TikZilla: Scaling Text-to-TikZ with High-Quality Data and Reinforcement Learning
Authors: Christian Greisinger
Steffen Eger
Venue: ICLR 2026 Poster (2026)
Keywords: Natural Language Processing
AI/NLP for Science
Large Language Models
Vision Language Models
Reinforcement Learning
Code Generation
Representation Learning
Primary area: foundation or frontier models, including LLMs

Abstract: Large language models (LLMs) are increasingly used to assist scientists across diverse workflows. A key challenge is generating high-quality figures from textual descriptions, often represented as TikZ programs that can be rendered as scientific images. Prior research has proposed a variety of datasets and modeling approaches for this task. However, existing datasets for Text-to-TikZ are too small and noisy to capture the complexity of TikZ, causing mismatches between text and rendered figures. Moreover, prior approaches rely solely on supervised fine-tuning (SFT), which does not expose the model to the rendered semantics of the figure, often resulting in errors such as looping, irrelevant content, and incorrect spatial relations. To address these issues, we construct DaTikZ-V4, a dataset more than four times larger and substantially higher in quality than DaTikZ-V3, enriched with LLM-generated figure descriptions. Using this dataset, we train TikZilla, a family of small open-source Qwen models (3B and 8B) with a two-stage pipeline of SFT followed by reinforcement learning (RL). For RL, we leverage an image encoder trained via inverse graphics to provide semantically faithful reward signals. Extensive human evaluations with over 1,000 judgments show that TikZilla improves by 1.5-2 points over its base models on a 5-point scale, surpasses GPT-4o by 0.5 points, and matches GPT-5 in the image-based evaluation, while operating at much smaller model sizes. Code, data, and models will be made available.

Published as a conference paper at ICLR 2026
TIKZILLA: SCALING TEXT-TO-TIKZ WITH HIGH- QUALITY DATA AND REINFORCEMENT LEARNING
Christian Greisinger & Steffen Eger University of Technology Nuremberg (UTN) (christian, greisinger, steffen. stt.eessttteeeeeeeettt....c..
ABSTRACT
Large language models (LLMs) are increasingly used assist scientists across di- verse workflows. A key challenge is generating high-quality figures from textual descriptions, often represented as TikZ programs that can be rendered as scientific images. Prior research has proposed variety of datasets and modeling approaches for this task. However, existing datasets for Text-to-Tikk are too small and noisy capture the complexity of TikZ, causing mismatches between text and rendered figures. Moreover, prior approaches rely solely on supervised fine-tuning (SFT), which does not expose the model to the rendered semantics of the figure, often resulting in errors such as looping, irrelevant content, and incorrect spatial rela- tions. To address these issues, construct DaTikZ-Va V4, dataset more than four times larger and substantially higher in quality than DaTikZZ-V3, enriched with L.MM-generated figure descriptions. Using this dataset, train TikZilla, a fam- ily of small open-source Qwen models (3B and 8B) with a two-stage pipeline of SFT followed by reinforcement learning (RL). For RL, we leverage an image en- coder trained via inverse graphics to provide semantically faithful reward signals. Extensive human evaluations with over 1,000 judgments show that TikZilla im- proves by 1.5-2 points over its base models on a 5-point scale, surpasses GPT-4o by 0.5 points, and matches GPT-5 in the image-based evaluation, while operating at much smaller model sizes. Code, data, and models will be made available.
INTRODUCTION
Natural language processing has become an increasingly valuable tool for scientists across do- mains (Bi et al., 2024 Eger et al. 2025), driven by continuous performance improvements in Large Language Models (LLMs) through scaling model size, hardware, and data (Minaee et al., 2025). well as by research expanding multimodal capabilities (Wu et al. (023) and enabling advanced reasoning (Lu et al. 2024). As a result, an increasing number of tools have been developed to sup- port scientists throughout the research process, which range from idea generation (Gottweis al.) 2025) to the full automation of scientific outputs (Lu et al. 2024).. However, these fully autonomous tools are still far from meeting the high scientific standards required for practical use. Achieving such standards involves overcoming complex subtasks, such as generating accurate scientific images based on textual descriptions (Rodriguez et al. 2023 2024 Zou al., 2024).
Graphics programming languages such as TikZ are the de facto standard in academia due to their precision, interpretability and seamless integration the LaTeX ecosystem. However, their steep learning curve and highly varied syntax make them difficult for both humans and L.LMs to mas- ter (Belouadi et al. 2024a). Prior works have attempted to bridge this gap by finetuning L.LMs on caption- TikZ pairs (Belouadi et al. 2024a] 2025). Due to the sparsely available data, Belouadi al. (2025) leverage captioned images without the underlying graphics program available, therefore hav- ing access to much richer dataset. However, these efforts remain limited by noisy captions, lack of executable and standardized TikZ code, as well as a lack direct visual feedback, leaving models prone to low compilation rates, hallucinations, overly long responses, and low-quality outputs.
We address these limitations by constructing DaTikZ- V4, a dataset more than 1.5M instances larger than its predecessor, sourced from arXiv, GitHub, TeX StackExchange (TeX SE), and synthetic data.

---

Published as a conference paper at ICLR 2026
Table I: Exemplary scientific TikZ figures produced by one bascline LLM (GPT-40) and two of our finetuned LLMs (TikZilla-3B and TikZilla-BB-RL) using the prompts from the first column which have been VLM augmented based on the Ground Truth figures in the second column. boxed figures have been rated as very good, annotators. Additional examples are provided in the Appendix (Table
good, O as bad, and as very bad by human and
Prompt
Ground Truth
GPT-4o
TikZilla-3B
TikZilla-BB-
RL
dngres
setar "Noa"" i libad with Shush a miee hi oe Bieecly skanak can bdeed with Sidd prcaiiakes Bakey dos tolo A cuppeni
geo hale haase caver
with "oeaadd "yo "Sa" ootrade "Br. 'id' conset B.: Eack connored code tolinien.
the
mye conaen
the vig litt and rendid rettage leoked coreec va Bicee retiaule
hat quantes cruncoo lage yelane dhencal lndell Aprophens Uunaatie" Furs de channad' sak, nighoard obet bac tosagii lubeid "Maussccca" A dowaward umey kab ls dos to aodde recunglo lahild "Tad"" amees Mact
aythor matargle lihiad
bettoeedd sepaenee of four secuaples conncd the The tho ructants on any anddad 555 Madk at canas and the shove
ackk
cree and Labited by thv mode i Nack Bilow the yana mategl (he gocn pectina thcemter of tae thadueeaa sccangl, wisk sad sad beoodel liveenion ceaieed A Mock mone pa five be crew partee Abose
hppe
Labcld rictanls "Duapa" commendd The lind 365 plooo abov mottaglo..
to and
Berranas har chan bhhied "saa tatad the sop righ coraur Eack yake dustard sanansall be cad cas lie duut sered top (2h22) ian (33.0,
one 1o) The fitredd (iee 2 (08.77 Chess 195. Coea
'Ags petternane vata" (11.0) Mapge 177.0) voea in, HiViaeer All, takk (15117) and Dilwaoe begge vake 1il..
To improve data quality, we introduce an LLM-based debugging pipeline that repairs uncompilable TikZ code, and employ Vision Language Models (VLMs) to generate accurate figure descriptions. Building on DaTikZ-V4, V4, we develop TikZilla, a family of small Qwen-based models (3B and 8B) trained with a two-stage pipeline: Supervised Finetuning (SFT) for syntax alignment, followed by Reinforcement Learning (RL) with a reward model trained on the Image-to- -Tkk task beforehand. We find that this approach substantially improves Text-to-Tikk TikZ generation quality, where even mod- els as small as 3B parameters outperform GPT-4o across automatic metrics and over 1,000 human judgments spanning four baseline LLMs. Table [ shows examples with corresponding human rat- ings. We summarize our key contributions as follows:
Caption Quality Analyisis: We show that widely available captions are insufficient for reconstructing figures.
Scaling Dataset Size: We introduce DaTikZ-V4 with over 2M unique TikZ samples, sourced from newer aiviv submissions and GitHub, quadrupling the scale of prior datasets.
Data Quality Enhancements: We combine (1) improved rule-based filtering (e.g.. dy- namic package inclusion), (2) VLMM-ased scientific figure descriptions, and (3) an LLM debugging pipeline for uncompilable TikZ code.
We finetune an image encoder on the Image-TikZ task using our larger TikZ corpus, providing more semantically meaningful rewards for RL optimization.
Reward Model:
TikZilla Models: We release TikZilla, a family of small open-source Qwen models (3B and 8B). TikZilla outperforms GPT-4o across automatic and human evaluation, and matches GPT-5 in image-hased evaluation, despite operating at much smaller model sizes.
2

---

Published as a conference paper at ICLR 2026
Structural Elements
Yea
Variant
BLEU-47
ROUGE-L1 1
STS1
Length
250
Prequerry
Owas2.5-VI-77
0,068
0.276
0.744
126.3
100-
Qwen2.5-VL VLLBBBB
0.047
0.242
0.719
177.8
0,057
0.264
0.743
141.6
GPT-4o-mini
140.9
GPT 4o
0.089
0.317
0.777
123.5
Figoto
Timsels
OCR
Cotlectio
Human
0,094
0.318
0.815
105.3
Figure 1: Left: human evaluation of caption quality by structural elements and usefulness ratings. Right: BLEU-4, ROUGE-L, STS, and average length for raw captions, VLM -LM-generated descriptions, and human- written descriptions (using other human descriptions references).
2 RELATED WORK
Text-Guided Graphics Program Generation for Scientific Figures Generating vector graphics such as SVG or TikZ is essential in scientific publishing due to their fidelity and interpretability. Early approaches relied on handcrafted heuristics or neural sequence models to approximate im- ages with path primitives (Lopes et al.) 2019 Carlier al. 2020), but these struggled with com- plex scientific figures. More recently, LLM-based methods have emerged: AutomaTikZ (Belouadi al. (2024)) finetunes on caption- caption-TikZ pairs from arviv and TeX SE, while StarVector (Rodriguez et al. 2024) focuses on SVG generation with a dedicated benchmark. Yet for TikZ, dataset spar- sity remains a bottleneck. TikZero (Belouadi et al. 2025) partially addresses this by combining an inverse-graphics model (Belouadi et al.) (024b) with a modality-brideee
adapter (Hu et al.) (202)), distilling supervision from text-image pairs. However, TikZero still depends on noisy captions and cannot finetune its text decoder without paired graphics programs, limiting performance. In con- trast, we construct a dataset over four times larger and of higher quality, pairing TikZ programs with VLM-generated descriptions, enabling small LLMs to be effectively finetuned for Text-to-Tine to-T..Z.
Post-training with Reinforcement Learning Advances in RL such as Group Relative Policy Op- timization (GRPO) (Zhihong Shao (024) allow to more efficiently align LLMs either with hu- man preferences (Ouyang et al.) (2022) or verifiable tasks (Lambert et al.) 2025). For example, RLEF (Gehring et [dl]] iteratively leverages execution feedback for code synthesis. Yoshihara math benchmarks, and VisionR1 (Huang et al.) 2025) ex- tends reasoning capabilities to the multimodal domain. Closest our setting, RLRF (Rodriguez et al. 2025) optimizes SVG code generation via composite rewards assessing code efficiency, se- mantic alignment, and visual fidelity. Our work differs in two ways: we focus on TikZ generation for scientific figures, and we introduce -ppaiic reward model, trained through inverse- graphics (Image-TikZ) -TkZ), which better captures semantics than general-purpose purpose metrics such as CLIP- Score (Hessel et al. 2021 DreamSIM (Fu et al. (023).
et al. (025) enhance LLM reasoning
3 CAPTION QUALITY ANALYSIS
Accurate Text-to- Tixtt-t-TTkkZooooo generation requires captions that specify objects, attributes, and spatial re- lations (Zhang al. 2025). To assess whether existing captions meet this need, we analyzed 200 samples from DaTikZ-V3 V3 with three annotators (Figure left). The annotators checked captions for missing structural elements (e. g. figure type, components, and labels) and judged usefulness on 1-5 Likert scale. Two findings emerged: (i) key details such figure types, components, and labels are often missing, and (ii) most captions received low usefulness scores (1-2). This indicates that raw captions insufficient for faithfully reconstructing scientific figures.
To quantify this further, a human annotator wrote reference descriptions for all 200 figures. We then compared these
descriptions against both the original captions and VLM-generated descriptions using BLEU-4 (Papineni et al.) 2002), ROUGE-L (Lin, 2004), and Semantic Textual Similarity (STS) RReimers & Gurevych] 2019) (Figure right). Across multiple VLMs (Qwen2.5- VL 7B/32B (Bai et al. 2025), Intern VL3 8B/38B (Zhu et al. (025). GPT-4o and GPT-4o-mini (Ope-) nAl al. 2024)), results show that VLMSs produce richer and more faithful descriptions than raw
3

---

Published as a conference paper at ICLR 2026
captions. For example, GPT-4o reaches 0.089 BLEU-4 compared to just 0.003 for captions, and comes close to human-human agreement (0.094 BLEU-4). VLM outputs are also substantially longer (120-170 170 vs. 34 characters), indicating that they capture additional detail necessary for fig- ure reconstruction. These results motivate our use of VL.M-generated descriptions in DaTikZZ-V4. V4. For additional information, we refer to the Appendix/A.1
DATASET
Building on DaTikZ-V3, we introduce DaTikZ-V4, a significantly expanded and refined dataset de- signed to support the training and evaluation of Text-to-TikZ models. The development of DaTikZ- V4 addresses the growing need for both larger and higher-quality datasets, which are critical for surpassing not only proprietary state-of-the-ar models like GPT-5 but also increasingly more capa- ble open-source LLMs such Qwen3.
Data Sourcing To enhance dataset scale, we valuable large-scale
Table 2: Unique TikZ graphics across all DaTikZ versions.
first identify GitHub source of high-quality graphics programs. With over one billion repositories, GitHub hosts a wealth of educational resources, tutorials, theses, books, and personal projects, many which con- tain TikZ code. From this, we clone approx- imately 5,500 repositories containing tex or -pg files with TikZ. content, resulting in over 400,000 unique TikZ samples. This GitHub-only subset is nearly as large as the entirety of DaTikZ- V3, To further expand coverage, we also extend sourcing from arXiv by including data post-2021 to mid 2025. The increasing amounts of arxiv submissions each year allows us source 1M additional samples, resulting in over 2M TikZ graphics in total. Of these, 35.55% originate from sources under permissive Creative Commons licenses (e.g.. CC-BY, CC-BY-SA, CCO) and can be redistributed. 40.03% originate from sources under Nonexclusive Disttribution contain no explicit license information. A comparison of DaTikZ-Va V4 previous releases seen in Table 2
Source
DaTikZ
V2
V3
V4
arXiv
85,656
326,450
407,851
1,471.083
GitHlu
0
0
413.178
TeX SE
29,238
30,609
42,654
97,909
Synthetic
1,957
1,958
2,256
13,514
Curated
981
1,566
3,646
5.196
Total
117,832
360,583
456,407
2,000,880
mmexclusive-Distribution licenses, and the remaining 24.43%
Filtering Beyond traditional tikzpicture environments, we now extract from other environ- ments such as tikz-cd (common in mathematical diagrams) and circuitika (used in elec- tronics). Since individual figures often contain multiple subfigures, we recursively split and extract all subfigure content. Furthermore, we enforce a standardized TikZ. code by wrapping the code in- side the ,document class [tik2] (aakk)(sstata (standalone, environment. Additionally, we implement dynamic package detection approach by using regular expressions to include necessary LaTeX packages (e.g., recognizing cir circuitin ikz from context such as resistor). We also remove any code that depends external files (e.g., \inputf... ... \ince includegraphics f... }), well as all inline comments, to improve compilation rates and reduce noise. Lastly, we apply exact dedu- plication and dismiss all samples where the number of characters is both smaller than 100 and larger than 4000.
LLM Debugging Due the low compilation success rate, especially from arXiv (success rate 31.3%), we introduce an LLM-based debugging pipeline. Given code snippet and its compiler error, an LLM is instructed fix the TikZ code. Using Qwen-32B across our corpus of 1.3M uncompilable TikZ samples, we successfully repair 600K instances in the first pass. This approach substantially boosts the proportion of usable TikZ programs scale.
VLMM-assed Image Description As shown in Section 3 raw captions are often unhelpful for fig- ure reproduction, potentially leading to severe hallucinations. To mitigate this, we employ VLMs to generate precise descriptions of TikZ figures. Using Qwee2.5-VL-77-Inntte
we annotate around 1.3M compilable samples, producing the first large-scale dataset of TikZ paired with se- mantically rich textual descriptions, providing stronger supervision for downstream model training. An overview of our dataset construction illustrated in Figure 2 about prompts and frameworks, we refer to A.2
For ablations and further details
4

