TECTONICdiagram
∇θ L(θ) = 0∑i=1n wixif(x) = α + βx + ε∫0∞ e−x² dx[ 1 0 ; 0 1 ]
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E=hν
LaTeX
\[ E = h\nu \]E=hν
LaTeX
\[ E = h\nu \]−∞0+∞
∫−∞∞e−x2dx=π
LaTeX
\[ \int_{-\infty}^{\infty} e^{-x^2}\,dx = \sqrt{\pi} \]ΔG=ΔH−TΔS
LaTeX
\[ \Delta G = \Delta H - T\Delta S \]ΔG=ΔH−TΔS
LaTeX
\[ \Delta G = \Delta H - T\Delta S \]a2+b2=c2
LaTeX
\[ a^2 + b^2 = c^2 \]OleaflyTeXFix-Bench: LaTeX Repair Bench…
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main.tex
CodeVisual
1% TeXFix-Bench technical report, manuscript version 0.2.
2% Compile with: tectonic -X compile main.tex
3
4\documentclass[journal,10pt]{IEEEtran}
5\usepackage{amsmath,amssymb,booktabs,array,graphicx}
6\usepackage{xcolor,microtype,url,fontspec,newtxsf}
7\usepackage{tikz,pgfplots,balance,hyperref}
8\pgfplotsset{compat=1.18}
9\usetikzlibrary{arrows.meta,positioning,fit,backgrounds}
10
11\definecolor{accentblue}{HTML}{006D9C}
12\definecolor{accentorange}{HTML}{D55E00}
13\definecolor{accentteal}{HTML}{0F766E}
14\definecolor{slate}{HTML}{64748B}
15
16\newcommand{\CR}{\textsc{cr}}
17\newcommand{\NEM}{\textsc{nem}}
18\newcommand{\MER}{\textsc{mer}}
19
20\title{TeXFix-Bench: Engine-Grounded Evaluation of\\
21Single-Fault \LaTeX{} Compilation Repair}
22\author{\textbf{Prajwal~S.~Venkateshmurthy}\\[0.10em]
23\small Independent Researcher\\[0.12em]
24\small San Jose, CA, USA}
25
26\begin{document}
27\maketitle
28
29\begin{abstract}
30Compilation repair for \LaTeX{} is easy to score badly. A candidate may
31compile after deleting or hiding content, while exact source equality
32rejects valid alternative repairs. We present TeXFix-Bench, a controlled
33benchmark for repairing complete, broken \LaTeX{} sources under a pinned engine~\cite{tan2024tex}.
34The generator applies 12 deterministic single-fault operators to 13
35hand-authored seed documents. It retains 627 engine-validated rows,
36comprising 300 unique hard-track inputs and 67 unique warning-only
37inputs after hash deduplication.
38\end{abstract}
39
40\begin{IEEEkeywords}
41automated program repair, \LaTeX{}, benchmark, compilation, large
42language models, reproducibility
43\end{IEEEkeywords}
44
45\section{Introduction}\label{sec:intro}
46\LaTeX{} compilation failures are a practical target for automated repair.
47A missing delimiter or misspelled control sequence can prevent a complete
48document from building, and the first reported diagnostic may be distant
49from the underlying fault. LLM-assisted writing systems now operate inside
50\LaTeX{} editors~\cite{wen2024overleafcopilot,hou2026paperdebugger}, but their
51publications do not provide a public, engine-pinned evaluation of
52compilation-failure repair.
53
54Program repair benchmarks pair a defect with an executable oracle and a
55fixed harness~\cite{just2014defects4j,legoues2015manybug}. SWE-bench extends
56the contract to repository-scale issues~\cite{jimenez2024swebench}.
57TeXFix-Bench studies a narrower input-output contract: a system receives
58an existing broken source document and must return a repaired full source
59under a specified engine.
60
61\section{Benchmark Construction}\label{sec:construction}
62\begin{figure*}[t]
63\centering
64\begin{tikzpicture}[font=\footnotesize\sffamily,node distance=0.58cm,
65 phase/.style={rounded corners=2.5pt,line width=0.7pt,
66 minimum width=2.55cm,minimum height=0.82cm,align=center}]
67\node[source] (seed) {13 known-good\\seed documents};
68\node[transform,right=of seed] (mutate) {12 deterministic\\mutation operators};
69\node[engine,right=of mutate] (gate) {Tectonic 0.17.0\\quality gate};
70\node[artifact,right=of gate] (rows) {627 retained\\rows};
71\draw[flow] (seed) -- (mutate);
72\draw[flow] (mutate) -- (gate);
73\draw[flow] (gate) -- (rows);
74\end{tikzpicture}
75\caption{TeXFix-Bench data and evaluation flow. Construction is
76deterministic. Evaluation reuses saved candidates and reruns only local
77engine and PDF-text checks.}
78\label{fig:pipeline}
79\end{figure*}
80
81\section{Results}\label{sec:results}
82Table~\ref{tab:main} reports the aggregate outcome.
83Figure~\ref{fig:paired} shows \CR{} and \NEM{} for each system, and
84Figure~\ref{fig:contrasts} shows the published paired contrasts.
85
86\begin{table*}[t]
87\caption{Version 0.2 results on 86 distinct hard-track inputs. \CR{} is
88intention-to-treat. \MER{} and \NEM{} use the same denominatior.}
89\label{tab:main}
90\input{results/tables/main-results-v02.tex}
91\end{table*}
92
93Claude Sonnet 5, GPT-5.6 Sol, and Grok 4.5 each recover 85 of 86 inputs.
94Their normalized exact-match counts are 52, 66, and 59, respectively.
95
96\begin{thebibliography}{19}
97\bibitem{just2014defects4j}
98R. Just, D. Jalali, and M. D. Ernst, \emph{Defects4J: A database of existing
99faults to enable controlled testing studies for Java programs}, ISSTA, 2014.
100\bibitem{legoues2015manybugs}
101C. Le Goues, N. Holtschulte, E. K. Smith, Y. Brun, P. Devanbu, S. Forrest,
102and W. Weimer, \emph{The ManyBugs and IntroClass benchmarks}, TSE, 2015.
103\bibitem{jimenez2024swebench}
104C. E. Jimenez, J. Yang, A. Wettig, S. Yao, K. Pei, O. Press, and
105K. Narasimhan, \emph{SWE-bench}, ICLR, 2024.
106\bibitem{chen2021codex}
107M. Chen et al., \emph{Evaluating large language models trained on code},
108arXiv:2107.03374, 2021.
109\bibitem{smith2015overfitting}
110E. K. Smith, E. T. Barr, C. Le Goues, and Y. Brun, \emph{Is the cure worse
111than the disease? Overfitting in automated program repair}, FSE, 2015.
112\bibitem{demillo1978}
113R. A. DeMillo, R. J. Lipton, and F. G. Sayward, \emph{Hints on test data
114selection}, Computer, 1978.
115\bibitem{jia2011mutation}
116Y. Jia and M. Harman, \emph{An analysis and survey of the development of
117mutation testing}, TSE, 2011.
118\bibitem{gupta2017deepfix}
119R. Gupta, S. Pal, A. Kanade, and S. Shevade, \emph{DeepFix}, AAAI, 2017.
120\bibitem{yasunaga2020drrepair}
121M. Yasunaga and P. Liang, \emph{Graph-based, self-supervised program repair
122from diagnostic feedback}, ICML, 2020.
123\bibitem{zhu2022eqfix}
124F. Zhu, Z. Liu, G. Yang, J. Li, and S. Cheung, \emph{EqFix}, ASE, 2022.
125\bibitem{wang2024texocr}
126K. Wang et al., \emph{TexOCR-Bench: Benchmarking page-image-to-LaTeX
127reconstruction}, 2024.
128\bibitem{wen2024overleafcopilot}
129H. Wen, Z. Zhang, and Y. Li, \emph{OverleafCopilot: Empowering academic
130writing in Overleaf with large language models}, 2024.
131\bibitem{hou2026paperdebugger}
132S. Hou et al., \emph{PaperDebugger: An in-editor assistant for manuscript
133repair}, 2026.
134\bibitem{tan2024tex}
135J. Tan, K. Zhang, and X. Chen, \emph{Instrumenting TeX produced diagnostics
136for repair}, 2024.
137\bibitem{zheng2023judging}
138L. Zheng et al., \emph{Judging LLM-as-a-judge with MT-Bench and Chatbot
139Arena}, NeurIPS, 2023.
140\bibitem{panickssery2024selfpref}
141A. Panickssery, S. R. Bowman, and S. Feng, \emph{LLM evaluators recognize
142and favor their own generations}, NeurIPS, 2024.
143\bibitem{hui2024qwen}
144B. Hui et al., \emph{Qwen2.5-Coder technical report}, 2024.
145\bibitem{metaai2024llama}
146Meta AI, \emph{The Llama 3 herd of models}, 2024.
147\bibitem{kassis2024reproduce}
148E. Kassis et al., \emph{On the reproducibility of research artifacts}, 2024.
149\end{thebibliography}
150
151\end{document}
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- Language analysisCurrent project revision analyzed
- CompilingProducing and verifying PDF output
- Rendering PDFWaiting for verified compile output
TeXFix-Bench: Engine-Grounded Evaluation of
Single-Fault LATEX Compilation Repair
Single-Fault LATEX Compilation Repair
Prajwal S. Venkateshmurthy
Independent Researcher
San Jose, CA, USA
Independent Researcher
San Jose, CA, USA
Abstract—Compilation repair for LaTeX is easy to score badly. A candidate may compile after deleting or hiding content, while exact source equality rejects valid alternative repairs. We present TeXFix-Bench, a controlled benchmark for repairing complete, broken LaTeX sources under a pinned engine. The generator applies 12 deterministic single-fault operators to 13 hand-authored seed documents. It retains 627 engine-validated rows, comprising 300 unique hard-track inputs and 67 unique warning-only inputs after hash deduplication.
Index Terms—automated program repair, LaTeX, benchmark, compilation, large language models, reproducibility
I. INTRODUCTION
LaTeX compilation failures are a practical target for automated repair. A missing delimiter or misspelled control sequence can prevent a complete document from building, and the first reported diagnostic may be distant from the underlying fault. LLM-assisted writing systems now operate inside LaTeX editors , but their publications do not provide a public, engine-pinned evaluation of compilation-failure repair.
Program repair benchmarks pair a defect with an executable oracle and a fixed harness . TeXFix-Bench studies a narrower contract: a broken source document in, a repaired full source out, under a specified engine.
II. RELATED WORK
TexOCR-Bench evaluates page-image-to-LaTeX reconstruction , and EqFix repairs errors in individual equations by synthesizing rules from examples . Overfitting to a weak oracle is a known hazard .
III. TASK AND SCOPE
A system receives one broken source file and returns one repaired source file. The engine, its version, and the build command are fixed for every attempt.
CR = compile(c) ∧ LCStok(Tc, Tr) ≥ 0.80(1)
IV. BENCHMARK CONSTRUCTION
Fig. 1. TeXFix-Bench data and evaluation flow. Construction is deterministic.
Every generated row passes an engine gate before it is retained, and duplicate broken sources are removed by SHA-256 so that no input is scored twice .
V. EVALUATION METHOD
Compile Recovery requires a successful Tectonic 0.17.0 run and at least 0.80 token-level LCS similarity between text extracted from the candidate and reference PDFs. Normalized exact match sits beside it, because compilation alone cannot separate a faithful repair from a deletion .
TABLE I
Version 0.2 results on 86 distinct hard-track inputs. CR is intention-to-treat.
Version 0.2 results on 86 distinct hard-track inputs. CR is intention-to-treat.
| System | CR | CR % | NEM | Fail |
| Identity | 0/86 | 0.0 | 0/86 | 0 |
| Rule | 34/86 | 39.5 | 28/86 | 0 |
| Qwen-7B + fb | 57/86 | 66.3 | 17/86 | 0 |
| MiniMax M3 | 75/86 | 87.2 | 50/86 | 2 |
| DeepSeek V4 | 81/86 | 94.2 | 38/86 | 2 |
| Gemini 3.5 | 82/86 | 95.3 | 26/86 | 0 |
| Claude S5 | 85/86 | 98.8 | 52/86 | 0 |
| GPT-5.6 Sol | 85/86 | 98.8 | 66/86 | 1 |
| Grok 4.5 | 85/86 | 98.8 | 59/86 | 0 |
Fig. 2. Compile Recovery and normalized exact match on the fixed cohort of n = 86 hard-track inputs. Circles denote CR, squares NEM.
VI. RESULTS
Claude Sonnet 5, GPT-5.6 Sol, and Grok 4.5 each recover 85 of 86 inputs, yet their normalized exact-match counts are 52, 66, and 59 . Compilation and edit precision must be reported together.
VII. CONCLUSION
The contribution is an auditable task definition, an executable oracle, a fixed cohort, and a replayable candidate ledger.
REFERENCES
[1]R. Just, D. Jalali, and M. D. Ernst, “Defects4J: A database of existing faults to enable controlled testing studies for Java programs,” in Proc. ISSTA, 2014, pp. 437–440.
[2]C. Le Goues, N. Holtschulte, E. K. Smith, Y. Brun, P. Devanbu, S. Forrest, and W. Weimer, “The ManyBugs and IntroClass benchmarks for automated repair of C programs,” IEEE Trans. Softw. Eng., vol. 41, no. 12, pp. 1236–1256, 2015.
[3]C. E. Jimenez, J. Yang, A. Wettig, S. Yao, K. Pei, O. Press, and K. Narasimhan, “SWE-bench: Can language models resolve real-world GitHub issues?” in Proc. ICLR, 2024.
[4]M. Chen et al., “Evaluating large language models trained on code,” arXiv:2107.03374, 2021.
[5]E. K. Smith, E. T. Barr, C. Le Goues, and Y. Brun, “Is the cure worse than the disease? Overfitting in automated program repair,” in Proc. ESEC/FSE, 2015, pp. 532–543.
[6]R. A. DeMillo, R. J. Lipton, and F. G. Sayward, “Hints on test data selection: Help for the practicing programmer,” Computer, vol. 11, no. 4, pp. 34–41, 1978.
[7]Y. Jia and M. Harman, “An analysis and survey of the development of mutation testing,” IEEE Trans. Softw. Eng., vol. 37, no. 5, pp. 649–678, 2011.
[8]R. Gupta, S. Pal, A. Kanade, and S. Shevade, “DeepFix: Fixing common C language errors by deep learning,” in Proc. AAAI, vol. 31, no. 1, pp. 1345–1351, 2017.
[9]M. Yasunaga and P. Liang, “Graph-based, self-supervised program repair from diagnostic feedback,” in Proc. ICML, PMLR, vol. 119, pp. 10799–10808, 2020.
[10]F. Zhu, Z. Liu, G. Yang, J. Li, and S. Cheung, “EqFix: Fixing LaTeX equation errors by examples,” in SETTA 2022, LNCS 13649, pp. 89–109.
[11]K. Wang et al., “TexOCR-Bench: Benchmarking page-image-to-LaTeX reconstruction,” in Proc. 64th ACL, Vol. 1, 2026.
[12]H. Wen, Z. Zhang, and Y. Li, “OverleafCopilot: Empowering academic writing in Overleaf with large language models,” arXiv:2403.09733, 2024.
[13]S. Hou et al., “PaperDebugger: Assisting scientific writing, review, and editing,” in Companion Proc. ACM Web Conf., 2026.
[14]J. Tan, K. Zhang, and X. Chen, “Inconsistencies in TeX-produced documents,” in Proc. ISSTA, 2024.
