# Opennovelty Scholarly Assessment

> Build agentic systems for transparent, evidence-based novelty analysis of research submissions through four-phase pipelines: contribution extraction, prior work retrieval, hierarchical comparison, and structured reporting with explicit citations—enabling fair peer review at scale.

- Skill: `adu2021/opennovelty-scholarly-assessment` (Agent Skill)
- Install (CLI): `npx skillmds@latest add adu2021/opennovelty-scholarly-assessment`
- Raw SKILL.md: https://api.skillmd.com/api/skills/adu2021/opennovelty-scholarly-assessment/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Research & Search
- License: MIT
- Author: adu2021 (https://skillmd.com/u/adu2021)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/adu2021/opennovelty-scholarly-assessment

---


## Overview

OpenNovelty is an LLM-powered agentic system designed to assess research novelty in a transparent, verifiable manner. It addresses a critical challenge in peer review: evaluating submissions against rapidly evolving literature requires extensive domain knowledge and careful analysis.

**Core Innovation:** Unlike naive LLM-based approaches that generate unsupported assessments, OpenNovelty grounds all novelty judgments in retrieved real papers, ensuring verifiable, evidence-backed evaluation.

## Four-Phase Assessment Pipeline

### Phase 1: Contribution Extraction

Extract core task and specific contribution claims from research submissions.

**Process:**
1. Identify the paper's central research task/problem
2. Extract explicit contribution claims (technical, methodological, empirical)
3. Generate retrieval queries capturing contribution scope
4. Structure contributions for downstream analysis

**Output:** Structured specification of contributions ready for evidence gathering.

### Phase 2: Prior Work Retrieval

Retrieve relevant prior work using semantic search and knowledge engines.

**Implementation:**
- Query generation from extracted contributions
- Semantic similarity search across paper databases
- Ranked retrieval of potentially relevant prior work
- Handles both well-known and obscure related work

**Advantages:**
- Discovers closely related papers authors may overlook
- Systematic coverage of related literature
- Evidence-based rather than recollection-based

### Phase 3: Hierarchical Taxonomy Construction & Full-Text Comparison

Build a hierarchical taxonomy of prior work organized by contribution category, then perform detailed comparisons.

**Taxonomy Structure:**
- **Level 1:** Core task (same research problem)
- **Level 2:** Methodological approach (similar techniques)
- **Level 3:** Specific innovations (targeted improvements)

**Comparison Process:**
- Extract relevant details from each prior work paper
- Compare against submission's contributions point-by-point
- Identify overlaps, incremental vs. novel aspects
- Document evidence snippets from papers

### Phase 4: Structured Novelty Report

Synthesize analyses into comprehensive report with explicit citations and evidence.

**Report Contents:**
1. Summary of submission's contributions
2. Identified related work organized by relevance
3. Contribution-level comparisons with evidence
4. Assessment of novelty claims with supporting citations
5. Recommendations for reviewers

**Key Feature:** Every claim is traceable back to evidence; includes page/section references to source papers.

## Agentic Architecture

OpenNovelty uses an agentic approach for four-phase pipeline execution:

```python
class NoveltyAssessor(Agent):
    """LLM agent performing novelty assessment."""

    def assess(self, submission: Paper) -> NoveltyReport:
        # Phase 1: Extract contributions
        contributions = self.extract_contributions(submission)

        # Phase 2: Retrieve prior work
        prior_works = self.retrieve_prior_work(contributions)

        # Phase 3: Build taxonomy and compare
        taxonomy = self.build_taxonomy(prior_works)
        comparisons = self.perform_comparisons(
            contributions, taxonomy, prior_works
        )

        # Phase 4: Generate structured report
        report = self.synthesize_report(
            submission, contributions, comparisons, prior_works
        )

        return report
```

## Deployment and Impact

**Large-Scale Pilot (ICLR 2026):**
- Processed 500+ research submissions
- Generated public novelty reports for all submissions
- Preliminary analysis validates effectiveness in identifying related work
- Authors report value in discovering overlooked references

**Scalability:**
- Processes submissions at scale without manual human effort
- Reduces reviewer cognitive load for novelty assessment
- Enables consistent, evidence-backed evaluation standards

## Advantages Over Manual Review

**Systematic Coverage:**
- Comprehensive search vs. reviewer recollection of related work
- Discovers obscure papers that domain experts might miss
- Reduces bias from reviewer familiarity with specific subfields

**Transparency:**
- Every claim supported by explicit evidence citations
- Reproducible assessment methodology
- Reviewers can verify or dispute specific comparisons

**Scalability:**
- Handles hundreds of submissions efficiently
- Consistent assessment standards across papers
- Reduces reviewer burden for novelty determination

## When to Use OpenNovelty

**Use when:**
- Assessing novelty of research submissions at scale
- Building evidence-backed peer review systems
- Evaluating patents or technical reports
- Supporting researchers in positioning their work
- Identifying overlooked related work in literature reviews

**When NOT to use:**
- Simple categorization or metadata extraction (direct LLM sufficient)
- Scenarios with extremely limited prior literature
- Real-time applications with latency constraints
- Specialized domains with proprietary literature not publicly available

## Implementation Considerations

**Retrieval Backend:**
- Semantic search engine over paper databases (arXiv, ACL, etc.)
- Embedding-based similarity for identifying relevant papers
- Support for multiple knowledge bases

**Comparison Strategy:**
- Fine-grained contribution-level comparison
- Hierarchical organization reduces false positives
- Evidence snippets provide human reviewers with context

**Report Generation:**
- Structured output (JSON/XML) for downstream processing
- Citation format supporting verification
- Confidence scores for uncertain assessments

## Research Contributions

- **Verifiable Novelty Assessment:** Framework for evidence-backed evaluation
- **Agentic Pipeline:** Multi-phase system enabling transparent analysis
- **Large-Scale Validation:** Pilot on 500+ ICLR submissions
- **Public Dataset:** Community resource for novelty assessment research

## Related Systems

- **Meta-review systems:** Automated review summarization vs. novelty-specific assessment
- **Literature mining:** Citation network analysis vs. contribution-level comparison
- **Patent analysis:** Similar concepts applied to patent landscapes

## Code and Data Availability

All code, reports, and datasets available at: https://opennovelty.org

**Public Outputs:**
- 500+ novelty reports for ICLR 2026 submissions
- Assessment methodology documentation
- Anonymized benchmark dataset for future research

## References

- OpenNovelty deployment on 500+ ICLR 2026 submissions
- Identifies relevant prior work with high recall
- Enables fair, transparent, evidence-backed peer review
- Reduces reviewer cognitive burden for novelty assessment