Knowledge catalogue
arXiv:2607.10669v2 Announce Type: replace Abstract: Bayesian optimization (BO) is widely used for expensive black-box problems, yet practical performance depends not only on high-level algorithmic cho
arXiv:2609.26978v1 Announce Type: cross Abstract: We study online inverse linear optimization with a fixed unknown linear utility: in each round, an environment presents a compact action set, the lear
arXiv:2609.27277v1 Announce Type: new Abstract: Time series agents answer analytical questions by calling external tools, and which tools they carry is decided by people before the agent runs. However
arXiv:2609.26972v1 Announce Type: new Abstract: Training Universal Differential Equations (UDEs) traditionally relies on backpropagating through numerical ODE solvers, creating memory footprints far e
arXiv:2609.27564v1 Announce Type: cross Abstract: The brain does not rely on a single type of neuron to perform all kinds of tasks; instead, it designs different neurons for different tasks. The conce
arXiv:2609.27868v1 Announce Type: cross Abstract: Octree-based 3D Gaussian Splatting organizes anchors into multi-level hierarchies for level-of-detail rendering, but features at different levels are
arXiv:2609.26990v1 Announce Type: cross Abstract: Natural Visibility Graph (NVG)-based representations provide a promising approach for capturing structural patterns in sequential network traffic. How
arXiv:2609.28272v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) have attracted significant attention for their strong reasoning ability. However, under a bidirectional attention mecha
arXiv:2609.27675v1 Announce Type: new Abstract: Understanding articulated objects is fundamental for robotic interaction, requiring accurate rigid-part discovery and the recovery of their kinematic re
arXiv:2609.28396v1 Announce Type: new Abstract: This paper addresses the control problem for robotic systems, including non-holonomic and underactuated platforms operating under unknown dynamics and s
arXiv:2609.27037v1 Announce Type: new Abstract: Wake word detection is a critical component of virtual assistants, serving as the gateway to seamless user interactions. This paper introduces a novel w
arXiv:2609.26959v1 Announce Type: new Abstract: Solar photovoltaic (PV) forecasting in regions affected by load shedding is challenging because reliable historical observations are scarce. This study
arXiv:2609.28199v1 Announce Type: new Abstract: Long physiological recordings contain many routine measurements, while predictive information is often concentrated in rare events, sustained burden, an
arXiv:2609.27312v1 Announce Type: cross Abstract: Robots deployed for competitive tasks must outmaneuver their opponents without sacrificing safety. Existing approaches, including safe reinforcement l
arXiv:2609.26911v1 Announce Type: new Abstract: A single locally plausible tool call can derail an otherwise successful agent trajectory. Suspicion alone does not justify intervention, because the rep
arXiv:2609.28187v1 Announce Type: new Abstract: Self-supervised vision transformers trained with DINO-style objectives exhibit striking emergent semantic representation quality across visual tasks, ye
arXiv:2609.27766v1 Announce Type: cross Abstract: In safe hypothesis testing with test supermartingals, Ville's inequality provides anytime-valid type-I error guarantees for every significance level a
arXiv:2609.27510v1 Announce Type: cross Abstract: Modern large language models are pretrained on massive datasets, making it difficult to prevent benchmark data from entering their training sets and u
arXiv:2609.27257v1 Announce Type: new Abstract: Enterprise data agents must preserve organization specific semantics, not just translate questions into queries. We present ChinaUnicom DataAgent (UniDa
arXiv:2609.26960v1 Announce Type: cross Abstract: A fundamental challenge in RF sensing is that Doppler signatures observed by a link entangle the target's motion with the sensing geometry, resulting
arXiv:2603.08930v2 Announce Type: replace-cross Abstract: This paper introduces a benchmark for evaluating whether vision-language models (VLMs) can generate plant simulation configurations from image
arXiv:2609.27915v1 Announce Type: new Abstract: Despite remarkable advancements in multimodal large language models (MLLMs), their fine-grained visual understanding is constrained by a primary relianc
arXiv:2609.26830v1 Announce Type: cross Abstract: Simulation success is not equivalent to structural correctness for LLM-generated circuits. We define and measure four evaluation levels -- schema vali
arXiv:2604.06039v2 Announce Type: replace-cross Abstract: Value iteration-type methods have been extensively studied for computing a nearly optimal value function in reinforcement learning (RL). Under
arXiv:2406.17699v3 Announce Type: replace-cross Abstract: Assume that we would like to estimate the expected value of a function F with respect to an intractable density pi, which is specified up to s
arXiv:2601.22524v2 Announce Type: replace Abstract: Graph generation aims to sample discrete node and edge attributes while satisfying coupled structural constraints. Diffusion models for graphs often
arXiv:2609.27577v1 Announce Type: new Abstract: Multimodal joint training often suffers from modality imbalance, where a dominant modality suppresses the optimization of others. Existing methods mainl
arXiv:2609.27321v1 Announce Type: new Abstract: Language-model agents increasingly face long-horizon tasks with evolving state, interdependent decisions, and delayed outcomes. Scaling their training r
arXiv:2609.28312v1 Announce Type: cross Abstract: We present VGM-VS, a visual servoing method built on a pretrained feed-forward visual geometry model. Given the current view and a reference image cap
arXiv:2609.27778v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has made real-time, high-fidelity 3D rendering practical, yet turning this explicit representation into a native generative
