Knowledge catalogue
arXiv:2610.15064v1 Announce Type: new Abstract: The world has seen several threats that include resource depletion, food security, poverty and inequality, difficulties associated with urbanization, mi
arXiv:2610.15297v1 Announce Type: new Abstract: Recently, AutoRegressive Visual Generation (ARVG) models have emerged as a competitive paradigm for high-quality image synthesis. However, their substan
arXiv:2610.17684v1 Announce Type: new Abstract: High-fidelity surgical video generation is increasingly important for surgical education, simulation, and downstream AI training, as conventional simula
arXiv:2610.14093v1 Announce Type: new Abstract: Despite advancements in sub-pixel decoding, modern Human Pose Estimation (HPE) architectures remain constrained by deterministic coordinate extraction f
arXiv:2610.14723v1 Announce Type: new Abstract: A method integrating policy gradient with fuzzy inference has been proposed. This method, hereafter referred to as the base model, has high flexibility
arXiv:2610.14743v1 Announce Type: new Abstract: We propose a Japanese handwriting generation framework that integrates human preference feedback using Direct Preference Optimization (DPO) to align gen
arXiv:2610.14290v1 Announce Type: new Abstract: The inner workings of deep neural networks are still not well understood. The information bottleneck framework, which characterizes deep networks by the
arXiv:2603.01786v2 Announce Type: replace Abstract: In this paper, we present a novel learning framework for finding shortest paths in graphs utilizing Generative Flow Networks (GFlowNets). First, we
arXiv:2610.14268v1 Announce Type: cross Abstract: The scalability and performance of edge federated learning are fundamentally constrained by synchronization bottlenecks. Clients operate under heterog
arXiv:2610.16224v1 Announce Type: new Abstract: Text-to-image models have made remarkable progress in visual fidelity and instruction following, yet complex compositional generation remains challengin
arXiv:2610.16368v1 Announce Type: new Abstract: Looped Language Models (LoopLMs) improve parameter efficiency by recursively reusing shared Transformer modules, yet their repeated computation over evo
arXiv:2610.14492v1 Announce Type: cross Abstract: Microsurgical anastomosis is one of the most challenging and sophisticated procedures for surgical robotics, requiring accurate manipulation of surgic
arXiv:2507.05291v2 Announce Type: replace Abstract: We propose a physics-informed machine learning framework called P-DivGNN to reconstruct local stress fields at the micro-scale, in the context of mu
arXiv:2610.18664v1 Announce Type: new Abstract: A patient's pathology case spans tens of slides from distinct specimens and anatomical sites, each carrying non-redundant, site-specific evidence. Formi
arXiv:2610.17936v1 Announce Type: new Abstract: Dynamic 3D Gaussian Splatting (3DGS) provides an efficient framework for dynamic scene reconstruction. Most existing approaches model scene dynamics wit
arXiv:2507.21437v3 Announce Type: replace Abstract: Physics-informed neural networks and physics-informed DeepONet excel at solving partial differential equations but often struggle with singularly pe
arXiv:2610.16302v1 Announce Type: new Abstract: Video-based interactive world models aim to enable real-time, user-controllable environments. Yet most existing systems are trained and rolled out at a
arXiv:2610.14474v1 Announce Type: new Abstract: Medication recommendations seek to deliver personalized drug combinations based on patients' clinical information, where ensuring transparent decision-m
arXiv:2610.14572v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards improves the reasoning capabilities of large language models, but remains computationally expensive and s
arXiv:2610.14911v1 Announce Type: cross Abstract: Multi-human cooperative object manipulation is a highly challenging topic in humanoid control. While existing methods can handle simple collaborative
arXiv:2610.14866v1 Announce Type: new Abstract: Cross-modal Thermal Geo-localization (TG) provides an illumination-robust positioning solution for Unmanned Aerial Vehicles (UAVs) in Global Navigation
arXiv:2610.15258v1 Announce Type: cross Abstract: We introduce Sequent-Prover, a family of verifier-using language-model agents, and the post-training framework that produces them. Agents reason in an
arXiv:2603.28916v2 Announce Type: replace Abstract: Football action-value approaches estimate scoring, possession, or other downstream outcome value; complementary descriptions can characterise how a
arXiv:2610.17069v1 Announce Type: new Abstract: In Nonlinear Model Predictive Control (NMPC), the control law is implicitly determined by the solution of an Optimal Control Problem (OCP). Consequently
arXiv:2610.14944v1 Announce Type: new Abstract: Specifying segmentation targets with natural language provides flexible semantic conditioning for medical image segmentation. However, existing methods
arXiv:2610.17636v1 Announce Type: new Abstract: The Safety-Aware Denoiser (SAD) is a recent, training-free, safety-guidance framework for text diffusion models. During inference, SAD steers text sampl
arXiv:2610.14590v1 Announce Type: cross Abstract: Wheel odometry provides an efficient motion prior for ground robots but is prone to transient violations such as wheel slip or terrain-induced tilt. I
arXiv:2610.15533v1 Announce Type: new Abstract: 3D editing aims to modify an existing 3D asset according to user intent while preserving geometry and appearance outside the intended edit region. Exist
arXiv:2610.18066v1 Announce Type: new Abstract: Reliable uncertainty evaluation is essential for assessing the trustworthiness of machine learning (ML) predictions, particularly in safety-critical app
arXiv:2610.16146v1 Announce Type: new Abstract: We present Unravel, a training-free framework that turns a single image into an interactive 3D scene, where every object is an independent and movable a
