Prajwal S. Venkateshmurthy

arXiv2026

Engine-Transfer-Bench: An Evidence-Based Benchmark for Document Compilation Engine Selection

There is no shared framework for selecting among document compilation engines (pdfLaTeX, XeLaTeX, LuaLaTeX, Tectonic, Typst, and pandoc PDF backends). We present Engine-Transfer-Bench (ETB): 1,784 open documents, four tasks covering reliability, latency, text consistency, and failures, a pinned harness, and host-tagged multi-OS results. On GitHub Actions (N=4,211 compiles per host across macOS, Ubuntu, and Windows), Tectonic success is stable within 0.9 percentage points (96.3-97.2%), whereas classic TeX Live-style engines vary by 12-20 percentage points according to distribution policy (Ubuntu apt, MiKTeX auto-install, or macOS BasicTeX). On 702 portable LaTeX documents, the tested engines succeed at 100%, making latency the primary selection factor; failures concentrate in 107 engine-specific templates. Within ETB, failures are architectural, involving fonts, layout, and assets, rather than missing packages on a provisioned host. A 50-pair validation of the S_pdf text-consistency metric achieves 94% precision for real content divergence. We release ETB, ETB-Porta, a recommender and portability gate, and a public cross-OS harness as shared infrastructure.

cs.SE↗

arXiv2026

TeXFix-Bench: An Empirically Grounded Multi-Format Benchmark for LLM-Based Document Source Repair

Scientific and technical writing depends on markup sources that must compile: LaTeX, Typst, and Markdown pipelines fail on missing delimiters, mismatched environments, broken imports, or package conflicts. Existing document-repair evaluations inject faults with ad-hoc edits that lack an empirical fault model. We present TeXFix-Bench, a multi-format benchmark for LLM-based full-source document repair grounded in a mined fault taxonomy. A Grounded-Theory study of localized hard-crash LaTeX faults from TeX Stack Exchange, GitHub commits, and package documentation (168 verified faults, dual open coding at $κ$=0.34) yields an 18-category taxonomy instantiated as DocMut: 48 AST-aware operators across three formats. A three-model cross-benchmark shows DocMut faults are 5.6-9.2 pp harder to repair than pattern-based mutations on the same seeds, and a real-error case study (88 mined human crashes, 67.0% repair success) brackets both synthetic sets from below. We construct 10,437 instances from 743 openly licensed seeds and evaluate seven LLMs under a fixed zero-shot protocol with provider-pinned routing, collecting 48,651 attempts at about USD 200 total inference cost. A complete 6,613-instance x 7-model balanced matrix confirms all rankings. A pinned engine gate yields a 27.5-point intention-to-treat compile spread (56.7-84.2%). Typst is markedly harder than LaTeX and Markdown. A restoration oracle over 28,129 compiling repairs shows that 13.6-18.5% of compiling repairs materially alter document text, and restoration rank diverges from compile rank: the model with the lowest compile rate restores content best among its successes. Compile success alone overstates repair quality. We release the taxonomy, DocMut, and all campaign artifacts.

cs.AI↗