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

Ming Zhang, Kexin Tan, Yueyuan Huang, Yujiong Shen, Chunchun Ma, Li Ju, Xinran Zhang, Yuhui Wang, Wenqing Jing, Jingyi Deng, Huayu Sha, Binze Hu, Jingqi Tong, Changhao Jiang, Yage Geng, Yuankai Ying, Yue Zhang, Zhangyue Yin, Zhiheng Xi, Shihan Dou, Tao Gui, Qi Zhang, Xuanjing Huang

arXiv:2601.01576v2cs.IRcs.AIcs.CL

TL;DR

Novelty assessment is difficult because reviewers must evaluate claims against a rapidly expanding literature while verifying fine-grained technical overlap. OpenNovelty addresses this with a four-phase agentic pipeline that extracts claims, retrieves and filters prior work, organizes related papers, and performs evidence-grounded comparisons. Deployed on 500+ ICLR 2026 submissions, it preliminarily identifies relevant prior work, including papers authors may overlook.

  • Problem

    Novelty assessment is challenging because literature is vast, claims require fine-grained verification, and existing LLM approaches may hallucinate references, miss technical details, or lack systematic organization.

    Source: §1 · pp. 1–2

  • Method

    OpenNovelty extracts core tasks and contribution claims, generates semantic queries, retrieves and filters prior work, constructs taxonomies, and performs contribution-level full-text comparisons.

    Source: §1 · p. 2, §2.1 · p. 3

  • Results

    OpenNovelty was deployed on 500+ highly rated ICLR 2026 submissions, with preliminary analysis suggesting it identifies relevant prior work that authors may overlook.

    Source: §1 · p. 3

  • Takeaways & Limitations

    The system provides transparent, traceable, and evidence-based novelty analysis through reports grounded in retrieved papers, citations, and evidence snippets.

    Source: §1 · pp. 2–3

    The authors note limitations regarding mathematical formulas and visual content extraction.

    Source: §2.1.3 · p. 4

Abstract

from arXiv · show

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.

1 Introduction

OpenNovelty addresses the difficulty of assessing novelty amid rapidly expanding literature and reviewer constraints by combining retrieval, structured analysis, and evidence-grounded reporting. It was deployed on 500+ highly rated ICLR 2026 submissions, with preliminary analysis indicating it can identify relevant prior work authors may overlook.

1 Introduction

  • Novelty assessment is difficult because literature is vast, claims require fine-grained verification, and reviewer judgments are subjective.Existing approaches may hallucinate references, compare only titles and abstracts, or lack systematic organization of related work.Source: §1 · pp. 1–2
  • OpenNovelty was deployed on 500+ highly rated ICLR 2026 submissions, and preliminary analysis suggests it identifies relevant prior work that authors may overlook.The reports were publicly released, while expansion to more than 2,000 submissions was planned.Source: §1 · p. 3
  • OpenNovelty integrates contribution extraction, semantic retrieval, taxonomy construction, and contribution-level comparison into an automated novelty-analysis pipeline.The framework is organized into four phases spanning query generation, prior-work retrieval, evidence-based comparison, and report generation.Source: §1 · pp. 2–3
  • The system grounds novelty judgments in retrieved real papers, explicit citations, and evidence snippets rather than relying on LLM parametric knowledge.Its design goal is to make novelty judgments verifiable and traceable.Source: §1 · pp. 2–3

2 OpenNovelty

OpenNovelty extracts abstracted tasks and author-stated contributions, expands them into semantic queries, retrieves broad candidate sets, and filters them into analyzable prior work. The phase produces structured inputs for later taxonomy construction and contribution-level verification.