[15]L. Zheng et al., “Judging LLM-as-a-judge with MT-Bench and Chatbot Arena,” Adv. Neural Inf. Process. Syst., vol. 36, pp. 46595–46623, 2023.
[16]A. Panickssery, S. R. Bowman, and S. Feng, “LLM evaluators recognize and favor their own generations,” Adv. Neural Inf. Process. Syst., vol. 37, 2024.
[17]B. Hui et al., “Qwen2.5-Coder technical report,” arXiv:2409.12186, 2024.
[18]Meta AI, “Llama 3.2 model card,” 2024.
[19]E. Kassis et al., “Agent skills for reproducible research agents,” arXiv:2609.00065, 2026.
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Comprehensive citation management for academic research. Search OpenAlex, PubMed, and Google Scholar for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.
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Formulate evidence-bounded scientific questions, candidate hypotheses, rival explanations, causal or associational claims, discriminating predictions, measurements, and preregistration-ready analysis plans. Use when turning observations or preliminary findings into transparent, testable research plans without treating hypotheses as facts.
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@article{open_science2015,2
title = {Estimating the Reproducibility of Psychological Science},1
@article{giglio2003,2
author = {Giglio, Louis and Descloitres, Jacques and Justice, Christopher O.},3
title = {An Enhanced Contextual Fire Detection Algorithm for {MODIS}},4
journal = {Remote Sensing of Environment},5
year = {2003}, doi = {10.1016/S0034-4257(03)00184-6}6
} Reject Approve
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Program repair benchmarks pair a defect with an executable oracle and a55
fixed harness~\cite{just2014defects4j,legoues2015manybug}. SWE-bench extends55
fixed harness~\cite{just2014defects4j,legoues2015manybugs}. SWE-bench extends56
the contract to repository-scale issues~\cite{jimenez2024swebench}.57
TeXFix-Bench studies a narrower contract: a system receives an existing57
TeXFix-Bench studies a narrower contract~\cite{zhu2022eqfix}: a system receives Reject Approve
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TeXFix-Bench: Engine-Grounded Evaluation of
Single-Fault LATEX Compilation Repair
Single-Fault LATEX Compilation Repair
Prajwal S. Venkateshmurthy
Independent Researcher
San Jose, CA, USA
Independent Researcher
San Jose, CA, USA
Abstract—Compilation repair for LaTeX is easy to score badly. A candidate may compile after deleting or hiding content, while exact source equality rejects valid alternative repairs. We present TeXFix-Bench, a controlled benchmark for repairing complete, broken LaTeX sources under a pinned engine. The generator applies 12 deterministic single-fault operators to 13 hand-authored seed documents. It retains 627 engine-validated rows, comprising 300 unique hard-track inputs and 67 unique warning-only inputs after hash deduplication.
Index Terms—automated program repair, LaTeX, benchmark, compilation, large language models, reproducibility
I. INTRODUCTION
LaTeX compilation failures are a practical target for automated repair. A missing delimiter or misspelled control sequence can prevent a complete document from building, and the first reported diagnostic may be distant from the underlying fault. LLM-assisted writing systems now operate inside LaTeX editors , but their publications do not provide a public, engine-pinned evaluation of compilation-failure repair.
Program repair benchmarks pair a defect with an executable oracle and a fixed harness . TeXFix-Bench studies a narrower contract: a broken source document in, a repaired full source out, under a specified engine.
II. RELATED WORK
TexOCR-Bench evaluates page-image-to-LaTeX reconstruction , and EqFix repairs errors in individual equations by synthesizing rules from examples . Overfitting to a weak oracle is a known hazard .
III. TASK AND SCOPE
A system receives one broken source file and returns one repaired source file. The engine, its version, and the build command are fixed for every attempt.
CR = compile(c) ∧ LCStok(Tc, Tr) ≥ 0.80(1)
IV. BENCHMARK CONSTRUCTION
Fig. 1. TeXFix-Bench data and evaluation flow. Construction is deterministic.
Every generated row passes an engine gate before it is retained, and duplicate broken sources are removed by SHA-256 so that no input is scored twice .
V. EVALUATION METHOD
Compile Recovery requires a successful Tectonic 0.17.0 run and at least 0.80 token-level LCS similarity between text extracted from the candidate and reference PDFs. Normalized exact match sits beside it, because compilation alone cannot separate a faithful repair from a deletion .
TABLE I
Version 0.2 results on 86 distinct hard-track inputs. CR is intention-to-treat.
Version 0.2 results on 86 distinct hard-track inputs. CR is intention-to-treat.
| System | CR | CR % | NEM | Fail |
| Identity | 0/86 | 0.0 | 0/86 | 0 |
| Rule | 34/86 | 39.5 | 28/86 | 0 |
| Qwen-7B + fb | 57/86 | 66.3 | 17/86 | 0 |
| MiniMax M3 | 75/86 | 87.2 | 50/86 | 2 |
| DeepSeek V4 | 81/86 | 94.2 | 38/86 | 2 |
| Gemini 3.5 | 82/86 | 95.3 | 26/86 | 0 |
| Claude S5 | 85/86 | 98.8 | 52/86 | 0 |
| GPT-5.6 Sol | 85/86 | 98.8 | 66/86 | 1 |
| Grok 4.5 | 85/86 | 98.8 | 59/86 | 0 |
Fig. 2. Compile Recovery and normalized exact match on the fixed cohort of n = 86 hard-track inputs. Circles denote CR, squares NEM.
VI. RESULTS
Claude Sonnet 5, GPT-5.6 Sol, and Grok 4.5 each recover 85 of 86 inputs, yet their normalized exact-match counts are 52, 66, and 59 . Compilation and edit precision must be reported together.
VII. CONCLUSION
The contribution is an auditable task definition, an executable oracle, a fixed cohort, and a replayable candidate ledger.
REFERENCES
[1]R. Just, D. Jalali, and M. D. Ernst, “Defects4J: A database of existing faults to enable controlled testing studies for Java programs,” in Proc. ISSTA, 2014, pp. 437–440.
[2]C. Le Goues, N. Holtschulte, E. K. Smith, Y. Brun, P. Devanbu, S. Forrest, and W. Weimer, “The ManyBugs and IntroClass benchmarks for automated repair of C programs,” IEEE Trans. Softw. Eng., vol. 41, no. 12, pp. 1236–1256, 2015.
[3]C. E. Jimenez, J. Yang, A. Wettig, S. Yao, K. Pei, O. Press, and K. Narasimhan, “SWE-bench: Can language models resolve real-world GitHub issues?” in Proc. ICLR, 2024.
[4]M. Chen et al., “Evaluating large language models trained on code,” arXiv:2107.03374, 2021.
[5]E. K. Smith, E. T. Barr, C. Le Goues, and Y. Brun, “Is the cure worse than the disease? Overfitting in automated program repair,” in Proc. ESEC/FSE, 2015, pp. 532–543.
[6]R. A. DeMillo, R. J. Lipton, and F. G. Sayward, “Hints on test data selection: Help for the practicing programmer,” Computer, vol. 11, no. 4, pp. 34–41, 1978.
[7]Y. Jia and M. Harman, “An analysis and survey of the development of mutation testing,” IEEE Trans. Softw. Eng., vol. 37, no. 5, pp. 649–678, 2011.
[8]R. Gupta, S. Pal, A. Kanade, and S. Shevade, “DeepFix: Fixing common C language errors by deep learning,” in Proc. AAAI, vol. 31, no. 1, pp. 1345–1351, 2017.
[9]M. Yasunaga and P. Liang, “Graph-based, self-supervised program repair from diagnostic feedback,” in Proc. ICML, PMLR, vol. 119, pp. 10799–10808, 2020.
[10]F. Zhu, Z. Liu, G. Yang, J. Li, and S. Cheung, “EqFix: Fixing LaTeX equation errors by examples,” in SETTA 2022, LNCS 13649, pp. 89–109.
[11]K. Wang et al., “TexOCR-Bench: Benchmarking page-image-to-LaTeX reconstruction,” in Proc. 64th ACL, Vol. 1, 2026.
[12]H. Wen, Z. Zhang, and Y. Li, “OverleafCopilot: Empowering academic writing in Overleaf with large language models,” arXiv:2403.09733, 2024.
[13]S. Hou et al., “PaperDebugger: Assisting scientific writing, review, and editing,” in Companion Proc. ACM Web Conf., 2026.
[14]J. Tan, K. Zhang, and X. Chen, “Inconsistencies in TeX-produced documents,” in Proc. ISSTA, 2024.
[15]L. Zheng et al., “Judging LLM-as-a-judge with MT-Bench and Chatbot Arena,” Adv. Neural Inf. Process. Syst., vol. 36, pp. 46595–46623, 2023.
[16]A. Panickssery, S. R. Bowman, and S. Feng, “LLM evaluators recognize and favor their own generations,” Adv. Neural Inf. Process. Syst., vol. 37, 2024.
[17]B. Hui et al., “Qwen2.5-Coder technical report,” arXiv:2409.12186, 2024.
[18]Meta AI, “Llama 3.2 model card,” 2024.
[19]E. Kassis et al., “Agent skills for reproducible research agents,” arXiv:2609.00065, 2026.
Stale
references.bib
1
@article{giglio2003,2
title = {An Enhanced Contextual Fire Detection Algorithm for MODIS},3
year = {2003},4
}6
@article{schroeder2014,7
title = {The New VIIRS 375 m Active Fire Detection Data Product},8
year = {2014},9
}11
@article{dampage2022,12
title = {Forest Fire Detection System Using Wireless Sensor Networks},13
year = {2022},14
}16
@article{adumanu2018,17
title = {Energy-Harvesting Wireless Sensor Networks: A Review},18
year = {2018},19
}21
@article{adelantado2017,22
title = {Understanding the Limits of LoRaWAN},23
year = {2017},24
}26
@article{bai2018,27
title = {An Empirical Evaluation of Generic Convolutional Networks},28
year = {2018},29
}31
@article{jacob2018,32
title = {Quantization and Training of Neural Networks},33
year = {2018},34
}36
@article{ray2022,37
title = {A Review on TinyML},38
year = {2022},39
}41
@article{geifman2017,42
title = {Selective Classification for Deep Neural Networks},43
year = {2017},44
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main.tex
CodeVisual
1% TeXFix-Bench technical report, manuscript version 0.2.
2% Compile with: tectonic -X compile main.tex
3
4\documentclass[journal,10pt]{IEEEtran}
5\usepackage{amsmath,amssymb,booktabs,array,graphicx}
6\usepackage{xcolor,microtype,url,fontspec,newtxsf}
7\usepackage{tikz,pgfplots,balance,hyperref}
8\pgfplotsset{compat=1.18}
9\usetikzlibrary{arrows.meta,positioning,fit,backgrounds}
10
11\definecolor{accentblue}{HTML}{006D9C}
12\definecolor{accentorange}{HTML}{D55E00}
13\definecolor{accentteal}{HTML}{0F766E}
14\definecolor{slate}{HTML}{64748B}
15
16\newcommand{\CR}{\textsc{cr}}
17\newcommand{\NEM}{\textsc{nem}}
18\newcommand{\MER}{\textsc{mer}}
19
20\title{TeXFix-Bench: Engine-Grounded Evaluation of\\
21Single-Fault \LaTeX{} Compilation Repair}
22\author{\textbf{Prajwal~S.~Venkateshmurthy}\\[0.10em]
23\small Independent Researcher\\[0.12em]
24\small San Jose, CA, USA}
25
26\begin{document}
27\maketitle
28
29\begin{abstract}
30Compilation repair for \LaTeX{} is easy to score badly. A candidate may
31compile after deleting or hiding content, while exact source equality
32rejects valid alternative repairs. We present TeXFix-Bench, a controlled
33benchmark for repairing complete, broken \LaTeX{} sources under a pinned engine~\cite{tan2024tex}.
34The generator applies 12 deterministic single-fault operators to 13
35hand-authored seed documents. It retains 627 engine-validated rows,
36comprising 300 unique hard-track inputs and 67 unique warning-only
37inputs after hash deduplication.
38\end{abstract}
39
40\begin{IEEEkeywords}
41automated program repair, \LaTeX{}, benchmark, compilation, large
42language models, reproducibility
43\end{IEEEkeywords}
44
45\section{Introduction}\label{sec:intro}
46\LaTeX{} compilation failures are a practical target for automated repair.
47A missing delimiter or misspelled control sequence can prevent a complete
48document from building, and the first reported diagnostic may be distant
49from the underlying fault. LLM-assisted writing systems now operate inside
50\LaTeX{} editors~\cite{wen2024overleafcopilot,hou2026paperdebugger}, but their
51publications do not provide a public, engine-pinned evaluation of
52compilation-failure repair.
53
54Program repair benchmarks pair a defect with an executable oracle and a
55fixed harness~\cite{just2014defects4j,legoues2015manybug}. SWE-bench extends
56the contract to repository-scale issues~\cite{jimenez2024swebench}.
57TeXFix-Bench studies a narrower input-output contract: a system receives
58an existing broken source document and must return a repaired full source
59under a specified engine.
60
61\section{Benchmark Construction}\label{sec:construction}
62\begin{figure*}[t]
63\centering
64\begin{tikzpicture}[font=\footnotesize\sffamily,node distance=0.58cm,
65 phase/.style={rounded corners=2.5pt,line width=0.7pt,
66 minimum width=2.55cm,minimum height=0.82cm,align=center}]
67\node[source] (seed) {13 known-good\\seed documents};
68\node[transform,right=of seed] (mutate) {12 deterministic\\mutation operators};
69\node[engine,right=of mutate] (gate) {Tectonic 0.17.0\\quality gate};
70\node[artifact,right=of gate] (rows) {627 retained\\rows};
71\draw[flow] (seed) -- (mutate);
72\draw[flow] (mutate) -- (gate);
73\draw[flow] (gate) -- (rows);
74\end{tikzpicture}
75\caption{TeXFix-Bench data and evaluation flow. Construction is
76deterministic. Evaluation reuses saved candidates and reruns only local
77engine and PDF-text checks.}
78\label{fig:pipeline}
79\end{figure*}
80
81\section{Results}\label{sec:results}
82Table~\ref{tab:main} reports the aggregate outcome.
83Figure~\ref{fig:paired} shows \CR{} and \NEM{} for each system, and
84Figure~\ref{fig:contrasts} shows the published paired contrasts.
85
86\begin{table*}[t]
87\caption{Version 0.2 results on 86 distinct hard-track inputs. \CR{} is
88intention-to-treat. \MER{} and \NEM{} use the same denominatior.}
89\label{tab:main}
90\input{results/tables/main-results-v02.tex}
91\end{table*}
92
93Claude Sonnet 5, GPT-5.6 Sol, and Grok 4.5 each recover 85 of 86 inputs.
94Their normalized exact-match counts are 52, 66, and 59, respectively.
95
96\begin{thebibliography}{19}
97\bibitem{just2014defects4j}
98R. Just, D. Jalali, and M. D. Ernst, \emph{Defects4J: A database of existing
99faults to enable controlled testing studies for Java programs}, ISSTA, 2014.
100\bibitem{legoues2015manybugs}
101C. Le Goues, N. Holtschulte, E. K. Smith, Y. Brun, P. Devanbu, S. Forrest,
102and W. Weimer, \emph{The ManyBugs and IntroClass benchmarks}, TSE, 2015.
103\bibitem{jimenez2024swebench}
104C. E. Jimenez, J. Yang, A. Wettig, S. Yao, K. Pei, O. Press, and
105K. Narasimhan, \emph{SWE-bench}, ICLR, 2024.
