VLDB 2026 Research / reviewers in the wild / expert
Kaushik T. Ranade
dblp:426/8593
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 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 |
Multi-agent systems · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Automated reasoning and model checking › model checking
counterexample analysis |
0.9 | 1 | 2025 | SpecMAS: A Multi-Agent System for Self-Verifying System Generation via Formal Model Checking · NeurIPS 2025 |
Automated reasoning and model checking
model repair |
0.9 | 1 | 2025 | SpecMAS: A Multi-Agent System for Self-Verifying System Generation via Formal Model Checking · NeurIPS 2025 |
Automated reasoning and model checking › model checking
temporal logic model checking |
0.9 | 1 | 2025 | SpecMAS: A Multi-Agent System for Self-Verifying System Generation via Formal Model Checking · NeurIPS 2025 |
Methods — techniques the papers use, named apart from their topics
temporal logic · 1.7symbolic model checking · 1.7counterexample analysis · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | SpecMAS: A Multi-Agent System for Self-Verifying System Generation via Formal Model CheckingabstractWe present SpecMAS, a novel multi-agent system that autonomously constructs and formally verifies executable system models from natural language specifications. Given a Standard Operating Procedure (SOP) describing a target system, SpecMAS parses the specification, identifies relevant operational modes, variables, transitions, and properties, and generates a formal model in NuSMV code syntax, an industry-standard symbolic model checker. A dedicated reasoning agent extracts both explicit and implicit properties from the SOP, and verification is performed via temporal logic model checking. If any properties fail to verify, an autonomous debugging agent analyzes counterexamples and iteratively corrects the model until all properties are satisfied. This closed-loop system design guarantees provable correctness by construction and advances the state of the art in automated, interpretable, and deployable verification pipelines. We demonstrate the generality, correctness, and practical feasibility of SpecMAS across a set of representative case studies and propose a new benchmark dataset for the evaluation and comparison of model checking performance. Kaushik T. Ranade, Aja Khanal, Kalyan S. Basu, Apurva Narayan |
NeurIPS | 2 |