Knowledge catalogue
arXiv:2607.08554v1 Announce Type: new Abstract: For urban managers and designers, improving the functional attributes of urban communities to enhance territorial resilience in the face of complexity a
arXiv:2508.11847v4 Announce Type: replace-cross Abstract: We propose a method for evaluating the robustness of widely used LLM ranking systems -- variants of a Bradley--Terry model -- to dropping a wo
arXiv:2607.08403v1 Announce Type: new Abstract: The application of lightweight Large Language Models in rule-based scientific domains remains severely limited by their tendency to mimic linguistic pat
arXiv:2607.07720v1 Announce Type: cross Abstract: Sleep physiology arises from the coordinated dynamics of the central nervous system (CNS) and autonomic nervous system (ANS), as reflected by multimod
Today, together with @kyutai_labs, we’re introducing our new Audio-to-MIDI model. It takes a finished recording, identifies the instruments playing, and returns separate MIDI tracks for each — voice,
arXiv:2403.01560v3 Announce Type: replace Abstract: Inspired by the impressive success of image-text foundation models, recent works have proposed to adapt these foundation models to video data, leadi
Cursor shared a link to their evals page that provides a comprehensive comparison of different AI models' performance and capabilities. The page likely displays benchmarks, metrics, or evaluation resu
arXiv:2607.05859v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are promising for construction-site monitoring, and recent construction-tailored VLMs have primarily adapted pretrained VL
arXiv:2606.08048v1 Announce Type: cross Abstract: Diffusion language models (DLMs) offer substantial speed advantages through parallel decoding, but the lack of token dependencies limits generation qu
arXiv:2601.21738v2 Announce Type: replace-cross Abstract: Evaluation of Image Quality Assessment (IQA) models has long been dominated by global correlation metrics, such as Pearson Linear Correlation
arXiv:2607.05481v1 Announce Type: cross Abstract: Detection models running in adversarial environments face a malicious distribution that drifts rapidly while the benign distribution stays comparative
arXiv:2607.06501v1 Announce Type: new Abstract: We consider an open-world planning setting in which service robots must operate in unknown environments with incomplete knowledge of objects and actions
arXiv:2607.05408v1 Announce Type: cross Abstract: The environmental impact of training large language models (LLMs) is increasingly scrutinised, yet most published estimates focus on operational energ
arXiv:2409.06067v3 Announce Type: replace Abstract: Previous studies on federated learning (FL) often encounter performance degradation due to data heterogeneity among different clients. In light of t
most agent labs are shy about acknowledging chinese model use because they need to sell to gov/defense cog team did the hard part to productionize: 1. build a multilingual propaganda & censorship eval
arXiv:2607.05577v1 Announce Type: new Abstract: Long-form fiction writers need memory that answers multi-hop questions about evolving story state: who knows a secret and when they learned it, whether
This entry covers the integration of vLLM, a high-performance inference engine, with Hugging Face's transformers library to enable faster language model inference. The native-speed backend allows user
arXiv:2509.24966v2 Announce Type: replace Abstract: Understanding how people interact with their surroundings and each other is essential for enabling robots to act in socially compliant and context-a
arXiv:2607.05174v1 Announce Type: new Abstract: Language agents, i.e., LLM agents, progress rapidly and are increasingly deployed in production environments. This trend underscores the urgent need for
arXiv:2409.14086v2 Announce Type: replace-cross Abstract: There have been several studies on automatically generating piano covers, and recent advancements in deep learning have enabled the creation o
arXiv:2607.03162v1 Announce Type: new Abstract: LLM-powered agents struggle with personalization when users issue raw, underspecified queries. In this setting, agents must infer latent intent, extract
arXiv:2607.05242v1 Announce Type: cross Abstract: Personalized incentive allocation is vital for e-commerce, where uplift modeling is the standard for estimating Individual Treatment Effects (ITE). Ho
arXiv:2607.02770v1 Announce Type: cross Abstract: We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family. Designed to advance compute effi
arXiv:2607.05257v1 Announce Type: new Abstract: Origin-destination (OD) flow modeling underpins urban planning and mobility analysis, but prevailing graph-based methods often neglect salient geographi
arXiv:2607.02980v1 Announce Type: cross Abstract: Scaling modern large language models (LLMs) to long contexts is limited by the quadratic computation cost, and poor length extrapolation of dense atte
arXiv:2607.03558v1 Announce Type: cross Abstract: Continuous video anomaly detection is dominated by reactive Multiple Instance Learning (MIL) that collapses spatiotemporal features into scalar scores
arXiv:2607.03644v1 Announce Type: cross Abstract: Decades of orbital missions have produced multi-modal remote sensing data for the Moon, spanning optical imagery, spectroscopy, thermal emission, rada
arXiv:2607.03855v1 Announce Type: new Abstract: We address the inverse problem of blind 3D scene reconstruction from extremely motion-blurred images, a scenario where traditional Structure-from-Motion
arXiv:2607.03788v1 Announce Type: new Abstract: Discrete diffusion promises orders-of-magnitude faster generation than autoregressive (AR) models for sequential discrete data, yet its full potential o
arXiv:2502.12096v5 Announce Type: replace-cross Abstract: In this paper, we introduce token communications (TokCom), a large model-driven framework to leverage cross-modal context information in gener
