OpenNovelty: An LLM-powered Agentic System for Verifiable Scholarly Novelty Assessment

TL;DR

OpenNovelty is an LLM-powered system that automates scholarly novelty assessment by retrieving and analyzing relevant prior work to produce verifiable, evidence-based review reports, enhancing fairness and transparency in peer review.

Contribution

The paper introduces OpenNovelty, a novel LLM-based system that performs structured, verifiable novelty analysis by integrating retrieval, hierarchical classification, and synthesis for peer review.

Findings

Successfully deployed on 500+ submissions with publicly available reports.

Effectively identifies relevant prior work, including overlooked related papers.

Provides transparent, evidence-backed novelty assessments.

Abstract

Evaluating novelty is critical yet challenging in peer review, as reviewers must assess submissions against a vast, rapidly evolving literature. This report presents OpenNovelty, an LLM-powered agentic system for transparent, evidence-based novelty analysis. The system operates through four phases: (1) extracting the core task and contribution claims to generate retrieval queries; (2) retrieving relevant prior work based on extracted queries via semantic search engine; (3) constructing a hierarchical taxonomy of core-task-related work and performing contribution-level full-text comparisons against each contribution; and (4) synthesizing all analyses into a structured novelty report with explicit citations and evidence snippets. Unlike naive LLM-based approaches, \textsc{OpenNovelty} grounds all assessments in retrieved real papers, ensuring verifiable judgments. We deploy our system on 500+ ICLR 2026 submissions with all reports publicly available on our website, and preliminary analysis suggests it can identify relevant prior work, including closely related papers that authors may overlook. OpenNovelty aims to empower the research community with a scalable tool that promotes fair, consistent, and evidence-backed peer review.