Euclid: Supercharging Multimodal LLMs with Synthetic High-Fidelity Visual Descriptions

Authors: Jiarui Zhang, Ollie Liu, Tianyu Yu, Jinyi Hu, Willie Neiswanger
Primary area: foundation or frontier models, including LLMs
Keywords: Multimodal LLMs, Geometric Perception, Low-level Visual Perception

Abstract:
Multimodal large language models (MLLMs) have made rapid progress in recent years, yet continue to struggle with low-level visual perception—particularly the ability to accurately describe the geometric details of an image. This capability is crucial for applications in areas such as robotics, medical image analysis, and manufacturing. To address this challenge, we first introduce Geoperception, a benchmark designed to evaluate an MLLM’s ability to accurately transcribe 2D geometric information from an image. Using this benchmark, we demonstrate the limitations of leading MLLMs, and then conduct a comprehensive empirical study to explore strategies for improving their performance on geometric tasks. Our findings highlight the benefits of certain model architectures, training techniques, and data strategies, including the use of high-fidelity synthetic data and multi-stage training with a data curriculum. Notably, we find that a data curriculum enables models to learn challenging geometry understanding tasks which they fail to learn from scratch. Leveraging these insights, we develop Euclid, a family of models specifically optimized for strong low-level geometric perception. Although purely trained on synthetic multimodal data, Euclid shows strong generalization ability to novel geometry shapes. For instance, Euclid outperforms the best closed-source model, Gemini-1.5-Pro, by up to 54.52% on benchmark tasks.

Decision: reject
Reviewer scores: 6, 6, 5, 3

Reviews and discussion:
<thread>
creation date:
1729108844519

thread_type:
official_review

<thread_object>
creator: reviewer_hlzb

<content>
summary:
The authors present Geoperception, a VQA dataset sourced from Geometry-3K. Geoperception requires reasoning over "surface-level geometric information" to identify points and line segments that satisfy conditions, and to classify angles. The authors evaluate a variety of existing MLLMs on the dataset and find that they do not perform consistently. Motivated by the results of the evaluation, the authors train their own MLLM, Euclid, on synthetic samples created using modified AlphaGeometry tooling. They also measure the effects of several design choices on their synthetic dataset.

strengths:
1. The writing of the paper is above-average.
2. Diagnostic benchmarks are valuable and relevant to the ICLR community.
2. The results of the visual-tokenizer evaluation are interesting.

weaknesses:
1. Performance on the benchmark seems likely moreso dominated by syntax than reasoning ability: "Certain models, such as GPT-4o-mini (Achiam et al., 2023) and Molmo-7B-D (Deitke et al., 2024), frequently either enumerate all potential components (e.g., all points in a diagram instead of the one on the lines) or every potential answer, leading to their poor accuracy scores." Given the authors have identified this as an issue, it seems difficult to conclude from the investigation that "Current MLLMs struggle to perceive low-level geometry annotations and relationships."

    Some questions are ungrammatical, such as asking "What is the point lying on circle with center G?" when the correct answer includes three points. Some are ambiguous, such as "Which line is longer, AE or ED?" where the answer can not be discerned from the symbols and requires measurement. Others are inconsistently labeled, like alphabetizing segment ends in answers ("CD") but not questions ("What is the line parallel to line EB?") or including duplicate answers ("ZV, VZ").
2. If the task itself had real-world application, the effect of syntax would be of lesser concern. However, "as a focused test bed" "designed to evaluate MLLMs’ ability to accurately perceive surface-level geometric information without requiring complex inference or reasoning" it is far removed from the "real-world applications" used to motivate the work: "spatial understanding for robotics, medical image analysis for accurate diagnosis, quality control in manufacturing to detect subtle defects, autonomous driving systems that rely on exact object localization or distance estimation, and augmented reality applications that demand precise overlay of virtual objects onto the real world."
3. The "detailed empirical exploration of MLLM architecture and training strategies" is disappointingly shallow. The only ablated architecture choice is the visual encoder. In their "investigation," the authors do not ask why the "CNN architecture performs better than ViT," they only present it as a "lesson." The second lesson, that "Tuning the visual encoder is beneficial," should be considered a given when one is evaluating on the training distribution. What is potentially compromised is generalization ability, which is why the ablations should have instead been measured on the evaluation set.
4. The authors' statement that they "demonstrate the limitations of leading MLLMs, and then conduct a comprehensive empirical study to explore strategies for improving *their* performance on geometric tasks" (emphasis added) is misleading. The authors do not improve the performance of existing MLLMs, opting instead to train their own from an existing visual encoder and language model. As such, it is difficult to say that "lessons we learn from this domain can be effectively generalized to a broader set of downstream domains that benefit from high-quality low-level visual perception." Had the authors tried finetuning an existing model, it's plausible that it would not be necessary to introduce a "data curriculum" to enable "models to learn challenging geometry understanding tasks which they fail to learn from scratch."
5. The curriculum training appears irrelevant as the "dataset generation engine can produce infinite samples for exhaustive task-specific training" and "Adaptive Curriculum" performance appears to match "Mixed Shape" at saturation when trained with 150% the samples (Figure 6). Why is it of practical value to "further enhance model efficiency on challenging shapes" and why is it true that "employing a curriculum-based training approach yields much more model potential than direct task training"?
6. The authors' claims are overstated. It is not true that "Euclid significantly outperforms current leading MLLMs," "surpassing the leading MLLMs by a substantial margin." It is outclassed by Gemini-1.5-Pro on one of the four tasks and exceeds it by less than a percent on another. The authors must correctly qualify their claims.

