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Alex Chao

Publications and source records attributed to Alex Chao.

8 recordsLinked to original sources
arXiv2026

I-Rex: An Interactive Debugger for SQL

SQL is declarative in nature and rich in its features. Writing semantically correct SQL queries and finding logical bugs in SQL are not easy, even for experienced programmers, who are often used to the mindset of working with general-purpose programming languages (GPLs). While there are many GPL debuggers, SQL debugging has received much less attention. In this paper, we present I-Rex, a SQL debugger that enables users to inspect the logical execution of SQL queries visually and interactively to identify and potentially fix logical bugs in the queries. I-Rex draws analogies to the debugging paradigm of GPLs (e.g., stepping, watchpoints, etc.), making it easier for programmers to adopt. However, unlike debugging GPLs, which involves executing the underlying program in full to the point of interest, I-Rex allows users to jump to arbitrary points of interest by leveraging the power of the database systems, through selective materialization and query rewrites. To simplify deployment, I-Rex acts as a lightweight middleware on top of the database system; it imposes no overhead to prepare a database for debugging and maintains no state in the database systems during debugging sessions. We demonstrate the effectiveness of I-Rex through performance experiments as well as a user study in an educational setting.

cs.DB↗
arXiv2026

When AI Is Your Pastor: A Benchmark for Theological Triage and Pastoral Guidance in Large Language Models

People increasingly ask large language models (LLMs) for counsel on questions of faith, doctrine, and pastoral care. These questions are not ordinary information requests. Some ask about core Christian beliefs, some ask about real disagreements among faithful traditions, some require humility because the issue is prudential, and some are pastoral situations where safety and human referral matter more than theological completeness. Existing benchmarks do not evaluate this structure. We introduce FMG-Bench, the Faith & Moral Guidance Benchmark, a 120-scenario benchmark for evaluating large language model behavior in English-language Christian theological triage and pastoral guidance contexts. FMG-Bench v1 evaluates 14 advanced models across 8,792 scored responses, comparing raw model behavior with three guided instruction settings. In our production run, placing models inside a structured harness improves over raw model behavior by +3.96 points on average, with every model improving. The most safety-critical finding is a +10.8 point gain in escalation appropriateness -- whether AI systems recognize when pastoral, clinical, legal, or emergency support is needed. The guided settings also improve robustness, meaning consistency when questions are reworded or pressured (92.88 to 98.02 stability). Asking a model to compare perspectives helps in secondary-doctrine questions but can be counterproductive when applied to primary doctrine or urgent pastoral situations. The benchmark is a measurement tool, not an endorsement of AI systems as pastoral authorities.

cs.CY↗
arXiv2024

From Local to Global: A Graph RAG Approach to Query-Focused Summarization

The use of retrieval-augmented generation (RAG) to retrieve relevant information from an external knowledge source enables large language models (LLMs) to answer questions over private and/or previously unseen document collections. However, RAG fails on global questions directed at an entire text corpus, such as "What are the main themes in the dataset?", since this is inherently a query-focused summarization (QFS) task, rather than an explicit retrieval task. Prior QFS methods, meanwhile, do not scale to the quantities of text indexed by typical RAG systems. To combine the strengths of these contrasting methods, we propose GraphRAG, a graph-based approach to question answering over private text corpora that scales with both the generality of user questions and the quantity of source text. Our approach uses an LLM to build a graph index in two stages: first, to derive an entity knowledge graph from the source documents, then to pregenerate community summaries for all groups of closely related entities. Given a question, each community summary is used to generate a partial response, before all partial responses are again summarized in a final response to the user. For a class of global sensemaking questions over datasets in the 1 million token range, we show that GraphRAG leads to substantial improvements over a conventional RAG baseline for both the comprehensiveness and diversity of generated answers.

cs.CL↗
arXiv2024

The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey

This survey paper examines the recent advancements in AI agent implementations, with a focus on their ability to achieve complex goals that require enhanced reasoning, planning, and tool execution capabilities. The primary objectives of this work are to a) communicate the current capabilities and limitations of existing AI agent implementations, b) share insights gained from our observations of these systems in action, and c) suggest important considerations for future developments in AI agent design. We achieve this by providing overviews of single-agent and multi-agent architectures, identifying key patterns and divergences in design choices, and evaluating their overall impact on accomplishing a provided goal. Our contribution outlines key themes when selecting an agentic architecture, the impact of leadership on agent systems, agent communication styles, and key phases for planning, execution, and reflection that enable robust AI agent systems.

cs.AI↗
arXiv2020

Dynamical Systems, Representation of Particle Beams

An overview of dynamical systems in accelerator physics is presented with a suggestion of a few issues to be addressed. Also mentioned are a few possible developments in the future. Technical details supporting the views are not presented.

physics.acc-ph↗
arXiv2013

A Feasibility Study of an e+e- Ring Collider for Higgs Factory

A ring-based Higgs factory with a center-of-mass energy of 240 GeV residing in the existing LEP tunnel is studied at a level of concrete lattice. We found that low-emittance lattice is essential to mitigate the effect of beamstrahlung on the beam lifetime. To achieve a luminosity of $1.0\times10^{34}{\rm cm}^{-2}{\rm s}^{-1}$, we simplified final focusing system and improved its momentum aperture by a factor of 5 to 2.5%.

physics.acc-ph↗
arXiv2013

Report of the ICFA Beam Dynamics Workshop 'Accelerators for a Higgs Factory: Linear vs. Circular' (HF2012)

This paper is a summary report of the ICFA Beam Dynamics Workshop 'Accelerators for a Higgs Factory: Linear vs. Circular' (HF2012). It discusses four types of accelerators as possible candidates for a Higgs factory: linear e+e- colliders, circular e+e- colliders, muon collider and photon colliders. The comparison includes: physics reach, performance (energy and luminosity), upgrade potential, technology maturity and readiness, and technical challenges requiring further R&D.

physics.acc-ph↗