106\bibitem{chen2021codex}
107M. Chen et al., \emph{Evaluating large language models trained on code},
108arXiv:2107.03374, 2021.
109\bibitem{smith2015overfitting}
110E. K. Smith, E. T. Barr, C. Le Goues, and Y. Brun, \emph{Is the cure worse
111than the disease? Overfitting in automated program repair}, FSE, 2015.
112\bibitem{demillo1978}
113R. A. DeMillo, R. J. Lipton, and F. G. Sayward, \emph{Hints on test data
114selection}, Computer, 1978.
115\bibitem{jia2011mutation}
116Y. Jia and M. Harman, \emph{An analysis and survey of the development of
117mutation testing}, TSE, 2011.
118\bibitem{gupta2017deepfix}
119R. Gupta, S. Pal, A. Kanade, and S. Shevade, \emph{DeepFix}, AAAI, 2017.
120\bibitem{yasunaga2020drrepair}
121M. Yasunaga and P. Liang, \emph{Graph-based, self-supervised program repair
122from diagnostic feedback}, ICML, 2020.
123\bibitem{zhu2022eqfix}
124F. Zhu, Z. Liu, G. Yang, J. Li, and S. Cheung, \emph{EqFix}, ASE, 2022.
125\bibitem{wang2024texocr}
126K. Wang et al., \emph{TexOCR-Bench: Benchmarking page-image-to-LaTeX
127reconstruction}, 2024.
128\bibitem{wen2024overleafcopilot}
129H. Wen, Z. Zhang, and Y. Li, \emph{OverleafCopilot: Empowering academic
130writing in Overleaf with large language models}, 2024.
131\bibitem{hou2026paperdebugger}
132S. Hou et al., \emph{PaperDebugger: An in-editor assistant for manuscript
133repair}, 2026.
134\bibitem{tan2024tex}
135J. Tan, K. Zhang, and X. Chen, \emph{Instrumenting TeX produced diagnostics
136for repair}, 2024.
137\bibitem{zheng2023judging}
138L. Zheng et al., \emph{Judging LLM-as-a-judge with MT-Bench and Chatbot
139Arena}, NeurIPS, 2023.
140\bibitem{panickssery2024selfpref}
141A. Panickssery, S. R. Bowman, and S. Feng, \emph{LLM evaluators recognize
142and favor their own generations}, NeurIPS, 2024.
143\bibitem{hui2024qwen}
144B. Hui et al., \emph{Qwen2.5-Coder technical report}, 2024.
145\bibitem{metaai2024llama}
146Meta AI, \emph{The Llama 3 herd of models}, 2024.
147\bibitem{kassis2024reproduce}
148E. Kassis et al., \emph{On the reproducibility of research artifacts}, 2024.
149\end{thebibliography}
150
151\end{document}
Logs
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- Language analysisCurrent project revision analyzed
- CompilingProducing and verifying PDF output
- Rendering PDFWaiting for verified compile output
TeXFix-Bench: Engine-Grounded Evaluation of
Single-Fault LATEX Compilation Repair
Single-Fault LATEX Compilation Repair
Prajwal S. Venkateshmurthy
Independent Researcher
San Jose, CA, USA
Independent Researcher
San Jose, CA, USA
Abstract—Compilation repair for LaTeX is easy to score badly. A candidate may compile after deleting or hiding content, while exact source equality rejects valid alternative repairs. We present TeXFix-Bench, a controlled benchmark for repairing complete, broken LaTeX sources under a pinned engine. The generator applies 12 deterministic single-fault operators to 13 hand-authored seed documents. It retains 627 engine-validated rows, comprising 300 unique hard-track inputs and 67 unique warning-only inputs after hash deduplication.
Index Terms—automated program repair, LaTeX, benchmark, compilation, large language models, reproducibility
I. INTRODUCTION
LaTeX compilation failures are a practical target for automated repair. A missing delimiter or misspelled control sequence can prevent a complete document from building, and the first reported diagnostic may be distant from the underlying fault. LLM-assisted writing systems now operate inside LaTeX editors , but their publications do not provide a public, engine-pinned evaluation of compilation-failure repair.
Program repair benchmarks pair a defect with an executable oracle and a fixed harness . TeXFix-Bench studies a narrower contract: a broken source document in, a repaired full source out, under a specified engine.
II. RELATED WORK
TexOCR-Bench evaluates page-image-to-LaTeX reconstruction , and EqFix repairs errors in individual equations by synthesizing rules from examples . Overfitting to a weak oracle is a known hazard .
III. TASK AND SCOPE
A system receives one broken source file and returns one repaired source file. The engine, its version, and the build command are fixed for every attempt.
CR = compile(c) ∧ LCStok(Tc, Tr) ≥ 0.80(1)
IV. BENCHMARK CONSTRUCTION
Fig. 1. TeXFix-Bench data and evaluation flow. Construction is deterministic.
Every generated row passes an engine gate before it is retained, and duplicate broken sources are removed by SHA-256 so that no input is scored twice .
V. EVALUATION METHOD
Compile Recovery requires a successful Tectonic 0.17.0 run and at least 0.80 token-level LCS similarity between text extracted from the candidate and reference PDFs. Normalized exact match sits beside it, because compilation alone cannot separate a faithful repair from a deletion .
TABLE I
Version 0.2 results on 86 distinct hard-track inputs. CR is intention-to-treat.
Version 0.2 results on 86 distinct hard-track inputs. CR is intention-to-treat.
| System | CR | CR % | NEM | Fail |
| Identity | 0/86 | 0.0 | 0/86 | 0 |
| Rule | 34/86 | 39.5 | 28/86 | 0 |
| Qwen-7B + fb | 57/86 | 66.3 | 17/86 | 0 |
| MiniMax M3 | 75/86 | 87.2 | 50/86 | 2 |
| DeepSeek V4 | 81/86 | 94.2 | 38/86 | 2 |
| Gemini 3.5 | 82/86 | 95.3 | 26/86 | 0 |
| Claude S5 | 85/86 | 98.8 | 52/86 | 0 |
| GPT-5.6 Sol | 85/86 | 98.8 | 66/86 | 1 |
| Grok 4.5 | 85/86 | 98.8 | 59/86 | 0 |
Fig. 2. Compile Recovery and normalized exact match on the fixed cohort of n = 86 hard-track inputs. Circles denote CR, squares NEM.
VI. RESULTS
Claude Sonnet 5, GPT-5.6 Sol, and Grok 4.5 each recover 85 of 86 inputs, yet their normalized exact-match counts are 52, 66, and 59 . Compilation and edit precision must be reported together.
VII. CONCLUSION
The contribution is an auditable task definition, an executable oracle, a fixed cohort, and a replayable candidate ledger.
REFERENCES
[1]R. Just, D. Jalali, and M. D. Ernst, “Defects4J: A database of existing faults to enable controlled testing studies for Java programs,” in Proc. ISSTA, 2014, pp. 437–440.
[2]C. Le Goues, N. Holtschulte, E. K. Smith, Y. Brun, P. Devanbu, S. Forrest, and W. Weimer, “The ManyBugs and IntroClass benchmarks for automated repair of C programs,” IEEE Trans. Softw. Eng., vol. 41, no. 12, pp. 1236–1256, 2015.
[3]C. E. Jimenez, J. Yang, A. Wettig, S. Yao, K. Pei, O. Press, and K. Narasimhan, “SWE-bench: Can language models resolve real-world GitHub issues?” in Proc. ICLR, 2024.
[4]M. Chen et al., “Evaluating large language models trained on code,” arXiv:2107.03374, 2021.
[5]E. K. Smith, E. T. Barr, C. Le Goues, and Y. Brun, “Is the cure worse than the disease? Overfitting in automated program repair,” in Proc. ESEC/FSE, 2015, pp. 532–543.
[6]R. A. DeMillo, R. J. Lipton, and F. G. Sayward, “Hints on test data selection: Help for the practicing programmer,” Computer, vol. 11, no. 4, pp. 34–41, 1978.
[7]Y. Jia and M. Harman, “An analysis and survey of the development of mutation testing,” IEEE Trans. Softw. Eng., vol. 37, no. 5, pp. 649–678, 2011.
[8]R. Gupta, S. Pal, A. Kanade, and S. Shevade, “DeepFix: Fixing common C language errors by deep learning,” in Proc. AAAI, vol. 31, no. 1, pp. 1345–1351, 2017.
[9]M. Yasunaga and P. Liang, “Graph-based, self-supervised program repair from diagnostic feedback,” in Proc. ICML, PMLR, vol. 119, pp. 10799–10808, 2020.
[10]F. Zhu, Z. Liu, G. Yang, J. Li, and S. Cheung, “EqFix: Fixing LaTeX equation errors by examples,” in SETTA 2022, LNCS 13649, pp. 89–109.
[11]K. Wang et al., “TexOCR-Bench: Benchmarking page-image-to-LaTeX reconstruction,” in Proc. 64th ACL, Vol. 1, 2026.
[12]H. Wen, Z. Zhang, and Y. Li, “OverleafCopilot: Empowering academic writing in Overleaf with large language models,” arXiv:2403.09733, 2024.
[13]S. Hou et al., “PaperDebugger: Assisting scientific writing, review, and editing,” in Companion Proc. ACM Web Conf., 2026.
[14]J. Tan, K. Zhang, and X. Chen, “Inconsistencies in TeX-produced documents,” in Proc. ISSTA, 2024.
[15]L. Zheng et al., “Judging LLM-as-a-judge with MT-Bench and Chatbot Arena,” Adv. Neural Inf. Process. Syst., vol. 36, pp. 46595–46623, 2023.
[16]A. Panickssery, S. R. Bowman, and S. Feng, “LLM evaluators recognize and favor their own generations,” Adv. Neural Inf. Process. Syst., vol. 37, 2024.
[17]B. Hui et al., “Qwen2.5-Coder technical report,” arXiv:2409.12186, 2024.
[18]Meta AI, “Llama 3.2 model card,” 2024.
[19]E. Kassis et al., “Agent skills for reproducible research agents,” arXiv:2609.00065, 2026.
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research/reading-list.mdAddedPreview+ # Wildfire sensing: reading list + + ## Satellite fire detection + Giglio et al. (2003). An Enhanced Contextual Fire + Detection Algorithm for MODIS. + DOI: 10.1016/S0034-4257(03)00184-6 + + Use: background on satellite detection methods. + Check separately: claims about ground-sensor energy use.
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\node (n17_nw4kz) at (16.25,-16.325) [draw=c1E293B, line width=0.025cm, fill=cFDE3C7, rounded corners=0.1cm, text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=4.25cm, minimum height=1.15cm] {Masked Multi-Head Attention};29
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\node (n19_9lw0g) at (16.25,-12.575) [draw=c1E293B, line width=0.025cm, fill=cFDE3C7, rounded corners=0.1cm, text=c0F172A, font=\rmfamily\fontsize{10}{12}\selectfont, inner sep=0pt, outer sep=0pt, minimum width=4.25cm, minimum height=1.15cm] {Multi-Head Attention};31
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\draw[->, line width=0.05cm, rounded corners=0.125cm] (16.675,-18.175) -- (17.175,-18.175) -- (18.875,-18.175) -- (18.875,-14.575) -- (18.375,-14.575);64
\draw[->, line width=0.05cm, rounded corners=0.125cm] (18.375,-14.575) -- (18.875,-14.575) -- (18.875,-10.825) -- (18.4,-10.825) -- (18.375,-10.825);65
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\end{scope}67
\end{tikzpicture}PREVIEWPNG scale2x ⌄Background
Output Probabilities
Softmax
Linear
Add & Norm
Feed Forward
Add & Norm
Multi-Head Attention
N×
Add & Norm
Feed Forward
Add & Norm
Multi-Head Attention
Add & Norm
Masked Multi-Head
Attention
Attention
N×
+
Positional Encoding
Input Embedding
Inputs
+
Positional Encoding
Output Embedding
Outputs (shifted right)
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Oleafly›diagram
Snippets □ ○ → — { }
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\draw[->, line width=0.05cm, rounded corners=0.125cm] (18.375,-14.575) -- (18.875,-14.575) -- (18.875,-10.825) -- (18.4,-10.825) -- (18.375,-10.825);65
\draw[->, line width=0.05cm, rounded corners=0.125cm] (18.375,-10.825) -- (18.875,-10.825) -- (18.875,-7.075) -- (18.4,-7.075) -- (18.375,-7.075);66
\end{scope}67
\end{tikzpicture}Drag Shapes to Move · Drag Handles to Connect · Space + Drag to Pan · Double-Click to Edit Text
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main.tex
CodeVisual
1% TeXFix-Bench technical report, manuscript version 0.2.
2% Compile with: tectonic -X compile main.tex
3
4\documentclass[journal,10pt]{IEEEtran}
5\usepackage{amsmath,amssymb,booktabs,array,graphicx}
6\usepackage{xcolor,microtype,url,fontspec,newtxsf}
7\usepackage{tikz,pgfplots,balance,hyperref}
8\pgfplotsset{compat=1.18}
9\usetikzlibrary{arrows.meta,positioning,fit,backgrounds}
10
11\definecolor{accentblue}{HTML}{006D9C}
12\definecolor{accentorange}{HTML}{D55E00}
13\definecolor{accentteal}{HTML}{0F766E}
14\definecolor{slate}{HTML}{64748B}
15
16\newcommand{\CR}{\textsc{cr}}
17\newcommand{\NEM}{\textsc{nem}}
18\newcommand{\MER}{\textsc{mer}}
19
20\title{TeXFix-Bench: Engine-Grounded Evaluation of\\
21Single-Fault \LaTeX{} Compilation Repair}
22\author{\textbf{Prajwal~S.~Venkateshmurthy}\\[0.10em]
23\small Independent Researcher\\[0.12em]
24\small San Jose, CA, USA}
25
26\begin{document}
27\maketitle
28
29\begin{abstract}
30Compilation repair for \LaTeX{} is easy to score badly. A candidate may
31compile after deleting or hiding content, while exact source equality
32rejects valid alternative repairs. We present TeXFix-Bench, a controlled
33benchmark for repairing complete, broken \LaTeX{} sources under a pinned engine~\cite{tan2024tex}.
34The generator applies 12 deterministic single-fault operators to 13
35hand-authored seed documents. It retains 627 engine-validated rows,
36comprising 300 unique hard-track inputs and 67 unique warning-only
37inputs after hash deduplication.
38\end{abstract}
39
40\begin{IEEEkeywords}
41automated program repair, \LaTeX{}, benchmark, compilation, large
42language models, reproducibility
43\end{IEEEkeywords}
44
45\section{Introduction}\label{sec:intro}
46\LaTeX{} compilation failures are a practical target for automated repair.
47A missing delimiter or misspelled control sequence can prevent a complete
48document from building, and the first reported diagnostic may be distant
49from the underlying fault. LLM-assisted writing systems now operate inside
50\LaTeX{} editors~\cite{wen2024overleafcopilot,hou2026paperdebugger}, but their
51publications do not provide a public, engine-pinned evaluation of
52compilation-failure repair.
53
54Program repair benchmarks pair a defect with an executable oracle and a
55fixed harness~\cite{just2014defects4j,legoues2015manybug}. SWE-bench extends
56the contract to repository-scale issues~\cite{jimenez2024swebench}.
57TeXFix-Bench studies a narrower input-output contract: a system receives
58an existing broken source document and must return a repaired full source
59under a specified engine.
