Knowledge catalogue
arXiv:2512.17992v2 Announce Type: replace Abstract: Long-horizon robotic tasks are hard due to continuous state-action spaces and sparse feedback. Symbolic world models help by decomposing tasks into
arXiv:2609.31766v1 Announce Type: new Abstract: Contextual image misuse pairs an image with a misleading claim. We study image-claim correspondence in fact-checked pairs containing out-of-context reus
arXiv:2609.34036v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student on its own rollouts using dense supervision from a teacher. In multi-turn environments, a mistake at a cri
arXiv:2609.32386v1 Announce Type: new Abstract: Impaired glucose regulation is a major contributor to metabolic dysfunction and type 2 diabetes. This study developed an interpretable machine-learning
Using the biggest model for every task can work in a demo. In production, cost, latency, and task-specific quality can tell a different story. At ODSC AI West 2026, Rob Ferguson, VP of Technology and
arXiv:2609.34390v1 Announce Type: new Abstract: Multi-animal tracking (MAT) supports the study of animal movement, behavior, and group interactions. However, general multi-object tracking (MOT) benchm
arXiv:2609.33264v1 Announce Type: new Abstract: Video world models are emerging as data engines, action planners, and generative simulators for autonomous driving, but existing benchmarks primarily as
arXiv:2609.35641v1 Announce Type: cross Abstract: Precise instruction following in image generation, such as satisfying object counts and spatial relations, remains an open challenge at least in part
arXiv:2609.34553v1 Announce Type: new Abstract: Formal verification can play a key role in ensuring the reliability of Deep Neural Networks (DNNs) deployed in safety-critical systems. Modern DNN verif
arXiv:2609.31697v1 Announce Type: new Abstract: Low-light conditions can significantly degrade the ability of vision-language models (VLMs) to accurately describe human actions in videos. In this work
arXiv:2609.33935v1 Announce Type: new Abstract: Adapting video models to new tasks typically requires dedicated data curation and fine-tuning. While visual analogy provides a training-free alternative
arXiv:2609.35134v1 Announce Type: new Abstract: Video editing has advanced substantially in recent years, with methods increasingly accounting for the visual consequences of edits, such as changes to
arXiv:2609.32918v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly being used in scientific workflows, but their ability as agents to directly control laboratory experime
arXiv:2503.10547v3 Announce Type: replace Abstract: Concept-based explanations help users understand vision models through recognizable visual patterns. However, existing methods often rely on correla
arXiv:2602.01915v2 Announce Type: replace Abstract: Recent advances in Large Language Models (LLMs) and Vision-Language Models (VLMs) have enabled powerful semantic and multimodal reasoning capabiliti
arXiv:2609.33104v1 Announce Type: new Abstract: While action-conditioned video prediction provides an intuitive world model for robotics, purely data-driven predictors often suffer from compounding er
arXiv:2609.34199v1 Announce Type: new Abstract: Humanoid loco-manipulation demands coordinated body and hand behavior, while conventional robot pre-training data provide limited coverage of such whole
arXiv:2609.33740v1 Announce Type: cross Abstract: Estimating causal effects with continuous treatments in observational studies is challenging due to confounding, model misspecification, and high-dime
arXiv:2609.32512v1 Announce Type: new Abstract: Models of vehicle dynamics learned from logged states and commands complement physics-based models, and latent world models, which predict in a learned
arXiv:2609.34981v1 Announce Type: new Abstract: World action models (WAMs) predict the future alongside actions during training. Due to the heavy computation cost of video denoising, whether the futur
arXiv:2609.33487v1 Announce Type: new Abstract: EEG foundation models hold promise for scalable brain-signal decoding across clinical and cognitive neuroscience applications, yet their pre-training pi
arXiv:2609.32452v1 Announce Type: cross Abstract: Reflective prompt optimization revises instructions using examples of a model's behavior, but which evidence the reflector should receive remains uncl
arXiv:2609.31760v1 Announce Type: cross Abstract: Can a robot improve itself the way coding agents now improve software? We built an agentic system to find out. It watches a robot fail, works out whic
arXiv:2609.31639v1 Announce Type: new Abstract: Diagnosing rolling-element bearing faults from vibration is a canonical physical-sensing task and a widely used benchmark for domain adaptation under op
arXiv:2609.34012v1 Announce Type: new Abstract: Neural PDE surrogates increasingly incorporate structural priors, yet it is often unclear whether their gains arise from physics-specific information or
arXiv:2609.34602v1 Announce Type: new Abstract: Frozen Earth-observation embeddings are judged almost entirely by spatially blocked cross-validation inside one study region. We show that this number d
arXiv:2609.31733v1 Announce Type: new Abstract: Retrieval-augmented generation is an attractive way to improve chest X-ray reporting, because reports from similar prior studies supply clinical context
arXiv:2609.35314v1 Announce Type: new Abstract: Neural residuals can improve satellite evapotranspiration (ET) estimates, but selectors must predict when a correction helps and reject unsupported inpu
arXiv:2609.35416v1 Announce Type: new Abstract: Continuous video counting requires distinguishing new observations from new objects or completed events. We introduce StaMina (State Maintenance), which
arXiv:2609.33334v1 Announce Type: new Abstract: Training-free KV-cache compression methods such as SnapKV, H2O, and PyramidKV evict tokens at the end of prefill, aiming to preserve the attention mass