---

Published as a conference paper at ICLR 2026
VLM Description
a
You akkznile
Roleshased Fittring
rwhhswayy
Sundandaatiiee
Tix.
Entornal Data Bazzens
Coumara Batsocal
Destepeantiss
Mip Complter Engins
LLM Dabug
sill prordd you
comppodiay sigs
For the Ticl that
sompthor mithort enners
Till cndh.
Figure 2: Overview of the data preprocessing workflow. We start by sourcing TikZ graphics pro- grams primarily from arXiv, GitHub, TeX SE, as well as synthetic data. Next, rulc-based filtering techniques are applied, and the TikZ code is rendered. Uncompilable code undergoes iterative debugging process using LLMs alongside the error messages to attempt error correction. Finally, all compilable code images described using VLMs.
5 METHOD
We train Text-toTTikZ models in two stages: SFT to ground models Tik2ayytax and task-specific token distributions, followed by RL for incorporating feedback from rendered images enforce enhanced visual alignment (Rodriguez et al. 2025). Similar two-stage paradigms have also proven effective in related domains such as code generation and mathematical reasoning, where surface- level syntax is complemented by execution-leva accuracy (Le et al. 2022 Gehring et al. 2025).
Stage 1: Supervised Finetuning Given figure description Fdes and ground truth TikZ sequence
Tik. = (Z1, ... ,T), we minimize the standard autoregressive negative log- log-likelihood:
STFT 0 E (nawo Drta)
log (a
Pett, Feees)
This ensures syntactic validity and prompt alignment. the same time, the model remains unaware of the rendered semantics of the figure, which leads to common errors such as loops, irrelevant content, or incorrect spatial relations.
Stage 2: Reinforcement Learning To address this, we reinterpret the SFT model Poa stochastic policy and apply reinforcement learning with GRPO. For each description, G rollouts (o1,..., ) Pe Zdese) are sampled, each of which is assigned a scalar reward {ri..... rG scored by a reward model, and updated with group-centered advantages A; mr,n(((.)) The std((r:))
GRPO objective we maximize is:
G lodl
Po Tree) Ass
(f) = zabs
min
LG
poas (os Ddese)
1=1
clip
pa(or Pa (oz] Pdoc)
Clow : 1 Ehigh A
B DKI(Po Po
where 33 regulates the KL penalty. We implement the "Dr.GRPO" variant (Liu et al. 2025). which
replaces the response-level normalization by token-level normalization with constant divisor
5

---

Published as a conference paper at ICLR 2026
(the maximum completion length L). This removes the response length bias in TikZ sequences, where longer responses are under-penalized. Furthermore, we apply the "Clip- strategy from DAPO (Yu al. 2025), which decouples the clipping threshold into Gow and Ehigh- This allows more headroom for increasing the probability of low-probability exploration tokens (by rais- ing (high), while still preventing collapse of high-prohability exploitation tokens (by keeping <ow smaller). As in DAPO, we set flow = 0.2 and Ehigh = 0.28. Additionally, we remove scaling the advantages by the standard deviation of the group rewards not introduce bias towards more or less difficult prompts, and mask all samples whose completion was cut by the length cap we find that it increases training stability. Finally, we disable the KL coefficient (B 0) and sample with
temperature=1: 0 and top.p=0......
1) SFT DeTikZity-22 V2 on DaTikZ-V4
2) image
DeTIZIN-
FTEX Compiler Engine
to. ladda ferw,, exctangie, wintear teight-ied (less)) in daae 1).. baade (1 /Wods (2, -1.0 (ovaee 218); (lates) (leen)
DBC
Copn
DaTikZ V4
Frew restaalle to wr aooen 2" the ropenttntty bilie.
Razmi
sachickky (131393 Aoat
Figure 3: Overview of our post-SFT optimization steps. We first fully finetune DeTikZify- con- sisting of an image encoder (IE), linear layer (LL) and LLM decoder (DEC) on our larger DaTikZ- V4 where we then use its enhanced IE to further finetune our LLMs based on the semantic similarity of the embeddings between ground truth and rendered image an online RL setting using GRPO. The IE is kept frozen during RL optimization to mitigate reward hacking.
Rewards Designing reward signals for Text-to-Tike is challenging: they must capture faithful- ness, scientific style, attributes, and spatial relations. Recent work has shown that metrics such as CLIPScore or DreamSim correlate poorly with human judgments as they fail to represent nuances in scientific figures (Belouadi al. 2025) and are prone reward hacking (e.g., embedding text into figures) (Rodriguez et al. 2025
To the best of our knowledge, we propose the first domain-specific reward model for Text-to-TikZ. It builds on the image encoder of DeTikZify-V2 (Belouadi et al. 2024b), originally trained on DaTikZ-V3 for inverse graphics (image + TikZ). DeTikZity consists of an image encoder followed by linear layer and an LLM decoder. By keeping the image encoder unfrozen during training, it incidentally learns to generate good low-dimensional representations of scientific figures in order to accurately reproduce the figure, allowing to utilize it to measure semantic similarity between the embeddings of two scientific figures more accurately. With DaTikz.. V4 providing much larger dataset, retrain DeTikZify-V2 end-to-end, yielding stronger encoder that produces richer, more generalizable embeddings of scientific diagrams. Subsequently, we use the retrained image encoder as our reward model in an online RL environment with GRPO. Both steps are illustrated in Figure Training details are provided A.3
For reward computation, pooled cosine similarity is not available since DeTikZify- outputs patch- level embeddings. We therefore adopt an Earth Mover's Distance (EMD) (Rubner al.) 1998] Kusner et al. 2015) formulation, inspired by test-time scaling approaches in TikZero (Belouadi et al. 2025). p. Given patch embeddings x [ and y = { from ground truth and predicted images, with distance matrix Dij = 1 cos (zi, Uj). ) the similarity reward is defined as
Σ ly j=
Dis
Rsim (Ssim(x,3) (x,y)
Σ x Σ Σ F.
x is the optimal flow matrix that minimizes the transport cost, subject to Σ, Fi,j = 1/ly and Σ, R., = 1//x/. This formulation yields a scalar reward in [o, 1] captur- ing semantic alignment. Finally, we add a format reward to ensure that the TikZ code starts and ends with valid document environments (i.e., (documentclass [tik2] {standa [ lnne), followed by b (document ), and ending with (end(document) }). con-cooforming outputs receive reward of zero
where F E 20

---

Published as a conference paper at ICLR 2026
6 EXPERIMENTS
Experimental Setup For evaluation, we construct contamination-free test set of 1,047 samples from DaTikZ-V4. To prevent overlap with training data, we (i) restrict to post-May 2025 samples, (ii) enforce per-source uniqueness (e.g.. one figure per arXiv paper or GitHub repo, removing the rest from training), (iii) filter with n-gram matching (OpenAl, 2023), and (iv) manual inspection to discard trivial corrupted figures. To avoid model bias, all test descriptions GPT-4o. For RL-tuning, we create DaTikZ-V4-BB uncompilable figures via second LLM debugging step and re-describing them with Qwen2.5-VL- 7B. This provides additional high-quality pairs beyond the training split.
generated by a 160K-sample subset obtained by repairing
Models We benchmark nine LLMs: (i) proprictary GPT-5 and GPT-4o (OpenAl et al. 2024 (ii) open-source Qwen3 (32B, 8B),
YYagg et al. 2025). Qwen2.5 (14B, 3B) Owen et al. 2025), TikZero-Plus- P0B (Belouadi et al. 2025). and Llama3.1-88 (Grattafiori et al. 2024), and (iii) fine-tuned Qwen2.5-3B and Qwen3-8B models. We refer to trained models as TikZilla, with the following variants: TikZilla-3B and TikZilla-8B (SFT only), and TikZilla-3B-RL and TikZilla-8B-RR (two-stage training). In addition, also test RL-only training.
Evaluation Metries We evaluate along four axes: (i) CLIPScore (CLIP) (Hessel al. 202. l) for
text-image alignment, (ii) DreamSIM (DSim) (Fu et al. 2023 for perceptual fidelity, (iii) TeX Edit
Distance (TED) (Kusner al. 2013) for code similarity, and (iv) Compilation Rate (CR) for exe- cutability. We also report average tokens (AT) for efficiency. To avoid
coupling in
our RL ablations, we additionally report DINOScore (DINO) (Caron et al. (021)) and LPIPS (Zhang eet al. 2018). which are independent of our domain-specift reward model. An aggregate score (AVG) is computed as the mean of CLIP/DINO, DSim//.PIPS, and 1-TED (depending on the eval- uation setting). Additional details reported in A.4
RESULTS
Main Results Table 3 reports results on automatic metrics, Our SFT+RL-tuned Qwen models achieve the best AVG performance, with TikZilla-3B-RL. reaching 0.385 and TikZilla-8B-RR 0.384. Both surpass GPT-5 (0.365), despite it being recently released as one of the strongest reasoning LLMs. evaluated with no output length restrictions. Compared to the recently released TikZero- Plus-10B, TikZilla- RB-RL improves by +0.085 on CLIP and +0.334 on DSim, while achieving a 37% higher compilation rate and requiring 261 fewer tokens average. Similar improvements hold for TikZilla-8B RB-RI. These results highlight the effectiveness of our two-stage training process, combining high-quality data with a domain-specific reward model. For qualitative examples with TikZ code, we refer to the Appendix (Figure 15
16 17
and 18
MODEL SIZE AND TRAINING REGIME Interestingly, the smaller Qwen2.5-3B not only closes the gap with Qwen3-8B but even slightly outperforms it once trained with SFT+RL. However, its low baseline (0.202) indicates that it strongly relies on SFT before RL, whereas Qwen3-8B benefits from RL directly (0.251 + 0.357). This suggests that SFT primarily provides syntax grounding for smaller models, while larger models already encode some TikZ knowledge that RL can amplify.
IMPLICIT EFFICIENCY EFFECTS RL consistently improves compilation rates to 95-98% and re- duces token length, indicating more efficient code generation. Unlike prior SVG studies (Rodriguez) et al.) 2025). which required explicit code efficiency rewards, we observe a natural reduction in se- quence length. We hypothesize this stems from our semantic reward model penalizing hallucinated or redundant elements, indirectly encouraging conciseness. A deeper comparison with explicit effi- ciency rewards is left for future work.
We conduct a human evaluation with expert annotators (6 PhD, 2 postdoc, 1 faculty member). Each annotator rated 30 randomized figures/descriptions across 4-5 models, using a 1-5 Likert scale (1 = uncompilable, 5 = publication-rradyy. Two criteria were considered:
Human Evaluation
wttp:iiicoomeemeeeeeeeeeeeee httpe//wwiiiied/eeeeeeeeee gpt http://///ppp---ssstteeeerr sysssm-strmmrrr

---

Published as a conference paper at ICLR 2026
Table 3: Results of all models on the evaluation subset of DaTikZ-V4, Both of our models trained with SFT and RL perform best, while GPT-5 and Qwen3-32B are the best proprietary and open- source LLMs respectively. Bold denotes best-performing while underline is second-best
LLM
CLIP
DSimt
TED1
AVGI
CRT
AT
GPT-5
0.181
0.679
0.765
0.365
88%
480
GPT-4o
0.147
0.580
0.767
0.320
78%
404
Qwen3-32B
0.149
0.583
0.765
0.322
79%
416
0.140
0.566
0.778
0.309
77%
472
Qwen2.5-14B
0.132
0.511
0.765
0.293
71%
376
1ii7Zen--luu-1088
0.104
0.397
0.807
0.231
61%
742
L-8m
0.088
0.339
0.786
0.214
50%
529
Qwen2.5-BB
0.081
0.315
0.789
0.202
52%
387
Qwen2.5-3B (+RL)
0.098
0.505
0.795
0.269
98%
234
TikZilla-3B
0.161
0.613
0.802
0.324
89%
672
TikZilla-3B-RR
0.189
0.731
0.766
0.385
98% %
481
Qwen3-8B
0.106
0.421
0.775
0.251
63%
412
Qwen3-8B (+RL)
0.169
0.669
0.768
0.357
98%
393
TikZilla-8B
0.158
0.602
0.793
0.322
86%
729
TikZilla-8B-RR
0.185
0.727
0.761
0.384
95%
459
(i) textual alignment (does the output follow the provided description?) and (ii) image alignment (does the output match the original ground-truth figure?). Annotator agreement was strong (Cohen's 0.814 for text, 0.794 for image). Full details are provided A.5
RESULTS Figure shows that GPT-5 achieved the highest textual score (4.18) and tied with our TikZilla-8B-RI. on image evaluation (3.48 vs. 3.46). TikZilla-3B-RRL also performed competitively (3.40 text, 3.30 image). Reinforcement learning substantially boosted both Qwen models (+0.75 and +0.67 points), while base models lagged 1.5-2 points behind. Interestingly, most models (especially GPT-5) scored higher on the textual evaluation than on the image evaluation. We hypothesize two possible explanations: (i) if VLM-generated captions omit or misrepresent visual details, models may score highly on textual alignment (satisfying the description) but lower on image alignment (failing to match the true figure). (ii) Human annotators may apply stricter criteria when comparing against ground-truth images than when comparing against text. Disentangling these two factors remains an open question, which leave for future work.
GPF44
Owes2.5520
T02.3
Soars
Imsage
Figure 4: Average Likert-scale ratings (1-5, higher is better) with 95% confidence intervals for eight LLMss, evaluated under two settings: (i) alignment with textual descriptions and (ii) alignment with ground-truth images. Combined scores are shown as the average of both settings.
CORRELATION WITH METRICS Finally, we compute correlations between automatic metrics and human scores using Spearman's, p. CLIP (PCLIP 0.260) and TED (PI-TED 0.307) show
8

---

Published as a conference paper at ICLR 2026
Table 4: Ablations on input data quality and debugging. VLM-based descriptions consistently out- perform captions, while mixing oversampling captions brings no gains. Our LLM-based debug- ging step yield improvements.
LLM
CLIP
DSimt
TED1
AVGT
CRT
AT
GPT-40cap.
0.105
0.469
0.763
0.270
80%
337
GPT-44dew::
0.143
0.568
0.767
0.315
76%
416
Qwen2.5-3B (+SFT cap.
0.134
0.511
0,809
0.279
79%
768
Qwen2.5-3B (+SFT desc.)
0.141
0.530
0.805
0.289
85%
651
Qwen2.5-BB (+SFT desc. cap.
0.154
0.589
0.804
0.313
85%
735
Qwen2.5-3B (+SFTdeso.
cap.
0.157
0.599
0.799
0.319
89%
787
Qwen2.5-3B (+SFT no debug
0.138
0.534
0.809
0.288
79%
762
TikZilla-3B
0.161
0.613
0.802
0.324
89%
672
Table 5: Ablation of our domain- reward models compared to CLIP Img and DreamSIM. The DaTikZ-V4 trained encoder achieves the strongest AVG performance.
LLM
DINO
LPIPSt
TED
AVG1
CR
AT
Qwen2.5-3B (4SFT+RLcLP...
0.751
0.418
0.779
0.463
97%
537
Qwen2.5-3B (+SFT+RLpa pSim)
0.759
0.439
0.777
0.474
99%
494
Qwen2.5-3B (+SFT+RL) R (DaTii
0.789
0.440
0.768
0.487
97%
496
TikZilla-BB-RL
0.809
0.451
0.766
0.498
98%
481
weak, DSim moderate (ppsim. 0.586), and our reward model strong (PR... 0.714) correlation. This validates our design of domain-specifie reward model aligned with human judgment.
Ablations We perform series of ablations to isolate the contribution of each component in our pipeline. These include: (i) the impact of input quality and LLM-based debugging (Table (ii) the effect of different reward models (Table 5). and (iii) the influence of dataset scale (Figure 5) We additionally evaluate TikZilla in an out-of dit-----sttitttiinn (OOD) setting using the SPIQA (Pramanick
al. 2024) benchmark (Table
CAPTIONS VS. DESCRIPTIONS VLM-generated descriptions consistently outperform raw cap- tions. At inference, GPT-4o achieves 0.315 AVG with descriptions versus 0.270 with captions, confirming our earlier analysis that captions are often unhelpful for figure reproduction. Examples are shown in Figure i4ji the Appendix. For training, Qwen2.5-3B also benefits from descriptions (0.289 vs. 0.279), though the gap is smaller, likely due to the limited caption subset (468k samples). Mixing captions//decriptions (desc. V cap.) and oversampling descriptions with captions (dese. cap.). degrade performance, suggesting that low-quality captions dilute training even when more data is added.
LLM-BASED DEBUGGING Models trained only first-try compilable code perform consider- ably worse than those trained on the full dataset (0.288 vs. 0.324), highlighting the necessity of our L.LM-based debugging pipeline to increase the size of our usable TikZ. corpus.
REWARD MODEL TRAINING We compare our domain-specific reward Rsim against CLIPScore (image-image) and DreamSIM (Table 5 We find that all reward functions improve over the SFT baseline, but Rym achieves the strongest AVG performance. DreamSIM performs slightly better than CLIPScore. Retraining DeTikZify-V V2 on DaTikZ-V4 yields a stronger reward model (0.389 vs. 0.375). Correlations with human judgments also improve (PRsim = 0.71 714 vs. 0.698), confirming that larger-scale scientific data produces more reliable image encoders for semantic evaluation.
DATASET SIZES To understand how performance scales with data, we supervised fine-tune Owen2.5-3B on subsets of DaTikZ-Va V4 at 75%, 50%, 25%, 12.5%, and 6.25% the full dataset (Figure 5). Performance increases sharply at small data scales (from 0-25%), after which improve- ments become more gradual from 25% up to the full dataset, suggesting that further data scaling (e.g., synthetic data) remains promising direction for improving text-to-TikZ. generation.