60
61\section{Benchmark Construction}\label{sec:construction}
62\begin{figure*}[t]
63\centering
64\begin{tikzpicture}[font=\footnotesize\sffamily,node distance=0.58cm,
65 phase/.style={rounded corners=2.5pt,line width=0.7pt,
66 minimum width=2.55cm,minimum height=0.82cm,align=center}]
67\node[source] (seed) {13 known-good\\seed documents};
68\node[transform,right=of seed] (mutate) {12 deterministic\\mutation operators};
69\node[engine,right=of mutate] (gate) {Tectonic 0.17.0\\quality gate};
70\node[artifact,right=of gate] (rows) {627 retained\\rows};
71\draw[flow] (seed) -- (mutate);
72\draw[flow] (mutate) -- (gate);
73\draw[flow] (gate) -- (rows);
74\end{tikzpicture}
75\caption{TeXFix-Bench data and evaluation flow. Construction is
76deterministic. Evaluation reuses saved candidates and reruns only local
77engine and PDF-text checks.}
78\label{fig:pipeline}
79\end{figure*}
80
81\section{Results}\label{sec:results}
82Table~\ref{tab:main} reports the aggregate outcome.
83Figure~\ref{fig:paired} shows \CR{} and \NEM{} for each system, and
84Figure~\ref{fig:contrasts} shows the published paired contrasts.
85
86\begin{table*}[t]
87\caption{Version 0.2 results on 86 distinct hard-track inputs. \CR{} is
88intention-to-treat. \MER{} and \NEM{} use the same denominatior.}
89\label{tab:main}
90\input{results/tables/main-results-v02.tex}
91\end{table*}
92
93Claude Sonnet 5, GPT-5.6 Sol, and Grok 4.5 each recover 85 of 86 inputs.
94Their normalized exact-match counts are 52, 66, and 59, respectively.
95
96\begin{thebibliography}{19}
97\bibitem{just2014defects4j}
98R. Just, D. Jalali, and M. D. Ernst, \emph{Defects4J: A database of existing
99faults to enable controlled testing studies for Java programs}, ISSTA, 2014.
100\bibitem{legoues2015manybugs}
101C. Le Goues, N. Holtschulte, E. K. Smith, Y. Brun, P. Devanbu, S. Forrest,
102and W. Weimer, \emph{The ManyBugs and IntroClass benchmarks}, TSE, 2015.
103\bibitem{jimenez2024swebench}
104C. E. Jimenez, J. Yang, A. Wettig, S. Yao, K. Pei, O. Press, and
105K. Narasimhan, \emph{SWE-bench}, ICLR, 2024.
106\bibitem{chen2021codex}
107M. Chen et al., \emph{Evaluating large language models trained on code},
108arXiv:2107.03374, 2021.
109\bibitem{smith2015overfitting}
110E. K. Smith, E. T. Barr, C. Le Goues, and Y. Brun, \emph{Is the cure worse
111than the disease? Overfitting in automated program repair}, FSE, 2015.
112\bibitem{demillo1978}
113R. A. DeMillo, R. J. Lipton, and F. G. Sayward, \emph{Hints on test data
114selection}, Computer, 1978.
115\bibitem{jia2011mutation}
116Y. Jia and M. Harman, \emph{An analysis and survey of the development of
117mutation testing}, TSE, 2011.
118\bibitem{gupta2017deepfix}
119R. Gupta, S. Pal, A. Kanade, and S. Shevade, \emph{DeepFix}, AAAI, 2017.
120\bibitem{yasunaga2020drrepair}
121M. Yasunaga and P. Liang, \emph{Graph-based, self-supervised program repair
122from diagnostic feedback}, ICML, 2020.
123\bibitem{zhu2022eqfix}
124F. Zhu, Z. Liu, G. Yang, J. Li, and S. Cheung, \emph{EqFix}, ASE, 2022.
125\bibitem{wang2024texocr}
126K. Wang et al., \emph{TexOCR-Bench: Benchmarking page-image-to-LaTeX
127reconstruction}, 2024.
128\bibitem{wen2024overleafcopilot}
129H. Wen, Z. Zhang, and Y. Li, \emph{OverleafCopilot: Empowering academic
130writing in Overleaf with large language models}, 2024.
131\bibitem{hou2026paperdebugger}
132S. Hou et al., \emph{PaperDebugger: An in-editor assistant for manuscript
133repair}, 2026.
134\bibitem{tan2024tex}
135J. Tan, K. Zhang, and X. Chen, \emph{Instrumenting TeX produced diagnostics
136for repair}, 2024.
137\bibitem{zheng2023judging}
138L. Zheng et al., \emph{Judging LLM-as-a-judge with MT-Bench and Chatbot
139Arena}, NeurIPS, 2023.
140\bibitem{panickssery2024selfpref}
141A. Panickssery, S. R. Bowman, and S. Feng, \emph{LLM evaluators recognize
142and favor their own generations}, NeurIPS, 2024.
143\bibitem{hui2024qwen}
144B. Hui et al., \emph{Qwen2.5-Coder technical report}, 2024.
145\bibitem{metaai2024llama}
146Meta AI, \emph{The Llama 3 herd of models}, 2024.
147\bibitem{kassis2024reproduce}
148E. Kassis et al., \emph{On the reproducibility of research artifacts}, 2024.
149\end{thebibliography}
150
151\end{document}
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demillo1978DeMillo, Richard A. and Lipton, Richard J. and Sayward, Frederi…
gupta2017deepfixGupta, Rahul and Pal, Soham and Kanade, Aditya and Shevade…
jia2011mutationJia, Yue and Harman, Mark · 2011 · An analysis and survey of th…
smith2015overfittingSmith, Edward K. and Barr, Earl T. and Le Goues, Claire and B…
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Title: Engine-Transfer-Bench
@article{venkateshmurthy2026etb, author = {Venkateshmurthy, Prajwal S.}, title = {Engine-Transfer-Bench: An Evidence-Based Benchmark for Document Compilation Engine Selection}, journal = {arXiv preprint}, year = 2026, eprint = {2608.18329}, }
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Working ⇆ Index
| 28 unchanged lines |
29\begin{abstract}
30Compilation repair for \LaTeX{} is easy to score badly. A candidate may
31compile after deleting or hiding content, while exact source equality
32rejects valid alternative repairs. We present TeXFix-Bench, a controlled
33benchmark for repairing complete, broken \LaTeX{} sources under a pinned engine.
34The generator applies 12 deterministic single-fault operators to 13
35hand-authored seed documents. It retains 627 engine-validated rows,
36comprising 300 unique hard-track inputs and 67 unique warning-only
37inputs after hash deduplication.
| 58 unchanged lines |
| 28 unchanged lines |
29\begin{abstract}
30Compilation repair for \LaTeX{} is easy to score badly. A candidate may
31compile after deleting or hiding content, while exact source equality
32rejects valid alternative repairs. We present TeXFix-Bench, a controlled
33benchmark for repairing complete, broken \LaTeX{} sources under a pinned engine~\cite{tan2024tex}.
34The generator applies 12 deterministic single-fault operators to 13
35hand-authored seed documents. It retains 627 engine-validated rows,
36comprising 300 unique hard-track inputs and 67 unique warning-only
37inputs after hash deduplication.
| 58 unchanged lines |
1% TeXFix-Bench technical report, manuscript version 0.2.
2% Compile with: tectonic -X compile main.tex
3
4\documentclass[journal,10pt]{IEEEtran}
5\usepackage{amsmath,amssymb,booktabs,array,graphicx}
6\usepackage{xcolor,microtype,url,fontspec,newtxsf}
7\usepackage{tikz,pgfplots,balance,hyperref}
8\pgfplotsset{compat=1.18}
9\usetikzlibrary{arrows.meta,positioning,fit,backgrounds}
10
11\definecolor{accentblue}{HTML}{006D9C}
12\definecolor{accentorange}{HTML}{D55E00}
13\definecolor{accentteal}{HTML}{0F766E}
14\definecolor{slate}{HTML}{64748B}
15
16\newcommand{\CR}{\textsc{cr}}
17\newcommand{\NEM}{\textsc{nem}}
18\newcommand{\MER}{\textsc{mer}}
19
20\title{TeXFix-Bench: Engine-Grounded Evaluation of\\
21Single-Fault \LaTeX{} Compilation Repair}
22\author{\textbf{Prajwal~S.~Venkateshmurthy}\\[0.10em]
23\small Independent Researcher\\[0.12em]
24\small San Jose, CA, USA}
25
26\begin{document}
27\maketitle
28
29\begin{abstract}
30Compilation repair for \LaTeX{} is easy to score badly. A candidate may
31compile after deleting or hiding content, while exact source equality
32rejects valid alternative repairs. We present TeXFix-Bench, a controlled
33benchmark for repairing complete, broken \LaTeX{} sources under a pinned engine~\cite{tan2024tex}.
34The generator applies 12 deterministic single-fault operators to 13
35hand-authored seed documents. It retains 627 engine-validated rows,
36comprising 300 unique hard-track inputs and 67 unique warning-only
37inputs after hash deduplication.
38\end{abstract}
39
40\begin{IEEEkeywords}
41automated program repair, \LaTeX{}, benchmark, compilation, large
42language models, reproducibility
43\end{IEEEkeywords}
44
45\section{Introduction}\label{sec:intro}
46\LaTeX{} compilation failures are a practical target for automated repair.
47A missing delimiter or misspelled control sequence can prevent a complete
48document from building, and the first reported diagnostic may be distant
49from the underlying fault. LLM-assisted writing systems now operate inside
50\LaTeX{} editors~\cite{wen2024overleafcopilot,hou2026paperdebugger}, but their
51publications do not provide a public, engine-pinned evaluation of
52compilation-failure repair.
53
54Program repair benchmarks pair a defect with an executable oracle and a
55fixed harness~\cite{just2014defects4j,legoues2015manybug}. SWE-bench extends
56the contract to repository-scale issues~\cite{jimenez2024swebench}.
57TeXFix-Bench studies a narrower input-output contract: a system receives
58an existing broken source document and must return a repaired full source
59under a specified engine.
60
61\section{Benchmark Construction}\label{sec:construction}
62\begin{figure*}[t]
63\centering
64\begin{tikzpicture}[font=\footnotesize\sffamily,node distance=0.58cm,
65 phase/.style={rounded corners=2.5pt,line width=0.7pt,
66 minimum width=2.55cm,minimum height=0.82cm,align=center}]
67\node[source] (seed) {13 known-good\\seed documents};
68\node[transform,right=of seed] (mutate) {12 deterministic\\mutation operators};
69\node[engine,right=of mutate] (gate) {Tectonic 0.17.0\\quality gate};
70\node[artifact,right=of gate] (rows) {627 retained\\rows};
71\draw[flow] (seed) -- (mutate);
72\draw[flow] (mutate) -- (gate);
73\draw[flow] (gate) -- (rows);
74\end{tikzpicture}
75\caption{TeXFix-Bench data and evaluation flow. Construction is
76deterministic. Evaluation reuses saved candidates and reruns only local
77engine and PDF-text checks.}
78\label{fig:pipeline}
79\end{figure*}
80
81\section{Results}\label{sec:results}
82Table~\ref{tab:main} reports the aggregate outcome.
83Figure~\ref{fig:paired} shows \CR{} and \NEM{} for each system, and
84Figure~\ref{fig:contrasts} shows the published paired contrasts.
85
86\begin{table*}[t]
87\caption{Version 0.2 results on 86 distinct hard-track inputs. \CR{} is
88intention-to-treat. \MER{} and \NEM{} use the same denominatior.}
89\label{tab:main}
90\input{results/tables/main-results-v02.tex}
91\end{table*}
92
93Claude Sonnet 5, GPT-5.6 Sol, and Grok 4.5 each recover 85 of 86 inputs.
94Their normalized exact-match counts are 52, 66, and 59, respectively.
95
96\begin{thebibliography}{19}
97\bibitem{just2014defects4j}
98R. Just, D. Jalali, and M. D. Ernst, \emph{Defects4J: A database of existing
99faults to enable controlled testing studies for Java programs}, ISSTA, 2014.
100\bibitem{legoues2015manybugs}
101C. Le Goues, N. Holtschulte, E. K. Smith, Y. Brun, P. Devanbu, S. Forrest,
102and W. Weimer, \emph{The ManyBugs and IntroClass benchmarks}, TSE, 2015.
103\bibitem{jimenez2024swebench}
104C. E. Jimenez, J. Yang, A. Wettig, S. Yao, K. Pei, O. Press, and
105K. Narasimhan, \emph{SWE-bench}, ICLR, 2024.
106\bibitem{chen2021codex}
107M. Chen et al., \emph{Evaluating large language models trained on code},
108arXiv:2107.03374, 2021.
109\bibitem{smith2015overfitting}
110E. K. Smith, E. T. Barr, C. Le Goues, and Y. Brun, \emph{Is the cure worse
111than the disease? Overfitting in automated program repair}, FSE, 2015.
112\bibitem{demillo1978}
113R. A. DeMillo, R. J. Lipton, and F. G. Sayward, \emph{Hints on test data
114selection}, Computer, 1978.
115\bibitem{jia2011mutation}
116Y. Jia and M. Harman, \emph{An analysis and survey of the development of
117mutation testing}, TSE, 2011.
118\bibitem{gupta2017deepfix}
119R. Gupta, S. Pal, A. Kanade, and S. Shevade, \emph{DeepFix}, AAAI, 2017.
120\bibitem{yasunaga2020drrepair}
121M. Yasunaga and P. Liang, \emph{Graph-based, self-supervised program repair
122from diagnostic feedback}, ICML, 2020.
123\bibitem{zhu2022eqfix}
124F. Zhu, Z. Liu, G. Yang, J. Li, and S. Cheung, \emph{EqFix}, ASE, 2022.
125\bibitem{wang2024texocr}
126K. Wang et al., \emph{TexOCR-Bench: Benchmarking page-image-to-LaTeX
127reconstruction}, 2024.
128\bibitem{wen2024overleafcopilot}
129H. Wen, Z. Zhang, and Y. Li, \emph{OverleafCopilot: Empowering academic
130writing in Overleaf with large language models}, 2024.
131\bibitem{hou2026paperdebugger}
132S. Hou et al., \emph{PaperDebugger: An in-editor assistant for manuscript
133repair}, 2026.
134\bibitem{tan2024tex}
135J. Tan, K. Zhang, and X. Chen, \emph{Instrumenting TeX produced diagnostics
136for repair}, 2024.
137\bibitem{zheng2023judging}
138L. Zheng et al., \emph{Judging LLM-as-a-judge with MT-Bench and Chatbot
139Arena}, NeurIPS, 2023.
140\bibitem{panickssery2024selfpref}
141A. Panickssery, S. R. Bowman, and S. Feng, \emph{LLM evaluators recognize
142and favor their own generations}, NeurIPS, 2024.
143\bibitem{hui2024qwen}
144B. Hui et al., \emph{Qwen2.5-Coder technical report}, 2024.
145\bibitem{metaai2024llama}
146Meta AI, \emph{The Llama 3 herd of models}, 2024.
147\bibitem{kassis2024reproduce}
148E. Kassis et al., \emph{On the reproducibility of research artifacts}, 2024.
149\end{thebibliography}
150
151\end{document}
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Alex Chen
Senior Software Engineer
[email protected] · (650) 555-0142 · Mountain View, CA · alexchen.dev · github.com/alexchen
SUMMARY
Senior Software Engineer (L5) with 9+ years designing and operating large-scale distributed systems. Deep experience in backend infrastructure, performance, and reliability.
EXPERIENCE
Senior Software Engineer (L5)Google
Mountain View, CAMar 2020 — Present
- Tech lead for a Search serving component handling 2M+ queries/second; cut p99 latency 38%.
