Knowledge catalogue
arXiv:2605.26788v1 Announce Type: cross Abstract: Large language models (LLMs) achieve impressive performance when a task is fully specified in a single turn, yet the same models lose up to 39% of tha
arXiv:2601.07737v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance in mainstream visual understanding tasks, but their ability
arXiv:2506.07813v2 Announce Type: replace-cross Abstract: Arbitrary-scale image super-resolution aims to upsample images to any desired resolution, offering greater flexibility than traditional fixed-
arXiv:2605.26744v1 Announce Type: new Abstract: Human motion generation has made tremendous progress in recent years, with state-of-the-art approaches surpassing ground truth data in leading evaluatio
arXiv:2510.06843v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have exhibited impressive capabilities across diverse application domains. Recent work has explored Multi-LLM Age
arXiv:2605.26132v1 Announce Type: new Abstract: Can post-trained large language models (LLMs) further improve themselves using only unlabeled prompts, without external teachers or feedback from tools?
arXiv:2605.27080v1 Announce Type: new Abstract: Appearance-based gaze estimation always suffers from poor generalization due to limited annotated samples and insufficient dataset diversity. Leading ap
arXiv:2605.26186v1 Announce Type: cross Abstract: Functionality-correct repository setup aims to configure execution environments (e.g., dependencies, build scripts) to successfully execute a reposito
arXiv:2605.27030v1 Announce Type: new Abstract: Test-Time Scaling (TTS) enhances the reasoning capabilities of large language models by allocating additional inference compute to explore the solution
arXiv:2605.27276v1 Announce Type: new Abstract: Humans are the bottleneck in building and improving AI. Both the models and the agents that wrap them are written, tuned, and corrected by people. The l
arXiv:2605.26509v1 Announce Type: new Abstract: Gaussian processes (GPs) provide a principled Bayesian framework for uncertainty estimation, but their computational complexity severely limits scalabil
arXiv:2605.26135v1 Announce Type: new Abstract: Unsupervised anomaly detection is widely used in transaction fraud detection where labels are scarce. Isolation Forest (IF) is among the most popular cl
arXiv:2605.26704v1 Announce Type: cross Abstract: Epidemic forecasting faces a fundamental challenge: human behavior dynamically responds to disease spread, creating feedback loops that induce distrib
arXiv:2508.05305v2 Announce Type: replace Abstract: The recently proposed Large Concept Model (LCM) generates text by predicting a sequence of sentence-level embeddings and training with either mean-s
arXiv:2601.08375v2 Announce Type: replace Abstract: Semantic segmentation of 3D geospatial point clouds is fundamental to remote sensing applications, yet domain shifts caused by regional and acquisit
arXiv:2605.27367v1 Announce Type: new Abstract: While spatial foundation models have demonstrated impressive performance on standard datasets, a critical question remains: are they truly all-round pla
arXiv:2605.26606v1 Announce Type: cross Abstract: Reinforcement learning (RL) is the dominant paradigm for post-training large language models. However, in the online, on-policy setting, rollout gener
arXiv:2605.26900v1 Announce Type: new Abstract: A fundamental open question in self-supervised learning (SSL) is the explicit characterization of the optimal geometry of the learned representations. R
arXiv:2604.18103v2 Announce Type: replace Abstract: Prefilling computational costs pose a significant bottleneck for Large Language Models (LLMs) and Large Multimodal Models (LMMs) in long-context set
arXiv:2605.26682v1 Announce Type: cross Abstract: This dataset provides high-resolution, annotated video sequences of shredded E40-grade steel and copper scrap on a conveyor belt. Captured in a contro
Stronger models do not always need lighter harnesses. Everyone believes more structured harnesses universally improve reliability, and that higher-capability models need proportionally less structural
arXiv:2508.07996v2 Announce Type: replace Abstract: Group Activity Detection (GAD) involves recognizing social groups and their collective behaviors in videos. Vision Foundation Models (VFMs), like DI
Super happy that a bunch of people are finding marlin useful. Thank you for the inference support @ZeroGPU_AI @huggingface 🥰🤝 We’ve got hands on more compute now so we’ll also release a series of blog
arXiv:2605.26436v1 Announce Type: cross Abstract: Discrete masked diffusion language models such as LLaDA generate text through iterative denoising, where mask tokens are progressively replaced with p
arXiv:2605.27239v1 Announce Type: new Abstract: Annotation quality is difficult to sustain when campaigns span weeks or months with small annotator pools. We present a Setswana sentiment dataset of 3,
arXiv:2605.26778v1 Announce Type: new Abstract: Retrieval-augmented generation promises to ground language model outputs in external evidence, yet the field has no reliable way to verify whether retri
arXiv:2605.26246v1 Announce Type: new Abstract: Knowledge distillation (KD) transfers knowledge from a large teacher model to a smaller student. In language modeling, the student is trained either on
arXiv:2605.26830v1 Announce Type: cross Abstract: State estimation is a fundamental problem in control and signal processing, for which the Kalman Filter provides an optimal solution under linear dyna
arXiv:2605.26494v1 Announce Type: new Abstract: We introduce the MiniMax-M2 series, a family of Mixture-of-Experts language models built around the principle that mini activations can unleash maximum
arXiv:2505.11063v3 Announce Type: replace Abstract: LLM-based agents solve complex tasks through iterative reasoning, tool use, and environment interaction, where each intermediate thought directly sh