---

Published as a conference paper at ICLR 2026
Compilation rale mC %
Averoge Lokeses (Z2)
700
Smore chigber
CLEP DSám
75
600
0.
69
500
04
55
400
40
60
50
100
40
80
100
Draction
fruction
Figure 5: SFT on Qwen2.5-3B with different dataset scales (75%, 50%, 25% 12.5%, and 6.25%).
Table 6: Model performance on the SPIQA benchmark.
LLM
CLIP
DSim
CR
AT
GPT-5
0.115
0.432
60%
1239
GPT-4o
0.098
0.326
48%
748
Owen3-Coder-30B-11
0.102
0.349
58%
1000
Qwen2.5-3B
0.038
0.117
24%
772
TikZilla-3B
0.114
0.374
64%
1170
TikZilla-3B-RL.
0.193
0.637
97 %
765
Qwen3-8B
0.070
0.228
37%
781
TikZilla-8B
0.131
0.428
70%
1184
TikZilla-BB-RL
0.174
0.584
90%
809
OOD DATA To assess TikZilla's robustness under distribution shift, we evaluate it on the SPIQA dataset. SPIQA figures are typically not generated TikZ. originate from tools such as mat- plotlib, ggplot2, and MATLAB, often containing multi-pane layouts, overlays, and varied diagram- matic structures. This makes SPIQA meaningful OOD benchmark from a structural-complextty perspective. For evaluation, use all samples only including figures from the test-A and test-B splits and generate textual descriptions with GPT-4o, yielding 397 test cases. Since ground-truth TikZ code unavailable, ble SPIQA exhibits substantially longer sequences, lower compilation rates, and overall lower performance, as expected due its non-TikZ origin and higher visual complexity. Notably, both TikZilla-8B--RL and especially TikZilla-3B RL outperform GPT-5 on this OOD benchmark.
omit TED in this evaluation. Relative to the DaTikZ-V4 test split (Ta-
8 CONCLUSION, LIMITATIONS, AND FUTURE WORK
We presented DaTikZ- V4, lcrge-scale, high-quality dataset for Text-to- TikZ, and a two-stage train- ing framework combining SFT with RL. Our key contributions arXiv and GitHub with LLL--based debugging to improve compilability, VL.M-generated descrip- tions that overcome the low quality of raw captions, and domain-specift reward model derived from an inverse-graphics image encoder, which correlates strongly with human judgments of figure quality. Building these components, els that achieve near-pertt pear-perfect compilation rates and even surpass much larger commercial systems such as GPT-4o across automatic and human evaluation. Beyond technical performance, TikZilla demonstrates the feasibility of building reproducible and efficient text-to-image generation systems with small-scale open models, reducing reliance on costly proprietary solutions.
richer dataset sourced from
introduced TikZilla, a family of small Qwen-based mod-
A key limitation is that our figure descriptions are generated automatically by VLMs, which may introduce omissions or hallucinations. This can bias training and, in rare cases, reward optimization may reinforce errors when descriptions diverge from figures. More reliable annotation methods and fine-grained reward functions are therefore crucial directions for future work. Beyond addressing these issues, future work should focus designing automatic metries with stronger alignment to human perception, and extending our approach to other structured generation tasks (c.g.. LaTeX tables, CAD, flowcharts), where programmatic fidelity is critical.
10

---

Published as a conference paper at ICLR 2026
REFERENCES
Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang. Jun Tang. Humenn Zhong, Yuanzhi Zhu, Mingkun Yang, Zhaohai Li, Jianqiang Wan, Pengfei Wang, Wei Ding, Zheren Fu, Yiheng Xu, Jiabo Ye, Xi Zhang, Tianbao Xie, Zesen Cheng, Hang Zhang, Zhibo Yang, Haiyang Xu, and Junyang Lin. Qwen2.5-vl technical report, 2025. URL pettps://azxie org/ http://bbs2220...532 13923
Jonas Belouadi, Anne Lauscher, and Steffen Eger. AutomaTikZ: Text-guided synthesis of scientific vector graphics with TikZ. In The Twelfth International Conference on Learning Representations, 2024a. httttipss//pppeeveieeeeess http:/iwwwweetffooooo fortt Fo
Jonas Belouadi, Simone Paolo Ponzetto, and Steffen Eger. DeTikZify: Synthesizing graphics pro- grams for scientific figures and sketches with Tikz. In The Thirly-eighth Annual Conference on Neural Information Processing Systems, 2024b. URL BRLhttts: /oppnreview.w.. wew.f/rrrummm
Ld-beVLE OCOjcc
Jonas Belouadi, Eddy 1lg. Margret Keuper, Hideki Tanaka, Masao Utiyama, Raj Dabre, Steffen Eger, and Simone Paolo Ponzetto. Tikzero: Zero-shot text-guided graphics program synthesis, 2025. URL htt.tttts///xxii..ooomm http://axw....or//a//5.......... 11509
Lucas Beyer, Andreas Steiner, André Susano Pinto, Alexander Kolesnikov, Xiao Wang, Daniel Salz, Maxim Neumann, Ibrahim Alahdulmohsin, Michael Tschannen, Emanuele Bugliarello, Thomas Unterthiner, Daniel Keysers, Skanda Koppula, Fangyu Liu, Adam Grycner, Alexey Gritsenko, Neil Houlsby, Manoj Kumar, Keran Rong, Julian Eisenschlos, Rishabh Kabra, Matthias Bauer, Matko Bošnjak, Xi Chen, Matthias Minderer, Paul Voigtlaender, Ioana Bica, Ivana Balazevic, Joan Puigcerver, Pinelopi Papalampidi, Olivier Henaff, Xi Xiong, Radu Soricut, Jeremiah Harm- sen, and Xiaohua Zhai. Paligemma: A versatile 3b vlm for transfer, 2024. URL https:
farxiv.org abs/ 22202
Zhenyu Bi, Minghao Xu, Jian Tang, and Xuan Wang. Al for science in the era of large language models. In Jessy Li and Fei Liu (eds.), Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Tutorial Abstracts, pp. 32-38, Miami, Florida. USA, November 2024. Association for Computational Linguistics. doi: 18.....5/1111000 http55/3/112222mmmmmmm wttp///22..mmn-uttttemee www..mmmttttutrils...... URL wwwpantwwwwwooooo./ooee2oeeem wwgg/////22222 www.eeeetpptttttall.......
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhari- wal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert- Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. Language models are few-shot learners. Proceedings of the 34th International Conference on Neural Information Processing Systems, NIPS '20, Red Hook, NY, USA, 2020. Curran Associates Inc. ISBN 9781713888 13829546..
Alexandre Carlier, Martin Danelljan, Alexandre Alahi, and Radu Timofte. Deepsvg: A hierarchical generative network for vector graphics animation. In H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin (eds.), Advances in Neural In- formation Processing Systems, volume 33, pp. 16351-16361. Curran Associates, Inc., fflles/paper//22/// pdf
2020. URL https::/ nwuriig.nneuiiie cctpsscccppppppeeeeeeeeeeeee http:///.....ooee///////ooaeaa/a
File/bcc htte://eeeeolll22222556eee360000
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the International Conference on Computer Vision (ICCV), 2021.
Steffen Eger, Yong Cao, Jennifer D'Souza Andreas Geiger, Christian Greisinger, Stephanie Gross, Yufang Hou, Brigitte Krenn, Anne Lauscher, Yizhi Li, Chenghua Lin, Nafise Sadat Moosavi, Wei Zhao, and Tristan Miller. Transforming science with large language models: A survey ai assisted scientific discovery, experimentation, content generation, and evaluation, 2025. URL https://arrs http://wwww..rr///////////.0//0/ 0515

---

Published as a conference paper at ICLR 2026
Stephanie Fu, Netanel Y. Tamir, Shobhita Sundaram, Lucy Chai, Richard Zhang, Tali Dekel, and Phillip Isola. Dreamsim: learning new dimensions of human visual similarity using synthetic data. In Proceedings of the 37th International Conference on Neural Information Processing Systems, NIPS '23, Red Hook, NY, USA, 2023. Curran Associates Inc.
Jonas Gehring, Kunhao Zheng, Jade Copet, Vegard Mella, Quentin Carbonneaux, Taco Cohen, and Gabriel Synnaeve. Rlef: Grounding code ilms execution feedback with reinforcement learning, 2025. URLbttps::/
org/ abs / 2 2410 10 02089
Juraj Gottweis, Wei-Hung Weng, Alexander Daryin, Tao Tu, Anil Palepu, Petar Sirkovic, Artiom Myaskovsky, Felix Weissenberger, Keran Rong, Ryutaro Tanno, Khaled Saab, Dan Popovici, Jacob Blum, Fan Zhang. Katherine Chou, Avinatan Hassidim, Burak Gokturk, Amin Vahdat, Pushmeet Kohli, Yossi Matias, Andrew Carroll, Kavita Kulkarni, Nenad Tomasev, Yuan Guan, Vikram Dhillon, Eeshit Dhaval Vaishnav, Byron Lee, Tiago R D Costa, José R Penadés, Gary Peltz, Yunhan Xu, Annalisa Pawlosky, Alan Karthikesalingam,, and Vivek Natarajan. Towards an ai co-scientist, 2025. hwt.hhttp////e //apxiv////xv..///// org/ abs 18864
Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, Amy Yang, Angela Fan, Anirudh Goyal, Anthony Hartshom, Aobo Yang. Archi Mitra, Archie Sravankumar, Artem Ko- renev, Arthur Hinsvark, Arun Rao, Aston Zhang, Aurelien Rodriguez, Austen Gregerson, Ava Spataru, Baptiste Roziere, Bethany Biron, Binh Tang, Bobbie Chern, Charlotte Caucheteux, Chaya Nayak, Chloe Bi, Chris Marra, Chris McConnell, Christian Keller, Christophe Touret, Chunyang Wu, Corinne Wong. Cristian Canton Ferrer, Cyrus Nikolaidis, Damien Allonsius, Daniel Song. Danielle Pintz, Danny Livshits, Danny Wyatt, David Esiobu, Dhruv Choudhary, Dhruv Mahajan, Diego Garcia-Olano, Diego Perino, Dicuwke Hupkes. Egor Lakomkin, Ehab AlBadawy, Elina Lobanova, Emily Dinan, Eric Michael Smith, Filip Radenovic, Francisco Guzmán, Frank Zhang, Gabriel Synnaeve, Gabrielle Lee, Georgia Lewis Anderson, Govind That- tai, Graeme Nail, Gregoire Mialon, Guan Pang. Guillem Cucurell, Hailey Nguyen, Hannah Kore- vaar, Hu Xu, Hugo Touvron, Iliyan Zarov, Imanol Arrieta Ibarra, Isabel Kloumann, Ishan Misra, Ivan Evtimov, Jack Zhang, Jade Copet, Jaewon Lee, Jan Geffert, Jana Vranes, Jason Park, Jay Ma- hadeokar, Jeet Shah, Jelmer van der Linde, Jennifer Billock, Jenny Hong. Jenya Lee, Jeremy Fu, Jianfeng Chi, Jianyu Huang, Jiawen Liu, Jie Wang, Jiecao Yu, Joanna Bitton, Joe Spisak, Jong- s0o Park, Joseph Rocca, Joshua Johnstun, Joshua Saxe, Junteng Jia Kalyan Vasuden Alwala, Karthik Prasad, Kartikeya Upasani, Kate Plawiak, Ke Li, Kenneth Heafield, Kevin Stone, Khalid El-Arini, Krithika Iyer, Kshitiz Malik, Kuenley Chiu, Kunal Bhalla, Kushal Lakhotia, Lauren Rantala-Yeary, Laurens van der Maaten, Lawrence Chen, Liang Tan, Liz Jenkins, Louis Martin, Lovish Madaan, Lubo Malo, Lukas Blecher, Lukas Landzaat, Luke de Oliveira, Madeline Muzzi, Mahesh Pasupuleti, Mannat Singh, Manohar Paluri, Marcin Kardas, Maria Tsimpoukelli, Mathew Oldham, Mathieu Rita, Maya Pavlova, Melanie Kambadur, Mike Lewis, Min Si, Mitesh Ku- mar Singh, Mona Hassan, Naman Goyal, Narjes Torabi, Nikolay Bashlykov, Nikolay Bogoy- chev, Niladri Chatterji, Ning Zhang, Olivier Duchenne, Onur Çelebi, Patrick Alrassy, Pengchuan Zhang, Pengwei Li, Petar Vasic. Peter Weng, Prajjwal Bhargava, Pratik Dubal, Praveen Krishnan, Punit Singh Koura, Puxin Xu, Qing He, Qingxiao Dong, Ragavan Srinivasan, Raj Ganapathy, Ra- mon Calderer, Ricardo Silveira Cabral, Robert Stojnic, Roberta Raileanu, Rohan Maheswari, Ro- hit Girdhar, Rohit Patel, Romain Sauvestre, Ronnie Polidoro, Roshan Sumbaly, Ross Taylor, Ruan Silva, Rui Hou, Rui Wang, Saghar Hosseini, Sahana Chennabasappa. Sanjay Singh, Sean Bell, Seohyun Sonia Kim, Sergey Edunov, Shaoliang Nie, Sharan Narang, Sharath Raparthy, Sheng Shen, Shengye Wan, Shruti Bhosale, Shun Zhang, Simon Vandenhende, Soumya Batra, Spencer Whitman, Sten Sootla. Stephane Collot, Suchin Gururangan, Sydney Borodinsky, Tamar Herman, Tara Fowler, Tarek Sheasha, Thomas Georgiou, Thomas Scialom, Tobias Speckbacher, Todor Mi- haylov, Tong Xiao, Ujjwal Karn, Vedanuj Goswami, Vibhor Gupta, Vignesh Ramanathan, Viktor Kerkez, Vincent Gonguet, Virginie Do. Vish Vogeti, Vítor Albiero, Vladan Petrovic, Weiwei Chu, Wenhan Xiong, Wenyin Fu, Whitney Meers, Xavier Martinet, Xiaodong Wang. Xiaofang Wang, Xiaoqing Ellen Tan, Xide Xia, Xinfeng Xie, Xuchao Jia. Xuewei Wang. Yaelle Gold- schlag, Yashesh Gaur, Yasmine Babaei, Yi Wen, Yiwen Song, Yuchen Zhang, Yue Li, Yuning Mao, Zacharie Delpierre Coudert, Zheng Yan, Zhengxing Chen, Zoe Papakipos, Aaditya Singh, Aayushi Srivastava, Abha Jain, Adam Kelsey, Adam Shajnfeld, Adithya Gangidi, Adolfo Victoria, Ahuva Goldstand, Ajay Menon, Ajay Sharma, Alex Boesenberg, Alexei Baevski, Allie Feinstein,
12

