Knowledge catalogue
arXiv:2609.19985v1 Announce Type: cross Abstract: Fine-tuning foundation models on new tasks inevitably suffer from catastrophic forgetting. While existing works attempt to mitigate this on the basis
arXiv:2609.19542v1 Announce Type: new Abstract: Open-vocabulary segmentation enables rich semantic perception for UAVs, but frame-wise predictions can remain temporally inconsistent across repeated ob
arXiv:2609.19776v1 Announce Type: new Abstract: Estimating temporal soil-loss change is challenging when physically meaningful input factors are noisy or corrupted, particularly because substantial ch
arXiv:2502.11153v4 Announce Type: replace-cross Abstract: Kernel selection for regression of physical observables is often heuristic. We investigate a physics-informed strategy in which functional for
arXiv:2609.20066v1 Announce Type: cross Abstract: Event cameras offer high temporal resolution and motion sensitivity for tiny UAV detection, yet distant targets generate sparse and fragmented events
arXiv:2609.19203v1 Announce Type: new Abstract: AI applications have shifted from single, monolithic foundation models (FM) to compound agentic systems. Yet today's stacks remain fragmented: even as p
arXiv:2609.19441v1 Announce Type: cross Abstract: World action models (WAMs) rely on video-generation backbones, requiring substantial memory and compute for deployment. Post-training quantization red
arXiv:2609.19782v1 Announce Type: cross Abstract: Figurative paintings are often approached as if they depict a single recoverable 3D scene: viewers infer depth and occlusion, and reconstruction pipel
arXiv:2609.20123v1 Announce Type: new Abstract: Modern smart vehicles leverage multimodal sensors, ranging from high-bandwidth vision systems to low-rate physiological monitors, to provide personalize
arXiv:2609.19983v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) achieve state-of-the-art performance on graph-structured data, but training and inference on large graphs are often bottl
arXiv:2609.20589v1 Announce Type: new Abstract: Current dense visual SLAM systems rely almost exclusively on 8-bit tonemapped Low Dynamic Range (LDR) inputs, limiting their robustness in extreme light
arXiv:2606.18372v2 Announce Type: replace-cross Abstract: Educational dialogue is a valuable but sensitive resource for research: the same transcripts that capture authentic learning often capture per
arXiv:2609.19831v1 Announce Type: cross Abstract: In this reproducibility study, we investigate the transparency and scrutability of recommender systems enhanced by incorporating generated natural-lan
arXiv:2609.19661v1 Announce Type: new Abstract: Imitation-learned robot policies are frequently overfit to the visual conditions present in their training demonstrations. Consequently, variations in o
arXiv:2609.20407v1 Announce Type: new Abstract: Free-flying robots rely on multiple thrusters to maneuver in space. If one or more of these thrusters fail, the robot may lose control authority and ris
arXiv:2312.01020v3 Announce Type: replace Abstract: Stock prices forecasting has always been a challenging task. Although many research projects try to address the problem, few of them pay attention t
arXiv:2609.19421v1 Announce Type: new Abstract: Gaussian Splatting has significantly improved the quality of novel view synthesis with explicit Gaussian representation. However, we observed that exist
arXiv:2609.20330v1 Announce Type: new Abstract: Blind and low-vision users often need to locate a specific personal object rather than an arbitrary instance of the same category. The task calls for a
arXiv:2609.20063v1 Announce Type: cross Abstract: The rapid advancement of speech synthesis and voice conversion technologies has made audio deepfakes increasingly realistic, posing serious security r
arXiv:2609.20629v1 Announce Type: new Abstract: Autonomous recovery of a micro unmanned aerial vehicle (UAV) onto a moving airborne carrier enables reusable deploy-mission-recover operation, but coupl
arXiv:2609.20209v1 Announce Type: cross Abstract: Urban cellular activity forecasting requires jointly modeling heterogeneous spatiotemporal signals, including SMS usage, mobile network traffic, and c
arXiv:2609.20235v1 Announce Type: new Abstract: Indoor mobile robots require open-vocabulary scene understanding that grounds natural-language queries in a consistent 3D map. Many existing systems ult
arXiv:2609.20419v1 Announce Type: cross Abstract: Graph foundation models (GFMs) aim to learn transferable representations across severely heterogeneous graph domains. However, severe domain shifts in
arXiv:2609.19444v1 Announce Type: new Abstract: Fine-grained evaluation of glomerular pathology must distinguish normal glomeruli from abnormalities such as global and segmental glomerulosclerosis, ob
arXiv:2609.20451v1 Announce Type: new Abstract: Numerical site-response predictions often deviate from observations, yet correcting these discrepancies is difficult because records are limited in both
arXiv:2609.19656v1 Announce Type: cross Abstract: Information retrieval is increasingly important as LLM agents tackle complex tasks involving diverse information needs. Because retrieval relies on an
arXiv:2609.19615v1 Announce Type: cross Abstract: Modern applications generate massive volumes of raw telemetry data, but translating those noisy, heterogeneous event streams into actionable business
arXiv:2609.19330v1 Announce Type: new Abstract: Autonomous robots operating in partially observed environments must navigate safely while acquiring observations that improve future planning. Existing
arXiv:2510.10232v2 Announce Type: replace-cross Abstract: Recursive self-modification is increasingly central in AutoML, neural architecture search, and adaptive optimization, yet no existing framewor
arXiv:2609.20455v1 Announce Type: new Abstract: External skills provide domain procedures without parameter updates, but existing methods often edit skills directly from failed rollouts without struct