arXiv:2609.27219v1 Announce Type: new Abstract: Aerial radiation surveys achieve higher sensitivity when the radiation detector is held close to the ground. Detector sensitivity falls off roughly with
arXiv:2609.28099v1 Announce Type: new Abstract: Deep vision systems remain vulnerable to corruption, occlusion, and distribution shift despite strong benchmark performance. Existing reliability method
arXiv:2609.27948v1 Announce Type: new Abstract: While Vision-Language Models (VLMs) demonstrate strong capabilities, they continue to suffer from a critical limitation: insufficient fine-grained visua
arXiv:2512.11490v2 Announce Type: replace Abstract: Satellite imagery differs from natural images in viewpoint, resolution, scale variation, and the prevalence of small objects -- demanding both regio
arXiv:2609.28184v1 Announce Type: cross Abstract: Robotic operation in previously unseen environments requires both semantic understanding and reliable metric information. While vision--language model
arXiv:2508.13680v5 Announce Type: replace Abstract: We introduce VMMU, a Vietnamese Multitask Multimodal Understanding and Reasoning Benchmark designed to evaluate how vision-language models (VLMs) in
arXiv:2510.01354v2 Announce Type: replace-cross Abstract: Multiple prompt injection attacks have been proposed against web agents. At the same time, various methods have been developed to detect gener
arXiv:2609.28429v1 Announce Type: new Abstract: An always-on robot faces an endless stream that never resets: instructions arrive and lapse, the scene changes, and its own past actions reshape what it
arXiv:2609.27088v1 Announce Type: new Abstract: Elongate multi-legged robots use coordinated body waves and distributed legs to move through cluttered terrestrial environments. However, as housing act
arXiv:2609.27712v1 Announce Type: new Abstract: Localizing an autonomous underwater vehicle without pre-deployed seabed transponders, or direct access to onboard vehicle sensors remains a core challen
arXiv:2609.28137v1 Announce Type: cross Abstract: Educational technology (EdTech) platforms collect highly sensitive student data, including behavioral logs, disability records, and academic histories
arXiv:2609.27865v1 Announce Type: cross Abstract: Sharing a deep encoder does not, by itself, fix the central confound of task-comparison scores. We show that cross-evaluated heads on a frozen shared
arXiv:2609.27064v1 Announce Type: new Abstract: A joint fact-verification score assesses answers and submitted evidence together. When the score improves, how much of the gain remains if the answers a
arXiv:2609.27822v1 Announce Type: cross Abstract: A common multi-agent design asks agents to report confidence and lets the highest-scoring agent speak next, implicitly using one scalar both to route
arXiv:2609.27252v1 Announce Type: cross Abstract: The Platonic Representation Hypothesis suggests that increasingly capable models converge toward shared representations. Recent work narrows this clai
arXiv:2609.27679v1 Announce Type: new Abstract: What reusable computation should a tabular foundation model learn when every table defines a new supervised task? We develop in-situ representation refi
arXiv:2609.27408v1 Announce Type: cross Abstract: Benchmarks for vision-language models offer their answer choices in some convention: a letter, a color name, a pixel coordinate. That convention is tr
arXiv:2609.26826v1 Announce Type: cross Abstract: Frontier benchmarks need tasks that current models cannot solve. But a task that no model solves is not automatically a hard task. The same zero pass
arXiv:2609.27762v1 Announce Type: cross Abstract: The conventional hearing aid is both costly and lim- ited in usage, as it is not intended to detect non-speech audio. Our objective is to develop a ma
arXiv:2609.27490v1 Announce Type: new Abstract: AI research agents need reliable knowledge of how their experiments change outcomes. We introduce WhatWorkedBench to measure experimental understanding,
arXiv:2609.27954v1 Announce Type: new Abstract: A scalar recalibration map fitted for an LLM judge on one task can fail when the task distribution changes, but the source-target accuracy gap is often
arXiv:2609.27819v1 Announce Type: new Abstract: Federated wearable models eventually serve people absent from source training, but favorable average accuracy does not establish that unlabeled onboardi
arXiv:2609.28385v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) supervises mathematical reasoning through final-answer correctness, but provides little guidance
arXiv:2609.27124v1 Announce Type: cross Abstract: Federated Learning enables decentralized model training by exchanging model updates--rather than raw data--with a central parameter server (PS). While
arXiv:2609.27603v1 Announce Type: cross Abstract: In-Context Learning (ICL) has become a cornerstone of modern LLM deployment. However, existing ICL post-training methods have a critical blind spot: t
arXiv:2609.27382v1 Announce Type: cross Abstract: Speech technology penalizes some voices: recognition errs nearly twice as often for Black speakers, and accuracy declines for second-language accents
arXiv:2609.27411v1 Announce Type: new Abstract: Machine fault diagnosis from vibration requires learning from scarce labelled fault recordings while meeting the computational constraints of edge devic
arXiv:2609.26976v1 Announce Type: new Abstract: Learned context planning selects evidence atoms before an answer model reasons over them. We test whether this learned selection improves long-context m
arXiv:2609.27396v1 Announce Type: new Abstract: DSpark-style parallel drafters have made speculative decoding highly effective, yet their draft phase remains serialized on the critical path of every r
arXiv:2609.26955v1 Announce Type: new Abstract: Fairness audits in production ML typically occur once, at deployment, on a single domain. Both fail in practice: fairness can shift after retraining or