- Designed a globally replicated feature store on Spanner backing 40+ ML models.
- Led migration of a 300-service fleet to a new RPC framework; reduced on-call pages 45%.
- Mentored six engineers and ran the team’s design-review program.
Software Engineer (L4)Google
Mountain View, CAJul 2017 — Mar 2020
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Software EngineerStripe
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- Reduced a reconciliation run from six hours to 25 minutes.
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M.S. Computer ScienceStanford University
B.S. Computer ScienceUniversity of Illinois Urbana-Champaign
SKILLS
Languages: C++, Go, Rust, Python, Java, SQL
Systems: Spanner, Bigtable, Kubernetes, Kafka, gRPC / Protobuf, Redis
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Jane Doe
Data Scientist
[email protected] · New York, NY · janedoe.dev · github.com/janedoe
SUMMARY
Data scientist focused on trustworthy machine learning, experimentation, and decision systems used by product and research teams.
EXPERIENCE
Senior Data ScientistAnthropic
New York, NY2021 — Present
- Built evaluation systems for large language models across safety and product quality.
- Designed experiments used to guide high-impact model launches.
Machine Learning EngineerAirbnb
San Francisco, CA2018 — 2021
- Developed ranking models and reliable offline evaluation pipelines.
- Partnered with product teams to improve marketplace search quality.
Data AnalystSpotify
New York, NY2016 — 2018
- Created retention models and executive experimentation dashboards.
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Model Cardsjanedoe.dev/model-cards
Open evaluation templates for production ML systems.
CausalKitgithub.com/janedoe/causalkit
Python package for reproducible causal inference workflows.
EDUCATION
M.S. StatisticsColumbia University
B.S. MathematicsUniversity of Michigan
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Languages: Python, R, SQL, PyTorch, JAX
Systems: Spark, dbt, BigQuery, Airflow, MLflow, Kubernetes
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Jane Doe
Data Scientist
[email protected] · New York, NY · janedoe.dev · github.com/janedoe
SUMMARY
Data scientist focused on trustworthy machine learning, experimentation, and decision systems used by product and research teams.
EXPERIENCE
Senior Data ScientistAnthropic
New York, NY2021 — Present
- Built evaluation systems for large language models across safety and product quality.
- Designed experiments used to guide high-impact model launches.
Machine Learning EngineerAirbnb
San Francisco, CA2018 — 2021
- Developed ranking models and reliable offline evaluation pipelines.
- Partnered with product teams to improve marketplace search quality.
Data AnalystSpotify
New York, NY2016 — 2018
- Created retention models and executive experimentation dashboards.
SELECTED PROJECTS
Model Cardsjanedoe.dev/model-cards
Open evaluation templates for production ML systems.
CausalKitgithub.com/janedoe/causalkit
Python package for reproducible causal inference workflows.
EDUCATION
M.S. StatisticsColumbia University
B.S. MathematicsUniversity of Michigan
SKILLS
Languages: Python, R, SQL, PyTorch, JAX
Systems: Spark, dbt, BigQuery, Airflow, MLflow, Kubernetes
Alex Chen
Senior Software Engineer
[email protected] · (650) 555-0142 · Mountain View, CA · alexchen.dev · github.com/alexchen
SUMMARY
Senior Software Engineer (L5) with 9+ years designing and operating large-scale distributed systems. Deep experience in backend infrastructure, performance, and reliability.
EXPERIENCE
Senior Software Engineer (L5)Google
Mountain View, CAMar 2020 — Present
- Tech lead for a Search serving component handling 2M+ queries/second; cut p99 latency 38%.
- Designed a globally replicated feature store on Spanner backing 40+ ML models.
- Led migration of a 300-service fleet to a new RPC framework; reduced on-call pages 45%.
- Mentored six engineers and ran the team’s design-review program.
Software Engineer (L4)Google
Mountain View, CAJul 2017 — Mar 2020
- Built a real-time aggregation pipeline processing 8B events/day.
- Cut batch-job cost 30% with sharding and incremental computation.
Software EngineerStripe
San Francisco, CAAug 2015 — Jun 2017
- Shipped ledger and reconciliation services processing $60B+/year.
- Reduced a reconciliation run from six hours to 25 minutes.
SELECTED PROJECTS
Ratelgithub.com/alexchen/ratel
Go, Raft, gRPC — distributed rate limiter doing 4M+ decisions/sec/node.
tqlgithub.com/alexchen/tql
Rust — typed query builder for Postgres with compile-time-checked SQL.
EDUCATION
M.S. Computer ScienceStanford University
B.S. Computer ScienceUniversity of Illinois Urbana-Champaign
SKILLS
Languages: C++, Go, Rust, Python, Java, SQL
Systems: Spanner, Bigtable, Kubernetes, Kafka, gRPC / Protobuf, Redis
Morgan Lee
Product Engineer
[email protected] · Brooklyn, NY · morganlee.design · github.com/morganlee
SUMMARY
Product engineer bridging design systems and frontend infrastructure to build accessible, high-craft tools for creative teams.
EXPERIENCE
Staff Product EngineerFigma
New York, NY2022 — Present
- Led architecture for collaborative prototyping workflows used by millions.
- Created an accessible component platform shared across six product teams.
Senior Frontend EngineerNotion
San Francisco, CA2019 — 2022
- Shipped core editor interactions and improved canvas rendering performance 42%.
- Partnered with design to establish product-wide interaction standards.
Interaction DesignerIDEO
Chicago, IL2016 — 2019
- Prototyped service experiences for health and education clients.
SELECTED PROJECTS
Prismmorganlee.design/prism
Open-source accessible color and typography toolkit.
Local First UIgithub.com/morganlee/lfui
Patterns for resilient collaborative interfaces.
EDUCATION
M.Des. Interaction DesignCarnegie Mellon University
B.S. Computer ScienceNorthwestern University
SKILLS
Languages: TypeScript, React, Rust, Swift, Figma
Systems: WebGL, Canvas, design systems, accessibility, prototyping
Setup
Modern ResumeTECTONICneeds setup
Morgan Lee
Product Engineer
[email protected] · Brooklyn, NY · morganlee.design · github.com/morganlee
SUMMARY
Product engineer bridging design systems and frontend infrastructure to build accessible, high-craft tools for creative teams.
EXPERIENCE
Staff Product EngineerFigma
New York, NY2022 — Present
- Led architecture for collaborative prototyping workflows used by millions.
- Created an accessible component platform shared across six product teams.
Senior Frontend EngineerNotion
San Francisco, CA2019 — 2022
- Shipped core editor interactions and improved canvas rendering performance 42%.
- Partnered with design to establish product-wide interaction standards.
Interaction DesignerIDEO
Chicago, IL2016 — 2019
- Prototyped service experiences for health and education clients.
SELECTED PROJECTS
Prismmorganlee.design/prism
Open-source accessible color and typography toolkit.
Local First UIgithub.com/morganlee/lfui
Patterns for resilient collaborative interfaces.
EDUCATION
M.Des. Interaction DesignCarnegie Mellon University
B.S. Computer ScienceNorthwestern University
SKILLS
Languages: TypeScript, React, Rust, Swift, Figma
Systems: WebGL, Canvas, design systems, accessibility, prototyping
Alex Chen
Research Engineer
[email protected] · Berlin, Germany · alexchen.dev
SUMMARY
Research engineer building fast, expressive systems for scientific communication and reproducible technical publishing.
EXPERIENCE
Research EngineerTypst
Berlin, Germany2022 — Present
- Built incremental layout and diagnostics for a modern typesetting engine.
- Improved large-document render latency and editor feedback loops.
Software EngineerMozilla
Remote2019 — 2022
- Worked on text shaping, browser layout, and developer tooling.
Research AssistantTU Berlin
Berlin, Germany2017 — 2019
- Published work on interactive document systems.
SELECTED PROJECTS
Inkstonegithub.com/alexchen/inkstone
A small language server for technical documents.
Glyphalexchen.dev/glyph
Experiments in high-performance text layout.
EDUCATION
M.Sc. Computer ScienceTU Berlin
B.Sc. Computer ScienceUniversity of Edinburgh
SKILLS
Languages: Rust, C++, TypeScript, Python
Systems: Compilers, text layout, language servers, WebAssembly
Software Engineer Resume
Lilac
Back
OleaflyAlex Chen - Google Resume
main.tex
CodeVisual
1
\documentclass[11pt,letterpaper]{article}2
\usepackage[T1]{fontenc}3
\usepackage[margin=0.5in]{geometry}4
\usepackage{titlesec}5
\usepackage{enumitem}6
\usepackage{hyperref}7
8
% ATS-friendly: single column, real selectable text, no tables-for-layout,9
% embedded subset fonts. Linear reading order so parsers extract cleanly.10
\setlength{\parindent}{0pt}11
\pagenumbering{gobble}12
\hypersetup{colorlinks=true, urlcolor=black, linkcolor=black}13
14
\titleformat{\section}{\large\bfseries\uppercase}{}{0em}{}[\vspace{2pt}\titlerule]15
\titlespacing*{\section}{0pt}{8pt}{4pt}16
\setlist[itemize]{leftmargin=14pt, itemsep=1pt, topsep=2pt, parsep=0pt}17
18
% role{Title}{Company}{Location}{Dates}19
\newcommand{\role}[4]{%20
\textbf{#1} \hfill #2 \\21
\textit{#3} \hfill \textit{#4}%22
}23
24
\begin{document}25
26
\begin{center}27
{\Huge \textbf{Alex Chen}}\\[3pt]28
\small29
Senior Software Engineer\\[2pt]30
\href{mailto:[email protected]}{[email protected]}\,$\cdot$\,31
(650) 555-0142\,$\cdot$\,32
Mountain View, CA\,$\cdot$\,33
\href{https://alexchen.dev}{alexchen.dev}\,$\cdot$\,34
\href{https://github.com/alexchen}{github.com/alexchen}\,$\cdot$\,35
\href{https://linkedin.com/in/alexchen}{linkedin.com/in/alexchen}36
\end{center}37
\vspace{2pt}38
39
\section*{Summary}40
Senior Software Engineer (L5) with 9+ years designing and operating large-scale41
distributed systems. Deep experience in backend infrastructure, performance, and42
reliability, with a track record of shipping products used by hundreds of43
millions of users and mentoring engineers toward senior roles.44
45
\section*{Experience}46
\role{Senior Software Engineer (L5)}{Google}{Mountain View, CA}{Mar 2020 -- Present}47
\begin{itemize}48
\item Tech lead for a Search serving component handling 2M+ queries/second; drove a redesign that cut p99 latency 38\% and saved an estimated \$14M/year in compute.49
\item Designed and launched a globally-replicated feature store on Spanner backing 40+ ML models, adopted by 12 teams across Search and Ads.50
\item Led migration of a 300-service fleet to a new RPC framework, improving tail latency and reducing on-call pages by 45\%.51
\item Mentored 6 engineers (2 promoted to senior); ran the team's design-review and readability programs.52
\end{itemize}53
\vspace{3pt}54
\role{Software Engineer (L4)}{Google}{Mountain View, CA}{Jul 2017 -- Mar 2020}55
\begin{itemize}56
\item Built a real-time aggregation pipeline (C++, Flume) processing 8B events/day for a Search-quality dashboard used org-wide.57
\item Cut batch-job cost 30\% by reworking sharding and introducing incremental recomputation.58
\end{itemize}59
\vspace{3pt}60
\role{Software Engineer}{Stripe}{San Francisco, CA}{Aug 2015 -- Jun 2017}61
\begin{itemize}62
\item Shipped core ledger and reconciliation services for a payments platform processing \$60B+/year.63
\item Reduced a reconciliation run from 6 hours to 25 minutes via parallelization and a columnar store.64
\end{itemize}65
66
\section*{Selected Projects}67
\textbf{Ratel} \hfill \href{https://github.com/alexchen/ratel}{github.com/alexchen/ratel} \\68
\textit{Go, Raft, gRPC} --- open-source distributed rate limiter doing 4M+ decisions/sec/node; 1.5k GitHub stars.\par\vspace{4pt}69
\textbf{tql} \hfill \href{https://github.com/alexchen/tql}{github.com/alexchen/tql} \\70
\textit{Rust} --- a typed query builder for Postgres with compile-time-checked SQL.71
72
\section*{Education}73
\textbf{M.S. Computer Science} \hfill Stanford University \\74
\textit{2013 -- 2015} \hfill \textit{Focus: Distributed Systems}\par\vspace{4pt}75
\textbf{B.S. Computer Science} \hfill University of Illinois Urbana-Champaign \\76
\textit{2009 -- 2013} \hfill \textit{GPA: 3.9 / 4.0}77
78
\section*{Skills}79
\textbf{Languages:} C++, Go, Rust, Python, Java, SQL \\80
\textbf{Systems:} Spanner, Bigtable, Kubernetes, Kafka, gRPC / Protobuf, Redis \\81
\textbf{Focus:} Distributed systems, performance, reliability (SLOs), system design, mentoring82
83
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QUEUEDAlex Chen
Senior Software Engineer
[email protected] · (650) 555-0142 · Mountain View, CA · alexchen.dev · github.com/alexchen
SUMMARY
Senior Software Engineer (L5) with 9+ years designing and operating large-scale distributed systems. Deep experience in backend infrastructure, performance, and reliability.
EXPERIENCE
Senior Software Engineer (L5)Google
Mountain View, CAMar 2020 — Present
- Tech lead for a Search serving component handling 2M+ queries/second; cut p99 latency 38%.
- Designed a globally replicated feature store on Spanner backing 40+ ML models.
- Led migration of a 300-service fleet to a new RPC framework; reduced on-call pages 45%.
- Mentored six engineers and ran the team’s design-review program.
Software Engineer (L4)Google
Mountain View, CAJul 2017 — Mar 2020
- Built a real-time aggregation pipeline processing 8B events/day.
- Cut batch-job cost 30% with sharding and incremental computation.
Software EngineerStripe
San Francisco, CAAug 2015 — Jun 2017
- Shipped ledger and reconciliation services processing $60B+/year.
- Reduced a reconciliation run from six hours to 25 minutes.
SELECTED PROJECTS
Ratelgithub.com/alexchen/ratel
Go, Raft, gRPC — distributed rate limiter doing 4M+ decisions/sec/node.
tqlgithub.com/alexchen/tql
Rust — typed query builder for Postgres with compile-time-checked SQL.
EDUCATION
M.S. Computer ScienceStanford University
B.S. Computer ScienceUniversity of Illinois Urbana-Champaign
SKILLS
Languages: C++, Go, Rust, Python, Java, SQL
Systems: Spanner, Bigtable, Kubernetes, Kafka, gRPC / Protobuf, Redis
main.tex
CodeVisual
Results
Claude Sonnet 5, GPT-5.6 Sol, and Grok 4.5 each recover 85 of 86 inputs, yet their normalized exact-match counts are 52, 66, and 59 just2014defects4j.
TABLE I
Version 0.2 results on 86 distinct hard-track inputs. CR is intention-to-treat.
Version 0.2 results on 86 distinct hard-track inputs. CR is intention-to-treat.
| System | CR | CR % | NEM | Fail |
| Identity | 0/86 | 0.0 | 0/86 | 0 |
| Rule | 34/86 | 39.5 | 28/86 | 0 |
| Qwen-7B + fb | 57/86 | 66.3 | 17/86 | 0 |
| MiniMax M3 | 75/86 | 87.2 | 50/86 | 2 |
| DeepSeek V4 | 81/86 | 94.2 | 38/86 | 2 |
| Gemini 3.5 | 82/86 | 95.3 | 26/86 | 0 |
| Claude S5 | 85/86 | 98.8 | 52/86 | 0 |
| GPT-5.6 Sol | 85/86 | 98.8 | 66/86 | 1 |
| Grok 4.5 | 85/86 | 98.8 | 59/86 | 0 |
Evaluation method
Compile Recovery requires a successful Tectonic 0.17.0 run and at least 0.80 token-level LCS similarity between text extracted from the candidate and reference PDFs. Normalized exact match sits beside it, because compilation alone cannot separate a faithful repair from a deletion.