---

Published as a conference paper at ICLR 2026
Amanda Kallet, Amit Sangani, Amos Teo, Anam Yunus, Andrei Lupu, Andres Alvarado, An- drew Caples, Andrew Gu, Andrew Ho, Andrew Poulton, Andrew Ryan, Ankit Ramchandani, An- nie Dong, Annie Franco, Anuj Goyal, Aparajita Saraf, Arkabandhu Chowdhury, Ashley Gabriel, Ashwin Bharambe, Assaf Fisenman, Azadeh Yazdan, Beau James, Ben Maurer, Benjamin Leon- hardi, Bernie Huang. Beth Loyd, Beto De Paola, Bhargavi Paranjape, Bing Liu, Bo Wu, Boyu Ni, Braden Hancock. Bram Wasti, Brandon Spence, Brani Stojkovic, Brian Gamido, Britt Mon- talvo, Carl Parker, Carly Burton, Catalina Mejia, Ce Liu, Changhan Wang. Changkyu Kim, Chao Zhou, Chester Hu, Ching-Hsiang Chu, Chris Cai, Chris Tindal, Christoph Feichtenhofer, Cynthia Gao, Damon Civin, Dana Beaty, Daniel Kreymer, Daniel Li, David Adkins, David Xu, Davide Testuggine, Delia David, Devi Parikh, Diana Liskovich, Didem Foss, Dingkang Wang, Due Le, Dustin Holland, Edward Dowling, Eissa Jamil, Elaine Montgomery, Eleonora Presani, Emily Hahn, Emily Wood, Eric-Tuaa Le, Frik Brinkman, Esteban Arcaute, Evan Dunbar, Evan Smoth- ers, Fei Sun, Felix Kreuk, Feng Tian, Filippos Kokkinos, Firat Ozgenel, Francesco Caggioni, Frank Kanayet, Frank Seide, Gabriela Medina Florez, Gabriella Schwarz, Gada Badeer, Georgia Swee, Gil Halpern, Grant Herman, Grigory Sizov, Guangyi, Zhang, Guna Lakshminarayanan, Hakan Inan, Hamid Shojanazeri, Han Zou, Hannah Wang. Hanwen Zha, Haroun Habeeb, Harri- son Rudolph, Helen Suk, Henry Aspegren, Hunter Goldman, Hongyuan Zhan, Ibrahim Damlaj, Igor Molybog, Igor Tufanov, Ilias Leontiadis, Irina-Elena Veliche, Itai Gat, Jake Weissman, James Geboski, James Kohli, Janice Lam, Japhet Asher, Jean- -aa-tttt Gaya, Jeff Marcus, Jeff Tang, Jen- nifer Chan, Jenny Zhen, Jeremy Reizenstein, Jeremy Teboul, Jessica Zhong, Jian Jin, Jingyi Yang, Joe Cummings, Jon Carvill. Jon Shepard, Jonathan McPhie, Jonathan Torres, Josh Ginsburg, Jun- jie Wang. Kai Wu, Kam Hou U, Karan Saxena, Kartikay Khandelwal, Katayoun Zand, Kathy Matosich, Kaushik Veeraraghavan, Kelly Michelena, Keqian Li, Kiran Jagadeesh, Kun Huang, Kunal Chawla, Kyle Huang, Lailin Chen, Lakshya Garg, Lavender A, Leandro Silva, Lee Bell, Lei Zhang, Liangpeng Guo, Licheng Yu, Liron Moshkovich, Luca Wehrstedt, Madian Khabsa, Manav Avalani, Manish Bhatt, Martynas Mankus, Matan Hasson, Matthew Lennie, Matthias Reso, Maxim Groshev, Maxim Naumov, Maya Lathi, Meghan Keneally, Miao Liu, Michael L. Seltzer, Michal Valko, Michelle Restrepo, Mihir Patel, Mik Vyatskov, Mikayel Samvelyan, Mike Clark, Mike Macey, Mike Wang, Miquel Jubert Hermoso, Mo Metanat, Mohammad Rastegari, Munish Bansal, Nandhini Santhanam, Natascha Parks, Natasha White, Navyata Bawa, Nayan Singhal, Nick Egebo, Nicolas Usunier, Nikhil Mehta, Nikolay Pavlovich Laptev, Ning Dong, Norman Cheng, Oleg Chernoguz, Olivia Hart, Omkar Salpekar, Ozlem Kalinli, Parkin Kent, Parth Parekh, Paul Saab, Pavan Balaji. Pedro Rittner, Philip Bontrager, Pierre Roux, Piotr Dollar, Polina Zvyagina, Prashant Ratanchandani, Pritish Yuvraj, Qian Liang, Rachad Alao, Rachel Ro- driguez, Rafi Ayub, Raghotham Murthy, Raghu Nayani, Rahul Mitra, Rangaprabhu Parthasarathy, Raymond Li, Rebekkah Hogan, Robin Battey, Rocky Wang. Russ Howes, Ruty Rinott, Sachin Mehta, Sachin Siby, Sai Jayesh Bondu, Samyak Datta, Sara Chugh, Sara Hunt, Sargun Dhillon, Sasha Sidorov, Satadru Pan, Saurabh Mahajan, Saurabh Verma, Seiji Yamamoto, Sharadh Ra- maswamy, Shaun Lindsay. Shaun Lindsay, Sheng Feng. Shenghao Lin, Shengxin Cindy Zha, Shishir Patil, Shiva Shankar, Shuqiang Zhang, Shuqiang Zhang, Sinong Wang, Sneha Agarwal, Soji Sajuyigbe, Soumith Chintala, Stephanie Max, Stephen Chen, Steve Kehoe, Steve Satter- field, Sudarshan Govindaprasad, Sumit Gupta, Summer Deng, Sungmin Cho, Sunny Virk. Suraj Subramanian, Sy Choudhury, Sydney Goldman, Tal Remez, Tamar Glaser, Tamara Best, Thilo Koehler, Thomas Robinson, Tianhe Li, Tianjun Zhang, Tim Matthews. Timothy Chou, Tzook Shaked, Varun Vontimitta, Victoria Ajayi, Victoria Montanez, Vijai Mohan, Vinay Satish Ku- mar, Vishal Mangla, Vlad Ionescu, Vlad Poenaru, Vlad Tiberiu Mihailescu, Vladimir Ivanov, Wei Li, Wenchen Wang, Wenwen Jiang. Wes Bouaziz, Will Constable, Xiaocheng Tang, Xiao- jian Wu, Xiaolan Wang, Xilun Wu, Xinbo Gao, Yaniv Kleinman, Yanjun Chen, Ye Hu, Ye Jia, Ye Qi. Yenda Li. Yilin Zhang, Ying Zhang, Yossi Adi, Youngjin Nam, Yu, Wang, Yu Zhao, Yuchen Hao, Yundi Qian, Yunlu Li, Yuzi He, Zach Rait, Zachary Veto,o, Zef Rosnbrick, Zhao- duo Wen, Zhenyu Yang. Zhiwei Zhao, and Zhiyu Ma. The llama 3 herd of models, 2024. URL htttps://arxxt
hbwpg//bb/2447... 217883
Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. CLIPScore: A reference-free evaluation metric for image captioning. In Marie-Francine Moens, Xuan- jing Huang, Lucia Specia, and Scott Wen-tau Yih (eds.), Proceedings of the 2021 Confer- ence on Empirical Methods in Natural Language Processing, pp. 7514-7528, Online and Punta Cana, Dominican Republic, November 2021. Association for Computational Linguis- tics. doi: 10. 1/266///1/2211mmmmmmmaa....5
wew....mmmmmmm5595.... URL httts::: / //acl antho onthelogy.oo orgg/221.
13

---

Published as a conference paper at ICLR 2026
emn emn.p-mainn
ma 5957
Ting- Yao Hsu, Chieh-Yare Yang Huang, Shih-Hong Huang. Ryan Rossi, Sungchul Kim, Tong Yu, Lee Giles, and Ting-Hao Kenneth Huang. Scicapenter: Supporting caption composition for scientific figures with machine--generate captions and ratings. In Extended Abstracts of the CHI Con- ference on Human Factors in Computing Systems, CHI EA '24, New York. NY, USA, 2024. Association for Computing Machinery. ISBN 9798400703317. doi: 10. 00.1145//613305335507777
URL httph: //o:: wtt.oooo.ogoooooo 1145/ 36155/331119.... 115.//////..11313333333335
3650738
Zhiqiang Hu, Lei Wang, Yihuai Lan, Wanyu Xu, Be-Peng Lim, Lidong Bing, Xing Xu, Soujanya Poria, and Roy Ka- Ka-Wei Lee. Am-adapters: An adapter family for parimeter-effiiient fine-tuning of large language models, 2023. URL //arxiv wttrg/bb/2230444099 01933
Wenxuan Huang, Bohan Jia, Zijie Zhai, Shaosheng Cao, Zheyu Ye, Fei Zhao, Zhe Xu, Yao Hu, and Shaohui Lin. Vision-rl: Incentivizing reasoning capability in multimodal large language models, 2025.
http://......////333.0022222.22 06749
Matt J. Kusner, Yu Sun, Nicholas I. Kolkin, and Kilian Q. Weinberger. From word embeddings to document distances. In Proceedings of the 32nd International Conference on International Conference on Machine Learning Volume 37, ICML' 15, pp. 957-966. JMLR.org. 2015.
Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng. Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang, and Ion Stoica. Efficient memory management for large language model serving with pagedattention. In Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles, 2023.
Nathan Lambert, Jacob Morrison, Valentina Pyatkin, Shengyi Huang, Hamish Ivison, Faeze Brah- man, Lester James V. Miranda, Alisa Liu, Nouha Dziri, Shane Lyu, Yuling Gu, Saumya Ma- lik, Victoria Graf, Jena D. Hwang, Jiangjiang Yang, Ronan Le Bras, Oyvind Tafjord, Chris Wilhelm, Luca Soldaini, Noah A. Smith, Yizhong Wang, Pradeep Dasigi, and Hannaneh Ha- jishirzi. Tulu 3: Pushing frontiers in open language model post-training, 2025. URL https: larxiv.orq/aaa http//oorrraaba/211111.....
2411.15124
Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, and Steven Chu Hong Hoi. Coderl: Mastering code generation through pretrained models and deep reinforcement learning. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh (eds.), Advances in Neural Information Processing Systems, volume 35, pp. 2132282 Curran Associates, Inc., 2022. URL http:://prooeeeingg...nee http:r/wwwo..c.coooooeeeaeaaaaaa fites/ppprr////// File/ 8636463644111 http:///0..aaeaeaeee...e20/220r.
cc/
pdf
Chin-Yew Lin. ROUGE: package for automatic evaluation of summaries. In Text Summarization Branches Out, pp. 74-81, Barcelona, Spain, July 2004. Association for Computational Linguis- tics. URL
Zichen Liu, Changyu Chen, Wenjun Li, Penghui Qi, Tianyu Pang, Chao Du, Wee Sun Lee, and Min Lin. Understanding r1 r1-zero-like training: A critical perspective, 2025. Uttptts::
orq/ hrg/abb/25533 .07883
Raphael Gontijo Lopes, David R Ha, Douglas Eck, and Jonathon Shlens. A learned represen- lation for scalable vector graphics. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 7929-7938, 2019. URL http://app...smttttccccccc...... Corpus_D:1022333397
Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization, 2019. URL https:
7/ //arxiv. wwwpxiioorg/abaaa abs
Chris Lu, Cong Lu, Robert Tjarko Lange, Jakob Foerster, Jeff Clune, and David Ha. The ai scientist: Towards fully automated open-ended scientific discovery, 2024. URL httptttst///ai..rrrrtt
abs/ / /2408 06292
Shervin Minaee, Tomas Mikolov, Narjes Nikzad, Meysam Chenaghlu, Richard Socher, Xavier Am- atriain, and Jianfeng Gao. Large language models: A survey, 2025. URL https: petteps//ane ://arxi/.. oorg/abs 2202. 061
14

---

Published as a conference paper at ICLR 2026
OpenAL GPT-44 report. CoRR, abtpa/b...33..07.,..2222 2023. doi: 1048550/AR2XV.233300877 URL wttoooooo//....55///////x.
OpenAI, :, Aaron Hurst, Adam Lerer, Adam P. Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, Aleksander Madry, Alex Baker- Whitcomb, Alex Beutel, Alex Borzunov, Alex Carney, Alex Chow, Alex Kirillov, Alex Nichol, Alex Paino, Alex Renzin, Alex Tachard Passos, Alexander Kirillov, Alexi Christakis, Alexis Con- neau, Ali Kamali, Allan Jabri, Allison Moyer, Allison Tam, Amadou Crookes, Amin Tootoochian, Amin Tootoonchian, Ananya Kumar, Andrea Vallone, Andrej Karpathy, Andrew Braunstein, Andrew Cann, Andrew Codispoti, Andrew Galu, Andrew Kondrich, Andrew Tulloch, Andrey Mishchenko, Angela Back, Angela Jiang, Antoine Pelisse, Antonia Woodford, Anuj Gosalia, Arka Dhar, Ashley Pantuliano, Avi Nayak, Avital Oliver, Barret Zoph, Behrooz Ghorbani, Ben Leimberger, Ben Rossen, Ben Sokolowsky. Ben Wang. Benjamin Zweig, Beth Hoover, Blake Samie, Bob McGrew, Bobby Spero, Bogo Giertler, Bowen Cheng, Brad Lightcap, Brandon Walkin, Brendan Quinn, Brian Guarraci. Brian Hsu, Bright Kellogg, Brydon Eastman, Camillo Lugaresi, Carroll Wainwright, Cary Bassin, Cary Hudson, Casey Chu, Chad Nelson, Chak Li, Chan Jun Shern, Channing Conger, Charlotte Barette, Chelsea Voss, Chen Ding, Cheng Lu, Chong Zhang, Chris Beaumont, Chris Hallacy, Chris Koch, Christian Gibson, Christina Kim, Christine Choi, Christine McLeavey, Christopher Hesse, Claudia Fischer, Clemens Winter, Coley Czarnecki, Colin Jarvis, Colin Wei, Constantin Koumouzelis, Dane Sherburn, Daniel Kappler, Daniel Levin, Daniel Levy, David Carr, David Farhi, David Mely, David Robinson, David Sasaki, Denny Jin, Dev Valladares, Dimitris Tsipras, Doug Li, Duc Phong Nguyen, Duncan Findlay, Edede Oiwoh, Edmund Wong, Ehsan Asdar, Elizabeth Proehl, Elizabeth Yang. Eric Antonow, Eric Kramer, Eric Peterson, Eric Sigler, Eric Wallace, Eugene Brevdo, Evan Mays, Farzad Kho- rasani, Felipe Petroski Such, Filippo Raso, Francis Zhang, Fred von Lohmann, Freddie Sulit, Gabriel Goh, Gene Oden, Geoff Salmon, Giulio Starace. Greg Brockman, Hadi Salman, Haiming Bao, Haitang Hu, Hannah Wong, Haoyu Wang, Heather Schmidt, Heather Whitney, Heewoo Jun, Hendrik Kirchner, Henrique Ponde de Oliveira Pinto, Hongyu Ren, Huiwen Chang, Hyung Won Chung. Ian Kivlichan, Ian O' O'Connell, Ian O'Connell, Ian Osband, Ian Silber, Ian Sohl, Ibrahim Okuyucu, Ikai Lan, Ilya Kostrikov, Ilya Sutskever, Ingmar Kanitscheider, Ishaan Gulrajani, Ja- cob Coxon, Jacob Menick, Jakub Pachocki, James Aung, James Betker, James Crooks, James Lennon, Jamie Kiros, Jan Leike, Jane Park. Jason Kwon, Jason Phang. Jason Teplitz, Jason Wei, Jason Wolfe, Jay Chen, Jeff Harris, Jenia Varavva, Jessica Gan Lee, Jessica Shich, Ji Lin. Jiahui Yu, Jiayi Weng. Jie Tang, Jieqi Yu, Joanne Jang, Joaquin Quinonero Candela, Joe Beutler, Joe Landers, Joel Parish, Johannes Heidecke, John Schulman, Jonathan Lachman, Jonathan McKay, Jonathan Uesato, Jonathan Ward, Jong Wook Kim, Joost Huizinga, Jordan Sitkin, Jos Kraaijeveld, Josh Gross, Josh Kaplan, Josh Snyder, Joshua Achiam, Joy Jiao, Joyce Lee, Juntang Zhuang, Justyn Harriman, Kai Fricke, Kai Hayashi. Karan Singhal, Katy Shi, Kavin Karthik, Kayla Wood, Kendra Rimbach, Kenny Hsu, Kenny Nguyen, Keren Gu-Lemberg, Kevin Button, Kevin Liu, Kiel Howe, Krithika Muthukumar, Kyle Luther, Lama Ahmad, Larry Kai, Lauren Itow, Lauren Work- man, Leher Pathak, Leo Chen, Li Jing, Lia Guy, Liam Fedus, Liang Zhou, Lien Mamitsuka, Lilian Weng, Lindsay McCallum, Lindsey Held, Long Ouyang, Louis Feuvrier, Lu Zhang. Lukas Kondraciuk, Lukasz Kaiser, Luke Hewitt, Luke Metz, Lyric Doshi, Mada Aflak, Maddie Simens, Madelaine Boyd, Madeleine Thompson, Marat Dukhan, Mark Chen, Mark Gray, Mark Hudnall, Marvin Zhang, Marwan Aljubeh, Mateusz Litwin, Matthew Zeng Max Johnson, Maya Shetty, Mayank Gupta, Meghan Shah, Mehmet Yatbaz, Meng Jia Yang. Mengchao Zhong, Mia Glaese, Mianna Chen, Michael Janner, Michael Lampe, Michael Petrov, Michael Wu, Michele Wang, Michelle Fradin, Michelle Pokrass, Miguel Castro, Miguel Oom Temudo de Castro, Mikhail Pavlov, Miles Brundage, Miles Wang, Minal Khan, Mira Murati, Mo Bavarian, Molly Lin, Murat Yesildal, Nacho Soto, Natalia Gimelshein, Natalie Cone, Natalie Staudacher, Natalie Summers, Natan LaFontaine, Neil Chowdhury, Nick Ryder, Nick Stathas. Nick Turley, Nik Tezak, Niko Fe- lix, Nithanth Kudige, Nitish Keskar, Noah Deutsch, Noel Bundick, Nora Puckett, Ofr Nachum, Ola Okelola, Oleg Boiko, Oleg Murk, Oliver Jaffe, Olivia Watkins, Olivier Godement, Owen Campbell-Moore, Patrick Chao, Paul McMillan, Pavel Belov, Peng Su, Peter Bak, Peter Bakkum, Peter Deng, Peter Dolan, Peter Hoeschele, Peter Welinder, Phil Tillet, Philip Pronin, Philippe Tillet, Prafulla Dhariwal, Qiming Yuan, Rachel Dias, Rachel Lim, Rahul Arora, Rajan Troll, Ran- dall Lin, Rapha Gontijo Lopes. Raul Puri, Reah Miyara, Reimar Leike, Renaud Gaubert, Reza Zamani, Ricky Wang. Rob Donnelly, Rob Honsby, Rocky Smith, Rohan Sahai, Rohit Ramchan- dani, Romain Huet, Rory Carmichael, Rowan Zellers, Roy Chen, Ruby Chen, Ruslan Nigmat-
15

