Knowledge catalogue
arXiv:2609.18697v1 Announce Type: new Abstract: Historians have reconstructed the twentieth-century transformation of general relativity and gravitation (GRG) at the field level and through individual
arXiv:2609.19119v1 Announce Type: new Abstract: Human videos contain rich causal evidence for robot manipulation: they reveal how hand motion induces object motion and produces task-relevant changes i
arXiv:2609.19122v2 Announce Type: new Abstract: Visual signals require compact yet sufficient representations for robust downstream prediction. Convolutional sparse coding (CSC) provides an explicit m
arXiv:2609.18321v1 Announce Type: cross Abstract: Offline on-policy distillation gains efficiency by collecting student trajectories and teacher supervision once and reusing them throughout optimizati
arXiv:2609.18958v1 Announce Type: cross Abstract: The natural way to review a long recording or document with a multimodal model is to hand it the raw source and ask for a review in one call. We show
arXiv:2609.18190v1 Announce Type: new Abstract: How neural representations preserve the structure of input changes connects representation analysis with internal intervention. We study operable repres
arXiv:2609.18525v1 Announce Type: new Abstract: Modern evaluation of learning pipelines often reduces to downstream accuracy, leaving open the question of why tasks succeed or fail. TriProbe addresses
arXiv:2609.18585v1 Announce Type: cross Abstract: Text-to-music (TTM) systems are increasingly used to generate musical audio from natural-language descriptions. Robust evaluation is therefore essenti
arXiv:2609.17984v1 Announce Type: new Abstract: Machine-learning libraries such as Weka and scikit-learn were designed for human programmers. Language-model agents now use these same libraries by reca
arXiv:2609.19011v1 Announce Type: new Abstract: We propose TwinMark, a watermarking scheme that reads a single SHAKE128 secret through two complementary linear functionals of model-output summaries: a
arXiv:2609.18326v1 Announce Type: new Abstract: Unmanned aerial vehicles (UAVs) extend embodied intelligence into continuous three-dimensional space, where perception, reasoning, physical embodiment,
arXiv:2510.09653v4 Announce Type: replace-cross Abstract: This paper presents a comprehensive overview of the Ultralytics YOLO family, emphasizing architectural evolution, benchmarking, deployment, an
arXiv:2609.18232v1 Announce Type: new Abstract: Real-robot demonstrations are limited, motivating the use of human manipulation data collected without robots, including egocentric videos and handheld
arXiv:2609.17866v1 Announce Type: cross Abstract: Real-world intelligent systems increasingly operate under open-world conditions, where user intents are not fixed or exhaustively known a priori and m
arXiv:2411.01289v2 Announce Type: replace Abstract: Complex events originate from other primitive events combined according to defined patterns and rules. Instead of using specialists' manual work to
arXiv:2609.18341v1 Announce Type: cross Abstract: When someone asks an AI assistant which doctor to see or which firm to trust with their savings, the answer is a referral. We audit AI provider recomm
arXiv:2609.18210v1 Announce Type: new Abstract: UAV-borne imaging has advanced from megapixel to gigapixel sensors, shifting aerial perception from recognizing individual targets to understanding enti
arXiv:2602.15868v3 Announce Type: replace Abstract: Large language models (LLMs) exhibit failure modes on seemingly trivial tasks. We propose a formalisation of LLM interaction using a deterministic m
arXiv:2609.18239v1 Announce Type: new Abstract: Structured pruning is commonly formulated as ranking individual channels, although channel responses can be complementary or cancel through downstream m
arXiv:2603.00210v2 Announce Type: replace-cross Abstract: Clustering is a central primitive in unsupervised learning, yet practice is dominated by heuristics whose outputs can be unstable and highly s
arXiv:2409.09253v2 Announce Type: replace-cross Abstract: Owing to the unprecedented capability in semantic understanding and logical reasoning, large language models (LLMs) have shown fantastic poten
arXiv:2609.18823v1 Announce Type: cross Abstract: How do VLMs map from pixels to semantics? To understand this general question, we focus on a narrow one: studying how VLMs perform optical character r
arXiv:2309.12668v3 Announce Type: replace Abstract: Omnidirectional camera is a cost-effective and information-rich sensor highly suitable for many marine applications and the ocean scientific communi
arXiv:2602.00408v3 Announce Type: replace-cross Abstract: This paper proposes a novel Variational Graph-to-Scheduler (VG2S) framework for solving the Job Shop Scheduling Problem (JSSP), a critical tas
arXiv:2609.18565v1 Announce Type: cross Abstract: We propose a compact NISQ-compatible quantum transformer architecture for synthetic QNLP sequence modelling. The model preserves the autoregressive ne
arXiv:2609.17834v1 Announce Type: new Abstract: A vehicle's heading affects both the surfaces beneath its tires and its pitch and roll. We present Vehicle-Conditioned Terrain Planning (VCTP), which re
arXiv:2604.04502v2 Announce Type: replace Abstract: Video generation models can produce coherent vi- sual sequences depicting object motion and interactions. We in- vestigate how frontier video genera
arXiv:2609.18769v1 Announce Type: new Abstract: Correctly answering a question grounded in normative documents often depends on information outside any single passage: whether the retrieved document i
arXiv:2609.18632v1 Announce Type: new Abstract: Multi-modal talking avatar synthesis aims to generate realistic talking videos from a reference portrait and speech. Despite rapid progress in diffusion
arXiv:2609.18077v1 Announce Type: cross Abstract: Open-source video generative models ship almost exclusively as PyTorch/CUDA reference implementations. This leaves Cloud TPU pods without a production
arXiv:2609.18667v1 Announce Type: new Abstract: Background.. Video-based markerless motion capture promises movement analysis without the cost, space and skin-marker constraints of optoelectronic syst
arXiv:2609.17714v1 Announce Type: new Abstract: Real-world robotic disassembly requires long-horizon execution, where robots must perform ordered sequences of manipulation tasks across multiple parts
arXiv:2609.18302v1 Announce Type: new Abstract: RAW-to-sRGB image signal processing (ISP) must recover perceptually faithful colors and fine details from sensor measurements, often under imperfect spa
arXiv:2609.18328v1 Announce Type: new Abstract: Recent advances in LLMs and VLMs have enabled safety systems to reason beyond simple risk patterns toward more contextual and semantic safety concerns.