arXiv:2609.19867v1 Announce Type: new Abstract: Joint learning across heterogeneous tasks is often treated as task coupling through feature sharing, distillation, or auxiliary supervision. However, in
arXiv:2609.19705v1 Announce Type: cross Abstract: Autonomous large language model (LLM) agents are moving rapidly into high-stakes domains, yet existing agentic-AI security studies remain largely doma
arXiv:2609.19747v1 Announce Type: new Abstract: Light field (LF) reconstruction from limited and noisy focal stack (FS) measurements is a highly ill-posed inverse problem. Although the LF-to-FS imagin
arXiv:2609.20347v1 Announce Type: cross Abstract: LEO satellite networks feature dynamic topologies, time-varying links, and diverse service requirements, which make conventional routing schemes diffi
arXiv:2602.11244v2 Announce Type: replace Abstract: Video-Language Models (VidLMs) achieve strong benchmark scores, yet these scores often hide whether models use the video at all. We show that VidLM
arXiv:2609.19824v1 Announce Type: new Abstract: Language-guided navigation for terrestrial-aerial bimodal robots requires selecting routes and locomotion modes that match scene context and task intent
arXiv:2609.20150v1 Announce Type: new Abstract: Conventional satellite remote sensing transmission follows a reconstruct-then-infer paradigm that optimizes pixel-level fidelity, creating an objective
arXiv:2609.20693v1 Announce Type: cross Abstract: Detecting phases of matter in general relies on identifying the correct order parameter - a task that remains notoriously difficult for unknown transi
arXiv:2609.19616v1 Announce Type: new Abstract: Complexity measured from generated code is failure-dependent: a difficult prompt can yield a short failing program and be assigned low output complexity
This week we kicked off the DevFest season in North America at Google Hudson Square in New York City with 80 engineers packed into the room. Typical technical workshops hand you a finished repo, tell
arXiv:2609.20435v1 Announce Type: new Abstract: Autonomous mobile robots require timeefficient planning and safety-critical dynamic obstacle avoidance under constrained onboard computation. While Iter
arXiv:2609.20186v1 Announce Type: new Abstract: Speculative Decoding (SD) has significantly accelerated Large Language Model (LLM) inference, yet existing approaches face a fundamental tradeoff betwee
arXiv:2609.20414v1 Announce Type: cross Abstract: Tactile signals provide direct contact and force measurements that are essential for understanding physical interactions and enabling dexterous roboti
arXiv:2609.19662v1 Announce Type: new Abstract: Cross-view object geo-localization (CVOGL) typically assumes a fixed query image, overlooking the ability of mobile agents to actively acquire more info
arXiv:2609.19155v1 Announce Type: new Abstract: In Human-LLM dialogue, follow-up user utterances may implicitly conflict with earlier intents, leading the LLM to misinterpret user needs and generate i
arXiv:2609.19897v1 Announce Type: new Abstract: Historical archives pose a difficult retrieval problem for retrievalaugmented generation systems: documents are OCR-degraded, heterogeneous across genre
arXiv:2510.09011v4 Announce Type: replace Abstract: In our deployed travel-planning service, most users give minimal inputs or free-form requests rather than the structured constraint checklists assum
arXiv:2609.19844v1 Announce Type: cross Abstract: AI-generated RTL verification plans can satisfy a provider schema yet fail at the boundary to trusted execution. We present SecTB-RTL, an auditable fr
arXiv:2609.19878v1 Announce Type: cross Abstract: Multimodal reasoning requires models to draw on information from multiple modalities throughout the reasoning process. Yet existing methods often conc
arXiv:2609.19690v1 Announce Type: cross Abstract: Daily locomotion encompasses diverse activities and frequent transitions between them, yet most exoskeleton controllers are designed for a single acti
arXiv:2609.20089v1 Announce Type: new Abstract: Self-evolving methods reduce the need for human-annotated trajectories by allowing tool-using agents to generate their own training data. Yet existing m
arXiv:2609.20630v1 Announce Type: new Abstract: Search advertising connects user intent with commercial content and plays a critical role in platform monetization. Recent systems typically align pretr
arXiv:2609.20582v1 Announce Type: new Abstract: Human manipulation videos provide rich motion and interaction cues for acquiring robot skills without robot demonstrations. Video generation models synt
arXiv:2609.19813v1 Announce Type: new Abstract: Predicting the future trajectories of surrounding vehicles in autonomous driving is important for collision risk assessment and safe ego-vehicle path pl
arXiv:2609.19664v1 Announce Type: new Abstract: Video agents have made substantial progress in long-video understanding. Yet effective video-agent systems require costly, time-consuming manual design
arXiv:2609.20423v1 Announce Type: new Abstract: Document parsing converts document images into structured content and requires reliable performance across diverse layouts and acquisition conditions. Y
arXiv:2609.19671v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) achieve strong performance on complex tasks but exhibit systematic inefficiency: they often overthink easy problems and un
arXiv:2609.19449v1 Announce Type: new Abstract: Robots must complete their tasks and maintain the achieved outcomes while avoiding safety failures at all times. Strict reach-avoid-stay (sRAS) formaliz
arXiv:2609.20310v1 Announce Type: new Abstract: Human activity traces record individuals' timestamped visits to points of interest and are essential for applications such as mobility prediction and ur
arXiv:2609.18610v1 Announce Type: new Abstract: Classical dynamic programming represents optimal sequential decisions through value functions and policies. While this functional representation is natu