---

Published as a conference paper at ICLR 2026
ullin, Ryan Cheu, Saachi Jain, Sam Altman, Sam Schoenholz, Sam Toizer, Samuel Miserendino, Sandhini Agarwal, Sara Culver, Scott Ethersmith, Scott Gray, Sean Grove, Sean Metzger, Shamez Hermani, Shantanu Jain, Shengjia Zhao, Sherwin Wu, Shino Jomoto. Shirong Wu, Shuaiqi. Xia, Sonia Phene, Spencer Papay, Srinivas Narayanan, Steve Coffey, Steve Lee, Stewart Hall, Suchir Balaji, Tal Broda, Tal Stramer, Tao Xu, Tarun Gogineni, Taya Christianson, Ted Sanders, Tejal Patwardhan, Thomas Cunninghman, Thomas Degry, Thomas Dimson, Thomas Raoux, Thomas Shadwell, Tianhao Zheng, Todd Underwood, Todor Markov, Toki Sherbakov, Tom Rubin, Tom Stasi, Tomer Kaftan, Tristan Heywood, Troy Peterson, Tyce Walters, Tyna Eloundou, Valerie Qi, Veit Moeller, Vinnie Monaco, Vishal Kuo, Vlad Fomenko, Wayne Chang. Weiyi Zheng, Wenda Zhou, Wesam Manassra, Will Sheu, Wojeiech Zaremba, Yash Patil, Yilei Qian, Yongjik Kim, Youlong Cheng, Yu Zhang, Yuchen He, Yuchen Zhang, Yujia Jin, Yunxing Dai, and Yury Malkov. Gpt-4o system card, 2024. URL httpt:tttss/xiiieee org/ abs /210 21276
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray. John Schulman, Jacob Hilton, Fraser Kel- ton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F Christiano, Jan Leike, and Ryan Lowe. Training language models to follow instructions with human feedback. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh (eds.), Advances in Neural Information Processing Systems, volume 35, pp. 27730-27744. Curran Associates, Inc., 2022. URL
cwtp:ripppcccppppppeeeeeeeee http://ppppppeeppappaaaeeeeepp files/paper/2022 orr.ap/ccceecc0aaee-eeee--e-eeer pdf
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. Bleu: method for automatic evaluation of machine translation. In Pierre Isabelle, Eugene Charniak, and Dekang Lin (eds.), Proceedings of the 40th Anmual Meeting of the Association for Computational Linguisties, pp- 311-318, Philadelphia, Pennsylvania, USA, July 2002. Association for Computational Linguis- tics. doi:
hnttlttt:://// http:////caaattoooro/.eoo wwtp://oooooooog/22--o2 1040/
Shraman Pramanick, Rama Chellappa, and Subhashini Venugopalan. Spiqa: A dataset for mul- timodal question answering on scientific papers. Advances in Neural Information Processing Systems, 37:1 18807-1188883 2024.
Qwen, :, An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang. Jingren Zhou, Junyang Lin, Kai Dang, Keming Lu, Keqin Bao, Kexin Yang. Le Yu, Mei Li, Mingfeng Xue, Pei Zhang, Qin Zhu, Rui Men, Runji Lin, Tianhao Li, Tianyi Tang, Tingyu Xia, Xingzhang Ren, Xuancheng Ren, Yang Fan, Yang Su, Yichang Zhang, Yu Wan, Yuqiong Liu, Zeyu Cui, Zhenru Zhang, and Zihan Qiu. Qwen2.5 technical report, 2025. URL https: /taps://aaxiiii http://rtxvvoo.o/////// abs.aaaaaaaa 2412. 15115
Nils Reimers and Iryna Gurevych. SeRtence-BERT: networks. In Kentaro Inui, Jing Jiang, Vincent Ng. and Xiaojun Wan (eds.), Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th In- ternational Joint Conference on Natural Language Processing (EMNLP-IJCNLL) pp. 3982- 3992, Hong Kong, China, November 2019. Association for Computational Linguistics. doi: 11116653v1/D19-111
Sentence embeddings using Siamese BERT-
htttpp::/aallanttooooo.rr/////// 1410/
Juan A Rodriguez, David Vazquez, Issam Laradji, Marco Pedersoli, and Pau Rodriguez. Figgen:
Text to scientific figure generation. arXiv preprint (wX.Xxi2226000000 2023.
Juan A. Rodriguez, Abhay Puri, Shubham Agarwal, Issam H. Laradji, Pau Rodriguez, Sai Rajeswar, David Vazquez, Christopher Pal, and Marco Pedersoli. Starvector: Generating scalable vector graphics code from images and text, 2024.
Juan A. Rodriguez, Haotian Zhang, Abhay Puri, Aarash Feizi, Rishav Pramanik, Pascal Wichmann, Arnab Mondal, Mohammad Reza Samsami, Rabiul Awal, Perouz Taslakian, Spandana Gella, Sai Rajeswar, David Vazquez, Christopher Pal, and Marco Pedersoli. Rendering-aware rein- forcement learning for vector graphics generation, 2025. URL hol.htttps://exxi.orro//ee
2505. 20793
Y. Rubner, C. Tomasi, and L.J. Guibas. A metric for distributions with applications to image databases. In Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36277), pp. 59-66, 1998. doi: 10.1 1091//C111999..10000000
16

---

Published as a conference paper at ICLR 2026
Benny Tang, Angie Boggust, and Arvind Satyanarayan. VisText: A benchmark for semantically rich chart captioning. In Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki (eds.), Pro- ceedings of the 6lst Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 7268-72679 Toronto, Canada, July 2023. Association for Computational Lin- URLhttts:: /acl wwt.aclog..oggg//a000..
guistics. doi: 10. acl-long. 4017
Jiayang Wu, Wensheng Gan, Zefeng Chen, Shicheng Wan, and Philip S. Yu. Multimodal large 2tt../rg..............
language models: A survey, 2023.
An Yang, Anfeng Li, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Gao, Chengen Huang, Chenxu Lv, Chujie Zheng. Dayiheng Liu, Fan Zhou, Fei Huang, Feng Hu, Hao Ge. Haoran Wei, Huan Lin, Jialong Tang, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang. Jing Zhou, Jingren Zhou, Junyang Lin, Kai Dang, Keqin Bao, Kexin Yang, Le Yu, Lianghao Deng, Mei Li, Mingfeng Xue, Mingze Li, Pei Zhang, Peng Wang, Qin Zhu, Rui Men, Ruize Gao, Shixuan Liu, Shuang Luo, Tianhao Li, Tianyi Tang, Wenbiao Yin, Xingzhang Ren, Xinyu Wang, Xinyu Zhang, Xuancheng Ren, Yang Fan, Yang Su. Yichang Zhang, Yinger Zhang, Yu Wan, Yuqiong Liu, Zekun Wang, Zeyu Cui, Zhenru Zhang, Zhipeng Zhou, and Zihan Qiu. Qwen3 technical report, 2025. URL https: http://aaavvvaa.a/aaaeee.eaawavv org/ abs/2505. 09388
Hiroshi Yoshihara, Taiki Yamaguchi, and Yuichi Inoue. A practical two-stage recipe for mathemat- ical Ilms: Maximizing accuracy with sft and efficiency with reinforcement learning, 2025, URL https:://axxi wwtp//////////oo//eoo.oo/eeeess/ abs/2507 222000.08820 08267
Qiying Yu, Zheng Zhang. Ruofei Zhu Yufeng Yuan, Xiaochen Zuo, Yu Yue, Weinan Dai, Tiantian Fan, Gaohong Liu, Lingjun Liu, Xin Liu, Haibin Lin, Zhiqi Lin, Bole Ma. Guang- ming Sheng, Yuxuan Tong, Chi Zhang, Mofan Zhang, Wang Zhang, Hang Zhu, Jinhua Zhu, Jiaze Chen, Jiangjie Chen, Chengyi Wang, Hongli Yu, Yuxuan Song, Xiangpeng Wei, Hao Zhou, Jingjing Liu, Wei- WeigYing Ma. Ya-Qin Zhang, Lin Yan, Mu Qiao, Yonghui Wu, and Mingx- uan Wang. Dapo: An open-source IIm reinforcement learning system at scale, 2025. URI. https: //rpxi////xvvv wrt./or/////boooooo
/ttp://rr......r//////55..3 14476
Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training, 2023. URL
/ttpgg/abb///33... 2303. 15343
Leixin Zhang, Steffen Eger, Yinjie Cheng, WEIHE ZHAI, Jonas Belouadi, Fahimeh Moafian, and Zhixue Zhao. Scimage: How good are multimodal large language models at scientific text-to- image generation? In The Thirteenth International Conference on Learning Representations, 2025. URL
net/ htt.:/ooummm.dugggEN01000..
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as perceptual metric. In Proceedings of the IEEE conference computer vision and pattern recognition, pp. 386-595,10 2018.
Qihao Zhu Runxin Xu Junxiao Song Mingehuan Zhang Y.K. Li Y. Wu Daya Guo Zhihong Shao, Peiyi Wang. Deepseekmath: Pushing the limits of mathematical reasoning in open language models, 2024. URL
abs 2402. 03300
Jinguo Zhu, Weiyun Wang, Zhe Chen, Zhaoyang Liu, Shenglong Ye, Lixin Gu, Hao Tian, Yuchen Duan, Weijie Su, Jie Shao, Zhangwei Gao, Erfei Cui, Xuehui Wang. Yue Cao, Yangzhou Liu, Xingguang Wei, Hongjie Zhang. Haomin Wang, Weiye Xu, Hao Li, Jiahao Wang, Nianchen Deng, Songze Li, Yinan He, Tan Jiang, Jiapeng Luo, Yi Wang, Conghui He, Botian Shi, Xingcheng Zhang, Wenqi Shao, Junjun He, Yingtong Xiong, Wenwen Qu, Peng Sun, Penglong Jiao, Han Lv, Lijun Wu, Kaipeng Zhang, Huipeng Deng, Jiaye Ge, Kai Chen, Limin Wang, Min Dou, Lewei Lu, Xizhou Zhu, Tong Lu, Dahua Lin, Yu Qiao, Jifeng Dai, and Wenhai Wang. In- ternv13: Exploring advanced training and test-time recipes for open-source multimodal models, 2025. URL /tttttst///aaat .rw.iiioorrg.ooooooo.
orgg abs 25044 10479
Bocheng Zou, Mu Cai. Jianrui Zhang, and Yong Jae Lee. Vgbench: Evaluating large language models on vector graphics understanding and generation. arXiv preprint 10997, 2024.
17

---

Published as a conference paper at ICLR 2026
A
APPENDIX
A.1 CAPTION QUALITY ANALYSIS
Our caption quality analysis involved three annotators: one bachelor's student, one PhD student, and one faculty member (all male). From subset of DaTikZ-V3, 74% of samples originate from arXiv and 26% from TeX SE. One annotator completed the evaluation sheet in Figure 6 based on the taxonomy Table 7 This annotator also manually described all 200 scientific figures, which we subsequently used as reference descriptions to compute BLEU-4, ROUGE-L, and STS with the all-mpnet-base-Ve sentence encoder between human descriptions and VLM -LM--generate descriptions.
The other two annotators each described 30 figures measure agreement, yielding unweighted 0.35 and weighted x = 0.63. The structural elements for scientific figure captions were adapted from best practices academic writing and prior research taxonomies (Tang et al. 2023
Hsu et al. 2024).
Functional Role
Structural Elements
Length & Complateness
Scere
Figure Type
Contentual
Useful
Longth
Completeness
Sample
Sample 2
Sample S
Very Poor
Sample
Sample 5
Poor
Sample
Average
Sample? 7
4- Good
Sumple 8
Very Good
Sample
Figure 6: Screenshot of our evaluation form for the first nine scientific figures.
Table 7: Caption analysis taxonomy for structural elements and usefulness scores.
Structural Elements
Figure type: names high-level typo (e.g. graph, tree, workflow).
Visual details: mentions colors, shopes, axes, layout/spatial relations.
OCR: includes texiual elements visible in the figure (axis labels, amnotations, math), aiding correct labeling. Contextual reference: points outside figure (e.g.. Sec. Useful reduces standalone utility.
Quantitative confent: numbers, Jormulas, code. Adds technical substance (oflen paired with OCR).
Usefulness Scores
Very Poor: not meaningfully descriplive. May he only label or inrelevant text.
Poor: somewhat relevant hut vague/incomplete. Mentions tnpiclelements without adequate clarity or context. is without highlighting key
Average: describes
content but lacks depth/specifies. States
details.
Good:
clear, specilic, and neur-complete. Covers important visullquantiative details and structure.
Very Good: precise, insightful, and largely self-contained. Explains key elements the figure almost unnecessary.
A.2 DATASET
To create synthetic data, we follow a strategy similar to ScImage (Zhang et al. 2025). We first generate 2,000 templates with varied terms, each used to produce 10 queries that generate TikZ code. All steps are performed using GPT-4o with minimal human intervention.
LLM Debugging For LLM-based debugging, we use the prompt in Figure 7 We first tested this on a subset of 753 samples spanning all sources, manually evaluating the percentage of compilable, non-empty, and non non-corrupted outputs. As shown in Table 8 Qwen3-32B (non-thinking) was the best-performing model, recovering 49.40% of errors in single pass and 59.04% after three repair rounds. Smaller Qwen variants and (wenn.55-BBIInstruu
(Qwen et al. (025) performed consider- ably worse. We therefore applied Qwen3-32B for large-scale debugging. which took days on A100 40GB GPUs using the vLLM framework (Kwon et al. 2023 Examples of the debugging
process are shown in Figure g and 9
18

---

Published as a conference paper at ICLR 2026
Table Accuracy of different LLMs in debugging TikZ code from error logs over three refinement iterations. Bold indicates the best-performing model.
LLM
Iteration
Iteration 2
Iteration 3
Qwen2.5-7B
17.49%
28.03%
34.08%
Qwen3-4B
14.17%
18.56%
21.98%
Qwen3-8B
35.11%
39.36%
41.49%
Qwen3-32B
49.40
55.42%
59.04%
GPT-4o-mini
36.82%
41.36%
43.62%
GPT-4o
48.10%
53.73%
58.89%
LLM Debug Prompt
correspancing LeTeX escor log. rike code so that it compiles without errers. only output the corrected 1ike code. original code: (tiksz_ode)]/p Compilation Srror Sogs {logunesaäge)
will previde you with some
VLM-based Image Description The prompt for image description shown in Figure TO We use few-shot in-context learning (Brown al., (020)) with two high-STS human descriptions as exemplars. We run Swen2.5-VL-7B--ee evaluation, to describe all figures in DaTikZ-Va V4. Processing required 2 days on A100 40GB GPUs.
which was the strongest open-source VLM in our
VLM Description Prompt
scientitic illustrator describing images precise redrawing in TikE. Your task is te describe the image in precise, coatinuous pruse without bullet points, lists, line breaks.
Start directly with the main ohject or scene. Avoid introductory phrases like 'Gertainly!'s The image deplets...'s "Here is precise description. Use clear, active language focused geometry, labela, colors, spatial relatienships, coordinates, and other visible properties.
Deacribe visible elements such shapes, lines, acrows, Labels, including their relative or absolute pasitions, dimensions, and orientation. Use consisteat, minimal naming for objects (e.g., 'cirale A', label positions relativa to shapes precisely.
LI") apscify
Only describe exect, concrete visual elements that enable precise image reconstruction Tike.
Avold vague, interpretive, or inferential language, and excluce summaries, image's meaning, function, sesthetics. \a
coaclusions, or commentary
sere are fex examples: thin
middle, containing nine evenly spaned black dota, and labeled at the left. Eacn doc 1s connected an alternatiag pattern to either 2y (placed at the top middle) or #1 (placed at the bottom middle).
horizontal.
thin black Line in
A line chart has diffecent instruction icn acales of 1/10, 1/4, 1/2, and 1 on the x axis. y-axis shews BLEU scores between contains three lines with th-an in blue, Be-an in red, and Fr-En in brown. All BLEu scores are initially increases, BLEU scores Improve for all pairs. De-En is the highest, closely Iollowed by Fr and then 2h fer below. The incresse is largest from 1/10 to 1/4 end only marginally
steps
lowest instruction scale.
trstruction scale
1/4. legend placed inaide chart write description this style given image.
instruction at the top left.
A.3 METHOD
For finetuning DeTikZify-V2 (Belouadi et al. 2024b), which is a SigLIP ZZai et al. 2023) vi- sion encoder of PaliGemma 3b mix 448 (Beyer al. 2024h), we use the training split of images with a max- imum output length of 2048 tokens. Training runs for two epochs with learning rate of 5e-5, AdamW (Loshchilov & Hutter 2019), cosine scheduler, and 3% linear warmup. The batch size is 128, trained on x H200 140GB GPUs for 12 days.
DaTikZ-Va V4 consisting of 1.3M Image-Tike -ikZ pairs. Inputs are 448x448-pixe
19