2.1.1 Core Task and Contribution Extraction

  • Phase I extracts a 5–15-word core task and structured author-stated contribution claims, excluding pure experimental results and performance numbers.Contribution records include a name, verbatim claim text, normalized description, and traceability hint.Source: §2.1.1 · p. 3

2.1.2 Query Generation with Semantic Expansion

  • Each paper generates 6–12 queries: three for the core task and three per contribution, using primary queries plus semantic variants.Contribution queries use a “Find papers about” prefix, while core-task queries are short phrases without that prefix.Source: §2.1.2 · p. 3, Example 1: Query Generation from… · p. 4

2.1.3 Implementation and Output

  • Phase I outputs the core task, contribution claims, and expanded queries for use by Phases II and III.The implementation also validates structured outputs, applies fallback mechanisms, and infers publication dates for temporal filtering.Source: §2.1.3 · p. 4

2.2.1 Semantic Search

  • Phase II sends generated natural-language queries directly to Wispaper [15] and retains only papers marked perfect by its verification verdict.The system uses broad-recall retrieval before sequential filtering, with configurable concurrent query execution.Source: §2.2 · p. 4, §2.2.1 · p. 5

2.2.2 Multi-layer Filtering

  • Multi-layer filtering applies quality checks, deduplication, self-reference removal, temporal filtering, and Top-K selection across core-task and contribution scopes.Core-task retrieval selects up to 50 papers, while each contribution selects up to 10; cross-scope merging typically yields 60–80 unique papers per submission.Source: §2.2.2 · p. 5

C. Cross-scope Deduplication

After retrieval, OpenNovelty organizes related work into validated taxonomies, checks textual overlap, and compares papers at core-task and contribution levels. Phase III serializes these analyses as structured inputs for report generation.

2.3 Phase III: Analysis & Synthesis

  • Phase II candidates feed Phase III, which produces taxonomies, textual-similarity findings, core-task distinctions, contribution comparisons, and an overall novelty assessment.These outputs are serialized as the sole input to Phase IV rendering.Source: C. Cross-scope Deduplication · p. 6, §2.3 · p. 6

2.3.1 Taxonomy Construction

  • The taxonomy organizes up to 50 core-task papers into interpretable hierarchical categories with explicit scope and exclusion rules.Typical depth is 3–5 levels, with leaves containing 2–7 semantically similar papers and MECE assignment requirements.Source: §2.3.1 · p. 6
  • Automatic validation checks coverage, uniqueness, and paper-ID validity, then repairs violations deterministically or with one LLM repair round.Taxonomies that remain invalid are marked needs_review rather than assigned artificial “Unassigned” categories.Source: §2.3.1 · p. 7

2.3.2 Textual Similarity Detection

  • Textual-similarity analysis identifies verified direct or paraphrased overlaps of at least 30 words and reports them for human interpretation without automated judgment.Reported segments are checked against both source texts using token-level anchor alignment with a 0.6 confidence threshold.Source: §2.3.2 · p. 7

2.3.3 Comparative Analysis

  • Core-task comparisons adapt to taxonomy structure, while contribution comparisons independently evaluate each extracted claim against each candidate using normalized full texts.The one-to-N design isolates judgments and supports parallel processing; contribution judgments use can_refute, cannot_refute, or unclear.Source: §2.3.3 · p. 7

A. Core-Task Distinction (Sibling-level):

The sibling-level comparison distinguishes AgentGym-RL from SkyRL-Agent by identifying different emphases within the same broad multi-turn reinforcement-learning category.

A. Core-Task Distinction (Sibling-level):

  • AgentGym-RL emphasizes horizon scaling through ScalingInter-RL, whereas SkyRL-Agent emphasizes an optimized asynchronous pipeline dispatcher for efficiency gains.Both papers are placed in the General-Purpose Multi-Turn RL Frameworks category.Source: A. Core-Task Distinction (Sibling-level) · p. 8

B. Contribution-level Comparison:

OpenNovelty compares each contribution against retrieved papers using explicit judgments and verified evidence, then renders the analyses into traceable reports. Phase IV is deterministic and adds no new analysis.