CR = compile(c) ∧ LCStok(Tc, Tr) ≥ 0.80
Discussion and limitations
These results show why compilation and edit precision should be reported together, but they do not establish a universal model ranking jimenez2024swebench.
Every generated row passes an engine gate before it is retained, and duplicate broken sources are removed by SHA-256 so that no input is scored twice.
Conclusion
The contribution is an auditable task definition, an executable oracle, a fixed cohort, and a replayable candidate ledger.
1% TeXFix-Bench technical report, manuscript version 0.2.
2% Compile with: tectonic -X compile main.tex
3
4\documentclass[journal,10pt]{IEEEtran}
5\usepackage{amsmath,amssymb,booktabs,array,graphicx}
6\usepackage{xcolor,microtype,url,fontspec,newtxsf}
7\usepackage{tikz,pgfplots,balance,hyperref}
8\pgfplotsset{compat=1.18}
9\usetikzlibrary{arrows.meta,positioning,fit,backgrounds}
10
11\definecolor{accentblue}{HTML}{006D9C}
12\definecolor{accentorange}{HTML}{D55E00}
13\definecolor{accentteal}{HTML}{0F766E}
14\definecolor{slate}{HTML}{64748B}
15
16\newcommand{\CR}{\textsc{cr}}
17\newcommand{\NEM}{\textsc{nem}}
18\newcommand{\MER}{\textsc{mer}}
19
20\title{TeXFix-Bench: Engine-Grounded Evaluation of\\
21Single-Fault \LaTeX{} Compilation Repair}
22\author{\textbf{Prajwal~S.~Venkateshmurthy}\\[0.10em]
23\small Independent Researcher\\[0.12em]
24\small San Jose, CA, USA}
25
26\begin{document}
27\maketitle
28
29\begin{abstract}
30Compilation repair for \LaTeX{} is easy to score badly. A candidate may
31compile after deleting or hiding content, while exact source equality
32rejects valid alternative repairs. We present TeXFix-Bench, a controlled
33benchmark for repairing complete, broken \LaTeX{} sources under a pinned engine~\cite{tan2024tex}.
34The generator applies 12 deterministic single-fault operators to 13
35hand-authored seed documents. It retains 627 engine-validated rows,
36comprising 300 unique hard-track inputs and 67 unique warning-only
37inputs after hash deduplication.
38\end{abstract}
39
40\begin{IEEEkeywords}
41automated program repair, \LaTeX{}, benchmark, compilation, large
42language models, reproducibility
43\end{IEEEkeywords}
44
45\section{Introduction}\label{sec:intro}
46\LaTeX{} compilation failures are a practical target for automated repair.
47A missing delimiter or misspelled control sequence can prevent a complete
48document from building, and the first reported diagnostic may be distant
49from the underlying fault. LLM-assisted writing systems now operate inside
50\LaTeX{} editors~\cite{wen2024overleafcopilot,hou2026paperdebugger}, but their
51publications do not provide a public, engine-pinned evaluation of
52compilation-failure repair.
53
54Program repair benchmarks pair a defect with an executable oracle and a
55fixed harness~\cite{just2014defects4j,legoues2015manybug}. SWE-bench extends
56the contract to repository-scale issues~\cite{jimenez2024swebench}.
57TeXFix-Bench studies a narrower input-output contract: a system receives
58an existing broken source document and must return a repaired full source
59under a specified engine.
60
61\section{Benchmark Construction}\label{sec:construction}
62\begin{figure*}[t]
63\centering
64\begin{tikzpicture}[font=\footnotesize\sffamily,node distance=0.58cm,
65 phase/.style={rounded corners=2.5pt,line width=0.7pt,
66 minimum width=2.55cm,minimum height=0.82cm,align=center}]
67\node[source] (seed) {13 known-good\\seed documents};
68\node[transform,right=of seed] (mutate) {12 deterministic\\mutation operators};
69\node[engine,right=of mutate] (gate) {Tectonic 0.17.0\\quality gate};
70\node[artifact,right=of gate] (rows) {627 retained\\rows};
71\draw[flow] (seed) -- (mutate);
72\draw[flow] (mutate) -- (gate);
73\draw[flow] (gate) -- (rows);
74\end{tikzpicture}
75\caption{TeXFix-Bench data and evaluation flow. Construction is
76deterministic. Evaluation reuses saved candidates and reruns only local
77engine and PDF-text checks.}
78\label{fig:pipeline}
79\end{figure*}
80
81\section{Results}\label{sec:results}
82Table~\ref{tab:main} reports the aggregate outcome.
83Figure~\ref{fig:paired} shows \CR{} and \NEM{} for each system, and
84Figure~\ref{fig:contrasts} shows the published paired contrasts.
85
86\begin{table*}[t]
87\caption{Version 0.2 results on 86 distinct hard-track inputs. \CR{} is
88intention-to-treat. \MER{} and \NEM{} use the same denominatior.}
89\label{tab:main}
90\input{results/tables/main-results-v02.tex}
91\end{table*}
92
93Claude Sonnet 5, GPT-5.6 Sol, and Grok 4.5 each recover 85 of 86 inputs.
94Their normalized exact-match counts are 52, 66, and 59, respectively.
95
96\begin{thebibliography}{19}
97\bibitem{just2014defects4j}
98R. Just, D. Jalali, and M. D. Ernst, \emph{Defects4J: A database of existing
99faults to enable controlled testing studies for Java programs}, ISSTA, 2014.
100\bibitem{legoues2015manybugs}
101C. Le Goues, N. Holtschulte, E. K. Smith, Y. Brun, P. Devanbu, S. Forrest,
102and W. Weimer, \emph{The ManyBugs and IntroClass benchmarks}, TSE, 2015.
103\bibitem{jimenez2024swebench}
104C. E. Jimenez, J. Yang, A. Wettig, S. Yao, K. Pei, O. Press, and
105K. Narasimhan, \emph{SWE-bench}, ICLR, 2024.
106\bibitem{chen2021codex}
107M. Chen et al., \emph{Evaluating large language models trained on code},
108arXiv:2107.03374, 2021.
109\bibitem{smith2015overfitting}
110E. K. Smith, E. T. Barr, C. Le Goues, and Y. Brun, \emph{Is the cure worse
111than the disease? Overfitting in automated program repair}, FSE, 2015.
112\bibitem{demillo1978}
113R. A. DeMillo, R. J. Lipton, and F. G. Sayward, \emph{Hints on test data
114selection}, Computer, 1978.
115\bibitem{jia2011mutation}
116Y. Jia and M. Harman, \emph{An analysis and survey of the development of
117mutation testing}, TSE, 2011.
118\bibitem{gupta2017deepfix}
119R. Gupta, S. Pal, A. Kanade, and S. Shevade, \emph{DeepFix}, AAAI, 2017.
120\bibitem{yasunaga2020drrepair}
121M. Yasunaga and P. Liang, \emph{Graph-based, self-supervised program repair
122from diagnostic feedback}, ICML, 2020.
123\bibitem{zhu2022eqfix}
124F. Zhu, Z. Liu, G. Yang, J. Li, and S. Cheung, \emph{EqFix}, ASE, 2022.
125\bibitem{wang2024texocr}
126K. Wang et al., \emph{TexOCR-Bench: Benchmarking page-image-to-LaTeX
127reconstruction}, 2024.
128\bibitem{wen2024overleafcopilot}
129H. Wen, Z. Zhang, and Y. Li, \emph{OverleafCopilot: Empowering academic
130writing in Overleaf with large language models}, 2024.
131\bibitem{hou2026paperdebugger}
132S. Hou et al., \emph{PaperDebugger: An in-editor assistant for manuscript
133repair}, 2026.
134\bibitem{tan2024tex}
135J. Tan, K. Zhang, and X. Chen, \emph{Instrumenting TeX produced diagnostics
136for repair}, 2024.
137\bibitem{zheng2023judging}
138L. Zheng et al., \emph{Judging LLM-as-a-judge with MT-Bench and Chatbot
139Arena}, NeurIPS, 2023.
140\bibitem{panickssery2024selfpref}
141A. Panickssery, S. R. Bowman, and S. Feng, \emph{LLM evaluators recognize
142and favor their own generations}, NeurIPS, 2024.
143\bibitem{hui2024qwen}
144B. Hui et al., \emph{Qwen2.5-Coder technical report}, 2024.
145\bibitem{metaai2024llama}
146Meta AI, \emph{The Llama 3 herd of models}, 2024.
147\bibitem{kassis2024reproduce}
148E. Kassis et al., \emph{On the reproducibility of research artifacts}, 2024.
149\end{thebibliography}
150
151\end{document}
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- Language analysisCurrent project revision analyzed
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TeXFix-Bench: Engine-Grounded Evaluation of
Single-Fault LATEX Compilation Repair
Single-Fault LATEX Compilation Repair
Prajwal S. Venkateshmurthy
Independent Researcher
San Jose, CA, USA
Independent Researcher
San Jose, CA, USA
Abstract—Compilation repair for LaTeX is easy to score badly. A candidate may compile after deleting or hiding content, while exact source equality rejects valid alternative repairs. We present TeXFix-Bench, a controlled benchmark for repairing complete, broken LaTeX sources under a pinned engine. The generator applies 12 deterministic single-fault operators to 13 hand-authored seed documents. It retains 627 engine-validated rows, comprising 300 unique hard-track inputs and 67 unique warning-only inputs after hash deduplication.
Index Terms—automated program repair, LaTeX, benchmark, compilation, large language models, reproducibility
I. INTRODUCTION
LaTeX compilation failures are a practical target for automated repair. A missing delimiter or misspelled control sequence can prevent a complete document from building, and the first reported diagnostic may be distant from the underlying fault. LLM-assisted writing systems now operate inside LaTeX editors , but their publications do not provide a public, engine-pinned evaluation of compilation-failure repair.
Program repair benchmarks pair a defect with an executable oracle and a fixed harness . TeXFix-Bench studies a narrower contract: a broken source document in, a repaired full source out, under a specified engine.
II. RELATED WORK
TexOCR-Bench evaluates page-image-to-LaTeX reconstruction , and EqFix repairs errors in individual equations by synthesizing rules from examples . Overfitting to a weak oracle is a known hazard .
III. TASK AND SCOPE
A system receives one broken source file and returns one repaired source file. The engine, its version, and the build command are fixed for every attempt.
CR = compile(c) ∧ LCStok(Tc, Tr) ≥ 0.80(1)
IV. BENCHMARK CONSTRUCTION
Fig. 1. TeXFix-Bench data and evaluation flow. Construction is deterministic.
Every generated row passes an engine gate before it is retained, and duplicate broken sources are removed by SHA-256 so that no input is scored twice .
V. EVALUATION METHOD
Compile Recovery requires a successful Tectonic 0.17.0 run and at least 0.80 token-level LCS similarity between text extracted from the candidate and reference PDFs. Normalized exact match sits beside it, because compilation alone cannot separate a faithful repair from a deletion .
TABLE I
Version 0.2 results on 86 distinct hard-track inputs. CR is intention-to-treat.
Version 0.2 results on 86 distinct hard-track inputs. CR is intention-to-treat.
| System | CR | CR % | NEM | Fail |
| Identity | 0/86 | 0.0 | 0/86 | 0 |
| Rule | 34/86 | 39.5 | 28/86 | 0 |
| Qwen-7B + fb | 57/86 | 66.3 | 17/86 | 0 |
| MiniMax M3 | 75/86 | 87.2 | 50/86 | 2 |
| DeepSeek V4 | 81/86 | 94.2 | 38/86 | 2 |
| Gemini 3.5 | 82/86 | 95.3 | 26/86 | 0 |
| Claude S5 | 85/86 | 98.8 | 52/86 | 0 |
| GPT-5.6 Sol | 85/86 | 98.8 | 66/86 | 1 |
| Grok 4.5 | 85/86 | 98.8 | 59/86 | 0 |
Fig. 2. Compile Recovery and normalized exact match on the fixed cohort of n = 86 hard-track inputs. Circles denote CR, squares NEM.
VI. RESULTS
Claude Sonnet 5, GPT-5.6 Sol, and Grok 4.5 each recover 85 of 86 inputs, yet their normalized exact-match counts are 52, 66, and 59 . Compilation and edit precision must be reported together.
VII. CONCLUSION
The contribution is an auditable task definition, an executable oracle, a fixed cohort, and a replayable candidate ledger.
REFERENCES
[1]R. Just, D. Jalali, and M. D. Ernst, “Defects4J: A database of existing faults to enable controlled testing studies for Java programs,” in Proc. ISSTA, 2014, pp. 437–440.
[2]C. Le Goues, N. Holtschulte, E. K. Smith, Y. Brun, P. Devanbu, S. Forrest, and W. Weimer, “The ManyBugs and IntroClass benchmarks for automated repair of C programs,” IEEE Trans. Softw. Eng., vol. 41, no. 12, pp. 1236–1256, 2015.
[3]C. E. Jimenez, J. Yang, A. Wettig, S. Yao, K. Pei, O. Press, and K. Narasimhan, “SWE-bench: Can language models resolve real-world GitHub issues?” in Proc. ICLR, 2024.
[4]M. Chen et al., “Evaluating large language models trained on code,” arXiv:2107.03374, 2021.
[5]E. K. Smith, E. T. Barr, C. Le Goues, and Y. Brun, “Is the cure worse than the disease? Overfitting in automated program repair,” in Proc. ESEC/FSE, 2015, pp. 532–543.
[6]R. A. DeMillo, R. J. Lipton, and F. G. Sayward, “Hints on test data selection: Help for the practicing programmer,” Computer, vol. 11, no. 4, pp. 34–41, 1978.
[7]Y. Jia and M. Harman, “An analysis and survey of the development of mutation testing,” IEEE Trans. Softw. Eng., vol. 37, no. 5, pp. 649–678, 2011.
[8]R. Gupta, S. Pal, A. Kanade, and S. Shevade, “DeepFix: Fixing common C language errors by deep learning,” in Proc. AAAI, vol. 31, no. 1, pp. 1345–1351, 2017.
[9]M. Yasunaga and P. Liang, “Graph-based, self-supervised program repair from diagnostic feedback,” in Proc. ICML, PMLR, vol. 119, pp. 10799–10808, 2020.
[10]F. Zhu, Z. Liu, G. Yang, J. Li, and S. Cheung, “EqFix: Fixing LaTeX equation errors by examples,” in SETTA 2022, LNCS 13649, pp. 89–109.
[11]K. Wang et al., “TexOCR-Bench: Benchmarking page-image-to-LaTeX reconstruction,” in Proc. 64th ACL, Vol. 1, 2026.
[12]H. Wen, Z. Zhang, and Y. Li, “OverleafCopilot: Empowering academic writing in Overleaf with large language models,” arXiv:2403.09733, 2024.
[13]S. Hou et al., “PaperDebugger: Assisting scientific writing, review, and editing,” in Companion Proc. ACM Web Conf., 2026.
[14]J. Tan, K. Zhang, and X. Chen, “Inconsistencies in TeX-produced documents,” in Proc. ISSTA, 2024.
