Knowledge catalogue
arXiv:2609.00058v1 Announce Type: cross Abstract: Developing high-performance CUDA kernels demands specialized knowledge in algorithm implementation, correctness validation, and hardware-aware paralle
arXiv:2603.18184v2 Announce Type: replace Abstract: Interlinear glossed text (IGT) is a standard notation for language documentation which is linguistically rich but laborious to produce manually. Rec
arXiv:2601.06853v3 Announce Type: replace Abstract: Chain-of-Thought (CoT) prompting is widely adopted for mathematical problem solving, including in low-resource languages, yet its behavior under irr
arXiv:2609.01038v1 Announce Type: new Abstract: A/B testing is the gold standard for evaluating product changes, but each experiment requires real user traffic, engineering effort, and weeks of measur
arXiv:2012.01194v3 Announce Type: replace-cross Abstract: In this article, we introduce a deep learning based approximation algorithm for SPDEs. Our approach employs neural networks to approximate the
arXiv:2609.01487v1 Announce Type: cross Abstract: Skill-augmented agents load reusable skills as persistent runtime context, improving task performance but also giving malicious skills a durable chann
arXiv:2603.04163v2 Announce Type: replace Abstract: Wildlife re-identification aims to recognise individual animals by matching query images to a database of previously identified individuals, based o
arXiv:2609.00267v1 Announce Type: cross Abstract: Autonomous LLM agents increasingly act on a user's behalf: they hold credentials, call tools and services, and spawn sub-agents that act further on th
arXiv:2609.00885v1 Announce Type: new Abstract: Denoising diffusion models are the dominant architecture for image generation, whereas most natural language generation and modeling are primarily handl
arXiv:2601.23276v4 Announce Type: replace-cross Abstract: Astronomical imaging remains noise-limited under practical observing conditions. Standard calibration pipelines remove structured artifacts bu
arXiv:2609.00833v1 Announce Type: new Abstract: Reinforcement learning (RL) for search agents typically relies on outcome rewards. However, it often fails to achieve effective credit assignment, due t
arXiv:2609.00045v1 Announce Type: cross Abstract: Under individual smoothness, the optimal incremental first-order oracle (IFO) complexity of nonconvex finite-sum optimization has remained open. Known
arXiv:2609.00413v1 Announce Type: new Abstract: Chain of thought prompting improves complex reasoning, but its long intermediate traces create substantial inference cost and hinder practical deploymen
arXiv:2609.00106v1 Announce Type: new Abstract: This paper presents FAIRY, a full-stack smart-agriculture agent system developed for and deployed to an operating soybean research farm at Harbin Instit
arXiv:2609.00730v1 Announce Type: new Abstract: Accurate tilt angle estimation is important in many engineering applications, such as robotics, motion tracking, and embedded control systems. However,
arXiv:2609.01588v1 Announce Type: cross Abstract: Writing involves diverse cognitive activities, from ideation to revision, and writers' needs vary across individuals and moments. Proactive AI promise
arXiv:2609.00530v1 Announce Type: new Abstract: Recovering compact explicit solutions from neural approximations is challenging when imperfect teacher data guide symbolic topology search and coefficie
arXiv:2609.00351v1 Announce Type: cross Abstract: Large language models can hide hidden behaviors that activate only under narrow conditions, such as backdoor triggers, sleeper-agent deployment cues,
arXiv:2609.00363v1 Announce Type: new Abstract: Conformance suites for quantized GEMM kernels ask whether two implementations agree within a tolerance. We measure what such a suite can detect. Injecti
arXiv:2609.00361v1 Announce Type: cross Abstract: Toxic language in digital workplaces such as pejoratives, sarcasm, condescension, and subtle incivility can erode trust, morale, and collaboration. Ex
arXiv:2609.00666v1 Announce Type: new Abstract: InfRared Small Target Detection (IRSTD) is a prominent and challenging task in computer vision. In recent years, text-guided methods have significantly
arXiv:2609.00768v1 Announce Type: new Abstract: Self-play is an effective paradigm for language-model self-evolution, but without guidance, solver performance can plateau or decline across rounds. Ung
arXiv:2502.03726v3 Announce Type: replace Abstract: Text-to-image diffusion models are capable of generating high-quality images, but suboptimal pre-trained text representations often result in these
arXiv:2609.01136v1 Announce Type: new Abstract: Change captioning is the task of generating natural language descriptions that explain the changes between a pair of images. Although different change t
arXiv:2609.00100v1 Announce Type: new Abstract: Psychometric questionnaires contain rich item-level information, yet it remains unclear whether different representation learning objectives recover the
arXiv:2609.00708v1 Announce Type: cross Abstract: Differentially private (DP) synthesis has been extensively studied for tabular and image data separately, yet many real-world datasets contain images
arXiv:2609.01449v1 Announce Type: new Abstract: Diffusion models and recursive reasoners are both iterative, but they carry information across iterations differently. We add a persistent hidden state
arXiv:2609.01370v1 Announce Type: new Abstract: Data is important in many deep learning-based inverse problem solvers. However, obtaining sufficient paired data in many scenarios remains highly challe
arXiv:2609.00899v1 Announce Type: cross Abstract: We present a direct optimization method for three-dimensional freeform reflectors that transform the light of a finite-etendue source into a prescribe
arXiv:2609.00644v1 Announce Type: cross Abstract: Bilevel optimization provides a natural modeling language for hierarchical decision problems. However, applying existing numerical solvers usually req
