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HalluCiteChecker: A Lightweight Toolkit for Hallucinated Citation Detection and Verification in the Era of AI Scientists

arXiv 2026 42 system, application

TLDR

A lightweight toolkit for detecting hallucinated citations in scientific papers, designed to reduce reviewer workload.

Reasoning

The paper introduces a practical tool for a specific NLP task, but lacks empirical evaluation or real-world benchmarks in the abstract. Its strength is addressing a timely problem with a lightweight, offline solution; weakness is no evidence of validation.

Read-first score

Read-first score 42, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 35.

Recency 8%
100

Uses a gentle age decay so recent papers surface without erasing older foundations. 2026

Reproducibility 25%
46

Screens links and visible text for paper, code, dataset, artifact, and repository signals. pdf=True; code=False; dataset=False; markers=code,github

Methodology quality 25%
40

Screens visible abstract and analysis fields for experiment, dataset, baseline, metric, and limitation evidence. markers=none

Topical relevance 42%
29.2

Uses existing LLM keyword relevance scores normalized to 0-100. AI scientist,automated scientific discovery,autonomous research agent,automated research,literature review agent,survey generation,automated experimentation,experiment design agent,AI for scientific research,paper writing agent,research automation,scientific discovery agent

Field roles

FrontierBridge

Rank sensitivity

Stability: volatile; rank range: 39.

Keyword Scores

AI for scientific research
6
paper writing agent
5
research automation
5
automated research
4
AI scientist
3
literature review agent
3
automated scientific discovery
2
autonomous research agent
2
scientific discovery agent
2
survey generation
1
automated experimentation
1
experiment design agent
1

Deep Analysis

Innovations

  • Formalizing hallucinated citation detection as an NLP task
  • Lightweight offline toolkit for hallucinated citation verification
  • Enabling systematic pre-review and publication checks

Methodology

The paper formalizes hallucinated citation detection as an NLP task and provides a lightweight toolkit that performs verification using only CPUs, running offline in seconds on a standard laptop.

Key Results

The toolkit can verify citations in seconds on a standard laptop, running entirely offline on CPUs.

Resources

Paper

Tags

CLAIDL