[15]L. Zheng et al., “Judging LLM-as-a-judge with MT-Bench and Chatbot Arena,” Adv. Neural Inf. Process. Syst., vol. 36, pp. 46595–46623, 2023.
[16]A. Panickssery, S. R. Bowman, and S. Feng, “LLM evaluators recognize and favor their own generations,” Adv. Neural Inf. Process. Syst., vol. 37, 2024.
[17]B. Hui et al., “Qwen2.5-Coder technical report,” arXiv:2409.12186, 2024.
[18]Meta AI, “Llama 3.2 model card,” 2024.
[19]E. Kassis et al., “Agent skills for reproducible research agents,” arXiv:2609.00065, 2026.
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main.tex · 7 files
Document
Words1,284
In text1,160
In headers44
Outside text80
Headers7
Figures2
Math inline6
Math displayed1
Characters8,946
Lines95
Proofreading · main.tex
Issues7
Spelling3
Grammar & style4
Renamed to "tab:main-results" (2 edits in 1 file)
Exported main.pdf
強力な研究ハーネス
最初の引用から、最終版の PDF まで。
下書き、コンパイル、校正、検索、引用、レビュー、公開。別々のツールをつなぎ合わせる必要はありません。Oleafly はソース、プレビュー、参考文献、AI、Git 履歴を、あなたが管理するひとつのワークスペースにまとめます。
paper.pdfmain.texlocal ✓
手元のものを取り込む
テキストベース PDF を編集可能な LaTeX にローカル再構成、管理 Pandoc で DOCX 変換、ページ写真をビジョンモデルで書き起こし。その後の AI 整形は任意。
main.tex→PDFDOCXHTML
行き先へ書き出す
コンテキストメニューから PDF、DOCX、HTML、Markdown などを書き出せます。プロジェクト全体をソース ZIP としてダウンロードすることもできます。
-- NORMAL --:wq
accent · themes · layouts
自分好みに
Vim モード、ライト/ダーク、アクセント色、7 つのレイアウト、編集可能なショートカット。エディタは手に合うべきです。
CodeVisual
\section{Results}
We find that \textbf{local-first}
editing \emph{wins} in every
benchmark we ran.
3 Results
We find that local-first editing wins in every benchmark we ran.
unsupported LaTeX stays visible, never dropped
コードでもビジュアルでも
LaTeX や Markdown をビジュアル編集へ：見出し、リスト、太字、リンクを直接編集。両モードで連動 undo。未対応コマンドは黙って消さず raw ブロックとして残し、ソース往復は非破壊。
main.tex
\section{Results}
We evaluate on
\cite{gatto24}
PDF
単語単位の SyncTeX
PDF の単語を ⌘-クリックするとソースの同じ語へ。⌘⇧J で戻る。
F2 · rename symbol
intro.tex \ref{fig:pipelinefig:pipelinefig:arch}
results.tex \ref{fig:pipelinefig:pipelinefig:arch}
コードインテリジェンス
定義へ移動、参照検索、ラベル・引用キー・マクロを全ファイルでリネーム。
…shown in Figure 3. /fig
/figure
/table
/cite
スラッシュコマンド
/ で Notion 風メニュー：/figure、/table、/section、/cite、/math。
doi:10.1145/3576915.3616679
@inproceedings{gatto24,
title = {Raft-based KV stores}, …
title = {Raft-based KV stores}, …
\cite{gatto24} inserted ✓
DOI で引用
DOI・arXiv id・URL を貼る。BibTeX を取得・重複排除し \cite を挿入。
library.bibpapers.risendnote.xmlzotero.rdf
→ references.bib
142 entries · 9 duplicates removed ✓
ライブラリごと取り込む
BibTeX・RIS・EndNote XML・Zotero RDF を一括。.bib に載る前にプロジェクトと重複排除。
⌘Krecompile
▸ Recompile document
Set recompile delay
Recompile on save
コマンドパレット
⌘K であらゆる操作をあいまい検索：プロジェクト、ファイル、設定など。
モデルを選ばない AI
好みのモデルプロバイダを接続するか、Ollama をローカルで実行するか、インストール済みの CLI エージェントをそれぞれのアカウントで使えます。承認モードは作業内容に合わせて設定できます。エディタは AI なしでも動作します。
“raft consensus in KV stores”
alphaXiv · Ongaro & Ousterhout, 2014
DOI verified via Crossref ✓
調査をすぐ手元に
エージェントが alphaXiv と OpenAlex を検索し、本文を取り、文献に入る前に Crossref で引用を検証。
The experimentexpermentexperiment ran offline.
experiment · Hunspell, on-device
内蔵の校正機能
Hunspell と Harper でスペルと文法をローカルで校正します。Oleafly は LaTeX コマンドと数式を無視するので、チェックは本文に集中します。
◈ task: literature review
✓ Read project sources
✓ Verify paper identifiers
Review reading-list.md
研究に専用のワークスペースを
タスクの概要を書いてエージェントを選べば、エージェントが隔離されたコピーで作業する間も執筆を続けられます。アクティビティを追い、変更されたファイルをプレビューし、必要な結果を適用します。
page 1page 214page 487600 pages ✓
still smooth
1 ページから 600 ページまで
1 枚の履歴書でも書籍級の学位論文でも、エディタと仮想化 PDF は滑らか。
本当に読むための PDF ビューア
プレビューを副画面へ、見開き、夜間の色反転、全画面プレゼン。
+−
ダイアグラムコンポーザー
図を説明すると AI が TikZ を書き、単独コンパイルし、描画を見て重なりを直す。自分で形と辺を描いても生成 TikZ と往復。透明高解像 PNG をオフライン出力。
main.texpaper.typnotes.md
one library · one Git history
3 エンジン、1 ワークスペース
LaTeX・Typst・Markdown のプロジェクトを同じライブラリで管理。プロジェクトごとにコンパイラを選び、同じプレビュー、参考文献、バージョン管理、AI ツールを使えます。
\begin{document}Hello, world.PDF compiled ✓
LaTeX エディタ
LaTeX・Typst・Markdown で執筆し、ライブ PDF プレビューと単語単位の SyncTeX を使えます。コンパイラは同梱なので、TeX セットアップもビルドキューも不要です。
edit→compile→verify ✓
AI アシスタント
アシスタントにソース編集、プロジェクトのコンパイル、PDF の確認を任せられます。モデルプロバイダ、Ollama、インストール済みの CLI エージェントを接続。長い作業は研究タスクに任せて、あなたは執筆を続けられます。
doi:10.1145/3576915@article{gatto24} ✓
参考文献マネージャ
BibTeX・RIS・EndNote・Zotero を自動重複排除付きでインポート。DOI や arXiv ID を貼り付ければ、Crossref で検証済みの参考文献を作成できます。
1 page ✓fork ×3 · per application
履歴書作成ツール
ATS 対応の履歴書テンプレートから始め、抽出テキストを確認し、応募ごとに合わせたフォークを作成。以前のバージョンも失われません。
+42 −7
バージョン管理
コンパイル成功後には変更されたソースのチェックポイントを自動作成。Git で意図したコミットを行い、リビジョンを比較し、同じワークスペースから GitHub へプッシュできます。
PDFDOCXHTMLZIP
PDF ツールキット
長い PDF を読み、テキストベース PDF の多くを LaTeX に再構成し、PDF・DOCX・HTML、投稿用のソースアーカイブとして書き出せます。
研究ワークスペース
一つの研究タスクを、最初から最後まで。
エージェントに的を絞ったタスクを任せ、提案された編集を確認し、準備ができたらコンパイルしましょう。成果物はプロジェクトに残ります。
θt+1=θt−η∇θL
Typst
$ theta_(t+1) = theta_t - eta nabla_theta cal(L) $- 01研究プロジェクトを作成原稿・資料・研究タスクを置く場所から始めます。
- 02エージェントにタスクを一つ任せる必要なファイルを選び、変更したい内容を伝えます。
- 03変更提案をレビューする差分を読み、どの編集を原稿に取り入れるか決めます。
- 04コンパイルして成果を残すPDF を確認し、あとで続きから再開。タスクはプロジェクトに残ります。
Oleafly睡眠と記憶ワークフロー例
新しい文書を始める
この原稿の始め方を選べます。どの部分も、後から自由に変更できます。
研究プロジェクト
節構成、資料、レビューノートを備えた研究フォルダと、研究問いを定義する最初のタスク。
プロジェクトをインポート
手元にある原稿を続けます。アーカイブ、Word や Markdown のドラフト、GitHub リポジトリなど。
テンプレートを使う
学術論文・学位論文・レポート・ポスター・講演用のレイアウトから始めます。
新規研究プロジェクト
開始時の構成を選び、Oleafly が作成する前に各ファイルを確認できます。
プロジェクト名
睡眠と記憶
文書エンジン
LaTeX
研究スターター
論文
最初の研究タスク
研究問いを定義し、論文のアウトラインをドラフトします。
プロジェクトプレビュー
- analysis
- figures
- research
- review
- main.tex
- references.bib
\documentclass{article}
\begin{document}
\section{方法}
…
\end{document}新規研究タスク
成果物は、レビューして適用するまで別のワークスペースに保持されます。
開始点
白紙のタスク
タイトル
方法の記述を明確にする
指示
review/notes.md を使って、方法の段落でどの回答を除外したのかを明確にしてください。main.tex への編集を提案してください。結果は変更しないでください。
エージェントとモデル
Codexデフォルトモデル
方法の記述を明確にする
要レビューCodexたった今
review/notes.md を使って、方法の段落でどの回答を除外したのかを明確にしてください。main.tex への編集を提案してください。結果は変更しないでください。
アクティビティレビュー出力
ファイル変更1 ファイル変更
適用前に、選択した各ファイルをプレビューできます。
✓
main.tex変更済み12
\section{方法}13−不完全な回答は除外した。
13+主要アウトカムを欠く回答は除外した。
14
コードビジュアル
再コンパイルmain.tex11
…12
\section{方法}13
主要アウトカムを欠く回答は除外した。14
15
\end{document}ログ✓ PDF コンパイル済み100%
睡眠と記憶
方法
主要アウトカムを欠く回答は除外した。
1
プロジェクトファイルと研究タスクは、いつも一緒に。
研究ループ
調査し、書き、検証し、公開する。
文献検索から投稿まで、文脈を失わずに進めます。Oleafly はソース、参考文献、図、コンパイラ出力、履歴を、中身を確認できるひとつのワークフローにまとめます。
τ=JTF
Typst
$ bold(tau) = bold(J)^T bold(F) $- doi:10.1145/3576915@article{gatto24} ✓1
調査
オープンな研究ソースを検索し、DOI と arXiv ID を BibTeX に変換し、文献リストに入る前に引用を検証します。
- \section{Results}2
執筆
テンプレート、プロジェクト全体のコードインテリジェンス、承認コントロール付きの任意の AI アシスタントを使って、LaTeX・Typst・Markdown で執筆します。
- compiled · 0 errors ✓3
検証
ローカルでコンパイルし、わかりやすいエラーを読み、ソースと PDF の間を行き来できます。プリフライトは、パーサーとスクリーンリーダーが抽出する内容を示します。
- PDFarXivGitHub ↑4
公開
PDF・DOCX・HTML を書き出し、投稿用にソースをパッケージ化し、作業を始めたのと同じフォルダから GitHub へ push します。
他製品との比較
ほかのすべてを置き換える、唯一の研究ワークスペース。
執筆、コンパイル、校正、文献検索、引用管理、PDFレビュー、編集の自動化、論文の提出まで、すべてを1か所で。Oleaflyは研究に必要なツール一式を、無料のオープンソースアプリに集約します。ほかの選択肢に何が足りないのか、以下でご確認ください。 ドキュメント.