arXiv:2609.00982v1 Announce Type: cross Abstract: Using a large language model to play the user is now standard in scalable evaluation. It has a repeatedly diagnosed failure: the simulated user is exc
arXiv:2609.00004v1 Announce Type: new Abstract: This paper studies a finite-horizon multi-item capacitated lot-sizing problem in which demand quantities are deterministic, while demand-arrival periods
arXiv:2609.00059v1 Announce Type: cross Abstract: Materials property prediction remains difficult in low-data settings, where many target properties are supported by only a limited number of labeled s
arXiv:2609.00206v1 Announce Type: cross Abstract: Despite their growing use in video moderation, multimodal large language models (MLLMs) exhibit a compositional safety blind spot: videos composed of
arXiv:2511.21415v2 Announce Type: replace Abstract: We introduce DiverseVAR, a framework that enhances the diversity of text-conditioned visual autoregressive models (VAR) at test time without requiri
arXiv:2609.00647v1 Announce Type: new Abstract: Multiple kernel k-means integrates complementary nonlinear similarities by learning a combination of base kernels. Its pointwise optimization, however,
arXiv:2609.01120v1 Announce Type: cross Abstract: Early recognition of lane-change intention is essential for proactive decision-making in autonomous driving and advanced driver assistance systems. Th
arXiv:2609.00177v1 Announce Type: cross Abstract: General-purpose NLP embedding models perform well on linguistic tasks, but their ability to capture symbolic ontological structure remains unclear. We
arXiv:2609.00345v1 Announce Type: new Abstract: Human mobility is central to urban planning, transportation, public health, and emergency response, yet fine-grained trajectory data are often proprieta
arXiv:2609.00067v1 Announce Type: cross Abstract: External text can override conflicting image evidence in multimodal large language models, a failure we call multimodal contextual sycophancy. We intr
arXiv:2609.00661v1 Announce Type: new Abstract: Satellite foundation models offer a globally available alternative to census data for commuting origin-destination (OD) generation, yet no study has sys
arXiv:2609.00854v1 Announce Type: cross Abstract: Fault localization can focus a code model's repair on the statements a failing test implicates, but a targeted edit may succeed merely because it is s
arXiv:2609.01453v1 Announce Type: cross Abstract: Dexterous manipulation policies learned by imitation are typically evaluated for robustness to variation in scenes, objects, or instructions, but thei
arXiv:2609.00464v1 Announce Type: cross Abstract: Neuro-Symbolic (NeSy) AI has recently emerged as a novel paradigm to enable trustworthy AI, aiming at integrating sub-symbolic neural perception with
arXiv:2609.01139v1 Announce Type: new Abstract: The LLM-as-a-judge (LLMaJ) framework has emerged as a promising solution for cheap, reproducible, reference-free Natural Language Generation (NLG) evalu
arXiv:2609.00996v1 Announce Type: new Abstract: Personalized video recommendation predicts user preference at the video level, while temporal video grounding localizes query-relevant moments. However,
arXiv:2609.00227v1 Announce Type: cross Abstract: LLM agents increasingly diagnose incidents and propose remediations. In a GitOps workflow, applying a fix means editing a version-controlled config fi
arXiv:2609.01148v1 Announce Type: new Abstract: Image retouching is commonly formulated as enhancing overall visual quality through color adjustment, but in practice, it also serves to emphasize visua
arXiv:2609.00365v1 Announce Type: new Abstract: Command-line coding agents (e.g., Claude Code, Gemini CLI) can already read and write files and sustain long sessions, yet end-to-end research still fra
arXiv:2609.00646v1 Announce Type: new Abstract: Commercial short-drama production follows a multi-stage chain: script, storyboard, keyframe imagery, shot-level video, and the finished short drama. Mos
arXiv:2609.00662v1 Announce Type: new Abstract: A multi-model language service must route each request while preserving workload-level budgets for compute, latency, memory, or monetary cost. Two featu
arXiv:2609.00619v1 Announce Type: new Abstract: In indoor environments, object positions frequently change due to human activities or embodied-agent interactions, causing previously constructed scene
arXiv:2609.01260v1 Announce Type: new Abstract: Robotic systems are deeply embedded in both industry and everyday life, where they are expected to act with speed, precision, and reliability. Classical
arXiv:2609.01516v1 Announce Type: new Abstract: While 3D Gaussian Splatting (3DGS) has revolutionized 3D reconstruction and novel-view synthesis, scenarios with limited input views often lead to poor
arXiv:2609.00935v1 Announce Type: cross Abstract: Deep Research agents tackle knowledge-intensive tasks through multi-round retrieval and decision-oriented generation. However, these agents suffer fro
arXiv:2609.01059v1 Announce Type: new Abstract: As Vision-Language Models (VLMs) tackle dynamic 3D spatial reasoning, ego-motion perception becomes essential to resolve monocular scale ambiguity. Howe
arXiv:2609.00407v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models enable efficient scaling of large language model (LLM) inference but suffer from substantial data-movement overhead wh
arXiv:2603.15604v2 Announce Type: replace Abstract: Battery-powered multirotor unmanned aerial vehicles (UAVs) can rapidly map unknown environments, but mission performance is often limited by energy
arXiv:2609.00712v1 Announce Type: new Abstract: Landslides are widespread geological hazards, yet their automated detection and mapping in remote sensing imagery remain challenging because of their ir
arXiv:2609.01360v1 Announce Type: new Abstract: Large language model (LLM) agent failures often contain multiple related errors rather than a single mistake. Existing attribution methods usually ident