B. Contribution-level Comparison:

  • Each contribution receives a can_refute, cannot_refute, or unclear judgment, with can_refute requiring explicit quotes from both papers.Unverified refutations are downgraded to cannot_refute before report generation.Source: B. Contribution-level Comparison · p. 8

2.3.4 Implementation and Output

  • Phase III serializes taxonomy, comparisons, similarity analysis, and novelty assessments as structured JSON for Phase IV rendering.The JSON is the sole input to report generation.Source: §2.3.4 · p. 8

2.4.1 Report Structure

  • Phase IV renders seven report modules, including taxonomy, contribution judgments, comparisons, similarity analysis, references, and metadata.All module content is read directly from Phase III outputs.Source: §2.4.1 · p. 9

2.4.2 Rendering

  • Deterministic rendering formats the reports as Markdown or PDF with consistent citations, truncated evidence quotes, and hierarchical taxonomy indentation.No LLM calls or analytical processing occur during this phase.Source: §2.4.2 · p. 9, §2.4.3 · p. 9

2.4.3 Implementation and Output.

  • Published reports provide full traceability, allowing users to verify judgments by examining cited evidence.The final output consists of Markdown and PDF reports.Source: §2.4.3 · p. 9

3 Discussion

The discussion explains design choices that favor broad semantic retrieval, contribution-focused judgments, and verifiable refutations. It also bounds the system through content, indexing, pipeline, and human-oversight limitations.

3.1.2 Retrieval Design

  • Multiple semantic query variants and broad retrieval prioritize recall because missed relevant papers cannot be recovered later.The system typically submits 6–12 queries per paper before multi-layer filtering.Source: §3.1.2 · p. 9, §3.1.1 · p. 9
  • Semantic relevance is preferred over citation counts and venue prestige to avoid disadvantaging recent, cross-disciplinary, or preprint literature.Natural-language queries are used instead of Boolean transformations to preserve semantic robustness.Source: §3.1.2 · p. 10

3.1.3 Analysis Design

  • OpenNovelty uses LLM-generated hierarchical taxonomies and three-way contribution-level judgments because reviewers need semantically labeled, actionable refutation signals.The design targets novelty verification rather than comprehensive survey generation.Source: §3.1.3 · p. 10
  • Unverified can_refute judgments are automatically downgraded to cannot_refute, prioritizing precision because false-positive refutations are more harmful to authors.Structured assembly also enables independent retries, error handling, parallel processing, and programmatic citation construction.Source: §3.1.3 · p. 10

3.2.1 Content Analysis Constraints

  • Mathematical formulas and visual content are not reliably analyzed, limiting detection of contributions conveyed through equations, figures, tables, diagrams, or pseudocode.Formula limitations affect both extraction and claim comparison, while visual limitations are especially relevant to computer vision, systems architecture, and algorithm design.Source: §3.2.1 · pp. 10–11

3.2.2 Retrieval Boundaries

  • Taxonomy structure may vary across runs, and retrieval coverage excludes papers outside Wispaper’s index, so cannot_refute means no conflict was found within search scope.These constraints can create blind spots and affect core-task comparisons.Source: §3.2.1 · p. 11, §3.2.2 · p. 11

3.2.3 Pipeline Dependencies

  • Upstream extraction, retrieval, taxonomy, and rendering errors can propagate unchanged into final reports despite query expansion, broad retrieval, and evidence checks.The system inherits biases toward well-indexed venues and English-language publications, while traceability makes those limitations visible.Source: §3.2.3 · p. 11

3.3 Ethical Considerations

  • OpenNovelty is intended as a retrieval and evidence-checking aid, not a replacement for human review or a standalone basis for rejection.Users should manually check can_refute findings and interpret them in context.Source: §3.3 · p. 12

4 Future Work

Future work proposes systematic evaluation of extraction, retrieval, analysis, and end-to-end usefulness. Planned benchmarks and human comparisons are intended to guide iterative improvement.

4.1 Phase I: Extraction Optimization

  • Future extraction studies will vary input scope and prompting strategy to measure completeness, accuracy, retrieval effects, and computational cost.Comparisons include abstract-only through full-text inputs, zero-shot versus few-shot prompting, and single-pass versus iterative refinement.Source: §4.1 · p. 12

4.2 Phase II: Retrieval Benchmarking

  • NoveltyBench will use peer-review evidence to evaluate retrieval recall against human-identified relevant papers.The benchmark will combine ML conference and transparent-journal review data.Source: §4.2 · p. 12
  • The project will compare Semantic Scholar, Google Scholar, OpenAlex, and Wispaper using recall@K, mean reciprocal rank, and reviewer-cited-paper coverage.These comparisons will use NoveltyBench.Source: §4.2 · p. 12