| Oleafly | OpenAI Prism | Overleaf | Typst Web App | Curvenote | LyX | TeXstudio | Texifier | VS Code + LaTeX | Paperpal | Writefull | 履歴書作成ツール | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 所有権とアクセス | 価格 | 無料かつオープンソース。Oleaflyのサブスクリプションは不要 | ChatGPTアカウントがあれば無料 | 無料プランあり。有料プランでは機能を追加 | 無料プランあり。Proは別料金 | 無料プランあり。有料プランでは非公開プロジェクトを追加 | 無料かつオープンソース | 無料、GPL | 有料。無料デモあり | 無料。AIには利用上限または有料プランあり | 無料プランあり。Primeは別料金 | 無料プランあり。Premiumは別料金 | 製品により異なる。大半はサブスクリプション制 |
| アカウント不要 | はい | いいえ | いいえ | いいえ | いいえ | はい | はい | はい | はい | いいえ | いいえ | 製品により異なる | |
| オフライン対応 | 執筆、校正、プレビュー、コンパイルをオフラインで実行可能 | 非対応。クラウドワークスペース | 非対応。クラウドサービス | 非対応。Webアプリには接続が必要 | 非対応。エディターはクラウドサービス | はい | はい | はい | はい | いいえ | いいえ | ほとんど非対応 | |
| ファイルをローカルに保存 | 自分が所有する普通のローカルフォルダー | クラウドストレージ | クラウドストレージ | クラウドストレージ。コンパイラーはローカル実行可能 | クラウドストレージ | はい | はい | ローカルファイルとファイルプロバイダー | はい | クラウドサービスとエディター連携 | ファイルはWordまたはOverleaf内に保持 | 通常はクラウドストレージ | |
| バージョン履歴 | 保存されたコンパイルチェックポイントに加え、Gitのコミット、差分、復元 | クラウド上のリビジョン | 有料プランで完全な履歴を提供 | ProでGit同期に対応 | 組み込みのバージョン履歴 | Gitなどのバージョン管理 | 手動またはSVN | ファイルプロバイダーまたはGitツールを使用 | 自分で設定する組み込みGit | 文書化されていない | ホストアプリの履歴を使用 | 製品により異なる | |
| リアルタイム共同編集 | 近日提供予定。人数無制限の共同編集。セルフホスト型 | 共同編集者数は無制限 | 組み込み。上限はプランにより異なる | 組み込み | リアルタイム編集とコメント | リアルタイム共同編集なし | リアルタイム共同編集なし | リアルタイム共同編集なし | Live Share経由 | Word、Google Docs、またはOverleafを使用 | WordまたはOverleafを使用 | 製品により異なる | |
| クラウド同期 | 近日提供予定。ファイルを自分のマシンに保持したままブラウザーからアクセス | クラウドワークスペース | クラウドワークスペース。有料でGitおよびDropbox同期に対応 | クラウドワークスペース。ProでGit同期に対応 | クラウドプロジェクトと連携型出版 | Gitまたは任意の同期サービスを利用 | Gitまたは任意の同期サービスを利用 | Texifier Connectによるリアルタイム同期 | Gitまたは任意の同期サービスを利用 | クラウドワークスペース | WordまたはOverleaf内で動作 | 通常はクラウドストレージ | |
| チームスペース | 近日提供予定。テンプレート、ライブラリ、レビューフローを共有 | 人数無制限で共同利用できる共有プロジェクト | グループ・機関向けプラン | 共有プロジェクト。組織向けオンプレミス版も提供 | チームワークスペースとアクセス制御 | 組み込みのチームスペースなし | 組み込みのチームスペースなし | Texifier Connectを介した共有プロジェクト | GitHub OrganizationsとLive Share | 機関向けダッシュボードとライセンス管理 | 機関向けアクセス | 製品により異なる | |
| 執筆と出版 | 文書エンジン | 1つのワークスペースでLaTeX、Typst、Markdownに対応 | LaTeXのみ | LaTeXのみ | Typstのみ | MyST MarkdownとJupyter | LaTeXとDocBook | LaTeXのみ | LaTeXとMarkdown | 拡張機能経由 | Word、Google Docs、Web、Overleaf | WordとOverleaf LaTeX | 履歴書形式のみ |
| TeXのセットアップ | TectonicとTypstを同梱。TeXのインストール不要 | インストール不要。クラウドでコンパイル | インストール不要。クラウドでコンパイル | TeXのインストール不要 | TeXのインストール不要 | TeXディストリビューションのインストールが必要 | TeXディストリビューションのインストールが必要 | 組み込み組版エンジンとパッケージマネージャー | 拡張機能とTeXのインストールが必要 | LaTeXにはOverleafを使用 | LaTeXにはOverleafを使用 | TeXのセットアップ不要 | |
| コード編集＋ビジュアル編集 | ソース編集とビジュアル編集をロスレスで相互変換 | ソース編集とプレビュー | ソース編集とリッチテキストモード | ソース編集とライブプレビュー | LaTeX数式に対応したビジュアルエディター | 構造化ビジュアルエディター | ソース編集のみ | ソース編集とPDFプレビュー | ソース編集のみ | ビジュアル型ライティングアシスタント | インラインの文章改善提案 | ビジュアル編集のみ | |
| プロジェクト全体のコードインテリジェンス | 自動補完、定義・参照の検索、プロジェクト全体の名前変更 | エディターとAI支援 | 自動補完と参照検索 | エラー表示、自動補完、Assists | 構造化された参照と数式 | ラベル、参照、アウトライン、自動補完 | 構造・参照チェッカー | 自動補完、アウトライン、参照、検索 | LaTeX Workshop IntelliSense | LaTeXコードツールなし | LaTeXコード向けTeXGPT | なし | |
| ソースとPDFの同期 | ソースとPDFを単語単位で双方向同期 | PDFを即時プレビュー | ソースとPDFの同期 | ライブプレビュー | ソースとPDFの同期なし | ソースと出力のプレビュー | ほぼ単語単位 | ソースとPDFの同期 | 順方向・逆方向のSyncTeX | なし | Overleafのプレビューを使用 | なし | |
| PDF閲覧モード | 組み込みビューアーに加え、別ウィンドウ表示、見開き、ダーク、リーダーの各モード | 組み込みプレビュー | 組み込みプレビュー | 組み込みプレビュー | 文書プレビュー | PDFビューアーを使用 | 統合ビューアー | 統合PDFビューアー | 色反転モード付き拡張ビューアー | PDFとのチャット | PDFリーダーなし | 文書プレビュー | |
| 文書テンプレート | 100種類以上のスターター、再利用可能なカスタムテンプレート、AIによるテンプレート生成 | 文書化されていない | 出版社テンプレートの大規模ギャラリー | Typst UniverseとProの非公開テンプレート | 有料プランでプロ向けテンプレートを提供 | 組み込みの文書クラス | 組み込みテンプレートとユーザーテンプレート | 文書化されていない | スニペットとコミュニティテンプレート | 文書化されていない | なし | 中核機能 | |
| プロジェクトと文書のインポート | ファイルとZIPに加え、PDF、DOCX、Markdown、画像、フォルダーに対応 | フォルダー、ZIP、またはドラッグ＆ドロップ | ファイル、ZIP、有料の外部連携 | LaTeX、Word、Markdownを変換 | Jupyterコンテンツと参考文献をインポート | LaTeXなどのテキスト形式 | ローカルのソースファイルを開く | ローカルフォルダーとクラウドファイル | ローカルのソースファイルを開く | PDFと文書のアップロード | Word文書とOverleaf文書 | 通常はPDFまたはDOCX | |
| エクスポート形式 | PDFとソースZIPに加え、DOCX、HTML、Markdownに対応 | PDFとプロジェクトファイル | PDFとソースZIP | PDF、PNG、SVG | PDF、Word、LaTeX、Typst、MyST | PDF、HTML、Word、RTF、ePubなど | PDFとソースファイル | PDFとソースファイル | ローカルツールに依存 | Wordワークフロー | 変更履歴付きWord文書 | 通常はPDFとDOCX | |
| 研究とAI | AIモデルの選択 | ホスト型プロバイダー、Ollama、カスタムエンドポイント、インストール済みのCLIエージェント | OpenAIモデルを組み込み | 研究向けAIを組み込み。1日あたりの利用枠はプランにより異なる | コンパイラーAssists | 文書化されていない | なし | なし | なし | Copilot、BYOK、または拡張機能 | Paperpal AI | WritefullモデルとGPT | 組み込み。モデルの選択肢は製品により異なる |
| エディター内のCLIエージェント | Claude Code、Codex、Gemini CLIなど | 文書化されていない | 文書化されていない | 文書化されていない | 文書化されていない | 文書化されていない | 文書化されていない | 文書化されていない | Copilot、Claude、Codex | 文書化されていない | 文書化されていない | 製品による | |
| お持ちのAIサブスクリプションを利用 | 対応するCLIエージェントを通じてClaudeやChatGPTのサブスクリプションを利用 | 文書化されていない | 文書化されていない | 文書化されていない | 文書化されていない | 文書化されていない | 文書化されていない | 文書化されていない | 対応エージェントのサブスクリプション | 文書化されていない | 文書化されていない | 製品による | |
| ローカルAI | ローカルOllama。ホスト型プロバイダーは不要 | なし | なし | なし | なし | なし | 組み込みAIなし | なし | BYOKまたは拡張機能 | なし | なし | なし | |
| 適用前にAIの編集内容を確認 | 承認モード、インラインの編集プレビュー、確認可能なタスク結果 | 保持または取り消し | AIの提案を確認 | 各Assistを確認 | 文書化されていない | 組み込みAIなし | 組み込みAIなし | 組み込みAIなし | 変更内容を確認 | 文章の改善提案を確認 | 変更履歴 | 製品により異なる | |
| AIによるレンダリング結果の検証 | ソースを編集・コンパイルし、レンダリング済みPDFを検証 | 再コンパイルして自分で確認 | 再コンパイルして自分で確認 | コンパイルプレビュー | なし | 組み込みAIなし | 組み込みAIなし | 組み込みAIなし | PDFレビューを伴わないビルドチェック | レンダリング済みPDFのレビューなし | レンダリング済みPDFのレビューなし | なし | |
| 文献・引用検索 | alphaXivとOpenAlexを横断する文献検索とCrossrefによる検証 | 組み込みの文献検索 | 参考文献検索とAIによる引用候補の提案 | 組み込み検索なし | DOIによる引用情報検索 | 組み込み検索なし | なし | 組み込み検索なし | AIまたは拡張機能経由 | 2億5,000万件以上の論文を横断検索 | Writefull Citeが不足している引用を検出 | なし | |
| 研究ワークスペースとタスク | 研究タスク：隔離されたワークスペース、エージェントのアクティビティ、ファイルプレビュー、選択的な適用 | 文書化されていない | 文書化されていない | なし | 文書化されていない | なし | なし | なし | AI拡張機能経由 | なし | なし | なし | |
| 参考文献ライブラリのインポート | BibTeX、RIS、EndNote、Zoteroに対応し、インポート前に重複を排除 | Zotero同期 | 有料プランで参考文献連携に対応 | ProでZoteroおよびMendeley同期に対応 | Zotero、Mendeley、EndNote | BibTeXファイル | BibTeXファイルに対応 | BibTeXファイル | 拡張機能経由 | 引用情報ジェネレーター | ライブラリのインポートなし | なし | |
| 外部AIエージェント向けMCP | MCPツールをアシスタントに接続、またはプロジェクトのツールを外部エージェントに公開 | なし | なし | なし | なし | なし | なし | なし | あり | なし | なし | なし | |
| スペル・文法チェック | LaTeXを認識する文章チェックにより、スペルと文法を非公開かつオフラインで校正 | AI校正 | 組み込みスペルチェックと研究文書に特化したAIフィードバック | 文書化されていない | 文書化されていない | Hunspell、Aspell、類語辞典 | 対話型のスペル・文法チェック | スペルチェック | 拡張機能またはAI経由 | 学術文書向けの文法・スタイルチェック | 学術文書向けの言語フィードバック | 基本チェックの内容は製品により異なる | |
| ビジュアル・AI作図ツール | ビジュアルキャンバスと、レンダリング検証付きのAI生成TikZ | 画像や音声からコードを生成 | 手動のTikZとAIツール | Typstパッケージとネイティブ描画 | インタラクティブなチャートと図 | ビジュアル表作成・画像ツール | エディター支援付きの手動TikZ | 手動のTikZ | 手動または拡張機能 | なし | AIによる表、数式、図の生成 | なし | |
| 品質と投稿 | 投稿前チェック | コンパイル、投稿先要件、ATS、アクセシビリティ、参考文献、プライバシーをチェック | 投稿支援スイートを伴わないAIレビュー | コンパイルチェック | コンパイラーエラーとAssists | SCMS準拠ツール | コンパイラーログと任意のChkTeX | エラーと警告 | わかりやすいコンパイラーエラー表示 | 拡張機能によるLint | 30項目以上の投稿前チェック | 文章品質レビュー | 履歴書チェックのみ |
| 履歴書のATSチェック | 採点式ATSチェックと抽出テキストのプレビュー | なし | なし | なし | なし | なし | なし | なし | 組み込みチェックなし | なし | なし | 製品により異なる。採点基準は不明瞭な場合が多い | |
| アクセシビリティ事前チェック | アクセシビリティチェックとタグ付きエクスポートの準備 | なし | なし | 文書化されていない | 構造化Web出版 | 構造化文書。事前チェックなし | なし | 文書化されていない | 組み込みチェックなし | なし | なし | 製品により異なる |
プライバシーを正確に
既定はローカル。ネットは選択時だけ。
プロジェクトはあなたのマシンに残ります。Oleafly がネットワークを使うのは、あなたが選んだ機能のときだけで、クラッシュレポートは送信前に表示されます。Oleafly アカウントなし。テレメトリなし。 プライバシー.
常にローカル
- プロジェクトファイル、下書き、保存済みチェックポイント、Git 履歴
- 執筆、コンパイル、PDF の確認、プリフライトチェック
- オフラインのスペルチェック、文法チェック、ダイアグラム、任意の Ollama
操作したときだけネットワーク
- ホスト型 AI が受け取るのは、あなたが送ると決めた文脈だけ
- GitHub 同期と文献検索は、リクエストしたときに実行
- パッケージ、テンプレート、フォント、アプリの更新は必要に応じてダウンロード
Edit · Compile · Commit · Verify · Research · Draft · Revise · Proofread · Cite · Search literature · Build bibliographies · Write equations · Draw diagrams · Review papers · Format manuscripts · Check references · Compare versions · Save checkpoints · Prepare submissions · Export PDFs · Import sources · Check grammar · Refine abstracts · Check accessibility ·
main.tex · refs.bib · figures/ · .git/ · LaTeX · Typst · Markdown · BibTeX · AI agents · ACP · MCP · Ollama · Tectonic · SyncTeX · DOI · arXiv · PubMed · OpenAlex · Theses · Journal articles · Conference papers · Literature reviews · Research posters · Writing tips ·
v=Km+[S]Vmax[S]
LaTeX
\[ v = \frac{V_{\max}[S]}{K_m + [S]} \]p2+2pq+q2=1
LaTeX
\[ p^2 + 2pq + q^2 = 1 \]哲学
研究は、それを生み出したツールよりも長く残るべきだ。研究は、それを生み出したツールよりも長く残るべきだ。
Yourthesisshouldn'tneedfivesubscriptions.YourAIshouldn'tguessateditsitneverchecked.Yourreferencesshouldn'ttakeanafternoon.YourfirstPDFshouldn'twaitinabuildqueue.Yourtoolsshouldn'tcostmorethanyourresearch.Onefreeapp.Allofit.
v=Km+[S]Vmax[S]
LaTeX
\[ v = \frac{V_{\max}[S]}{K_m + [S]} \]p2+2pq+q2=1
LaTeX
\[ p^2 + 2pq + q^2 = 1 \]
本気のオープンソース
意図して独立。コミュニティのために開発。
Oleafly はオープンに開発されています。信頼性も製品の一部です。キャンセル可能なコンパイルや安全なファイルパスから、Git 復旧、PDF フォールバック、macOS・Windows・Linux にわたるテストまで。
コードの確認、issue の報告、修正の提供、Oleafly への Star はこちら： GitHub.
よくある質問（FAQ）
乗り換える前に知っておきたいこと。
01Oleafly とは何ですか？+
Oleafly は、論文・学位論文・履歴書などの技術文書向けの無料デスクトップワークスペースです。LaTeX・Typst・Markdown で執筆し、PDF をプレビューし、参考文献を管理し、任意の AI アシスタントを利用できます。プロジェクトはコンピュータ上の普通のファイルのまま残ります。
02本当に無料ですか？+
はい。Oleafly は無料でオープンソースです。デスクトップアプリがサブスクリプションを課すことはありません。有料の AI プロバイダを接続した場合、そのプロバイダの利用料金が発生することがあります。Ollama 経由のローカルモデルに有料 API は不要です。
03どの AI モデルに対応していますか？+
ホスト型モデルプロバイダ、ローカルの Ollama、OpenAI 互換のカスタムエンドポイントを接続できます。Claude Code や Codex など、サポートされた CLI エージェントを、それぞれのインストール済みツールとアカウントで実行することも可能です。ランタイムとモデルはアシスタント内で選びます。
04アカウントは必要ですか？+
いいえ。デスクトップアプリに Oleafly アカウントやログインはありません。プロジェクトは普通のローカルフォルダに置かれます。接続する AI や Git ホスティングサービスには、それぞれの資格情報が必要な場合があります。
05どのツールを置き換えられますか？+
多くのワークフローでは、Oleafly が執筆・校正・コンパイル・引用・PDF・履歴書・AI・Git の個別クライアントを置き換えられます。普通のファイルと GitHub をそのまま扱うので、仕事を独自形式に閉じ込めることなく導入できます。
06既存の論文や履歴書をインポートできますか？+
はい。既存のフォルダを開くか、オンライン LaTeX エディタからの書き出しを含む ZIP をインポートできます。Oleafly がメイン文書を検出します。テキストベース PDF の LaTeX へのローカル変換、DOCX の Pandoc 経由での処理、スキャンや写真のビジョンモデルによる書き起こしに対応します。
07PDF 出力は ATS 対応ですか？+
ATS 適合度はテンプレートと内容次第なので、Oleafly はスコアを保証しません。履歴書テンプレートは選択可能な Unicode テキスト、埋め込みフォント、1 段レイアウト、線形な読み順を採用しています。プリフライトは、PDF を送る前にパーサーが実際に抽出する内容を示します。
08共同編集と同期はどうなりますか？+
現時点でのコラボレーションは Git を使います。GitHub に公開し、Oleafly 内から push と pull を行えます。リアルタイム共同編集、コメント、管理された同期は計画されていますが、まだアプリにはありません。
09オフラインや AI なしで執筆できますか？+
はい。執筆、ローカルコンパイル、PDF レビュー、校正は AI なしで動作します。オフラインにする前に、プロジェクトで必要なパッケージ、フォント、ツールをダウンロードしてください。ローカル AI では、Ollama でモデルをダウンロードし、そのローカルサーバーを接続します。
10私が執筆を続けている間に、エージェントは作業できますか？+
はい。研究タスクを開き、タスクに概要を伝え、エージェントを選びます。エージェントは隔離されたコピーで作業し、あなたは論文を続けられます。アクティビティを追い、変更されたファイルをプレビューし、選んだ結果を適用してください。
E=hν
LaTeX
\[ E = h\nu \]