---

Published as a conference paper at ICLR 2026
LLM-based TikZ Debugging
Original TikZ Code:
idecumentciors tike] (standaione) [ut cnd
suaepackage: cireu. tix)]
(uaepackage:
(bcgin| socuncaz]
(bwgini ririiit (e: straight voltages)
[amezican voltage soures, V-VV, invert, voltage shirtri] 1-1,4)
20 [R, 8-08_PS, 1-9-01_99] (2,4)
[R-3R_ -15, vix-sy 4,91, voltage shistr1.5] (,4
to (Taig Tua.gi,, laddyciddel (10,4;
Lo [shoeu] [12,4]
voltage
v'e-sp veltage shitt-1] (12,01
12. shezt] 1-1,0)
12.0,4)
Cis, (0i1e) (2.0.1.5)
1_-50_
(cDs) (2.6,0)
[7.2, 4) to Taagbu ahaaa indydi -nvect] (7.2.31
(10-8.4) 4) to [RedR iest [cul)
(culs) (10.0,1.5)
< by (0510] 11000,01
18.5,31 modela. cen 196
16.1, 21 nodels a.dela.ignneee cen 196 (56_15)
17 01 nodel
(2,05 05 mode feire, acale=1.5]
(2,4] node loice, ocloresed, scale=l.3]
noda
coicrered, scalesi.5)
110,0]
(end[document
Compiler Error Log:
Package Like Ecross A mude must have pply eapty) label Lext. See the Like packege domanent aciin for: explanation Type Stebario fur inmediate help. 1.26 (2,01 nedelctro, scale=1.5]
sccurred, ourput Tranecript written tigure.
produced!
Corrected TikZ Code (Changed Parts):
13.2, 41 modelatra, sealor1 1110
scaleet
(2.4) node (cl coloewsed, scalael.5]
(10,4) nodelciro, colorered, scelusl
(10,0) node (eirt, colorezed, sceluel.
mm
Rdi
Reo
Yeu
Row
Figure 8: An example of the LLM debugging pipeline. The original TikZ. code failed to compile. The compiler error log was passed the LLM, which generated corrected TikZ code. The fixed code produces the valid figure shown above.
20

---

Published as a conference paper at ICLR 2026
LLM-based TikZ Debugging
Original TikZ Code:
kusepackapeitt it-ietii
tke (etcerfbrrrag shapess
(Lscpackaca/azza)
Abeeini
(bcein [iqure) [b]
(hegtmi n [>: (12n)t///cnnnrring aeriirggrrrraareeee las Vidiexeerd ..5 St extextiii h- abost sep'rel a
comp gragh
Meegintt urg [state// s/appeed Syllemmiinmmm sl sixe=5mn]]
(nod: [state]
10] at (-2, [1ab61 (ost1-1 1l
Unode [state]
" (laeell eilllleee leet p31 11p
(nade [s:alm] [3] at 1.25, 0.251 [labeler (ggt:c) c0311
[label above:
[0]
vdrax [4] to [1]
[3]]
Vreaw
deex []] to [2)]
(()
dreaw
drex [0] t0 [2]]
Vdraw [2] to
kendit.kapicturss
Mlegenci tt (Thit TARLI: 1nnmmn:1
Ngcnewcormande barayyytttttraytoottttt./. (1.3)
Docgiaitabliatt cocco)
canditabular Aendi Eigurs?
Compiler Error Log:
lowed See the TaTel nanual TaTex Congant on for ixplanation.
Type <returno for innediste help.
1.6 Nborin| [b]
Lass Fatal Girez securred, no ourpuz PDF file produced! franscript writton on fiquie.loc.
Corrected TikZ Code (Changed Parts):
Abagia docunen:] |
Abegini tabular: 2 Accntteiing wawrriiggrgrrreeeeeeeeoooo lash \dimexpro. waapgrr...leeeee www.ettwidth--e--e.............e ataboo sep' 1 Abeginite kikgi sturel jetate/ oppend etyle-ininimon zizebmmi]
Nawnewoornand tarrayatzetel) (1.3)
Abegin
cooco)
cenditaollst
tend doccameni
Figure 9: An example of the LLM debugging pipeline, The original TikZ code failed to compile. The compiler error log was passed the LLM, which generated corrected TikZ code. The fixed code produces the valid figure shown above.
21

---

Published as a conference paper at ICLR 2026
A.4 EXPERIMENTS
The prompt template for all models is shown in Figure 11 We also experimented with templates without the standalone environment but found that this reduced performance and compilation rates.
Models Except for GPT-5, decoding uses t temperature-1.. top-p-0. 0000.-0.. 9, and max length 2048. For GPT-5, we set reasoning=mediun um, verbosity=medi um, and evaluate a random subset of 100 samples due to cost. For TikZero, trained on caption-Tikk TikZ pairs, we only provide the figure deseription as prompt. For SFT, Qwen2.5-3B is finetuned on DaTikZ- 4 for two days with a learning rate of le-4, warmup ratio 3%, cosine scheduler, and batch size 128. Qwen3-8B is trained for four days with reduced learning rate of 5e-5. For RL. on DaTikZ-V4-RI, TikZilla-3B is trained with GRPO for 4,000 iterations (batch size 144. 8 rollouts) using learning rate 5e-6 and weight decay le-2. TikZilla-8k 8B uses learning rate 2c-6. RL-only runs were also tested. Training took 5 days for TikZilla-3B and 10 days for TikZilla-8B, 8B, all on 4 H200 140GB GPUs.
Dream- SIM (DSim) uses CLIP, DINO, and OpenCLIP (ViT-B/16). TeX Edit Distance (TED) TexLexer. Average tokens (AT) are measured with o200k_base tokenizer. DINOScore (DINO) calculated using the cosine similarity of the patch embeddings produced by dino-vitsl6. Learned Perceptual Image Patch Similarity (LPIPS) uses the alex network.
Metrics CLIPScore (CLIP) is computed with siglip-so4000 -wtt000mmmhhh---000
Prompt Template
Generate a complete LeTeX document that contains a TikE figure according to the requirements:
following requi remants:
(fiqure.ceacr/pg descriptioo)
Mzap your code using (documentelese [Likz] (standalonel, include
(eaafdoccuuttt Only cuzput valid LaTex code with no extra text.
A.5 RESULTS
Human Evaluation We split 9 annotators (6 male, 3 female) into groups. Group (5 notators) evaluated GPT-5, GPT-4o, Qwen2.5-3B, TikZilla-3B, and TikZilla-3B-RR Group (4 annotators) evaluated GPT-5, GPT-4o, Qwen3-8B, TikZilla-&B, and TikZilla-BB-RL. Each anno- tator received two Excel sheets (textual vs. image alignment), each with 30 randomized samples. We ensured least five overlapping samples for inter-annotator agreement and five GPT-5 samples (scarcer due to cost). Annotation interfaces are shown in Figures and 13 Likert scale definitions are shown below:
5 Excellent: Figure fulfills all requirements. Few minor issues (e.g., slightly imperfect layout, one or two mislabeled/cxtra elements) are acceptable. Think about it as publication almost publication ready where only small tweaks needs to be made.
4 Good:
Figure broadly fulfills the requirements and contains no major errors, but it is clearly not perfect. Typical cases include multiple minor flaws (e.g., clutter, small inaceu- racies, awkward design) or one moderate issue.
3 Fair: The figure has about one to two major issues (e.g., important elements missing, wrong trends in charts, and/or some minor issues. It is still usable with corrections as parts of the figure are clearly correct.
2 Poor: Several major issues and/or many minor ones. The figure no longer meaningfully reflects the description or GT image (e.g., severe overlaps, high amounts of hallucinated content, ...).
1 Failed: Non-compilable code (already auto-assigned).
Ablations For inference, ablate input quality by comparing GPT-4o the evaluation sub- set where captions are available (GPT-40 versus the same subset with VLM-generated de- scriptions instead (GPT-40mec:). For training, we finetune Qwen2.5-3B different input vari- ants: (i) Qwen2.5-3B (SFT using only caption-Tike TikZ pairs (468k samples), (ii) Qwen2.5-3B (SFTaesc.) using the same subset but replacing captions with VLM descriptions, (iii) Qwen2.5-3B
22

---

Published as a conference paper at ICLR 2026
Figure 12: Example of text-image annotations.
Betts
+ 2 +
Figure 13: Example of image-image annotations.
(SFT desc. v ). using the full DaTikZ-V4 dataset, but preferring captions whenever they exist, and (iv) Qwen2.5-3B (SFT ). oversampling by including both descriptions and captions for all samples with paired captions. This setup isolates whether captions add robustness simply dilute supervision from richer descriptions.
Table shows that arXiv data alone achieves strong results (0.305 AVG). Adding GitHub yields further gains (0.320), while TeX SE and synthetic data provide marginal benefits. This highlights that lcrge-scale, naturally occurring TikZ. from arXiv and GitHub are the most valuable sources.
Table 9: Ablation study of different data sources. Using only data from arXiv already leads to very good performances and arXiV + GitHub almost reaches its full potential.
Source
CLIP
DSimt
TED
AVG
CR
AT
arXiv
0.152
0.568
0.805
0.305
84%
550
GitHub
0.158
0.605
0.802
0.320
88%
548
+ TeX SE
0.159
0.608
0.806
0.320
88%
569
0.161
0.613
0.802
0.324
89%
529
We conducted manual inspection randomly sampled outputs to compare the typical error patterns of TikZilla and GPT-5. Our observations reveal several systematic differences:
FAILURE CASES
23

---

Published as a conference paper at ICLR 2026
Compilation. GPT-5 frequently produces TikZ code that fails to compile for complex sci- entific figures, often due to missing library imports, incorrect macro nesting, or hallucinated commands. In contrast, our RL-tuned TikZilla models almost always generate syntactically valid code.
Code structure. TikZilla generally relies on basic primitives such as \node, \draw, and \fill, leading code that is casy to interpret and modify. GPT-5 tends to generate more elaborate constructs (c.g.. macros, loops, nested coordinate definitions), which are more compact but also more brittle and error-prone.
Category-speciffe strengths. TikZilla performs best on diagrams with strong geometric or mathematical constraints-charte function plots, schematics, and commutative diagrams (tikzed), GPT-5 performs better on high-level conceptual figures and network style dia- grams where spatial layout loosely specified.
Effect of RL tuning. TikZilla without RL exhibits similar structural strengths but more fre- quent spatial misalignments (e.g., misplaced labels or arrows), RL substantially improves geometric coherence and spatial consistency.
24

---

Published as a conference paper at ICLR 2026
Captions vs. Descriptions
Outline of our algorithm for enumerating Williamson sequences of order n. The boxes on the left correspond to the preprocessing which encodes and decomposes the orig- inal problem into SAT instances. The boxes on the right correspond to an SMT-like setup where the system that computes the discrete Fourier transform takes on the role the theory solver.
Caption:
Description: A block diagram illustrating with several components. There are four main labeled rectangular blocks connected by arrows indicating the direction. At the bottom left, there is an input labeled n entering rectangular block titled 'Driver script', which sends an arrow labeled "External call' upward to block titled 'Diophantine solver / Fourier trans- form'. From this block another arrow labeled 'Result" points downwards back to the 'Driver script'. From the 'Driver script' horizontal black arrow point to the right and is labeled 'SAT instances' connected to block titled 'Programmatic SAT solver'. It outputs hori- zontal black arrow labeled 'Enumeration in order pointing the right out of the diagram. Above the Programmatic SAT solver' is another block labeled Fourier transform' and con- nected with an upward arrow labeled Partial assignment' and a downward arrow labeled "Conflict clause'. A dashed arrow labeled 'Ecoddin information' points from the 'Driver script' block back to the 'Diophantine selver Fourier transform' to the Fourierr transform',
Ground Truth
Caption (GPT-4o)
Description (GPTT-4))
the Salg
Cacomn actlio)
udevs
Tranller
Caption: A set $\/igma in PW+S inside rectangle R. The blue region S frac (R (cup \ partial R)SS can always be triangulated. Description: A blue rectangle labeled R in the top-left corner. Inside the rectangle, there are two black geometric figures. At the lower-left side, is a layered square pattern composed of three squares, small black square at the center, surrounded by blue square matching the background color of the rectangle, surrounded by larger black square. Diagonally toward the upper-right an irregular black polygon labeled $ siggmS.. Inside the polygon two shapes have the black background color of the rectangle, one is hexagonal at the top and the other is diamond shaped at the bottom.
Ground Truth
Caption (GPT-4o)
Description (GPT-40)
R
R
Bloueregion
Figure 14: Captions versus VI.M-generated descriptions and their produced figures by GPT-4o. Striked-out text indicates incorrect VLM-descriptions and red text indicates improved descriptions made by human annotators. We observe that VLMs most frequently omit low-level stylistic details (e.g., small decorative elements), while hallucinations omission of key structural elements occur less frequently.
25

---

Published as a conference paper at ICLR 2026
Examples with TikZ Code
Description GT Image:
large circle centered around point 01. points labeled $x'as and 5w'+5 55 aced the cirole near the tep, atzaiaht black horiiontal Line connacts boch peints and is 1absled exactly aligned wist vertical straight 20116 inn
Bithetas tha circle. cenzer of the the cizcle. Thess line form an isosceles triangia with the linas labeled 1. vertica. dashed line, shich 18 the vert axes 2ot3 the triangle re goes from the point 39,01 spwards
isialleft (Ntrac (-baaa) (2] (eieat) and
GPT-5:
Adocurent 1kz) (standd lone)
(amsmathh)
Ncseti84lioes (ssetals11lbary(saall
(hegi (occument
[acale=]]
\d22v1813)
Accondinat-
[F\MRccs 190 atthet 1, i Vit acle
mate [3] [/V/Rcne hetttaetteeee//11 ( (908 We a2leg/2 (2
Apsordi
(2) 10. (K1
[chick] circle
Adzow dasbad) 10) -- 10, V.R node midway, right] |sVithetaf1i
sesaithhee (0) -- 121 nose mi dxay, sloped, a3ove] 101917
nodo dway, zloped, asove] 101917
Adraw hiick) (2)
Anade [above] as (9) 15 (5LAtheta3)
161
Arode [below]
(92V Mete (V Www.e/WWWeeaee.eoo (athleW
(E111 [2] purale
(2pL) node Jabove l=ft] 15w 15w (+1817
candit/kaiccture
Lendido
TikZilla-B--RL.
Adoourenteles 1kr) (sten]ssten)))
NusetiRs
pooratioas.pathropp..oo positisaing, shapos, cale]
Mheginl (t
Adrew 10. 0) circle (3em1:
(8,0) 2,558: (0.05em) rode (belox left] (308):
(S=",81;
Miill [-1.5. 2.398) circle (c.355m) aodelabove lest] 15v
ttickt 10.01 - 12.3, 2,3981 nose micxey, asove left] (515))
1-1.5, modelmiwway,
hedelnisvay, abeve
15103)
dashad] 10,0)
Arode at 10.8, 3,2)
(92Vistalller: Maaallletttteeeee (atraa (tthett Sthetai:2 / A
(end Likapici ure]
Figure 15: TikZ code and rendered figures shown for GPT-5 and TikZilla-3B-RL using the de- scription above. The code produced by GPT-5 more accurately follows the description compared to TikZilla- RL.
26