questions:
1. Why are only four out of seven benchmark tasks used for evaluation of the proposed model (Table 4)?
2. In the related work, the authors write that "VDLM (Wang et al., 2024b) transcribes raster images into vector graphics and uses LLMs to reason over the SVG code. They find that although SVG code is not straightforward to understand, using LLMs to reason over SVG is consistently more effective than directly using MLLMs on original raster images." If reconstructing prior to reasoning is "consistently more effective," why is it not evaluated against? Why is it important that Geoperception be solved through intermediate-free VQA?
3. The authors state their task is "straightforward for humans" but do not provide quantitative comparison. Have they measured this?

Further comments:
1. The title does not seem very representative of the paper content and appears overly sensational. The authors are suggested to change it to remove mention of Euclid, which the authors have not demonstrated to be generally applicable outside the benchmark, and focus on Geoperception which seems to be the real contribution of the work.
2. The authors write "For our Euclid model, we include all geometry shape code used for training, along with demonstration diagrams and pseudocode for generating training questions and answers," but it does not appear that supplementary materials were submitted.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730632155865

thread_type:
official_review

<thread_object>
creator: reviewer_hwjq

<content>
summary:
The paper proposes a new benchmark, Geoperception, sourced from Geometry-3K, to evaluate MLLMs' ability to perform geometric perception. The dataset is in QA form, and the benchmark has seven tasks. Most are about element relationships in geometric images, such as points, lines, and angles. The author also proposes a method called Euclid, a fine-tuned MLLM that may outperform other MLLMs.

strengths:
Geometric perception benchmark may seem a novel topic to me. The benchmark can contribute to the MLLM community.

weaknesses:
- Overall, this paper devotes a significant amount of space to introducing background information and other methods, while providing relatively fewer details about its own proposed approach. I think the amount of work is below the average level for ICLR. Therefore, I believe the contributions of this paper are insufficient for acceptance at a top-tier conference like ICLR.

- The proposed task is sourced from Geometry-3K and seems to be easier than it. Since Geometry-3K contains some calculations, while this benchmark is mainly about some relationships between elements, such as whether point A is on Line A. I know the paper is trying to explore the geometric perception topic, but Geometry-3K also needs some level of perception, or models cannot do the harder calculation.  

- For baselines, why not directly fine-tune some MLLMs on Geoperception, to compare with the method of Euclid?

- If an MLLM has been fine-tuned on this perception dataset, will it gain the ability of other types of perception, such as real images or medical images?

questions:
- Why not consider the multiple-choices form for question answering?

- Is it possible to transform geometric figures into SVG code, just as VDLM, to evaluate the performance of the OpenAI-o1 model? Moreover, the authors may add some evaluation on some geometry MLLMs or math MLLMs.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730716335826

thread_type:
official_review

<thread_object>
creator: reviewer_e5tw

<content>
summary:
This paper addresses the limitations of MLLMs in low-level visual perception. Specifically,  It introduces a benchmark, Geoperception, to evaluate MLLMs' ability to transcribe 2D geometric information.  Effective strategies for enhancing model performance are identified, including high-fidelity synthetic data and a data curriculum approach. It also develops a new family of models, named Euclid, optimized for geometric perception, which significantly outperforms existing models, demonstrating strong generalization to novel geometric shapes.

strengths:
1. The paper is well-written and easy to follow.
2. The paper conducts extensive experiments to systematically explore the impact of geometric shapes of increasing complexity and identifies some key lessons.
3. The paper shows that the Euclid model trained on high-fidelity synthetic data exhibits strong generalization capabilities, especially in real-world geometric shape understanding tasks, significantly surpassing existing leading MLLMs.

weaknesses:
1. Concerns about Data Filtering with MLLM. Since Geoperception is a new benchmark intended to evaluate the precise geometric perception capabilities of MLLMs, and given that these models exhibit limited geometric understanding, it appears unreasonable to utilize gpt-4o-mini for data filtering purposes.
2. Limited Comprehensive Geometric Understanding. The results in Table 4 shows the performance limitations of Euclid on multiple tasks, such as POC and LHC. They may be linked to the benchmark data distribution. It would be beneficial for the authors to conduct further experiments aimed at enhancing this aspect.

questions:
Please refer to the weaknesses above.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730772131654

thread_type:
official_review

<thread_object>
creator: reviewer_2bjh

<content>
summary:
- The paper studies multimodal large language models (MLLMs) on the task of low-vele visual perception — particularly, testing and improving MLLMs for tasks related to Euclidean geometric concepts like points, lines, and angles.
- The first part of the study highlights significant failures of the recent open-sourced and closed-sourced MLLMs including GPT4o, Gemini, and Molmo.
- In the second part, the authors trained the MLLM on a very simple generated synthetic geometric data, which is composed of only 3 shapes (but multiple questions/answers per shape) for each geometric concept/axiom.
- They also introduce a geometry concept benchmark, named Geoperception, which is a filtered version of Geometry-3K corpus (Lu et al., 2021), as a test-bed to support the above two points.

