llamaindex/BooBench

👻 Boo Bench

Quick links: [💻 Code: ParseBench] [📊 ParseBench dataset]

Boo bench is built to capture the pages that scare VLMs (cause them to make mistakes) but most specialized document OCR APIs perform well on. It is a playful and simple litmus test for understanding the need for specialized models, tools, and agent harnesses for document processing, beyond simply straight shotting documents to VLMs.

A Boo is an element on a page that scares a parser. We frequently see failure modes where certain pages cause parsers to go into repitition loops or stop parsing properly.

Boo Bench is a haunted house of 179 pages built to scare document parsers into repeating themselves.

The four rooms

Room (split)	Pages	Ground truth	What is inside
🕯️ real	100	the page's text layer	Real pages with a repetition: dot leaders, fill-in blanks, rules of =, rows of 0.00, repeated lines, in 23 kinds.
🕸️ trap	48	exact, generated	16 trap families × 3 Halloween-themed pages: near-identical rows, sequences and lists that stop early, fill-in forms, repeated blocks, repetition written as an instruction (la la la (×3)), dense number grids, codes (Morse, binary, bar glyphs), box drawing and text art, tiled watermarks, homoglyphs (O0, l1I), stacked diacritics, right-to-left and vertical text, rare emoji sequences, nearly blank pages, giant words.
👻 ghost	16	exact, generated	Grids, rows and tallies of ghost, pumpkin, bat, spider and skull emoji.
💀 script	15	none needed	Scans of the UDHR's Article 1, the alphabet and the digits in 15 scripts (Armenian, Canadian syllabics, Cherokee, Ethiopic, Gujarati, Kannada, Khmer, Malayalam, Mongolian, Myanmar, Sinhala, Syriac, Tamil, Thaana, Vietnamese).

Trap, ghost and script pages are image-only PDFs, so a parser has to read the pixels.

What a Boo looks like

A rotated table of study criteria marks each cell with an x: 159 of them on the page. The parser wrote 8,181 x in one run, until its output ran out. The page is on the left, the parser's output on the right, the loop in red.

Running it

Boo Bench runs with the ParseBench CLI, on any of its pipelines:

pip install parse-bench            # or: uv tool install parse-bench

parse-bench run llamaparse_agentic --dataset llamaindex/BooBench                # downloads to ./data/boobench, runs, scores
parse-bench run llamaparse_agentic --dataset llamaindex/BooBench --group script # one room: real, trap, ghost or script
parse-bench download --dataset llamaindex/BooBench
parse-bench status --dataset llamaindex/BooBench

The score is the rule pass rate: the share of pages with no Boo, overall and per room.

On your own documents

parse-bench repetition make ./my_docs                  # writes <name>.test.json next to every PDF
parse-bench run llamaparse_agentic --input_dir ./my_docs

repetition make reads each PDF's text layer and writes one repetition_overshoot rule budgeted from it, the way the real room was built. A PDF without a text layer is skipped (--scans tests it as a page that repeats nothing).

The rule: repetition_overshoot

The output is cleaned of markdown and HTML syntax (table separators, grids, rules, bold markers, tags), look-alike characters are folded (O/0, I/l/ǀ/1, $/5), and the page fails on the worst overshoot of:

Budget	Limit
One fill run (dots, dashes, underscores, =, ·, …, ▪)	2 × the page's longest + 40, one line of the page (250), or 2 × the page's block of leader lines + 40
All fill together	2 × the page's total + 40, or 250 per line of the page
A run of one character, a 2-8 character unit or a token	2 × its count on the page + 20
A repeated line or HTML table row	2 × the count of its rarest word on the page + 40
A whole table written out again	2 × its count on the page (at least once)

On a text-layer page, a value the text layer lacks (a number drawn in a chart) is not budgeted, and a value repeated across a table's distinct rows (a chart transcribed point by point) is data, not a loop. On a generated page the reference is exact, so every copy counts. Every generated page passes with its exact transcription, written plainly, with blank lines, as a markdown list or as an HTML table, and fails when its last row is looped 150 times.

Leaderboard

Ghost-free: pages answered without a Boo, out of 179. Boos count loops, loops hidden inside an answer they broke (for example a JSON response), and pages that came back empty. Lost counts pages still being written when the 10-minute clock ran out. Equal scores share a rank.

