Demo Data from VLDB 2026 / reviewers in the wild / expert
Marc Hudak
dblp:421/7039
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Software engineering, systems software and programming languages · 1 · 1 since 2021Theory of computation · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Theoretical computer science
1 paper |
Automated reasoning and model checking · 100% | |
| Artificial intelligence
1 paper |
Language models and text generation · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Automated reasoning and model checking › automated reasoning › mathematical reasoning
autoformalization |
1.0 | 1 | 2026 | A Neurosymbolic Approach to Natural Language Formalization and Verification · CAV (2) 2026 |
Natural language and speech › Language models and text generation › large language model safety
LLM guardrail |
0.3 | 1 | 2026 | A Neurosymbolic Approach to Natural Language Formalization and Verification · CAV (2) 2026 |
Methods — techniques the papers use, named apart from their topics
large language model · 2.0automated reasoning · 2.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | A Neurosymbolic Approach to Natural Language Formalization and VerificationabstractAbstract Large Language Models perform well at natural language interpretation and reasoning, but their lack of formal correctness guarantees limits their adoption in regulated industries like finance and healthcare that operate under strict policies. To address this limitation, we launched Automated Reasoning checks (ARc) : a public service that (1) uses LLMs with optional human guidance to formalize natural language policies, allowing fine-grained control of the formalization process, and (2) uses inference-time autoformalization to validate logical correctness of natural language statements against those policies. ARc performs multiple redundant formalization steps at inference time, checking the formalizations for semantic equivalence. Our benchmarks show that ARc exceeds 99% soundness and achieves a near-zero false positive rate in identifying logical validity. Our approach produces auditable artifacts that substantiate the verification outcomes and can be used to improve the original text. ARc is the first commercial offering from a major cloud provider to integrate automated reasoning into a generative AI guardrail. Chenyang An, Sam Bayless, Stefano Buliani, Darion Cassel, Byron Cook, Duncan Clough, Rémi Delmas, Nafi Diallo, Ferhat Erata, Nick Feng, Dimitra Giannakopoulou, Aman Goel, Aditya Gokhale, Joe Hendrix, Victor Heorhiadi, Marc Hudak, Dejan Jovanovic, Andrew M. Kent, Benjamin Kiesl-Reiter, Jeffrey J. Kuna, Nadia Labai, Joe Lilien, Divya Raghunathan, Zvonimir Rakamaric, Niloofar Razavi, Michael Tautschnig, Ali Torkamani, Nathaniel Weir, Michael W. Whalen, Jianan Yao |
CAV (2) | 16 |