---

Published as a conference paper at ICLR 2026
Examples with TikZ Code
Description GT Image:
arranged labeled
From leftt right Each circle connected the nex: by raghteard point gAS. Belov wach diicle, there is conresgondi mg blue square Tabelld 50 05, $0_ $0.28, and fo_(r-115 ccspeasivoly. Bach ciecle
co-responding square vertical black image, intersocting aztoNs. The zequence continues MiZE ellipses between 9X_29 and MK:I-11 and arter PLIT-1]9, indicating consinuaticn.
aztow lsbeled 585, dasned
Xo
X2
Xr-1
B
B
O
O
Or-1
GPT-40:
\decurentclas [Lik)] (standalone) luaepackage [amamach)
(begini socument:
Abeginit: sturel
aistenoe= auto, bestes]th"
Anode circll, drav-green 150 tblckk,
120 minimam size=10mm) Cmni 1X91 (5X 0913
Arode ccicll, drav-green Iall lblack, e:l --ggegenn 20,
mintmum size= Dmn, right o5-X01 20, ninimam
Aneda
meda|circles dravnrreen
Cap,
:bblack,, fill-qrean
right 02-X2,
cn) (X11) [6x_(111]81)
Apede tright c2-x2, node distancesi, Scn] (dots) [8Vhedots5)]
(rode redt
draweb uet se1501black
fi 1-blue150 120,
mirimum s:ze-1 0mm, be of=x01
100)
(rode rect rect englm, drav=bl uel 5011) 201black ack, fi 1111-lee1900 mirimur s:ze= (mn, be cf=X1
(01)
Srode reattangle drewebl ue ackk, 1-hlee mirimur Imm,
1021
s122-10mm
of-X
-1]517
\draw|->> [-] (X0) nods (nidaay, sbove 1355) [X11)
[->]
node inidaay,
(821)
\draw (22)
6851
Adrax >1 (dotx) node Inidway, above
Adrax ", thick 1801 node (righ
(00:
Adras: 5. thick 1821
node Iright 1985 (02):
Adeak -2, thick] (K71) rodbrright 1985) 10T.);
red.
(8(00) (0111 (5(071) 10.5; (doc62)81)
(endit indrttkkpicttre
TikZilla-BB-RL:
idecurentclas [tke (standalone)
Ausepackade [L1k,, pafslots]
\csetikellnrasy harlllaaaayyaoroaaaaa. (arsoya......tta decorations lecratiionspaahheppllio pathrepiscinn, positisnina, shapas, cale]
Abegini hikpiii
Arode idrax, circle, fill-green mirimun size=toni 1x01 at
18. 01 18x 091;
Arode Idnew, cirele, Fill-green 130, mirimun sizeeton iell at
12-5,
14x 151;
Acods at (7.5 (7.5, 0) 1 (SViedetsā] \oode/draw, circlo, fill
mirimun sizeslent (x2) at
(10, 0) (8X11-11181)
soctangio, fill blac!3c,
minimum size 1.20m6.Bc0
a2 (0, 150 180_0811
Anods
(0l)
(o2) st (5, (.5 150
Arode dd rect rect-ang fill-hh sel minimun sise=0, Stm
Arode at 17.5,
(eint <il;
anglangle fillehi
xise=0,
CD, 170-17-11817
(draw >) (x0) (x1) node jnidway above] [5A5);
Adrax > [x]] (x2) mode mideey, above] 15A6
(X2)
nade
(5201;
[ni
\dtow ->) (X0) (00) neds mnidwy, ictt] (925):
Ageax ->) [X] (01) neds nidway, ieft 11
adraw >] (xT) (ot) node midday, ieft] (5851;
Adeak dashed, red:
[-1, -1) il., -1);
(endi document
Xo
X₁
Xr-1
B
Og
O₂
Or-1
Figure 16: TikZ code and rendered figures shown for GPT-4o and TikZilla-3B-RL using the descrip- tion above. The code produced by GPT-4o could not be compiled while TikZilla-3B-RL produced correct code.
27

---

Published as a conference paper at ICLR 2026
Examples with TikZ Code
Description & GT Image:
sequence ig colored circies arzanged berizortal each cennecced nistrectioral positiored the
number "i" centered irside. To its right, cirale 2 is yol,ow, labeles 12- ineide. A bidirectional асгои labeled sbove and te belcy cornecte circle and dircie Next. Rext, cire 3 rea, lateled circle connected
isbeled
inside,
above is blue, labeled *4* insida, cornected to circle by bidirestions arrew labeled fe" above and below. The circies are everly spaced along the horizontal axis.
bidirect
labeled
C
2
TikZilla-8B:
Idocumenteionn wwt.:titktttttaaaeeoeee (ctandaionn)
Yusepackagg annamaah
lusepacka@s (ooccebco
Ausepackage (tik2)
(Lawt abracylyyyy wel.yooooeaaaiiii
ptw.:aaa.......oooaaiiiiog. decuzal kinga, whteppaaaii Lag
chapes]
lusetikt (ereiiikiotrry kocety
laaepackage bookkeeeaa
bookneskaoge kaogann (hyperswE)
Muesin(docunen doccunen
Anode kkaiiioo
01
(5151;
Arode 12) 01
(525);
Arode [3] st (4
(235);
(1
(085)7
node
(5a5
:212
\draw [-2 (2) pode [above] (5c5
[312
[->]
node
[below]
\draw [<- (2) nods [bolow] (5dE
(312
Adeaw [c]
node [eloo]] 19151 2012
(draw [sje low, spacity=).5)
12) circle 10,2511
Adrex [: [lllerdd opacity=0.51 (5) circle 10,251;
Adraw
cirele (0.251)
kandit.kkpicturr Lendi Iocament
C
2
b
d
TikZiLla-BB-RL:
Ldocurentclaes stands
Musepockagetticc ansmath, anssybb, pofplats, agffori
ikset Torarylantone positi backgreinds, external:
staate..sylleemiii eetyleeiiiiine eixe=2gth]
(beein (ocunsaz)
\becin Abecinitskappe [tskppiztuze]
Arodo| d234, fill-ysllex
10,0) (S18))
Arada ccircl.
Xrode ccircle drev, [illared 3n) (3) at (4,01 133817
Arode icirlee draw,
me130] 141 at 16,0) (845)7
Idraw (2) nodde[bbove [5:5 (3)
Adeak ->] (3) --nsdo[bber> (e5 (4)
(2)
-ncdelbclon
nede [bclow]
Adron [-] (3) nedo [cdc|bclok]
(525) (3)
(endi kkkiictuuur
a
C
e
2
3
4
b
d
f
Figure 17: TikZ code and rendered figures shown for TikZilla-BB and TikZilla-BB-RL using the description above. Despite both figures being correct, the code produced by TikZilla-BB-RL is much shorter compared to TikZilla-8B.
28

---

Published as a conference paper at ICLR 2026
Examples with TikZ. Code
Description & GT Image:
discinet curvex. The axis zamped from Lu 3, and the axia labeled lines al each integes, The fieal oueve, labeled ry Stexi (texi (hwlil) 1x17 from 2 Lo 2, with grid Tunstion scarl
Carlesian coard
with
Labeled
in red, is a piecesise nesa.ive for positivs x-valnes, The axia curve, iskeled by = Stanh x' in biue, is B-chaped curve passing through the origin, asympiotically app apgrosching y = a5 approscnes positive intinity ara y
grigan, excending Lerizoni Lalilllly slope
x-values,
rising linearly
x approaches negative gradual slope, also orange, 15 snother S-enaped curve, sinilar through the
antirity,
curve, labeled
Veigna 00
blue carve bat with asypptot approsching y and y Cor posiiiie and negat ive x-valuea, respectively poaltioned label,
and
gzaph, with zed label as the
tolioved
label
2
ReLU(z)
y tanh
y (x)
X
TikZilla-8B:
tikk] (standaion)))
\usepackaes jutteeeetttti1 (noetl-))ttons)
Musepeckegee annemahh
amosynb]
cosppacka==_d-ppaaaee. (dy-psnancs, table, xcdran] \usepackaes
\usepackag2
Seaet 1kx lorarylbrrrylaa pexit ioning, arrocs.tetd, backgrounds, cals, 3dl
(ocola docuncnt]
Abcgini Eikipiztuzos [demainn-22 23.
Ieary
2010x gray] 1-2.9,
(3,2.9); iight
Adeow (-3,0) 13,07
Ade >] 10, 2,21 [0, sodelabosy 15y8);
(coloseleedr
thick] plot (M6, mode (anchoessuch west, Lext widthel.Son]] (8y=3)
Ade (coloseBle BBlu,, bicck] plot (1x, (0.5 (esp())a) (xp(-10111 mude (below elght, Lext, widthell Sen]
symistanb xe
Orange, thick] plot (ts.
(bslow rexht, vidta-1.ton] (0ym1)
lendi
lenditikan
tikspicture
(end docament.
TikZilla-BB-RI:
idccumentelass tike] (standalone]
Nssepackass/t (ticka pafsiots, amonach]
susctikgllorasy www.lorryy(srsowaa... (arsylooooossss...
wtw.tianns.pahhppciiiigg positisning, shapes, cale]
thegini dicuunent
(beginit kkpii
Adrax 33.
(3, 0) rodelright) (5x8);
Adzow halp lines, gravi30] (-3,-2) arid (3:21)
Adeow thick, 206] [-3, 0) (2, 0) 13, 31
Arado right] at 13.2,
(8c10)
Arode blue, right at (3.2, 0.5) [5y Wlaan x51:
chick.
camples (VX, (tanb/V/a0))
Adraw hiickk orange plot (3mniire
sample==103.
(38, (1/11 expt Vod
Visign
Arodo bolox at [-2, D1 1-21)
Arode bello at
Arodo balox 32 (2, 0) (2)
Arode at (3.
Lenditikapictun
decamnntt
(eLU(x)
(w)
2
11
2
tank
Figure 18: TikZ code and rendered figures shown for TikZilla-8B and TikZilla-8B-R using the description above. The code produced by TikZilla-8P could be compiled while TikZilla-8B-RU produced correct code.
29

---

Published as a conference paper at ICLR 2026
Table 10: Exemplary scientific TikZ figures produced by one baseline LLM (GPT-40) and two of our finetuned LLMs (TikZilla-3B and TikZilla-B-RLL) using the prompts from the first column which have been VLM augmented based on the Ground Truth figures in the second column. boxed figures have been rated as very good, as good, O as bad, and as very bad by human annotators. Empty cells indicate non-compilable TikZ code.
Prompt
Ground
GPT-4o
TikZilla-3B
TikZilla-3B-
Truth
RL
columa consir shale IS Se th. de 36, Sc 18, and Sc 15, laer dade tatuglo glo luided be 1S, So 25, St and be 183, abo let onlane coree shught baok
soll inc betuglo dlead vinicaty belvem The labak Sa henwers audlastay de copective lacs
bt as arz teb 25, 38 red ase widde restaglo..
son' us 100nt wode cepuunat of ti th anded Acaunn 197 rpuins it and Farts consine thowe perted hers "sar hartich
muesti "MPPAD Meth and thid not, appceaati libe
and the and at for caeeore I the bladi hut libridd it Viu Mac 14, and sot but the cingory btack bubciad laico at Mad the anl the
ligial is messoel tepe The chan budkgreed iaciades dodted each
duggas camtra aneral baddel amos and aades amaged mnde
conecnal verceral lonkel Shidk 37% froe
5rGaeze 5. seec Stouss ad cnancted by dontnadd mowe jus is loud cottr made
to mode veaian itt Rilos tighword tabale biud
d'Geaaa ixconactidd
gover Chaane is Froes lpdcia Jii1s
9 th yt bend arow tilvle g misias is 3030 the aotted tade tu 30 settoal aoee bincled 3f Shouss cos. mien us as Eveies shiud Shaa wil
dannd libeled
sumar bettonal martay bow The cnnrunee deste is the bervee loah the maregle ki5 and 507 bate Ni5 from up boscee Each stods torieead saapks paa kis Shupe The capede heve bettorrd thne abeled fant sowedd ripenced to Madk laccee sks. amancnal
countt
with and smis buatng a careutie
merrggle theikal femoved
Sus facelick the toops frves thn dedle complerig rerseeeee
Arotagsta diggen cadmad by dackel banky wit assaid can- with deveroue amso landel Toadseil miee Yoe bad
lubckd "oard KIN, Bet The right puth cast dreandd anme tahele " TIF"
sardee dow an and anoe conseca ao
ane to aciect with taak exprctss pudisticato
nydaoaad amose bualing the rutiple sicke clicke with plac mndd expoorciin pddaiio coeraios. The left pur ano lei rodunglo biddel "Cend kS" The recaage la righoned cesk draded aare inds labe "Duputti TIs. entre tasked the seties conaning the lepadox 115 put
motingle
right
Sooes
peir corsrd tee end Vigea pelgge knis toee is destased in bisk wes The axudies sincaly aliped and dlmmeae les ofd i
playee
Ao Mil
Top's pull
S'uakaa an oight gigi prstical right pelyge.
Stare daames was deeits lchcldd So and Sy 25 Clrck Sq
lacks uno sered ii tel "ake 18l 6 with
the thd kop had donr
las roala '22" cour "ae- aed "vodge
"agar "aags "Tex" ""koyss fae "aaa of...
30

---

Published as a conference paper at ICLR 2026
Table II: Exemplary scientific TikZ figures produced by one baseline LLM (GPT-5) and two of finetuned LLMs (TikZilla-8B, and -iLZ using the prompts from the first column which have been VLM augmented based on the Ground Truth figures in the second column. O- boxed figures have been rated as very good, as good, - as bad, and - as very bad by human annotators. Empty cells indicate non-compilable TikZ code.
Prompt
Ground Truth
GPT-5
TikZilla-8B
TikZilla-8B- RL
and secaugie the bahded wish 5 taaa "apedd a the 315 sepii un le age dad will thre Sead the 513 sed. As
retangler, "ppaie thciggt Mask abdedd civle "aafes in Seee pet te Mandeeee sithe like tngting with aroos pointing in both directons
labled
lef,
peas fun rod
the
Brndart with wao of conacied dapca top laaidd lesks
sagi abdad "Gad ieraaee.. visca is inse a bapet gny rat pateph. "Chi wasaiee. dovesseat unee bedo to sie lihded Cee. baddy a recragle ubild "Geenrtion dhddile Planing Regie
furved bekd "Adtieel and snow lenading times Deaaa. The ply
glo conneing to rensggee taokll Gnd wirktie...
tb. Abore de ame dane adjcees cakeed mosajo dlased lest- Hak al cote The soneal saa regees ladded DX kil Nark an coner Bales arove thes ventcal blck adow aone The
to anddal 58 dasale reggrs and teet fire the anoihhed
Cataiia cand syoce with ileeionna not stgrng line skeg stggs
skog
onts tiack fer Ubeled S25 A choted cack with Sabed lettie
Hacaii tho wask kellc tiga dee seja sadde dee caikk A Mask loc This lines wboked 58'5 ser the do les pu lsoed cutade coertiatt
law dan with the sustia libcled "Nowee sie"" saginy lives raspin bves to in kummens The daa ceease anda woldd Muck eathen loekel Sue Iar graanss the libiid "1 line dil a draed black the babke "iin dil daubed bas loe luboli "Jas bon barddaa moady becoonal unead tebi whelld NNNEY The logeed in plave baste white with the
shodel IA nnttviuting of
anvewars stas aboued sant fross ivd indiciad the
cow oa banaeg Suae har will koop laonr cue 2 loepchacc inticand de cilles worrd
stgang
wndee sax valin sare vericaly paree ian tou
he's Nav
las connectog the apaas the tlack die Thi allomdke tas Mea iie Anvonaati,, with back
zad baten with Mack alecel Sirk beke belennaao Muck det, with math dabae and the madke narbid liee 1oth top bour aiteet "jel re 37A and 1333
31

