Knowledge catalogue
arXiv:2605.16238v1 Announce Type: new Abstract: Probabilistic forecasting of infectious diseases is crucial for public health but relies on labor-intensive manual model curation by expert modeling tea
arXiv:2605.15609v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) generate text by iteratively denoising masked token sequences. Although dLLMs can predict all masked positions i
arXiv:2511.09884v2 Announce Type: replace Abstract: Mission critical (MC) applications such as defense operations, energy management, cybersecurity, and aerospace control require reliable, determinist
arXiv:2512.04457v2 Announce Type: replace Abstract: Removing specific data influence from large language models (LLMs) remains challenging, as retraining is costly and existing approximate unlearning
arXiv:2603.18396v3 Announce Type: replace Abstract: Bus holding control is challenging due to stochastic traffic and passenger demand. While deep reinforcement learning (DRL) shows promise, standard a
arXiv:2605.15782v1 Announce Type: new Abstract: In this work, we address the problem of ensuring real-time safety in autonomous robot navigation, in spatially constrained dynamic environments, by util
arXiv:2605.15243v1 Announce Type: cross Abstract: When reliable target structures are unavailable at scale or phenotypes arise from dysregulated pathways, transcriptomic perturbations provide a system
arXiv:2605.16080v1 Announce Type: new Abstract: The rise of AI-generated images (AIGIs) poses growing challenges for digital authenticity, prompting the need for efficient, generalizable image forgery
arXiv:2605.16052v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have significantly enhanced automated legal reasoning. Yet, it remains unclear whether their performance
arXiv:2602.16274v2 Announce Type: replace Abstract: We present the first regret bound for classical online Q-learning in infinite-horizon discounted Markov decision processes (MDPs), without relying o
arXiv:2605.15480v1 Announce Type: cross Abstract: Stochastic communication delays in teleoperation introduce signal discontinuities that undermine control stability and degrade control performance. Co
arXiv:2605.15792v1 Announce Type: new Abstract: The long-standing goal of multimodal AI is to build unified models in which visual understanding and visual generation mutually enhance one another. Des
arXiv:2605.16065v1 Announce Type: cross Abstract: 3D Gaussian Splatting (3D-GS) enables real-time 3D scene reconstruction but lacks robust segmentation for editing tasks such as object removal, extrac
arXiv:2510.02734v2 Announce Type: replace-cross Abstract: Deep learning, particularly with the advancement of Large Language Models, has transformed biomolecular modeling, with protein language models
arXiv:2507.17572v3 Announce Type: replace Abstract: Global optimization has gained attraction over the past decades, thanks to the development of both theoretical foundations and efficient numerical r
arXiv:2601.21636v3 Announce Type: replace Abstract: We study privacy amplification for differentially private model training with matrix factorization under random allocation (also known as the balls-
arXiv:2510.22665v3 Announce Type: replace-cross Abstract: Synthetic Aperture Radar (SAR) is a critical imaging modality due to its all-weather operational capability. Although recent advances in self-
arXiv:2504.00663v2 Announce Type: replace Abstract: Existing hardware-aware NAS (HW-NAS) methods typically assume access to precise information circa the target device, either via analytical approxima
arXiv:2605.16165v1 Announce Type: cross Abstract: Autoregressive next-token training offers a unified formulation for image generation and text understanding, but it also creates strong modality compe
arXiv:2511.17426v2 Announce Type: replace-cross Abstract: Self-supervised learning (SSL) has recently advanced through non-contrastive methods that couple an invariance term with variance, covariance,
arXiv:2510.25404v3 Announce Type: replace-cross Abstract: Optimizing an experimental system can be extremely challenging when each experiment is expensive, time-consuming, or difficult to perform. Exi
arXiv:2605.15720v1 Announce Type: new Abstract: Medical referring image segmentation (MRIS) requires pixel-level masks aligned with textual descriptions of anatomical locations, making annotation cost
arXiv:2605.15877v1 Announce Type: cross Abstract: Continual learning enables neural networks to learn tasks sequentially without forgetting previously acquired knowledge. However, neural networks suff
arXiv:2604.02268v2 Announce Type: replace Abstract: Agent skills, structured packages of procedural knowledge and executable resources that agents dynamically load at inference time, have become a rel
arXiv:2605.15424v1 Announce Type: new Abstract: Human trajectory forecasting is crucial for safe navigation in crowded environments, requiring models that balance accuracy with computational efficienc
arXiv:2605.15307v1 Announce Type: cross Abstract: Motion-centric video editing remains difficult for large generative video models, which often respond well to appearance changes but struggle to produ
arXiv:2507.15778v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become an effective post-training method for improving the reasoning abilities of Large La
arXiv:2603.05917v3 Announce Type: replace-cross Abstract: Stock market prediction presents considerable challenges for investors, financial institutions, and policymakers operating in complex market e
arXiv:2605.15508v1 Announce Type: cross Abstract: The quadratic complexity of attention imposes severe memory and computational bottlenecks on Large Language Model (LLM) inference. This challenge is p
arXiv:2605.16163v1 Announce Type: cross Abstract: Skillful medium-range precipitation forecasting at kilometer scale remains challenging over complex terrain because precipitation arises from multisca
