Knowledge catalogue
arXiv:2610.17827v1 Announce Type: new Abstract: Existing approaches to training and evaluating search agents focus heavily on final-answer quality, largely neglecting how generated answers map back to
arXiv:2610.19254v1 Announce Type: new Abstract: Physics-informed neural networks provide a flexible framework for learning parametric PDE solution families without labeled data, yet preserving accurat
arXiv:2610.20367v1 Announce Type: new Abstract: Retrieving information from visually rich documents requires matching queries against pages that contain complex evidence in tables, figures, and layout
arXiv:2610.19306v1 Announce Type: new Abstract: Jailbreak attacks against large language models (LLMs) are evaluated using attack success rate (ASR), which depends on correctly identifying responses t
arXiv:2610.19523v1 Announce Type: new Abstract: Robotic exploration, coverage, and multi-agent deployment algorithms require reproducible environments with controllable geometry and visibility. This p
arXiv:2509.21193v2 Announce Type: replace Abstract: Large language models (LLMs) have recently shown strong progress on scientific reasoning, yet two major bottlenecks remain. First, explicit retrieva
arXiv:2112.09646v2 Announce Type: replace Abstract: The generation of discontinuous distributions is a difficult task for most known frameworks such as generative autoencoders and generative adversari
arXiv:2610.17763v1 Announce Type: new Abstract: Long-term memory supports agents across sessions. Repeated retrieval and write-back can let one historical claim occupy multiple retrieval positions and
arXiv:2610.19587v1 Announce Type: new Abstract: Social psychology has long described how people can be persuaded to interpret a harmful act as moral. Recent research suggests that Large Language Model
arXiv:2610.15930v1 Announce Type: new Abstract: Border crossing points are stochastic service systems in which traffic demand, traveler categories, registration status, inspection requirements, staffi
arXiv:2610.19539v1 Announce Type: new Abstract: Aggregate performance metrics do not identify local modelling inaccuracies in machine learning (ML) surrogates used for thermal-system optimisation. Thi
arXiv:2610.15231v1 Announce Type: new Abstract: Generative artificial intelligence (GenAI) models, particularly large language models (LLMs), are increasingly used in educational settings for tasks su
arXiv:2610.14149v1 Announce Type: new Abstract: VETNET represents an ecosystem of live benchmark datasets and evaluation frameworks designed to accelerate AI-driven innovation in animal science and ve
arXiv:2610.17126v1 Announce Type: cross Abstract: The rapid proliferation of autonomous AI agents in decentralized ecosystems challenges existing identity paradigms. While human identity is traditiona
arXiv:2610.15623v1 Announce Type: new Abstract: Hybrid federated semantic communication enables user equipment (UEs) to contribute model updates or semantic features under a shared training framework.
arXiv:2610.15590v1 Announce Type: new Abstract: In practical engineering and scientific computing, a real physical system can be reduced to a boundary value problem, and the adequate boundary informat
arXiv:2610.16440v1 Announce Type: new Abstract: Medication recommendation requires jointly considering diagnoses, comorbidities, longitudinal patient history, and treatment interactions. Although rece
arXiv:2610.14598v1 Announce Type: new Abstract: The effective integration of features derived from RGB images and 3D point clouds presents a substantial challenge in the field of multimodal anomaly de
arXiv:2610.17926v1 Announce Type: new Abstract: Autoregressive video generation relies on memory retrieval to model long-range dependencies. This capability becomes particularly critical for multi-sho
arXiv:2610.14473v1 Announce Type: cross Abstract: We propose a new framework for banner ad auctions, leveraging diffusion models to allow for multiple bidders to co-win a single banner ad opportunity,
arXiv:2610.15274v1 Announce Type: new Abstract: Training humanoid robots to learn human behaviors requires expensive data collection, and human-robot interaction data can be even more difficult and co
arXiv:2610.15207v1 Announce Type: new Abstract: Large Language Models (LLMs) have been shown to be computationally expensive and vulnerable to jailbreak attacks, in which adversarial prompts bypass sa
arXiv:2610.17337v1 Announce Type: cross Abstract: Liveness requires a dynamical system to repeatedly accomplish a desired objective over time. In this work, we consider a particular form of liveness k
arXiv:2610.14445v1 Announce Type: new Abstract: In film production, chroma keying composites an approved foreground performance into a new environment, but conventional workflows require coordinated b
arXiv:2610.15735v1 Announce Type: new Abstract: This paper presents a geometry-aware predictive control framework for bipedal obstacle crossing across walking and running regimes. The proposed approac
arXiv:2610.18045v1 Announce Type: cross Abstract: High-fidelity three-dimensional turbulent flow fields are essential for scientific and engineering analyses, yet obtaining them through direct numeric
arXiv:2610.16548v1 Announce Type: new Abstract: Low dose Computed Tomography (CT) is the standard imaging modality for lung cancer screening. On CT scans, lung cancer typically manifests as pulmonary
arXiv:2610.15177v1 Announce Type: new Abstract: As agentic AI systems take on increasingly complex and consequential tasks, securing them against prompt injection becomes critical. Existing defenses o
arXiv:2610.18691v1 Announce Type: new Abstract: This paper extends the non-unique games framework introduced in Bandeira et al. (2020) to address symmetric structures. Precisely, we consider the probl
arXiv:2610.14840v1 Announce Type: new Abstract: We present a learning-based framework that enables a full-scale bipedal humanoid to sprint at over 7 m/s with motion-prior-guided gait and perform aggre