strengths:
- The paper studies an important and open topic of the application of MLLMs on geometric problem datasets. Furthermore, comparisons of prior SOTA MLLMs and their failures further enhance the importance of the paper.
- The release of the filtered GeoPerception benchmark will support future works.
- Achieves 3x performance boost compared to Gemini1.5-Pro on `PointsonLines` questions and roughty +15% on average.
- Paper writing is overall clear and related works are well covered.

weaknesses:
- Clarification regarding sampling multiple answers per question
    - "Molmo predicts every potential answer, leading to their poor accuracy scores" – If this means, that instead of only answering the required/asked points on the line, Molmo outputs all the points on the line –– this seems less critical. It would be great if the authors could also share the accuracy if the prediction is a superset of the required answer.
    - How are multiple possible answers handled if some answers are incorrect, but there is at least one correct answer? As per evaluation score formulae, score = |Prediction| / |Ground Truth|. It will be again good to know the score of each MLLM if the formulae would have been score=1 if P is a subset of G. This will help understand if the accuracy of MLLMs can be enhanced by non-deterministic nucleus sampling.
    - On the above point, how many solutions are sampled for each question? Is any form of nucleus sampling performed i.e. sampling multiple answers per question? Please provide details for the same.

- What does it mean to compose a dataset of only 3 shapes? Were all the training and the validation question-answer pairs for training generated only from 3 shape instances? Clarification on the differences between the terminology "shapes" and geometric question/answer pairs would be great.

- Following the above question, it might be important to increase the dataset by sampling more shapes from the geometric shape generation engine and then training on that dataset.

- Comparison against a baseline that performs supervised fine-tuning of existing MLLMs rather than training the vision part of MLLMs (fine-tuning vision backbone and adapter) from scratch.

- Furthermore, it seems that the generated training dataset is relatively quite small. Comparison among different convolution and ViT-based image backbones might not be optimal under such small dataset settings. It would be nice to either test this on larger datasets or be subtle of this observation made. The same might be true for the training data curriculum.

- Rather than designing a human-designed curriculum (based on what human finds complex), doing negative-hard mining might be a strategy to design a curriculum automatically. It would be great if the authors could compare against such a strategy.

questions:
- My main concerns are regarding the small size of the dataset (any clarifications on the true train dataset size), or results on enhanced dataset size would be appreciated. Conclusions made on such a small dataset cannot be accepted to scale with dataset size.
- It would be great if the authors could share results from the requested baseline (supervised fine-tuning of existing MLLM) and automatic curriculum training based on negative-hard mining.

If my main concerns on the small training dataset size are addressed, I would be happy to increase my rating.
</content>
</thread_object>

</thread>
<thread>
creation date:
1732512948491

thread_type:
official_comment

<thread_object>
creator: authors

<content>
comment:
## **General Response to All Reviewers**

We want to thank the reviewers for their constructive and thoughtful reviews! We feel encouraged that reviewers find our work’s topic to be fundamental, important (reviewer 2bjH) and novel (reviewer HWJq); our dataset to be valuable (reviewer 2bjH and hLZB); our empirically study to be extensive and interesting (reviewer E5Tw and hLZB); and our Euclid model to be strong and robust (reviewer 2bjH and E5Tw).

In response to reviewer comments, we have made a number of additions to our experiments and paper, which we summarize below:
- Added experiments studying the effect of scaling the size of large language models.
- Added experiments incorporating all seven tasks in Geoperception for Euclid’s training.
- Added an experiment expanding our training dataset to 1 million data instances, which shows that the dataset volume in our empirical study is sufficient.
- Additional evaluation of Cambrian-1 on Geoperception tasks, showing even MLLM trained on the same images and augmented text annotations still struggle in Geoperception.
- Additional evaluation of G-LLaVA on Geoperception tasks, showing the model struggles to properly follow instructions.
- Incorporating two additional evaluation metrics for Geoperception, showing the validity of our evaluation.

Again we thank reviewers and ACs for their great efforts and valuable insights! All updates will be consistently incorporated into the updated version of this paper.
</content>
</thread_object>

</thread>


Metareview:
metareview:
This paper studies multimodal large language models (MLLMs) on the topic of low-level visual perception task. Specifically, the authors study low-level geometric understanding in MLLMs with a new benchmark and conduct empirical studies with proposed models. The manuscript was reviewed by four experts in the field. The recommendations are ("3: reject, not good enough", "5: marginally below the acceptance threshold", 2 x "6: marginally above the acceptance threshold"). The reviewers raised many concerns regarding the paper, e.g., unclear motivation and statement, unconvincing experimental benchmarks and evaluation results, etc. Considering the reviewers' concerns, we regret that the paper cannot be recommended for acceptance at this time. The authors are encouraged to consider the reviewers' comments when revising the paper for submission elsewhere.