arXiv:2609.34300v1 Announce Type: new Abstract: World models offer a powerful substrate for safety reasoning in high-dimensional robotic systems, but they are also fallible: their predictions can be b
arXiv:2609.31722v1 Announce Type: new Abstract: Fixed image distortions do not cover an attacker that learns from paired clean and watermarked images. We study this paired-training threat with single-
arXiv:2609.34078v1 Announce Type: new Abstract: Domain adaptation is crucial for addressing distributional shifts that degrade model performance across domains. While most existing research has center
arXiv:2609.34826v1 Announce Type: new Abstract: Humans often solve spatial problems by mentally simulating visual transformations. In contrast, conventional vision-language models (VLMs) reason primar
arXiv:2609.33208v1 Announce Type: cross Abstract: Constructing complex physical worlds from language requires coordinating extensive 3D environments, detailed structures and objects at different spati
arXiv:2609.35560v1 Announce Type: new Abstract: Interactive world models require responding in real time to versatile controls and maintaining long-horizon consistency. However, modeling heterogeneous
arXiv:2609.32336v1 Announce Type: new Abstract: Human-robot interaction (HRI) failures remain a major barrier to deploying robots in real-world environments. Prior work often treats failures as isolat
arXiv:2609.34212v1 Announce Type: new Abstract: Mixture-of-Depths (MoD) enables conditional computation across Transformer depth by routing only a subset of tokens through selected layers, but its ori
arXiv:2609.34513v1 Announce Type: new Abstract: Existing text-guided segmentation models in the medical domain cover only a narrow set of anatomical structures and lesions in chest X-rays (CXRs), and
arXiv:2609.33066v1 Announce Type: new Abstract: Contemporary neural inference architectures rely on dense floating-point weight matrices stored in high-bandwidth memory (VRAM), incurring severe memory
arXiv:2609.34010v1 Announce Type: cross Abstract: We present ZeroBot, a real2sim framework for learning a robot manipulation task from scratch in minutes under challenging conditions: zero human demon
arXiv:2609.30975v1 Announce Type: cross Abstract: Speech content representations are central to voice conversion, speech-to-speech translation, and multimodal language models, yet they are rarely comp
arXiv:2012.02655v4 Announce Type: cross Abstract: In this article, we define a new non-archimedean metric structure, called cophenetic metric, on persistent homology classes of all degrees. We then sh
arXiv:2609.31358v1 Announce Type: cross Abstract: The Model Context Protocol (MCP) provides a common interface through which AI applications discover and use external resources and tools. It allows la
arXiv:2609.30859v1 Announce Type: cross Abstract: Contingency analysis using the AC power flow (AC-PF) model is a critical tool for accurate grid security assessment, but its computational burden incr
arXiv:2609.31224v1 Announce Type: cross Abstract: Full-duplex speech language models continuously accumulate acoustic key-value (KV) states, making long-running interactions memory-intensive. During l
arXiv:2609.30428v1 Announce Type: cross Abstract: Foundation models provide robots with the ability to interpret natural language and reason about environmental context, yet most language-conditioned
arXiv:2609.30631v1 Announce Type: cross Abstract: Online audio-visual target-speaker extraction aims to remove competing voices while preserving speech quality and bounding lookahead. Existing extract
arXiv:2609.30405v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in settings where responses must reflect multiple, potentially interacting social norms and human
arXiv:2609.30891v1 Announce Type: cross Abstract: Semantic communication (SemCom) and integrated sensing and communication (ISAC) are promising technologies for future 6G wireless networks. Existing s
arXiv:2609.30356v1 Announce Type: new Abstract: Cities differ in built form, land cover and development history, complicating comparison across places and time. Satellite foundation models map Earth's
arXiv:2609.30396v1 Announce Type: cross Abstract: This paper presents an end-to-end causal machine learning (ML) pipeline designed for real-world applications with continuous treatments. The proposed
arXiv:2609.30725v1 Announce Type: new Abstract: Although effective, coding agents often incur substantial monetary costs. Their recurring cost-inefficient behaviors remain underexplored. We conduct th
arXiv:2609.30566v1 Announce Type: new Abstract: We present a new inference-time sampler for diffusion models that gives a pretrained model a capability it was never trained for: constructing the atlas
arXiv:2609.31168v1 Announce Type: new Abstract: My doctoral work aims to explore Audio Emotion Recognition (AER) in the context of atypical listening. This research focuses on auditory hypersensitivit
arXiv:2609.30557v1 Announce Type: cross Abstract: Runtime failure monitors can use a model's internal representations to anticipate failures. We audit this monitoring strategy across two autonomous-dr
arXiv:2609.31110v1 Announce Type: new Abstract: Large language models are increasingly used as high-level planners for mobile robots, robot manipulators, and autonomous vehicles. Recent studies show t
arXiv:2609.30541v1 Announce Type: new Abstract: Optimizing embedding systems for production recommendation pipelines demands systematic exploration that consumes disproportionate engineering effort at
arXiv:2509.19384v2 Announce Type: replace-cross Abstract: Reconstructing high-resolution regional significant wave height (SWH) fields from sparse buoy observations is a critical challenge for ocean m
arXiv:2609.31431v1 Announce Type: new Abstract: Accurate segmentation of axons in 3D microscopy data is important for analyzing white-matter organization, but dense ground truth labels are expensive t