---

Published as a conference paper at ICLR 2026
Table 12: Exemplary scientific TikZ. figures produced by one baseline LLM (GPT-40) and two of our finetuned LLMs (TikZilla-8B and TikZilla8B-RRL) using the prompts from the first column which have been VLM augmented based on the Ground Truth figures in the second column. boxed figures have been rated as very good, as good, as bad, and as very bad by human annotators. Empty cells indicate non-compilable TikZ code.
Prompt
Ground Truth
GPT-40
TikZilla-8B
TikZilla-8B- RL
The inage with the aniciiee
dew
pats $100-m lxo axd mpporeiig erteray les The fhe liw the win the prosteacly The soond lig load with miattar nurber spprees appoonaads will pee sas youks A grea issiosall laco lecadd booke thn labckd's lis with ra andedd hagle happe LolIS. and the banded Yveie
oxgin Eoe nutted at namaak
dyn consasee exstec DECEs
Mad dagaas for aatiliaaee workdew, sko lubied reds tos. dhe praces taae was pinzi blck backed Teratntt Gown- isskeel me or inder litebe Froms
Stih aco metiigle Booeds mucke recrag lieed Spac
CTI Einssmar beth las rectagho an boxt
and "ya- "wilge dae In mevive nun apent dut hot is the line and meeker amer oith the bhec sh andell Opeed
wakkhee for cve ca wing fold consio of tou cits- Heem, The for
tase and aoaedd Uniied
becert Theakin Teead tep maaning inteeaa thnd once uedal "Ik goin
boucth Paos ttad "Bee"
aroee leal viil landio 1he tert on
"speardd b times caing & sobi.
(oencluat dinded rettied sestonn, cach doobal banke. Tinse
DG Lay
the right Fochs socise conpiee hyprace hoom creaaced the they bugin at the op with aoaald ocnsgle bibied Sacc glad petr 'trcin Andia deesead ama acrodt a bock bekd thans gren visk doveand urceangl landel
consive fone the
amoo kook tingle bilred
pose io the rigt conwes to te to keep sectie with recauule whic des spti raa Ind beaader addda Poosed grves and Torad
didrraar gi clor diicee two right colord ragea was disnets
aapeen lubild Breat wash peocasee 333% sad is cakend and 1.3 The this
Nedde" wit
The
with proceaaa
a byth Mac. tis
arams laccs
lnorkiedd rnd The onien
ubiled "Paria with pen
"Luss" vith poocraaee par and sokeed pelin, The aats eq- con Buck lade a lood oatude compooddin segieet with bees
Too Mark momaggle poctioed herloveally
snc The lnf seniaaa comaie (sbel whi opti canoll more pointng
and lddeed
Aboo
dnvoparts con wes the lart incsogle the rite metangle bnsded tn grr ain sapls abded 5 -igg -0 2.
Larg Mack carved dap sccrdia the lett munchleg watke of jendkdagans siniia dugondly villa ducked landal Alos the veerra the nnd dereead fro Hack lacke
Tho
connanng at homse ride, ovedkate soae Tes to Hal the wotte laboel
""2"" ecrade live Rack dee latated 35t on the barowass sos The Mad lal ioe top rutk de curvec stape.
32

---

Published as a conference paper at ICLR 2026
Table 13: Exemplary scientific TikZ. figures produced by one baseline LLM (GPT-4o) and two of our finetuned LLMs (TikZilla-8B, and -ikZ using the prompts from the first column which have been VLM augmented based on the Ground Truth figures in the second column. boxed figures have been rated as very good, as good, as bad, and as very bad by human annotators. Empty cells indicate non-compilable TikZ. code.
Prompt
Ground Truth
GPT-40
TikZilla-BB
TikZilla-8B-
RL
So toud und SN foo5 posthed uptiaily with sD dous op and reak Wos abiid book yrurec holes To blak edic crnsecu bottonall tooi was thead bracknt fabell "htupp" dreanunt, righ of to1s, lestored snow Sddl "Dika
Duo
Al
9.15 Aboeenta
Aay
andd Bakke thes dee veskel tracke lubcked 3/ Decta in Deta uB pateo lett and lacker Af bakk venge t. "Dde mnd HB, parnting reaovent.
The dan conaae
netiiall landad "GSSUKK" and "Muti scads, with yunls Libekd "Futveraase Chang"" free 100
"ISMEK" greap ber Each contin The soord groee atweling mres ts
ppetec "Huuf"
will lubced of al bodtes MMATH" amag andt be- pore. rxteddigg frve to bdbold
"
by The tast ideathor
hettmeed crousu an theser the lead "iilanale di groas Libanc 1-00) Liane 30--8.
sectoes thoee cenael poir from wcidh for back fees
to lulcced not wil. aakaac and haliee be all, Jutii
belton dill in "Nuther puatee ther the spaar. quatret, thas and panapine
"Andee caadren Aaaln blcc unrved arlyy Mack fives Maa
there untegie. thi beard
conspeeriin ding with aathers and the wos is litvtd len didn.
for A in Men and the
eepally
bessea lett sonas an poo poskasnce ahone bate dastud squar tahaod tp 35 dugh potnet scaan stidd
The coke bos
tildd with
waane tap oener bike same
parto
"The
ind sadreaingt the with the and spa The uibe in codiicpla ploudd takke
Hac aquare The poeao 15 and 9p 35 as coutted with and cancles by
(x)
preza dindls Linded SX 5S0 15, S3.25 -73
anaaaae berlinndll from left to rylh, SNS, Bukir
conaucted
right mudgenniin 1%, sequstivlyy bock cicle coneecee reccooeeeediing squ the keaas, cnlmeciiig vativval amorvs The aoquenee cenliaoo with con sulion.
On the lel,, lik ouraal las oeeedt two Mack dos landal beld by is poodiee reen riglet dishonatt anddd cona 1ack teakel 555
bekc SW biy the zame The lidd Wuseeniee
consuco
Mack de thoke be sing portened vanile the the saaall "he-- - iidta qpadica ddduu Thil TM mome (S. ciole codeooosiingg nwo doe protonee thth right lahoed Sps
yoe
aea ldool to dete BRo Muet May"" Rog your Resshuf TecDler Back the konuseal lis sout the las adoniig th modes whe Wieken
thar NMA (il
NMAA valo with makas oatien The tlep mode
11%% with
10 ti pr madion cless to 2oo The Roddaal model as wale: abonc 155% Paals backer dhan a skublyy wades geeal 1.
33


Reviews:
[{"review_id": "lKZPezZsze", "reviewer": "Reviewer_XPus", "summary": "This paper presents DaTikZ-V4, a new large-scale dataset designed for text-to-TikZ generation.\nIn addition to the conventional supervised fine-tuning (SFT) approach, the authors introduce a reinforcement learning framework that explicitly takes into account the visual quality of the generated images.\nBy employing a two-stage training strategy that combines large-scale data with reinforcement learning, the proposed method achieves a substantial improvement in text-to-TikZ generation performance.", "questions": "In Section 3, under Caption Quality Analysis, the paper states that “Accurate text-to-TikZ generation requires captions that specify objects, attributes, and spatial relations.”\nWhile it is understandable that detailed captions are helpful for reducing hallucinations during model training, in practical applications it is often costly to describe every element in an image exhaustively. In many real-world scenarios, users may prefer to generate diagrams from shorter or more abstract textual instructions rather than from fully detailed descriptions.\nGiven that the proposed model is trained only on detailed captions, how does it perform when provided with insufficiently detailed or abstract instruction texts? What kind of outputs would it produce in such cases?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "- The authors newly construct a large-scale TikZ dataset, approximately four times larger than the existing dataset.\n- They define a reward function based on image similarity and demonstrate the effectiveness of reinforcement learning.\n- They conduct not only automatic evaluations but also human evaluations, demonstrating the effectiveness of the proposed method.", "weaknesses": "- In Section 4, the paper describes the collection of a new large-scale dataset; however, there appears to be no mention of its license information. Since license details are essential for enabling data reuse, it would be helpful to provide not only the total number of data samples but also a breakdown of the dataset by license type.\n- The correlation between the automatic evaluation metrics and the human evaluation results appears to be low, making it difficult to accurately assess the quality of the generated results based solely on the automatic scores. For example, in Table 4, the discussion is based only on the automatic evaluation values. It would therefore be desirable to develop or employ automatic evaluation metrics that exhibit a higher correlation with human judgments.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "VWfKecOXSp", "reviewer": "Reviewer_LicD", "summary": "This paper proposes a data generation methodology to generate 1.5M tikz examples. These are then used to finetune QWen (with RL + SFT) in order to obtain small-ish (3B/8B) models that surpass GPT-4 and are similar in performance to GPT-5.\n\nThe authors do this by first sourcing data from Github and other sources and then using a pipeline to identify valid tikz and leveraging a VLM to obtain descriptions.\n\nThis data is used to finetune and then with RL using GRPO.", "questions": "See above.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. This paper describes how to obtain a large scale text-to-tikz dataset. They use a combination of choices (e.g. 1 tikz per website; ensuring compilation; VLM style description) to obtain a large scale and high quality dataset.\n\n2. They describe howt o use such a dataset for SFT and for RL. For RL, they add a couple of domain specific changes -- e.g. the model for the reward; the scalar rewards for capturing semantic alignment. \n\n3. Their strategy seems to work -- leading to clear improvements across model sizes and leading to performance that is comparable with GPT-4o / GPT-5 though much smaller by using a human rating comparison.", "weaknesses": "1. It would be good to quantify the difference in model size in Figure 4 -- how much smaller is the model than GPT-5/4o in terms of sheer size of the model as well as compute at inference time.\n\n2. it would be good to plot / understand performance as a function of the size of the data. If we want better performance, can we just collect more data or are we already hitting diminishing returns here ?\n\n3. Is the dataset planning to be made public? It would be useful for other researchers I imagine.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "gQ2Osr1xHC", "reviewer": "Reviewer_9Bdk", "summary": "This paper focuses on advancing the state-of-the-art in the application of text-to-Tikz, the challenge of generating code for a tikz diagram based on a text specification. The paper provides several contributions. The first is an analysis of existing text-to-Tikz datasets. The paper finds that relying on captions for the textual description of a figure, as many of these datasets apparently do, is probably insufficient for the development of strong models since captions fail to capture major structural components. To mitigate this, the paper introduces DaTikZ-V4, which revises and extends a previous dataset called DaTikZ-V3. This includes more careful filtering, additional data sources like GitHub, and the inclusion of LVLM figure descriptions. After this, the paper describes its approach to training text-to-TikZ models using supervised finetuning and then RL with a special emphasis on the RL rewards used. Finally, the paper describes a range of comparisons between models trained with the proposed methods (the paper calls these TikZilla models) and other relevant LLMs, showing that Tikzilla models are competitive with much larger, proprietary systems.", "questions": "-\tLine 478: Why is this approach “ethical”?\n-\tWill models or datasets be released?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "**Clarity:** The paper is well-written. It effectively communicates its contributions relative to other work in this area so that even a reader that is not familiar with the text-to-TikZ problem can appreciate the results. DaTikZ-V4 is well-motivated by an analysis of existing datasets (though the reviewer would have appreciated a few more examples). The figures are all of high quality and efficiently visualize key ideas in the paper.\n\n**Effective, small, specialized models:** One potential criticism of this paper is that the TikZilla models are at best marginally better than many existing models (e.g., GPT5). The important point though (from this reviewer’s perspective) is that the TikZilla models are much, much smaller than GPT5 and other proprietary models that achieve comparable performance. This reviewer believes that the generation of small, reliable, specialized models is an important contribution to the community.", "weaknesses": "**A lot of the paper feels routine:** The approach described in the paper appears to result in substantially better performance than prior small-model approaches. However, the methods used to achieve these improvements are exactly what most readers would likely expect: increase the size of the dataset, increase the quality of the dataset, implement recent approaches that have been successful in language modeling broadly (e.g., RL). One exception to this is the section on reward signals specific for the text-to-TikZ problem. Together, the reviewer believes that this will somewhat limit what a reader will take away from the work. On the other hand, a result being in line with community intuition should not be a barrier to publication. As such, the reviewer did not weight this ‘weakness’ very strongly when evaluating the work.\n\n**More analysis of failure cases would be interesting:** The paper devotes considerable space to describing how current text-to-TikZ datasets fall short of what would likely be needed to train strong text-to-TikZ models. The reviewer appreciated this contribution. It would have been nice to see a similar type of analysis applied to the results section where text-to-TikZ models were compared. TikZilla and GPT5 score comparably in certain respects, but this doesn’t mean that they fail in the same ways. If large proprietary models and TikZilla have different strengths and weaknesses, this would be very important to know in practice. \n\n**Nitpicks**\n- Line 268: A parenthesis is incorrectly oriented.\n- Line 365: This would probably come across as more interesting if it was not labeled as ‘emergence’, which feels like a strong word for this particular phenomenon.\n- Table 3: It would be helpful to remind the reader what the acronyms stand for so they don’t have to flip back to earlier sections.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "IZ6e8lcEYg", "reviewer": "Reviewer_fGdx", "summary": "This paper presents TikZilla, a new family of open-source models for generating scientific figures from text by producing TikZ code. The authors' primary contributions are twofold: 1) the creation of a large-scale, high-quality dataset named DaTikZ-V4, and 2) a two-stage training pipeline. The dataset involves an LLM-based pipeline to debug and repair uncompilable code and a VLM-based process to generate rich figure descriptions, overcoming the sparseness of typical captions. The training methodology first uses SFT to teach the model TikZ syntax, followed by RL to align the output with the desired visual semantics. A key aspect of the RL stage is a domain-specific reward model, derived from a fine-tuned inverse-graphics encoder, which proves to be more effective than generic multimodal metrics. Through extensive automatic and human evaluations, the paper demonstrates that even small TikZilla models (3B/8B) achieve SOTA results, outperforming strong proprietary baselines like GPT-4o and matching the performance of next-generation models on image-based evaluations.", "questions": "1. Could you provide a more quantitative analysis of the VLM's error rate? For example, by manually evaluating a random sample of generated descriptions against their ground-truth images, could you estimate how often the VLM omits key details, hallucinates elements, or misinterprets spatial relationships?\n2. While I understand that a full training run with a different reward model is resource-intensive, could you provide more direct evidence of $R_{sim}$'s practical superiority?\n3. Have you observed any limitations regarding the model's ability to render specific styles, such as different dash patterns, line weights, or precise color shades, when these are explicitly requested in the prompt? Since the reward is based on patch embeddings, it might not penalize deviations in these subtle attributes as strongly.\n4. Table 3 shows an interesting result: RL applied directly to the base Qwen3-8B model (+RL) yields a significant performance boost (AVG 0.251 → 0.357), whereas for Qwen2.5-3B, the improvement is much smaller. The paper suggests this is because larger models already encode some TikZ knowledge. Could you elaborate on this hypothesis? Is the primary role of SFT for smaller models simply to get them into a \"syntactically plausible\" region where RL can effectively take over?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "This is a strong paper with several key strengths that make it a compelling contribution to the field.\n\n1.  The paper's most significant contribution is the creation of DaTikZ-V4, a large-scale, high-quality dataset for the Text-to-TikZ task. The authors have addressed the critical problem of data scarcity and quality through a meticulous, multi-source collection process and two highly innovative enhancement steps: (1) an LLM-based debugging pipeline to repair uncompilable code, substantially increasing the usable data, and (2) the use of VLMs to generate semantically rich descriptions, which are demonstrably superior to the sparse original captions. The appendix further validates the data quality with detailed analysis.\n\n2.  The proposed domain-specific reward model for RL is a standout feature. By finetuning an inverse-graphics (Image-to-TikZ) model and using the semantic distance between image embeddings as a reward signal, the authors have developed a method that is simple, intuitive, and highly effective. This approach is more semantically faithful for scientific figures than general-purpose metrics, and its strong correlation with human judgment validates its design and utility in guiding the model towards generating visually accurate figures.\n\n3.  The paper presents a solid two-stage training pipeline (SFT followed by RL) that is executed with rigor, evidenced by the detailed description of the multi-GPU, long-duration training process. The authors clearly articulate the role and limitations of each stage, providing a strong justification for their approach. For instance, the paper notes that while SFT trains the model in syntax, it \"remains unaware of the rendered semantics,\" which perfectly motivates the necessity of the subsequent RL stage for visual alignment.\n\n4. The experimental evaluation is extensive and highly convincing. The authors benchmark their models against a wide range of the latest and most powerful systems on a carefully constructed, contamination-free test set. The results are outstanding: TikZilla achieves state-of-the-art performance across multiple metrics, significantly outperforming top open-source models and even much larger proprietary systems like GPT-4o. Crucially, these strong quantitative results are corroborated by a thorough human evaluation with expert annotators, confirming that TikZilla produces publication-quality figures.\n\n5.  The paper is exceptionally well-written and clearly structured. The motivation is compelling, the problem is well-defined, and the proposed solutions are presented in a logical, easy-to-follow narrative. The authors excel at explaining the rationale behind each design choice, making the entire research story coherent and persuasive.", "weaknesses": "1. The entire training pipeline is fundamentally dependent on the quality of the VLM-generated figure descriptions. While the paper demonstrates that these are superior to raw captions, VLMs are known to hallucinate and omit critical details.\n2. The paper convincingly argues for its domain-specific reward model (R_sim) by showing its high correlation with human judgment. However, it doesn't contain a direct experimental comparison against general-purpose reward models (like CLIPScore or DreamSIM) within the RL training loop.\n3. The reward model operates on the semantic similarity of image patch embeddings. It may not be sensitive enough to fine-grained stylistic details that are crucial for scientific figures, such as precise line weights, specific dash patterns, exact color hex codes, or font styles.\n4. The dataset is sourced primarily from arXiv, GitHub, and TeX StackExchange, which likely results in a corpus rich in diagrams, plots, and graphs common to fields like computer science, mathematics, and physics. But it's unclear how well TikZilla would generalize to more structurally different (OOD) types of scientific figures, such as complex chemical reaction pathways, intricate biological diagrams, or detailed engineering schematics, which may be underrepresented in the training data.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)