Benchmark LLM reference recommendation capabilities by verifying every cited paper against Crossref, PubMed, arXiv, and DBLP. Measures hallucination rate, per-field accuracy (title/author/year/DOI), discipline breakdown, and year constraint compliance. Supports tool-augmented (ReAct + web search) mode.
SkillScore80Maintained
Automatic
◆AI Engineer pack
Last verified 10/11/2026
What it changes
Passed 20 of 25 test cases with the skill, and 4 of 25 without it.
Without skill16%
With skill80%
Checks it passed that the model alone missed
Stores parsed citations in extracted_refs.json.
Classifies scores in the 60% to 75% range as Good.
Assigns the SUSPECT status to partial metadata matches.
and 26 more in the full report
16 fixed4 passed either way5 still failing0 broke
Pass-rate lift
+64 pp
Tokens
+54%
Agent turns
0%
Response time
-64%
Model gemini-3.6-flashMeasured 10/11/2026v120 of 25 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 16 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 16
Prompt
What is the default output folder path where benchmark run outputs are saved, and where are parsed BibTeX references stored?
What was checked, without and with the skill
Checked
Without skill
With skill
Saves outputs under ./evaluation_results/ref_hallucination_arena by default.
Missed
Passed
Stores parsed citations in extracted_refs.json.
Missed
Passed
case-13 · gemini-3.6-flash · 10/11/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md249 linessingle file
Reference Hallucination Arena Skill
Evaluate how accurately LLMs recommend real academic references using the
OpenJudge RefArenaPipeline:
Load queries — from JSON/JSONL dataset
Collect responses — BibTeX-formatted references from target models
Extract references — parse BibTeX entries from model output
Use when the user has nothing — no traces, no labels, no eval set — and needs to build a v0 evaluation from scratch. Also use when the user says "I need to start evaluating my app but don't know where to begin," "I want to set up eval for a new product," or has just identified failure modes and needs to turn them into principles. Outputs a v0 grader in 30 minutes using OpenJudge SimpleRubricsGenerator, plus a roadmap to reach calibrated evaluation.