by adaptively partitioning the problem and applying off-the-shelf verifi
arXiv:2607.29479v1 Announce Type: new Abstract: Text-to-molecule generation is typically formulated as a one-shot sequence generation problem, where a model directly maps target descriptions to molecu
arXiv:2505.21736v2 Announce Type: replace Abstract: Translation equivariance is a central reason convolutional neural networks have been successful in computer vision. Other symmetries, such as rotati
arXiv:2607.29115v1 Announce Type: cross Abstract: Link prediction aims to identify potential or future connections within a given graph structure. Position information is essential for link prediction
arXiv:2401.04890v2 Announce Type: replace-cross Abstract: This work introduces a novel principle for disentanglement we call mechanism sparsity regularization, which applies when the latent factors of
arXiv:2607.29633v1 Announce Type: new Abstract: Single-image 3D hand avatar reconstruction is fundamentally ill-posed and particularly challenging due to limited visual evidence under severe self-occl