4.3 Phase III: Analysis Quality

  • Phase III evaluation will test taxonomy validity and coherence, textual-similarity accuracy, and calibration of can_refute judgments against experts.Planned analyses include precision–recall trade-offs, false positives, false negatives, and confidence calibration.Source: §4.3 · p. 13

4.4 End-to-End Evaluation

  • End-to-end studies will measure agreement with reviewers, effects on review efficiency and quality, and relationships with acceptance decisions and post-publication impact.These experiments are intended to inform iterative improvements and contribute benchmarks to the research community.Source: §4.4 · p. 13

5 Related Work

Related work shows both promise and recurring limitations in LLM-assisted review and novelty assessment. OpenNovelty differentiates itself by grounding claim-level judgments in retrieved papers and traceable full-text evidence.

5.1 AI-Assisted Peer Review

  • LLM-assisted review systems can produce review-like feedback but exhibit scoring bias, methodological blind spots, difficulty identifying deep limitations, and stylistic homogenization.These limitations motivate restricting LLMs to defined, auditable subtasks.Source: §5.1 · p. 13

5.2 Novelty Assessment

  • Novelty-assessment research includes statistical prediction, retrieval-augmented ranking, dimensional decomposition, abstract-level idea evaluation, review assistance, graph representations, and reviewing frameworks.These paradigms differ in evidence sources, comparison granularity, and human oversight.Source: §5.2 · pp. 13–14
  • OpenNovelty grounds assessments in retrieved real papers and performs claim-level full-text comparisons with traceable citations for every judgment.This directly addresses approaches that rely on limited context, small retrieval sets, abstract-level analysis, or unverified related-work identification.Source: §5.2 · p. 14

6 Conclusion

OpenNovelty is presented as a transparent, evidence-based novelty-analysis system whose judgments are grounded in retrieved papers and verified evidence. The appendices specify structured Phase I outputs for core tasks, contributions, and semantic queries.

6 Conclusion

  • OpenNovelty grounds novelty judgments in retrieved real papers with explicit citations and verified evidence, supporting traceable and verifiable assessments.The system has been deployed on 500+ ICLR 2026 submissions, with reports publicly available; planned work includes scaling and systematic evaluation.Source: §6 · p. 14
  • The authors plan to scale OpenNovelty beyond 500 submissions and evaluate it systematically through NoveltyBench and component optimization.Source: §6 · p. 14

A.1 Phase I Output Field Definitions

  • Phase I produces structured outputs for a core task description, contribution claims, and semantic queries.Tables define the fields and constraints for each output type.Source: §A.1 · p. 17

A.1.1 Core Task Output

Phase I converts paper text into structured task and contribution representations, then generates semantic retrieval queries under reliability and formatting constraints. Its prompts, validation layers, and temperature settings are designed for deterministic, parseable batch processing.

A.2 Temperature and Query Configuration

  • Each paper generates 6–12 queries, with lower temperatures for extraction and temperature 0.2 for controlled semantic-variant diversity.The query count depends on the number of extracted contributions.Source: §A.2 · pp. 17–18, Table 4 · p. 18

A.3 Prompt Design Principles

  • Strict JSON schemas, semantic inclusion and exclusion rules, prompt-injection defenses, and layered parsing validation improve robustness for batch processing.Fallbacks include code-fence removal, JSON-span extraction, bracket truncation, field constraints, and defaults for missing optional fields.Source: §A.3 · p. 18, §A.4 · pp. 18–19

A.1.2 Contribution Output

  • Contribution extraction records names, verbatim author claims, normalized descriptions, and source hints while excluding pure performance results.The prompt limits outputs to clearly claimed contributions and supports attribution and query generation.Source: §A.5 · p. 20

B Phase II: Detailed Specifications

Phase II filters and retrieves candidates with quality checks and fault tolerance, while Phase III organizes retrieved work into taxonomies and structured comparison outputs. Filtering removes most candidates, and quality flags are computed from verification verdicts.

