MINOS: A Multimodal Evaluation Model for Bidirectional Generation Between Image and Text
Pith reviewed 2026-05-19 11:20 UTC · model grok-4.3
The pith
Minos achieves state-of-the-art multimodal evaluation on 16 out-of-domain datasets using under half the training data of prior models.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
Through rigorous quality control strategies during the construction of the Minos-57K dataset with evaluation samples across 15 datasets, and training the Minos model with SFT and preference alignment training strategies, the model achieves state-of-the-art evaluation performance across 16 out-of-domain datasets covering both I2T and T2I tasks among all open-source multimodal evaluation models and remains competitive with closed-source models, despite using less than half the scale of the training data of prior work.
What carries the argument
Minos-57K dataset built with rigorous quality control, used for SFT plus preference alignment training of a bidirectional multimodal evaluator
If this is right
- Quality control during dataset construction outweighs raw data scale for building general multimodal evaluators.
- Joint training on both image-to-text and text-to-image evaluation data produces consistent strength across directions.
- Preference alignment after supervised fine-tuning further raises accuracy on unseen generation tasks.
- Smaller but carefully filtered datasets can match or exceed larger collections for evaluator training.
Where Pith is reading between the lines
- The same quality-first curation approach could be tested on evaluators for video or audio generation tasks.
- Open-source multimodal models might narrow the gap with closed-source systems by adopting comparable data filtering.
- Future multimodal benchmarks could add explicit checks for training-data quality to measure true generalization.
- Researchers could verify whether the benefit of bidirectional training appears when the base model changes.
Load-bearing premise
The quality control steps used to build Minos-57K produce samples that are sufficiently unbiased and representative to train an evaluator that generalizes to truly out-of-domain data.
What would settle it
Measuring Minos performance on a fresh collection of multimodal generation samples drawn from entirely new domains or datasets never seen during the original 15-source curation would show whether the out-of-domain gains depend on the specific quality biases of the training set.
read the original abstract
Evaluation is important for multimodal generation tasks, while traditional multimodal evaluation metrics suffer from several limitations. With the rapid progress of MLLMs, there is growing interest in applying MLLMs to build general evaluation systems. However, existing researches often simply collect large-scale evaluation data for training, while overlooking the quality of evaluation data. What's more, current proposed evaluation models often struggle to achieve consistently strong performance across both image-to-text (I2T) and text-to-image (T2I) tasks. In this paper, through rigorous quality control strategies, we construct a comprehensive multimodal evaluation dataset, Minos-57K, with evaluation samples across 15 datasets, for developing the multimodal evaluation model Minos with SFT and preference alignment training strategies. Notably, despite using less than half the scale of the training data of prior work, our model achieves state-of-the-art evaluation performance across 16 out-of-domain datasets covering both I2T and T2I tasks among all open-source multimodal evaluation models and remain competitive with closed-source models. Extensive experiments demonstrate the importance of leveraging quality control process, jointly training on evaluation data from both I2T and T2I generation tasks and further preference alignment.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript introduces MINOS, a multimodal evaluation model for bidirectional image-to-text (I2T) and text-to-image (T2I) generation tasks. The authors construct the Minos-57K dataset from 15 source datasets using rigorous quality control, then apply supervised fine-tuning (SFT) and preference alignment. They report that, despite using less than half the training data scale of prior work, MINOS achieves state-of-the-art performance among open-source multimodal evaluators across 16 out-of-domain datasets for both task directions while remaining competitive with closed-source models. Experiments are said to demonstrate the contributions of quality control, joint I2T/T2I training, and preference alignment.
Significance. If the out-of-domain generalization holds, the work would be significant for multimodal evaluation research. It provides evidence that targeted quality control and bidirectional joint training can yield strong evaluators with substantially reduced data scale, addressing limitations of traditional metrics and prior MLLM-based approaches that often lack consistent performance across directions. This could encourage more emphasis on curation over sheer volume in building reliable evaluation systems.
major comments (1)
- [§3 (Dataset Construction) and §4 (Experiments)] The central claim of SOTA performance on 16 truly out-of-domain datasets (abstract and §4) rests on the assumption of distributional separation from the 15 sources used to build Minos-57K (§3). The manuscript provides no quantitative verification of this separation, such as image duplicate detection via hashing, text n-gram overlap, CLIP similarity histograms between train and test sets, or a provenance table. Without such evidence, performance gains cannot be confidently attributed to quality control rather than possible leakage or domain overlap.
minor comments (2)
- [Abstract and §4] The abstract and §4 would benefit from explicit reference to the specific tables or figures reporting per-dataset scores, baseline comparisons, and any statistical significance tests supporting the SOTA claims.
- Notation for training strategies (SFT, preference alignment) and dataset names could be introduced with brief definitions on first use for improved readability.
Simulated Author's Rebuttal
We thank the referee for the careful review and the constructive comment on substantiating the out-of-domain claims. We agree that additional quantitative evidence would strengthen the manuscript and will incorporate it in the revision.
read point-by-point responses
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Referee: [§3 (Dataset Construction) and §4 (Experiments)] The central claim of SOTA performance on 16 truly out-of-domain datasets (abstract and §4) rests on the assumption of distributional separation from the 15 sources used to build Minos-57K (§3). The manuscript provides no quantitative verification of this separation, such as image duplicate detection via hashing, text n-gram overlap, CLIP similarity histograms between train and test sets, or a provenance table. Without such evidence, performance gains cannot be confidently attributed to quality control rather than possible leakage or domain overlap.
Authors: We acknowledge that the current manuscript does not present explicit quantitative verification (e.g., perceptual hashing for image duplicates, n-gram overlap statistics, or CLIP similarity distributions) to demonstrate distributional separation between the Minos-57K training sources and the 16 evaluation datasets. The 16 test datasets were selected from distinct public benchmarks whose original publications and collection protocols differ from the 15 sources used for Minos-57K; however, we did not include a formal provenance table or overlap metrics. In the revised manuscript we will add (i) a provenance table in §3 listing the exact source and split for every training and test dataset, (ii) a brief analysis of text n-gram overlap and image duplicate rates where feasible, and (iii) CLIP similarity histograms comparing the training and test distributions. These additions will allow readers to directly assess the degree of distributional separation and will make the generalization claims more transparent. revision: yes
Circularity Check
No circularity: empirical claims rest on external benchmarks
full rationale
The paper constructs Minos-57K from 15 source datasets via quality control, trains Minos via SFT and preference alignment, then reports performance on 16 separate out-of-domain datasets. No equations, derivations, or mathematical steps exist that could reduce results to fitted inputs or self-definitions by construction. Claims rely on measured outcomes against independent external test sets rather than any internal renaming, self-citation chain, or ansatz smuggling. The evaluation is therefore self-contained against external benchmarks.
Axiom & Free-Parameter Ledger
Lean theorems connected to this paper
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IndisputableMonolith/Cost/FunctionalEquation.leanwashburn_uniqueness_aczel unclear?
unclearRelation between the paper passage and the cited Recognition theorem.
We propose Minos, a multimodal evaluation model ... trained using a Mix-SFT training and DPO alignment strategy on Minos-Corpus.
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IndisputableMonolith/Foundation/AlphaCoordinateFixation.leanJ_uniquely_calibrated_via_higher_derivative unclear?
unclearRelation between the paper passage and the cited Recognition theorem.
rigorous quality control strategies ... Minos-57K ... state-of-the-art evaluation performance across 16 out-of-domain datasets
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- uses
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- unclear
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discussion (0)
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