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Jerry Xu

Publications and source records attributed to Jerry Xu.

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Early Planet Formation in Embedded Disks (eDisk). XXIV: Systematic Investigation of Disk Structures based on Visibility Analysis

The dust continuum emission from young protostellar disks encodes key information about their mass distribution and early evolution, yet uniform high-resolution comparative studies remain limited. We present a systematic uv-plane analysis of parametric intensity models applied to ALMA Band-6 (1.3 mm) observations of 23 disks (19 protostellar systems with 4 being in binary) from the eDisk sample, spanning Gaussian profiles to power-law cores with exponential tails (PLCT), including asymmetric extensions. Gaussian models generally fail to reproduce the centrally peaked emission and extended outer structure observed in most disks, whereas the PLCT framework provides a significantly improved description of radial brightness profiles. Incorporating azimuthal asymmetries further reduces residuals in 15 of 17 inclined disks, indicating that departures from axisymmetry are common at early stages. Only two disks, L1489 IRS and Oph IRS63, exhibit clear gap and ring substructures, while most appear smooth at the spatial resolution and sensitivity of our observations. These systems are among the most evolved in the sample, and the absence of flat-spectrum sources limits the evolutionary range probed, {suggesting that the detection of prominent gaps and rings is not common} in the earliest phases of disk evolution. Using a uniform definition of disk radius based on the 95\% enclosed flux, we find a positive correlation with stellar mass, $R_{\rm disk} \propto M_{\star}^{1.5 \pm 0.1}$, with disks in binary systems systematically smaller than those around isolated protostars. While the models capture overall morphology and large-scale asymmetries, distinguishing intrinsic structures from radiative transfer effects in optically thick regions remains challenging.

astro-ph.SR

Open Agent Specification (Agent Spec): A Unified Representation for AI Agents

The proliferation of agent frameworks has led to fragmentation in how agents are defined, executed, and evaluated. Existing systems differ in their abstractions, data flow semantics, and tool integrations, making it difficult to share or reproduce workflows. We introduce Open Agent Specification (Agent Spec), a declarative language that defines AI agents and agentic workflows in a way that is compatible across frameworks, promoting reusability, portability and interoperability of AI agents. Agent Spec defines a common set of components, control and data flow semantics, and schemas that allow an agent to be defined once and executed across different runtimes. Agent Spec also introduces a standardized Evaluation harness to assess agent behavior and agentic workflows across runtimes - analogous to how HELM and related harnesses standardized LLM evaluation - so that performance, robustness, and efficiency can be compared consistently across frameworks. We demonstrate this using four distinct runtimes (LangGraph, CrewAI, AutoGen, and WayFlow) evaluated over three different benchmarks (SimpleQA Verified, $\tau^2$-Bench and BIRD-SQL). We provide accompanying toolsets: a Python SDK (PyAgentSpec), a reference runtime (WayFlow), and adapters for popular frameworks (e.g., LangGraph, AutoGen, CrewAI). Agent Spec bridges the gap between model-centric and agent-centric standardization & evaluation, laying the groundwork for reliable, reusable, and portable agentic systems.

cs.AI

GRASP: Municipal Budget AI Chatbots for Enhancing Civic Engagement

There are a growing number of AI applications, but none tailored specifically to help residents answer their questions about municipal budget, a topic most are interested in but few have a solid comprehension of. In this research paper, we propose GRASP, a custom AI chatbot framework which stands for Generation with Retrieval and Action System for Prompts. GRASP provides more truthful and grounded responses to user budget queries than traditional information retrieval systems like general Large Language Models (LLMs) or web searches. These improvements come from the novel combination of a Retrieval-Augmented Generation (RAG) framework ("Generation with Retrieval") and an agentic workflow ("Action System"), as well as prompt engineering techniques, the incorporation of municipal budget domain knowledge, and collaboration with local town officials to ensure response truthfulness. During testing, we found that our GRASP chatbot provided precise and accurate responses for local municipal budget queries 78% of the time, while GPT-4o and Gemini were only accurate 60% and 35% of the time, respectively. GRASP chatbots greatly reduce the time and effort needed for the general public to get an intuitive and correct understanding of their town's budget, thus fostering greater communal discourse, improving government transparency, and allowing citizens to make more informed decisions.

cs.AI