Knowledge catalogue
arXiv:2610.07114v1 Announce Type: cross Abstract: The Frechet Inception Distance (FID) is widely used to evaluate generative models, but its empirical plug-in estimator suffers from finite-sample bias
arXiv:2610.07086v1 Announce Type: cross Abstract: LLM agents interact with external systems by generating structured tool calls. Given a user request, conversational context, and a catalog of tool sch
arXiv:2610.08691v1 Announce Type: new Abstract: Large language model agents are accelerating scientific automation, yet verified executions rarely become persistent program-level improvements, and exi
arXiv:2610.07335v1 Announce Type: cross Abstract: Improving the reliability of large language model (LLM) agents in long-horizon decision-making remains a key challenge. When deployed as autonomous ag
arXiv:2610.07881v1 Announce Type: new Abstract: Social preferences can promote cooperation in multi-agent reinforcement learning, but existing approaches often require agents to observe the rewards of
arXiv:2610.07385v1 Announce Type: new Abstract: Fine-tuning vision-language models (VLMs) is typically evaluated at a single downstream checkpoint, obscuring whether a semantic capability was never ac
arXiv:2610.07033v1 Announce Type: new Abstract: Predicting transient urban winds is fundamental to understanding urban microclimates and designing climate-resilient cities. Building-resolving large-ed
arXiv:2610.08778v1 Announce Type: new Abstract: Large language models (LLMs) have become increasingly capable problem solvers, but being able to solve a problem is not the same as being able to teach
arXiv:2610.07810v1 Announce Type: new Abstract: Search filters help guests navigate vast catalogs in two-sided marketplaces like Airbnb, and recommending the right filters can meaningfully lift bookin
arXiv:2610.07935v1 Announce Type: new Abstract: LLM agents are increasingly capable of executing complex tasks and of recursively improving themselves on easy-to-verify objectives such as software eng
arXiv:2610.07378v1 Announce Type: new Abstract: Reconstructing cortical WM and pial surfaces from structural magnetic resonance imaging (MRI) is a prerequisite for surface-based neuroanatomical analys
arXiv:2604.07925v2 Announce Type: replace-cross Abstract: The self-attention mechanism is central to the success of Transformer architectures. However, standard row-stochastic attention has been shown
arXiv:2610.07121v1 Announce Type: new Abstract: Diffusion large language models dLLMs) have emerged as a promising alternative to autoregressive language models through bidirectional diffusion-based t
arXiv:2610.08573v1 Announce Type: new Abstract: Cooperative perception improves autonomous driving by sharing complementary observations among vehicles and roadside infrastructure for 3D object detect
arXiv:2610.07204v1 Announce Type: cross Abstract: Recent AI alignment work often frames alignment as a pre-deployment optimization problem: collect human feedback, learn preferences or principles, fin
arXiv:2610.07237v1 Announce Type: new Abstract: A model that learns from its own outputs inherits more than their correctness: it inherits which solutions it produces. We formulate Looped Self-Distill
arXiv:2610.07756v1 Announce Type: new Abstract: Vision-language-action (VLA) models increasingly rely on diffusion- or flow-matching-based action heads to generate continuous robot actions. These acti
arXiv:2610.07348v1 Announce Type: cross Abstract: Training large language models (LLMs) is resource-intensive, and adapting them for diverse deployment scenarios with varying computational constraints
arXiv:2512.23441v2 Announce Type: replace-cross Abstract: Temporally aware image representations are crucial for capturing disease progression in 3D volumes of longitudinal medical datasets. However,
arXiv:2610.07006v1 Announce Type: new Abstract: Learning effective representations helps characterize the structure and dynamics of equity markets from financial data with a low signal-to-noise ratio.
arXiv:2610.07726v1 Announce Type: new Abstract: Videofluoroscopic Swallowing Study (VFSS) is one of the gold standard for diagnosing swallowing disorders, providing dynamic X-ray imaging of the swallo
arXiv:2610.07472v1 Announce Type: new Abstract: Remotely operated vehicles (ROVs) are widely used to explore and inspect underwater environments such as caves, shipwrecks, and submerged infrastructure
arXiv:2610.06926v1 Announce Type: new Abstract: Robot manipulation systems using Vision-Language-Action (VLA) model backbones typically use just one VLA for task execution. However, individual VLAs do
arXiv:2610.08161v1 Announce Type: cross Abstract: Ambient documentation systems are rapidly gaining adoption, yet their impact on clinical note quality remains poorly characterized. We introduce MedCo
arXiv:2610.05437v2 Announce Type: replace Abstract: Computer-use agents need to capture procedural knowledge of how people use software. User telemetry offers a scalable source of this knowledge. Howe
arXiv:2610.06914v1 Announce Type: new Abstract: Text2Dashboard is a DataBrain-specific prototype that turns natural-language analytic requests into inspectable dashboards. An installable Codex plugin
arXiv:2610.07925v1 Announce Type: new Abstract: Partially Relevant Video Retrieval (PRVR) aims to retrieve untrimmed videos containing moments relevant to a given text query. Despite recent progress,
arXiv:2610.07131v1 Announce Type: new Abstract: We study the implicit bias of Riemannian gradient flow for hyperbolic multiclass classification with fixed class prototypes in hyperbolic space H^n. Our
NVIDIA’s Seattle Robotics Lab and Isaac engineering team created reusable robotic systems to automate GB300 tester‑tray assembly, achieving over 95 % success on busbar tasks with a 160‑second cycle ti
arXiv:2610.07309v1 Announce Type: new Abstract: Long-term-memory agents can retrieve relevant information that is inadmissible for the current request because it belongs to another principal, violates