arXiv:2503.01125v4 Announce Type: replace Abstract: Although acrobatic flight control has been studied extensively, one key limitation of the existing methods is that they are usually restricted to sp
arXiv:2605.15284v1 Announce Type: new Abstract: We introduce Tadpole, a novel foundation model for three-dimensional partial differential equations (PDEs) that addresses key challenges in transferabil
arXiv:2605.15528v1 Announce Type: new Abstract: Autonomous underwater vehicle (AUV) swarms are emerging as intelligent underwater networks, where each node must sense, communicate, process local data,
arXiv:2605.15207v1 Announce Type: new Abstract: Multi-agent LLM systems have shown promise for complex reasoning, yet recent evaluations reveal they often underperform single-model baselines. We ident
arXiv:2505.15692v5 Announce Type: replace Abstract: Reinforcement learning (RL) has emerged as an effective paradigm for enhancing model reasoning. However, existing RL methods like GRPO typically rel
The Open Agent Leaderboard is a benchmarking system hosted on Hugging Face that evaluates and ranks AI agents based on their performance across various tasks and capabilities. It provides a standardiz
arXiv:2605.15692v1 Announce Type: new Abstract: We study episodic reinforcement learning with fixed reward and transition functions, but with episode-dependent admissible action sets that are observed
arXiv:2605.15812v1 Announce Type: cross Abstract: Recent advances in foundation models have enabled conversational agents that aim for sustained companionship rather than mere task completion. Yet mos
arXiv:2602.08324v3 Announce Type: replace Abstract: Chain-of-Thought (CoT) reasoning successfully enhances the reasoning capabilities of Large Language Models (LLMs), yet it incurs substantial computa
arXiv:2605.16087v1 Announce Type: cross Abstract: Deep Neural Networks have become the dominant solution for Autonomous Driving perception, but their opacity conflicts with emerging Trustworthy AI gui
arXiv:2605.15608v1 Announce Type: new Abstract: Decoder-only transformers compute the conditional probability of the next token from a sequence of past observations. This paper derives, from first pri
arXiv:2412.06853v4 Announce Type: replace-cross Abstract: This paper proposes a novel loss function, called 'Tube Loss', for simultaneous estimation of bounds of a Prediction Interval (PI) in the regr
arXiv:2605.15533v1 Announce Type: cross Abstract: Video editing poses a significant challenge. While a series of tuning-free methods circumvent the need for extensive data collection and model trainin
arXiv:2605.15579v1 Announce Type: cross Abstract: To fit diverse display and bandwidth constraints, high-frame-rate videos are temporally downscaled to low-frame-rate (LFR) and later upscaled, requiri
arXiv:2605.15421v1 Announce Type: new Abstract: In this study, we explore in depth a few under-studied topics at the intersection of uncertainty estimation and segmentation. Prior work has shown that
arXiv:2605.15894v1 Announce Type: cross Abstract: Rapid and accurate wildfire smoke severity assessment from satellite images is essential for emergency response, air quality modeling, and human healt
arXiv:2605.16230v1 Announce Type: cross Abstract: Magnetic order is a fundamental property of materials, governing collective behavior and enabling a broad range of functionalities. Yet magnetic struc
arXiv:2605.15876v1 Announce Type: new Abstract: Vision-Language Models (VLMs) excel at 2D tasks such as grounding and captioning, yet remain limited in 3D understanding. A key limitation is their text
arXiv:2605.16020v1 Announce Type: new Abstract: Monte Carlo methods are essential across diverse scientific fields, yet their efficiency is frequently hampered by critical slowing down-a sharp increas
arXiv:2511.03606v2 Announce Type: replace-cross Abstract: The study of self-normalized processes plays a crucial role in a wide range of applications, from sequential decision-making to econometrics.
arXiv:2504.05451v2 Announce Type: replace Abstract: Traditional methods for view-invariant learning rely on controlled multi-view training data with minimal scene clutter. However, they struggle with
arXiv:2511.02342v3 Announce Type: replace Abstract: Aerial manipulation combines the maneuverability of multirotors with the dexterity of robotic arms to perform complex tasks in cluttered spaces. Yet
arXiv:2605.15843v1 Announce Type: new Abstract: Recent 3D world modeling systems based on generative scene synthesis, such as Marble, can create coherent and explorable 3D environments, yet their outp
arXiv:2605.15964v1 Announce Type: cross Abstract: Aerial vision-language navigation (VLN) requires agents to follow natural-language instructions through closed-loop perception and action in 3D enviro
arXiv:2605.15505v1 Announce Type: new Abstract: In enterprise operations, the context required for an AI agent task is scattered across systems of record, static information stores, and communication
arXiv:2605.16046v1 Announce Type: cross Abstract: Semantic code search has been widely adopted in both academia and industry. These approaches embed natural-language queries and code snippets into a s
LlamaIndex held an offsite event in 2026, as announced by Jerry Liu, the project's founder, on social media. The post likely covered updates on the project's direction, team initiatives, or announceme
Often overlooked is the Frankfurt School’s central role, alongside French postmodernists, in forging Critical Theory and exporting it to America, where it evolved into one of the most influential (des
arXiv:2605.13858v1 Announce Type: cross Abstract: Large Language Models have demonstrated remarkable capabilities in generating contextually relevant and grammatically correct text. However, they fund
arXiv:2605.14489v1 Announce Type: new Abstract: Building black-box models for dynamical systems from data is a challenging problem in machine learning, especially when asymptotic stability guarantees