VLDB 2026 Research / reviewers in the wild / expert
Petros Raptopoulos
dblp:409/8253
· DBLP profile ↗
1ranked-venue papers
1as 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 first-author · 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.
| Artificial intelligence
1 paper |
Question answering and dialogue systems · 77% Multi-agent systems · 23% | |
| Databases, data mining, and information retrieval
1 paper |
Information retrieval · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Question answering and dialogue systems › domain-specific question answering
legal question answering |
0.9 | 1 | 2025 | PAKTON: A Multi-Agent Framework for Question Answering in Long Legal Agreements · EMNLP 2025 |
Information retrieval
retrieval-augmented generation |
0.9 | 1 | 2025 | PAKTON: A Multi-Agent Framework for Question Answering in Long Legal Agreements · EMNLP 2025 |
Methods — techniques the papers use, named apart from their topics
retrieval-augmented generation · 1.7multi-agent framework · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | PAKTON: A Multi-Agent Framework for Question Answering in Long Legal AgreementsabstractContract review is a complex and time-intensive task that typically demands specialized legal expertise, rendering it largely inaccessible to non-experts. Moreover, legal interpretation is rarely straightforward—ambiguity is pervasive, and judgments often hinge on subjective assessments. Compounding these challenges, contracts are usually confidential, restricting their use with proprietary models and necessitating reliance on open-source alternatives. To address these challenges, we introduce PAKTON: a fully open-source, end-to-end, multi-agent framework with plug-and-play capabilities. PAKTON is designed to handle the complexities of contract analysis through collaborative agent workflows and a novel retrieval-augmented generation (RAG) component, enabling automated legal document review that is more accessible, adaptable, and privacy-preserving. Experiments demonstrate that PAKTON outperforms both general-purpose and pretrained models in predictive accuracy, retrieval performance, explainability, completeness, and grounded justifications as evaluated through a human study and validated with automated metrics. Petros Raptopoulos, Giorgos Filandrianos, Maria Lymperaiou, Giorgos B. Stamou |
EMNLP | 1 |