B.1 Filtering Statistics

  • Approximately 90–95% of retrieved papers are filtered, with quality-flag filtering contributing the largest reduction.Source: §B.1 · p. 23, Table 9 · p. 23

B.2 Fault Tolerance Mechanisms

  • Retrieval uses retries, token refresh, graceful degradation, and detailed logging to continue batch processing under transient failures.Failed queries can preserve partial results for downstream phases.Source: §B.2 · p. 23

B.3 Quality Flag Generation

  • Quality flags classify retrieved papers as perfect, partial, or no from criterion-level verification assessments, removing no-flag papers before ranking.The mapping distinguishes single-criterion and multi-criterion cases and excludes time-only support from partial status when multiple criteria apply.Source: §B.3 · p. 23, Table 10 · p. 24

C.1 Taxonomy Node Definitions

C.3 Core-Task Comparison Output Schema

The comparison pipeline verifies generated evidence against source documents using token-level alignment and explicit acceptance criteria. It filters similarity segments to retain only sufficiently supported, representative evidence.

C.4 Evidence Verification Algorithm

  • Token-level verification normalizes text and uses SequenceMatcher to find the longest contiguous matching subsequence, with similarity ratio 2M/T.Source: §C.4 · p. 25
  • Quotes are verified when the confidence score exceeds 0.6, combining anchor coverage and hit ratio with a compactness-dependent weighting.The score uses 0.7 × mean anchor coverage + 0.3 × hit ratio for compact anchors, and halves that value otherwise.Source: §C.4 · p. 25

C.5 Similarity Verification Details

  • Similarity segments are accepted only when both quotes achieve at least 60% coverage, contain at least 30 words at the shorter length, and are found in both sources.Source: §C.5 · p. 25
  • When more than three segments pass verification, only the three longest are retained and classified as Direct or Paraphrase.Source: §C.5 · p. 25

C.6 Core-Task Comparison Path Definitions

The system uses structured Phase III prompts and schemas to compare core-task neighbors, assess contribution-level refutation, and assemble evidence-backed novelty reports. Phase IV then renders the validated report deterministically into Markdown and optionally PDF.

C.6 Core-Task Comparison Path Definitions

  • Core-task comparisons adapt their execution path to the paper’s structural position in the taxonomy, using sibling and subtopic context where applicable.The implementation distinguishes cases with siblings, without siblings but with subtopic siblings, and without either.Source: §C.6 · pp. 25–26, Table 14 · p. 26

C.7 Prompt Templates for Phase III

  • Phase III prompt templates enforce strict JSON output, MECE validation, evidence verification, and hallucination-prevention constraints across taxonomy, survey, comparison, and similarity tasks.The templates cover taxonomy construction and repair, survey narratives, one-liners, textual similarity, contribution comparisons, novelty assessment, and core-task or subtopic comparisons.Source: §C.7 · p. 26, Table 15 · pp. 26–28, Table 16 · p. 28

C.9 Refutation Entry Schema

  • Contribution-level refutation entries classify each comparison as can_refute, cannot_refute, or unclear, with verbatim evidence pairs required when prior work challenges a claim.Evidence pairs include quotes, source locations, verification results, match scores, and rationales; unclear cases receive brief notes and are counted with non-refutable cases in statistics.Source: §C.7 · p. 31, §C.9 · pp. 35–36

C.7 Prompt Templates for Phase III

  • The novelty-assessment prompt combines taxonomy position, neighboring papers, literature-search scope, and per-contribution statistics while requiring cautious interpretation of limited candidate searches.Its output emphasizes field density, nearby directions, contribution-level overlap, and explicit acknowledgment that non-exhaustive searches cannot establish definitive novelty judgments.Source: §C.7 · pp. 32–34

D Phase IV: Report Rendering

  • Phase IV performs no LLM calls: it reads the complete Phase III JSON report, applies section templates, and produces a Markdown report that can optionally be converted to PDF.Rendering includes quote truncation, unified citation formatting, indented taxonomy visualization, and deterministic output filenames.Source: §C.8 · p. 35, D Phase IV: Report Rendering · p. 36, §D.1 · p. 36

D.2 Report Structure

  • The rendered report presents the paper header, core-task survey, sibling comparisons, contribution analysis, and detected textual-overlap segments when applicable.These sections organize taxonomy context, comparison results, overall novelty assessment, and textual-similarity findings for the final reader-facing report.Source: §D.2 · p. 37, §1 · p. 37
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