Rank	Parser	Kind	Ghost-free	Boos	Lost
1	LlamaParse Agentic	Document parser	100.0%	0	0
1	LlamaParse Agentic Plus	Document parser	100.0%	0	0
1	Datalab Accurate	Document parser	100.0%	0	0
1	Datalab Fast	Document parser	100.0%	0	0
5	Datalab Balanced	Document parser	99.4%	1	0
5	Extend (2.0)	Document parser	99.4%	1	0
5	GPT-6 Luna	Frontier VLM	99.4%	1	0
5	GPT-6.1 Terra	Frontier VLM	99.4%	1	0
5	Reducto	Document parser	99.4%	1	0
5	Reducto (non-agentic)	Document parser	99.4%	1	0
5	Reducto (r-1)	Document parser	99.4%	1	0
12	Azure DI (Layout)	Document parser	98.9%	2	0
12	Claude Sonnet 5.5	Frontier VLM	98.9%	2	0
12	Grok 4.6	Frontier VLM	98.9%	2	0
12	Muse Spark 1.3	Frontier VLM	98.9%	2	0
16	LlamaParse Cost Effective	Document parser	98.3%	3	0
17	AWS Textract	Document parser	97.8%	4	0
17	Claude Haiku 5.5	Frontier VLM	97.8%	4	0
17	Gemini 3.8 Flash	Frontier VLM	97.8%	4	0
17	Gemini 4 Argon	Frontier VLM	97.8%	4	0
21	Claude Opus 5.5	Frontier VLM	97.2%	5	0
22	Gemini 3.5 Flash-Lite	Frontier VLM	96.6%	6	0
23	Extend (v1)	Document parser	96.1%	7	0
24	Mistral OCR 4.1	Document parser	95.0%	9	0
24	Mistral OCR 4.1 (Annotation)	Document parser	95.0%	9	0
26	Infinity-Parser 2 Flash	Open VLM	93.3%	12	0
27	Databricks AI Parse	Document parser	92.7%	13	0
28	HunyuanOCR 1.5	Open VLM	92.2%	14	0
28	Pulse Ultra 2	Document parser	92.2%	14	0
30	MinerU 2605 Pro	Open VLM	91.6%	15	0
31	Chandra 2	Open VLM	91.1%	16	0
32	Extend Light (1.0)	Document parser	89.4%	19	0
33	olmOCR 2 7B	Open VLM	88.8%	20	0
34	GLM-OCR	Open VLM	86.6%	24	0
35	Qwen 3.8 27B	Open VLM	82.1%	24	8
36	PaddleOCR-VL 1.6	Open VLM	77.1%	14	27
37	Nemotron Omni 30B	Open VLM	61.5%	12	57

Data format

The rows follow the ParseBench format, one per page:

Field	Meaning
pdf	path of the page, docs/<room>/<id>.pdf
category	the room: real, trap, ghost or script
id	the page's name
type	repetition_overshoot
rule	the rule's budgets, as a JSON string: reference_text, page_fill_runs, page_lines, and the slacks
page	unused (every document is one page)
expected_markdown	the exact transcription, for trap and ghost pages
tags	the room, plus kind: (real pages), family: (trap pages) or script: (scans)

Known limitations

• The real room's ground truth is a text layer, not a human transcription. The budgets are loose on purpose (a text layer can undercount a leader drawn with a symbol font), and a garbled text layer can still undercount a value the page shows many times.
• The factor of 2 means a loop shorter than the page's own count of that unit passes.
• real/Exhibit_D_Gift-Loan_Disclosure_p1 has a resource cycle that some PDF libraries refuse to open. A parser that cannot read it returns nothing for it, which counts as a Boo.

License and citation

Apache 2.0, like ParseBench. The real pages are single pages of publicly available documents. There is no paper yet; if you use Boo Bench, please cite the dataset:

@misc{doulcet2026boobench,
      title={Boo Bench: A Haunted House for Document Parsers},
      author={Pierre-Loïc Doulcet},
      year={2026},
      publisher={Hugging Face},
      howpublished={\url{https://huggingface.co/datasets/llamaindex/BooBench}},
}

It runs on ParseBench.