arXiv:2607.04686v1 Announce Type: cross Abstract: Tool calling is central to modern language model agents, but aggregate benchmark scores often hide where tool use fails. A model that never calls a ne
arXiv:2607.05055v1 Announce Type: new Abstract: Healthcare appointment scheduling remains a persistent operational bottleneck, driven by manual coordination, fragmented legacy systems, and high admini
arXiv:2607.02608v1 Announce Type: cross Abstract: Lightweight neuromorphic computing offers a promising route to efficient AI, with particular benefits for resource-constrained edge deployments. Howev
arXiv:2509.25136v3 Announce Type: replace Abstract: Activation-aware low-rank factorization techniques yield strong compression results but are generally confined to linear layers, while existing whit
Tesla has begun initial production of the Model Y Long Wheelbase variant at its Gigafactory Texas facility. The Long Wheelbase version offers extended interior space and cargo capacity compared to the
arXiv:2607.01502v1 Announce Type: new Abstract: Recent advances in automatic speech recognition (ASR) have explored different sequence models, including Conformer-based models and newer state space mo
arXiv:2607.00678v1 Announce Type: new Abstract: Mobile manipulation is a key capability for general-purpose robots, yet remains challenging for current embodied learning methods. VLA policies are typi
This post critiques Fable AI's text generation style, suggesting it produces overly verbose or exaggerated outputs that parody Claude's characteristic writing patterns taken to an extreme. The comment
arXiv:2607.00275v1 Announce Type: cross Abstract: Federated Learning (FL) is a distributed machine learning (ML) paradigm with collaboration among multiple clients without sharing data. FL is challeng
arXiv:2607.01012v1 Announce Type: new Abstract: Data assimilation models state dynamics conditioned on sequential observations, and has wide-ranging scientific applications. In the filtering setting,
arXiv:2606.13695v2 Announce Type: replace-cross Abstract: Mineral prospectivity modelling (MPM) underpins exploration economics, yet most operational pipelines reduce to data-driven classifiers traine
arXiv:2603.22055v2 Announce Type: replace-cross Abstract: Underground mining robots are increasingly modeled for planning, operator training, and digital-twin workflows, where reliable actuator-level
arXiv:2607.00248v1 Announce Type: new Abstract: We present Seed2.0, a model series that takes a meaningful step toward solving complex, real-world tasks. Our approach begins with identifying users' ge
arXiv:2606.31483v1 Announce Type: new Abstract: Personalized driving can improve the user acceptance of automated driving systems. However, existing methods still provide limited support for translati
arXiv:2404.10034v3 Announce Type: replace Abstract: Weakly Supervised Object Localization (WSOL) allows training deep learning models for classification and localization (LOC) using only global class-
arXiv:2511.17979v2 Announce Type: replace Abstract: Diffusion models have achieved remarkable success in generative modeling, yet how to effectively adapt large pretrained models to new tasks remains
arXiv:2606.28578v1 Announce Type: cross Abstract: Closed-loop materials discovery iterates between proposing candidate structures and evaluating their properties, and property evaluation dominates the
arXiv:2511.05934v2 Announce Type: replace Abstract: Generative modeling frameworks have emerged as an effective approach to capture high-dimensional image distributions from large datasets without req
arXiv:2606.20470v2 Announce Type: replace-cross Abstract: Agentic AI systems increasingly rely on language-model components to interpret instructions, process external data, invoke tools, and coordina
arXiv:2606.30319v1 Announce Type: new Abstract: Modeling the bidirectional correspondence between external sensory stimuli and internal neural activity has emerged as a critical frontier in neuroscien
arXiv:2509.21530v2 Announce Type: replace Abstract: Data augmentation is a widely used strategy to improve model robustness and generalization by enriching training datasets with synthetic examples. W
arXiv:2606.30054v1 Announce Type: new Abstract: The advancement of generative AI models capable of producing text and image marks a critical step forward in the realm of multimodal intelligence, parti
arXiv:2606.29697v1 Announce Type: new Abstract: For uncrewed aerial vehicles (UAVs), estimating six-degree-of-freedom (6-DoF) poses is essential for airspace situational awareness, target tracking, an
arXiv:2405.16472v2 Announce Type: replace Abstract: Contemporary AI faces the challenge of balancing generality with user-specific personalization. In federated learning (FL), this challenge is amplif
arXiv:2606.28573v1 Announce Type: new Abstract: Modern machine learning models are trained by optimizing high-dimensional non-convex empirical risk functions. Such cost functions can have a multitude
arXiv:2606.27547v1 Announce Type: new Abstract: Human motion generation models are fundamentally constrained by the limited diversity of motion capture datasets, which predominantly contain common, re
arXiv:2606.27484v1 Announce Type: new Abstract: Autism spectrum disorder (ASD) affects 1 in 31 US children, yet median age at diagnosis exceeds four years. Artificial intelligence pipelines that provi
arXiv:2606.28182v1 Announce Type: cross Abstract: Embodied agents operating in decentralized and partially observable environments have attracted growing attention in recent years. However, existing l
arXiv:2602.22284v3 Announce Type: replace Abstract: Recent advancements in deep learning have actively addressed complex challenges within the Computer-Aided Design (CAD) domain.However, most existing
arXiv:2606.26373v1 Announce Type: cross Abstract: Dense embeddings power semantic search and retrieval-augmented generation, but embedding-inversion attacks can reconstruct source text from a vector: