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.
SkillScore96Maintained
Automatic
◆AI Engineer pack
Last verified 10/11/2026
What it changes
Passed 24 of 25 test cases with the skill, and 9 of 25 without it.
Without skill36%
With skill96%
Checks it passed that the model alone missed
Sets max_workers to 50.
Awaits pipeline.evaluate().
Includes the --fresh CLI option.
and 17 more in the full report
15 fixed9 passed either way1 still failing0 broke
Pass-rate lift
+60 pp
Tokens
+45%
Agent turns
0%
Response time
-67%
Model gemini-3.6-flashMeasured 10/11/2026v124 of 25 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 15 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 15
Prompt
When running the reference hallucination benchmark CLI module, developers often guess flags like `--clean`, `--reset`, or `--ignore-checkpoint`. Which specific flag forces the execution to ignore any pre-existing `checkpoint.json` and start from scratch?
What was checked, without and with the skill
Checked
Without skill
With skill
Includes the --fresh CLI option.
Missed
Passed
case-18 · 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
Automatically evaluate and compare multiple AI models or agents without pre-existing test data. Generates test queries from a task description, collects responses from all target endpoints, auto-generates evaluation rubrics, runs pairwise comparisons via a judge model, and produces win-rate rankings with reports and charts. Supports checkpoint resume, incremental endpoint addition, and judge model hot-swap. Use when the user asks to compare, benchmark, or rank multiple models or agents on a cust