Generating Business Process Model and Notation (BPMN) process models with LLMs is costly at the serialization layer. Standard XML and JSON formats consume hundreds to thousands of output tokens per model, creating severe latency and cost bottlenecks for interactive modeling assistants. We present BPCF (Business Process Compact Format), a compact, indentation-based notation designed specifically for LLM emission. BPCF-Full is lossless with respect to the structural constructs found in the PMo benchmark. At 221 tokens per model on average, BPCF-Full is roughly half the size of a Mermaid encoding, 8× smaller than verbose JSON representations (PME JSON) and about 29× smaller than BPMN XML. We evaluated nine BPMN representations across five LLMs and found that BPCF is the most token-efficient format by a wide margin, delivering the lowest cost and latency. Although exact accuracy rankings vary across models, BPCF offers a highly viable accuracy-efficiency trade-off for production systems, successfully modeling complex BPMN constructs where other compact formats fail. In the full-expressivity arm, BPCF-Full+Strict scores 0.469/0.472 on GPT-4o/Gemini at 148–155 visible output tokens. We publicly release our grammar, a dependency-free parser, and the full evaluation pipeline.

This paper introduces the ‘An Gaodhal’ project, which aims to serve the historically under-resourced and endangered language of Irish (known as Gaeilge) by providing new digital tools and resources. The initial goal of the project was the extraction of full text of ‘An Gaodhal’, a monthly bilingual Irish-English newspaper produced from 1881 to 1898, to the highest possible degree of accuracy via Optical Character Recognition (OCR), with a view to making its printed content searchable. The methodology applied toward achieving this goal yielded additional digital outputs including: 1. a new OCR model for the Irish language as printed in Cló Gaelach type; 2. a new OCR model for bilingual Irish-English content printed in Cló Gaelach and Roman types respectively; 3. a BART-based OCR post-correction model for historical bilingual Irish-English data; 4. a historical Irish training set for Named Entity Recognition (NER). All but the first of these four additional outputs appear to be the first of their kind. Each of the project outputs, including the full-text OCR outputs in ALTO XML format, is set for public release to enable open-access research. The paper also identifies the challenges historical Irish data poses to Natural Language Processing (NLP) in general and OCR in particular, and reports on project results and outputs to date. Finally, it contextualises the project within the wider field of NLP and considers its potential impact on under-resourced languages worldwide.