arXiv:2605.27003v1 Announce Type: cross Abstract: W4A4 quantization of large video diffusion Transformers offers substantial memory savings but is hindered by two main challenges: sparse large-magnitu
arXiv:2605.26463v1 Announce Type: cross Abstract: Data consistency between unstructured clinical notes and structured tables in Electronic Health Records (EHRs) is essential for patient safety and cli
arXiv:2605.26776v1 Announce Type: cross Abstract: In recent years, Deep Reinforcement Learning (DRL) has achieved substantial progress on Vehicle Routing Problems (VRPs). However, existing DRL-based m
arXiv:2605.27038v1 Announce Type: new Abstract: Vision-Language Models (VLMs) provide a promising foundation for autonomous driving planning, yet bridging semantic reasoning and precise 3D spatial for
arXiv:2605.27071v1 Announce Type: new Abstract: Key knowledge for steel-industry volatile organic compounds (VOCs) governance is scattered across unstructured scientific literature, making it difficul
arXiv:2605.26576v1 Announce Type: new Abstract: Referring 3D Gaussian Splatting (R3DGS), which utilizes natural language for 3D object segmentation, has emerged as a crucial capability for embodied AI
arXiv:2605.26156v1 Announce Type: cross Abstract: The known stylistic biases in LLM judges, such as a preference for verbosity or specific sentence structures, present an underexplored security vulner
arXiv:2605.26503v1 Announce Type: new Abstract: Vision-Language Navigation (VLN) requires an agent to navigate 3D environments following natural language instructions. During navigation, existing agen
arXiv:2605.26447v1 Announce Type: new Abstract: Underwater scene reconstruction is essential for immersive exploration of aquatic environments, yet remains challenging due to complex participating-med
arXiv:2510.17759v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) extend large language models with visual reasoning, but their multimodal design also introduces new, underexplor
arXiv:2605.26457v1 Announce Type: cross Abstract: AI coding agents are increasingly used to write real-world software, but ensuring that their outputs are correct remains a fundamental challenge. Form
arXiv:2507.06513v3 Announce Type: replace Abstract: Advances in vision-based sensors and computer vision algorithms have significantly improved the analysis and understanding of traffic scenarios. To
arXiv:2605.26183v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) have emerged as a structurally natural approach for molecular toxicity prediction, operating directly on atomic connectiv
arXiv:2605.24906v2 Announce Type: replace Abstract: Detecting AI-generated images (AIGI) remains challenging because detectors often fail to generalize to unseen generators. Although existing methods
arXiv:2605.26200v1 Announce Type: cross Abstract: This paper argues that workflow closure is not scientific closure in auto-research systems. Current systems can increasingly complete research-like lo
arXiv:2605.26383v1 Announce Type: new Abstract: Object re-identification (ReID) in egocentric kitchen videos is challenging due to rapid viewpoint changes, frequent occlusions, cluttered scenes, and l
arXiv:2605.25640v1 Announce Type: cross Abstract: Accurate reconstruction of magnetic fields in inaccessible regions is vital for many high-precision experiments in physics. Traditional methods, such
arXiv:2605.24274v1 Announce Type: new Abstract: Input-convex neural networks (ICNNs) are widely used for log-concave density estimation, convex-potential normalizing flows, optimal transport, and tran
arXiv:2605.25536v1 Announce Type: cross Abstract: Context. Large language models (LLMs) are increasingly applied to code-generating tasks (CGTs) in software engineering. While reported results are pro
arXiv:2605.25652v1 Announce Type: new Abstract: Free-form legal essay evaluation in NLP treats expert inter-rater stability as a single ceiling number, and treats LLM-judge agreement with that ceiling
arXiv:2605.25156v1 Announce Type: cross Abstract: Prediction models trained under the source distribution do not generalize well to a different target distribution. A valid inference about an unseen d
arXiv:2605.24011v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models exhibit remarkable action generation for embodied intelligence, but their heavy compute make deployment on edge pl
arXiv:2605.25814v1 Announce Type: cross Abstract: Dirty entity resolution (ER), which identifies records referring to the same real-world entity from a single, messy dataset, is a fundamental task in
arXiv:2605.24410v1 Announce Type: new Abstract: Graph few-shot learning, which aims to classify nodes from novel classes with only a few labeled examples, is a widely studied problem in graph learning
arXiv:2605.25377v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have advanced multimodal understanding, yet their reliability is limited by hallucination, where generated conten
arXiv:2605.24203v1 Announce Type: new Abstract: Vision-language-action (VLA) models have shown strong potential for generalist robot manipulation, yet they remain limited by insufficient spatial reaso
arXiv:2605.25748v1 Announce Type: new Abstract: Trajectory prediction methods have demonstrated remarkable capabilities in capturing complex motion patterns. However, existing methods rely on global s
arXiv:2605.24216v1 Announce Type: cross Abstract: Monitoring autonomous large language model (LLM) agents for covert malicious behavior is challenging due to delayed, context-dependent, and long-horiz
arXiv:2605.23978v1 Announce Type: new Abstract: In algorithmic markets, predictive models become part of the data-generating process they aim to forecast. Once their outputs are converted into trades,
arXiv:2605.25166v1 Announce Type: cross Abstract: Time series forecasting models are increasingly scaled through large Transformer backbones, yet most existing approaches process all series through a