arXiv:2610.07597v1 Announce Type: cross Abstract: A robot description does more than specify a physical mechanism: it also encodes arbitrary conventions, such as joint-axis direction, joint-angle zero
arXiv:2610.07803v1 Announce Type: new Abstract: Small reasoning models (SRMs) have shown strong performance on complex reasoning tasks by generating extended chain-of-thought trajectories, but they of
arXiv:2610.07004v1 Announce Type: new Abstract: Task planning for LLM agents requires workflows that satisfy both user intent and complex sub-task dependencies. While existing planners work well for s
arXiv:2610.08733v1 Announce Type: new Abstract: Language models provide a plug-and-play interface between humans and robots, but important challenges remain when speech, dialogue, fast interaction, an
arXiv:2610.07320v1 Announce Type: new Abstract: Multi-robot missions in infrastructure-denied environments frequently rely on reliable communication links between spatially separated locations. We pro
arXiv:2610.07767v1 Announce Type: cross Abstract: Reinforcement learning (RL) for post-training large language models (LLMs) incurs substantial computation and memory overhead during rollout generatio
arXiv:2610.06858v1 Announce Type: cross Abstract: This demo paper presents the conceptual foundations and the first steps towards implementation of a novel no-code solution for movement data analytics
arXiv:2603.05670v2 Announce Type: replace Abstract: When humans learn new manipulation skills, they are able to generalize these skills to new contexts and environments. In particular, when learning,
arXiv:2610.07070v1 Announce Type: new Abstract: Small language models are inexpensive to serve and can run on private infrastructure, but base models are often not good enough at multi-turn tool calli
arXiv:2610.07570v1 Announce Type: new Abstract: In clinical practice, agreement among independent experts is treated as evidence of reliability, and multi-round consensus has become a core mechanism o
arXiv:2610.08353v1 Announce Type: new Abstract: While graph neural networks (GNNs) have shown substantial promise in connectome-based diagnostic classification, deterministic models inevitably suppres
arXiv:2610.07311v1 Announce Type: new Abstract: Agentic memory allows LLM agents to reuse past experience, yet retrieved memories can also distort inference even when they are benign, correctly stored
arXiv:2610.06938v1 Announce Type: new Abstract: Medical image segmentation remains fragmented along two axes: segmentation paradigms and data dimensionality. Existing methods are typically developed s
arXiv:2610.07689v1 Announce Type: new Abstract: Vision-Language Models (VLMs) such as CLIP excel in global semantic alignment but often lack fine-grained perceptual capabilities. This hinders dense pr
arXiv:2610.08463v1 Announce Type: new Abstract: Long-context inference and Retrieval-Augmented Generation (RAG) handle evidence selection at vastly different scales, from a single long prompt to an en
arXiv:2610.07899v1 Announce Type: cross Abstract: Generative actors are transforming offline reinforcement learning (RL) by enabling expressive policy classes that model complex action distributions.
arXiv:2610.08761v1 Announce Type: new Abstract: Self-improving policies continually expose new failure patterns, changing what their judges must be able to verify. However, current fixed judges constr
arXiv:2509.26189v2 Announce Type: replace Abstract: The rapid proliferation of Large Language Models has intensified the challenge of distinguishing LLM-generated text from human writing in non-Englis
arXiv:2610.08179v1 Announce Type: new Abstract: Text-driven 3D Gaussian editing commonly does not distinguish the editing reliability of rendered views, although different viewpoints provide supervisi
arXiv:2610.08170v1 Announce Type: cross Abstract: Agentic VLM pipelines increasingly pass the same static visual evidence through multiple specialist agents and tools. This design creates an orchestra
arXiv:2610.08220v1 Announce Type: cross Abstract: Portable mobile-manipulation demonstrations can help alleviate data scarcity for embodied intelligence, but obtaining reliable, low-cost, and robot-fr
arXiv:2605.05780v2 Announce Type: replace Abstract: In the mid-twentieth century, mathematician and polymath John von Neumann created a computational system on an array of cells as a simple model of t
arXiv:2610.07077v1 Announce Type: new Abstract: Class-incremental learning must recognize all classes seen so far without task labels. Logit replay methods such as DER and DER++ mitigate forgetting by
arXiv:2610.07396v1 Announce Type: new Abstract: Navigation for humanoid robots is critical, yet large-scale evaluation on physical hardware is often impractical due to cost and safety concerns, making
arXiv:2610.06992v1 Announce Type: new Abstract: Improved traffic forecasts do not necessarily yield better signal-control decisions. We investigate this gap through a layered diagnostic study using 29
arXiv:2610.07100v1 Announce Type: new Abstract: Persistent agent memory is only as reliable as its retention decision: an assertion weakly supported by its source can be stored and later reused as est
arXiv:2610.07018v1 Announce Type: new Abstract: Vision-language models (VLMs) have achieved strong performance in multimodal reasoning, yet they remain prone to generating plausible but incorrect answ
arXiv:2610.08097v1 Announce Type: cross Abstract: Mathematical problem solving often requires deterministic computational steps that agents delegate to tools and implicitly trust. Yet tools can fail s
arXiv:2610.07657v1 Announce Type: new Abstract: LLM-based multi-agent systems (MAS) engage tools, share memory, and delegate tasks, often encountering adversarial content. Current defenses for MAS are
arXiv:2610.07553v1 Announce Type: cross Abstract: LoRA fine-tuning adapts small language models (SLMs) to heterogeneous instruction data within a low-rank update subspace, making it vulnerable to thre