arXiv:2610.18374v1 Announce Type: new Abstract: RGB-based category-level 9D pose estimation remains highly challenging because a monocular system must generalize across unseen object instances while j
arXiv:2604.01477v2 Announce Type: replace Abstract: Reinforcement learning (RL) and model predictive control (MPC) offer complementary strengths, yet existing MPC-RL frameworks commonly rely on gradie
arXiv:2610.15585v1 Announce Type: new Abstract: World Action Models (WAMs), often built upon pretrained video-generation world models, have emerged as a promising framework for general-purpose and ope
arXiv:2610.15038v1 Announce Type: new Abstract: Real-world Vision-Language Navigation (VLN) requires agents to act under partial observability, where map structure, semantic cues, and target locations
arXiv:2610.18356v1 Announce Type: new Abstract: City-scale 3D grounding is commonly formulated as candidate ranking, which requires precomputed instance proposals and becomes inefficient in large outd
arXiv:2610.13663v1 Announce Type: new Abstract: Many machine learning approaches are two-level: an expert selects a class of methods, within this class the choice of key components is critical and usu
arXiv:2610.12658v1 Announce Type: new Abstract: We develop a data-driven sensor-placement framework and apply it to aerosol monitoring in the United Kingdom (UK). A trained convolutional Gaussian neur
arXiv:2502.08598v3 Announce Type: replace Abstract: Diffusion models have been widely adopted as a powerful generative framework across numerous scientific domains, yet their inherently iterative samp
Introducing QwenGyre, bringing elastic RL to extreme-long-horizon (XLong) agent training. ✨ It launches with two core designs: 🥳 - Elastic Scheduler: reallocates GPUs between rollout & training as dem
arXiv:2601.07665v2 Announce Type: replace-cross Abstract: We introduce a principled learning to optimize (L2O) framework for solving fixed-point problems involving general nonexpansive mappings. Our i
arXiv:2610.13397v1 Announce Type: cross Abstract: We develop a model-free, noise-aware Bayesian clustering framework that constructs a martingale posterior over clusterings from a predictive Bayesian
arXiv:2606.19821v2 Announce Type: replace Abstract: Key Performance Measurement (KPM) forecasting is essential for proactive network management of 5G and next-generation telecom networks. However, exi
arXiv:2610.12813v1 Announce Type: new Abstract: LLM agents increasingly operate over sensitive resources, such as private documents and mutable system states, which can create serious safety and secur
arXiv:2610.11218v1 Announce Type: cross Abstract: Dense associative memory (DAM) provides an energy-based framework for memory retrieval with close connections to attention mechanisms in modern artifi
arXiv:2512.17302v2 Announce Type: replace Abstract: We propose a generative framework for producing high-quality PBR textures on a given 3D mesh. As large-scale PBR texture datasets are scarce, our ap
arXiv:2407.14562v3 Announce Type: replace Abstract: Large language models have demonstrated remarkable capabilities as general-purpose assistants, excelling in a wide range of reasoning tasks and supp
arXiv:2610.09712v1 Announce Type: cross Abstract: We develop a continuous-time deterministic policy gradient framework for reinforcement learning with reflected state dynamics, where the state process
arXiv:2610.09907v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have shown strong performance across a wide range of downstream vision tasks, thanks to the complementary information cont
arXiv:2610.09838v1 Announce Type: new Abstract: We present a framework for building and interpreting minimal transformer models. By constraining a transformer's embedding dimension and head size to 2,
arXiv:2610.10499v1 Announce Type: new Abstract: Online learning is an attractive framework in many domains because it permits well-defined learning even when data are dependent or chosen adversarially
arXiv:2610.10012v1 Announce Type: new Abstract: In the framework of edge-weighted graphs, watersheds have proven to be linked to well-known optimization problems, as Minimum Spanning Tree, which allow
arXiv:2610.10453v1 Announce Type: new Abstract: Flow-based policies provide an expressive framework for continuous robot control, but their iterative ODE integration incurs substantial inference cost.
arXiv:2610.09617v1 Announce Type: cross Abstract: We introduce a novel second-order optimization framework for minimizing so-called variable projection functionals. We demonstrate that the proposed fr
arXiv:2610.09687v1 Announce Type: cross Abstract: Structured recovery phenomena, such as restricted isometry properties in compressed sensing, have shown that high-dimensional objects can often be rec
arXiv:2610.10506v1 Announce Type: cross Abstract: Many of the questions now put to large language models have no correct answer to score against: what a policy is worth, which option a user should cho
arXiv:2610.10039v1 Announce Type: new Abstract: This paper presents a video-to-model framework for automatically modeling the motion of a deformable suture thread from an input video. We utilize a rec
arXiv:2610.07391v1 Announce Type: cross Abstract: We present a new perspective on the problem of likelihood approximation within the framework of simulation-based inference that promotes scalable and
arXiv:2610.07271v1 Announce Type: new Abstract: Linkage algorithms for hierarchical clustering (HC) are a powerful and efficient framework for constructing clustering trees, yet it is often unclear wh
arXiv:2610.07902v1 Announce Type: new Abstract: Cantonese lyric writing requires close alignment between lexical tones and melodic pitch. Existing melody-guided lyric generation methods typically rely
arXiv:2610.07868v1 Announce Type: new Abstract: Large language model agents have been used to search over symbolic structures such as programs and equations. We propose CueRator, an agentic framework