arXiv:2610.16793v1 Announce Type: new Abstract: Interacting with unfamiliar objects requires understanding how their parts connect and move. We introduce ViewStruct, a framework for jointly predicting
arXiv:2610.18147v1 Announce Type: new Abstract: We present a real-time framework for expressive legged locomotion that enables independent control over velocity and style. Unlike existing data-driven
arXiv:2610.15929v1 Announce Type: new Abstract: Recent trackers built on foundation models such as the Segment Anything Model (SAM) achieve strong performance by propagating object masks through memor
arXiv:2610.14861v1 Announce Type: new Abstract: Time-series forecasting plays a vital role in a wide range of real-world applications. Recent approaches have achieved strong performance by modeling te
arXiv:2610.13273v1 Announce Type: cross Abstract: API specifications serve as critical documentation for modern software development, directly impacting integration efficiency and client code generati
arXiv:2610.12494v1 Announce Type: new Abstract: In this article, we propose algebraic integer number formats (AINF) as a framework for highly configurable and accurate compressed computation. A key pr
arXiv:2510.00734v2 Announce Type: replace-cross Abstract: Bayesian optimal experimental design provides a principled framework for selecting experimental configurations that maximize obtained informat
arXiv:2603.07427v3 Announce Type: replace Abstract: As Large Language Models (LLMs) evolve into autonomous agents, existing safety evaluations face a fundamental trade-off: manual benchmarks are costl
arXiv:2610.12776v1 Announce Type: new Abstract: Automatic differentiation (AD) is a cornerstone of modern machine learning. Since all AD frameworks implicitly fix a basis of Euclidean space to define
arXiv:2610.12936v1 Announce Type: new Abstract: Vision-language-action (VLA) models provide a unified framework for visual understanding, language grounding, and robot control, yet anticipating how th
arXiv:2610.13025v1 Announce Type: new Abstract: Self-evolving agents are increasingly used to improve language model systems without human intervention through either weight updates or harness improve
arXiv:2610.12762v1 Announce Type: new Abstract: Hardware datasheets contain engineering specifications essential for component selection, system integration, and hardware compatibility verification. H
arXiv:2610.13849v1 Announce Type: new Abstract: We present HorizonNav, a video diffusion-based framework for anticipatory robot navigation that transforms a single RGB image and a text prompt into a g
arXiv:2610.13632v1 Announce Type: new Abstract: Large language model (LLM) agents are rapidly improving on long-horizon scientific and olympiad tasks. Recent systems have produced a Lean-formalized re
arXiv:2609.35328v2 Announce Type: replace Abstract: Meta-Black-Box Optimization (MetaBBO) is one of the highlights in the recent AI for Optimization trend. This paradigm's bi-level workflow leverages
arXiv:2610.13442v1 Announce Type: cross Abstract: Compression schemes provide a general framework for deriving data-dependent risk bounds for learning algorithms. For learning rules based on preferent
arXiv:2610.13386v1 Announce Type: new Abstract: Stance detection remains challenging for large language models (LLMs) due to implicit opinions, sarcasm, and context-dependent expressions of stance. Al
arXiv:2610.13305v1 Announce Type: new Abstract: We introduce MORAD, an online reinforcement learning framework for backbone-conditioned RNA diffusion models that trains a single shared policy across d
arXiv:2610.12754v1 Announce Type: new Abstract: We propose NV-Tesseract-AD, a curriculum learning-enhanced conditional diffusion framework for multivariate time-series anomaly detection. Our central h
arXiv:2610.12874v1 Announce Type: new Abstract: Long-term memory has been widely adopted in LLM-based agents to support persistent interaction and personalized assistance. Long-term multi-party intera
arXiv:2610.13221v1 Announce Type: new Abstract: Cancer diagnosis and treatment increasingly rely on multi-modal diagnostic technologies that generate high-dimensional datasets. Although these datasets
arXiv:2610.12864v1 Announce Type: new Abstract: Existing sequence models share a critical limitation: they lack an explicit, interpretable mechanism for content-aware temporal recall. LSTMs compress e
arXiv:2610.13098v1 Announce Type: new Abstract: Visual agents for symbolic graphics reasoning depend on executable world representations, yet continued improvement remains bottlenecked by ambiguous su
Mistral AI outlines its vision for engineering, manufacturing, and earth observation by demonstrating how the model can inspect mechanical components, extract evidence from PDFs, and analyze gigapixel
arXiv:2610.13886v1 Announce Type: new Abstract: Personal agents increasingly connect to services across the web and applications by using tools. As the effects of their actions accumulate and interact
arXiv:2509.02302v2 Announce Type: replace Abstract: We study history-dependent online problems with a possibly inaccurate prediction of the future request sequence. Motivated by several real-world app
arXiv:2507.14897v2 Announce Type: replace Abstract: Methods to build LLM agents have evolved from prompt engineering and supervised finetuning to agentic reinforcement learning (agentic RL). However,
arXiv:2610.12340v1 Announce Type: new Abstract: We introduce RefineMix, a framework for training discrete diffusion models under severe data scarcity, a common constraint in scientific applications. R
arXiv:2610.11537v1 Announce Type: cross Abstract: Flow models generate trajectories from an initial distribution to a target distribution by solving an ordinary differential equation defined by a velo
arXiv:2610.10875v1 Announce Type: new Abstract: Motivated by recent applications in generative modeling and sampling, we introduce a framework for optimal measure transport where cost captures the not