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Structured interactions improve distributed coordination beyond model scaling in a real-world multi-robot system
arXiv:2605.30383v1 Announce Type: new Abstract: Scaling individual robot capabilities is common but costly. Here we investigate a system-level design question in real-world multi-robot coordination: given matched hardware budgets, does restructuring communication among robots yield larger gains than increasing onboard model size? Using a representative transport-and-mapping task with 10 physical robots (5 runs per condition, 60 runs total), we find that switching from fully connected to...
EmpiriGraph-Psy: A Dataset and LLM Pipeline for Extracting Empirical Relation Graphs from Psychology Abstracts
arXiv:2606.08362v1 Announce Type: new Abstract: Existing scientific relation extraction benchmarks mainly target domains such as computer science, where entities are tasks, methods, datasets, materials, or metrics. This leaves a gap in variable-oriented empirical fields such as psychology, where findings are expressed as relations among constructs, measurements, interventions, and outcomes. We introduce variable-centered empirical graph extraction, the task of mapping scientific abstracts to...
Brain dynamics supporting high cognitive performance reorganize after midlife
Quantifying functional brain aging trajectories at scale remains a fundamental challenge due to the scanner-bound limitations of traditional neuroimaging. Here, we deploy whole-head Time-Domain functional Near-Infrared Spectroscopy (TD-fNIRS) to map task-evoked cortical dynamics during a 30-minute cognitive battery across the adult lifespan (N = 302, age 18 to 87, 45% racial or ethnic minority). We developed a robust General Cognitive Factor (GCF) tracking age-related performance decline (r...
The Token Not Taken: Sampling, State, and the Variability of AI Agent Outputs
arXiv:2606.08998v1 Announce Type: new Abstract: Agentic AI systems can behave differently across runs: the same request may produce a different plan, a different tool call, a different code edit, or a different final answer. Such variability arises from several layers that are often conflated. A foundation model is a large pretrained model, usually adaptable to many downstream tasks, that maps an input context to predictions over outputs.
Intercepting the Future: Latent-Space Predictive World Model for Dynamic VLA Manipulation
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PolyBuild: An End-to-End Method for Polygonal Building Contour Extraction from High-Resolution Remote Sensing Images
arXiv:2606.08920v1 Announce Type: new Abstract: Extracting building polygon contours from high-resolution remote sensing images is a fundamental task for various mapping applications. However, the presence of varying imaging conditions and complex building structures, makes automatic contour extraction extremely challenging. Mainstream approaches for building extraction often rely on pixel-level segmentation followed by multiple post-processing steps to produce building contour, which can be...
StandardE2E: A Unified Framework for End-to-End Autonomous Driving Datasets
arXiv:2606.04271v1 Announce Type: new Abstract: Autonomous driving has shifted from modular perception-prediction-planning stacks toward end-to-end (E2E) models that map sensor inputs directly to vehicle control, often regularized by auxiliary tasks such as 3D detection, motion forecasting, and HD-map perception. Progress is driven by a fast-growing ecosystem of sensor-rich driving datasets, yet each ships its own file formats, APIs, coordinate conventions, and modality coverage, leaving...
A disappearing Service Processor (2025)
One of the considerations in designing our Oxide rack is asking which parts we expect to be accessible and by what means. The Oxide rack is designed to live in a data center with exclusive access via the network. The only reason an engineer should ever need to physically visit a rack is to replace a failing part, such as a disk.
Think Before You Act: Intention-Guided Reasoning for LLM-Based Location Prediction
Announce Type: new Abstract: Predicting a user's next Point-of-Interest (POI) based on their historical check-in records is a fundamental task in location-based services. While recent methods incorporating large language models have shown strong reasoning capabilities and promising results, they typically formulate the prediction task as a one-step trajectory-to-location mapping problem, making predictions prone to shallow trajectory correlations and historical frequency bias. We argue that...
Mixture of Concept Bottleneck Experts
Announce Type: replace Abstract: Concept Bottleneck Models (CBMs) promote interpretability by grounding predictions in human-understandable concepts. However, existing CBMs typically constrain their task predictor to a single expression whose functional form is set a priori, limiting both predictive accuracy and adaptability to diverse user needs. We propose Mixture of Concept Bottleneck Experts (M-CBE), a framework that generalizes existing CBMs along two dimensions: the number of...