arXiv:2609.18345v1 Announce Type: new Abstract: Large vision-language models (LVLMs) are commonly used with only a single text prompt as the input, or plus an image. In this paper, we demonstrate that
arXiv:2508.11584v3 Announce Type: replace-cross Abstract: Deploying multiple machine learning models on resource-constrained robotic platforms for different perception tasks often results in redundant
arXiv:2609.18663v1 Announce Type: cross Abstract: Billion-parameter vision--language--action (VLA) policies demand substantial onboard power, while communication delays in remote inference hinder time
arXiv:2508.06291v2 Announce Type: replace Abstract: Semantic scene understanding in robotics requires representations that are both metric-accurate and queryable via natural language in real-time. Whi
arXiv:2609.18451v1 Announce Type: new Abstract: We present a hierarchical UAV navigation framework that aligns natural-language intent with dynamically feasible flight behaviors in cluttered indoor en
arXiv:2609.18413v1 Announce Type: new Abstract: What is a gait? Appearance-based gait networks consider a gait as the human shape and motion information from images. Model-based gait networks treat a
arXiv:2609.18677v1 Announce Type: new Abstract: Speech language models enable richer spoken interactions between humans and machines than cascaded systems, allowing access to paralinguistic informatio
arXiv:2609.18521v1 Announce Type: cross Abstract: Speech retrieval has become increasingly important as spoken content continues to grow across meetings, lectures, podcasts, and videos. Existing bench
arXiv:2609.17901v1 Announce Type: cross Abstract: Generative modeling of time-dependent data is typically formulated on a discrete temporal grid, restricting supervision to the observed timestamps in
arXiv:2609.17556v1 Announce Type: cross Abstract: Edge-deployed AI operate under dynamically changing power budgets, reliability requirements, and input distributions, requiring continuous adaptation.
arXiv:2609.18193v1 Announce Type: new Abstract: World models can anticipate the consequences of navigation actions, but predicted action sequences may become invalid during execution, especially when
arXiv:2609.18812v1 Announce Type: new Abstract: Time-series language models provide a shared natural-language interface across temporal tasks, but plausible text does not guarantee reliable task outpu
arXiv:2609.18612v1 Announce Type: cross Abstract: We analyze the learned input-output behavior of GLU-based neurons in large language models (LLMs). We propose a simple analysis method: For each neuro
arXiv:2609.18685v1 Announce Type: new Abstract: Reinforcement learning allows robots to acquire complex skills, but producing policies for geometrically complex manipulation remains difficult. A promi
arXiv:2609.18435v1 Announce Type: new Abstract: Automating biological research requires general-purpose, reproducible robot systems that allow individual wet-lab researchers to delegate robot tasks wi
arXiv:2609.18182v1 Announce Type: new Abstract: Real-world agents fundamentally require persistent non-parametric knowledge for dynamic reasoning, i.e., long-term memory and retrieval-augmented genera
arXiv:2609.18286v1 Announce Type: new Abstract: Chess has long served as a model domain for studying search, expertise, decision-making, and artificial intelligence. The emergence of large language mo
arXiv:2609.17637v1 Announce Type: new Abstract: Restricting what a module can read may improve what a system learns to compute. We test this in a preregistered confirmation with sixty four-cell system
arXiv:2609.18970v1 Announce Type: new Abstract: Adults with intellectual and developmental disabilities (IDD) are increasingly transitioning from family homes towards semi-inde-pendent living. As pare
arXiv:2609.17772v1 Announce Type: cross Abstract: AI-generated covariates from notes, conversations, images, and wearable streams can change the causal question when their roles are left unspecified.
arXiv:2609.18960v1 Announce Type: new Abstract: Quality-aware synthetic-data selection rests on a proxy: examples that an LLM judge rates as good should also help a downstream model learn. In a contro
arXiv:2606.15121v3 Announce Type: replace Abstract: Predicting the timing of individual and collective panic emotional arousal before manifestation is essential for timely emergency intervention. Exis
arXiv:2609.18745v1 Announce Type: new Abstract: Antibody lead optimization calls for a small, bounded set of edits to an existing candidate: substitutions, but also insertions and deletions. Edit-base
arXiv:2609.18099v1 Announce Type: new Abstract: Graph-based retrieval-augmented generation (RAG) can help answer questions that require information from many documents. However, building a graph often
arXiv:2609.17594v1 Announce Type: new Abstract: Can gradient-based training learn the rank needed to store and compose associations in a matrix memory? In our earlier study, we used a matrix-augmented
arXiv:2609.17977v1 Announce Type: new Abstract: Emotion recognition in conversation (ERC) is a production capability behind agent-assist prompts, escalation routing, and post-call analytics in contact