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REVIEW 4 major objections 4 minor 56 references

YINYANG-ALIGN: Benchmarking Contradictory Objectives and Proposing Multi-Objective Optimization based DPO for Text-to-Image Alignment

T0 review · 4 major / 4 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read YinYangAlign and CAO claim that a multi-objective DPO extension can balance six contradictory text-to-image alignment goals at once, and provide a benchmark to measure the trade-offs.

desk verdict A useful benchmark idea buried under an unverifiable CAO claim and circular evaluation. read the letter →

arxiv 2502.03512 v2 pith:72VRMDZB submitted 2025-02-05 cs.AI

Amitava Das , Yaswanth Narsupalli , Gurpreet Singh , Vinija Jain , Vasu Sharma , Suranjana Trivedy , Aman Chadha , Amit Sheth This is my paper · ORCID
classification cs.AI
keywords text-to-imagealignmentmulti-objectiveoptimizationdirectpreferencecontradictoryobjectivesbenchmarkculturalsensitivitySinkhornWassersteindistanceParetofrontier
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper has two intertwined aims. It introduces YinYangAlign, a benchmark of 40,000 human-verified images built around six contradictory alignment objectives: faithfulness versus artistic freedom, emotional impact versus neutrality, realism versus artistic freedom, originality versus referentiality, verifiability versus creative freedom, and cultural sensitivity versus artistic freedom. It then introduces Contradictory Alignment Optimization (CAO), an extension of Direct Preference Optimization that treats each objective pair as a local loss and combines them through a global synergy aggregator. The paper's central claim is that optimizing any single objective alone disrupts the others, whereas CAO's multi-objective procedure achieves a stable balance across all six at once. A sympathetic reader should care because real text-to-image deployment requires exactly such trade-offs, and the benchmark makes them measurable.

What carries the argument

The load-bearing object is the CAO loss, which stacks three modules: local axiom-wise Bradley-Terry preferences over per-axiom losses $f_a(I)=\alpha_a L_p(I)+(1-\alpha_a)L_q(I)$; a global synergy preference over $S(I)=\sum_a \omega_a f_a(I)$; and axiom-specific regularizers using Sinkhorn-regularized Wasserstein distance. The synergy Jacobian $J_S=\partial S/\partial \theta$ is used to scale back gradients that benefit one axiom at the expense of others. This machinery is what lets the paper claim a Pareto-aware joint optimum rather than a winner-take-all alignment.

What would settle it

Take a held-out set of prompt-image pairs and have independent human judges rank them on one axiom, say cultural sensitivity; if the SCCM score orders pairs opposite to the human rankings on a majority of items, the cultural-sensitivity loss is not measuring the intended objective, and CAO's balanced gain on that axis would not transfer to real use.

Watch

Extended reading notes

Core claim

The discovery the authors claim is that contradictory alignment objectives in text-to-image models can be jointly optimized rather than traded off by accident. On six axiom pairs, single-objective DPO training improves the target by roughly 40% while dragging down others: for example, artistic freedom training reduces cultural sensitivity by 30% and verifiability by 35%. CAO instead builds each axiom into a Bradley-Terry preference over a per-axiom loss, aggregates these with a synergy function $S(I)=\sum_a \omega_a f_a(I)$, and regularizes the optimization with Sinkhorn-regularized Wasserstein distances and a synergy Jacobian that dampens conflicting gradients. The authors report that CAO achieves balanced gains across all six objectives, for instance +10% artistic freedom and +44% cultural sensitivity, and remains close to DPO in generalization as measured by the Weighted Alpha metric.

Load-bearing premise

The load-bearing premise is that the automated scoring rules and the machine-plus-human labels truly capture the human judgements they stand for, so if the scores misjudge what people care about, both the benchmark rankings and the claimed balance collapse.

Editorial extensions

If this is right

  • Training DPO on a single axiom improves that axiom by about 40% but consistently lowers at least one other measured objective, so single-axis alignment is measurably lopsided.
  • CAO's balanced profile, such as +10% artistic freedom alongside +44% cultural sensitivity versus baseline, shows the six objectives can be moved together rather than one at a time.
  • Varying the synergy weights traces a Pareto frontier, so a developer can choose a point that favors, say, verifiability over artistic freedom without losing sight of the other five.
  • The Weighted Alpha analysis indicates the extra multi-objective machinery costs only a small generalization drift, within about 0.1 on the reported metric, so the balance is not bought with catastrophic overfitting.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the per-axiom proxies hold up under human validation, YinYangAlign could become a shared measurement standard for text-to-image alignment, turning 'balanced alignment' from a slogan into a number that can be audited.
  • A natural extension the paper does not run is to compare CAO against a DPO model trained on a convex combination of all six preference pairs; that comparison would separate the benefit of the synergy aggregator from the benefit of simply seeing all six objectives.
  • The slider-to-weight mapping suggests CAO could serve as a controllable interface for deployed systems, letting operators trade verifiability for creativity per use case, but the paper does not demonstrate a deployed system with that interface.
  • Because the six objectives are likely correlated, for instance cultural sensitivity and verifiability both suppress hallucinated detail, what looks like synergy may partly reflect redundancy; a PCA-style analysis of the six loss gradients could test this.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper proposes YinYangAlign, a benchmark for evaluating Text-to-Image (T2I) alignment across six contradictory objective pairs, and Contradictory Alignment Optimization (CAO), a multi-objective extension of Direct Preference Optimization (DPO). The benchmark is constructed from multiple source datasets, T2I-generated outputs, VLM-based annotation with human review, and a reported final set of 40,000 images. CAO combines per-axiom losses, a weighted global synergy aggregator, Bradley-Terry preference terms, axiom-specific regularizers, and a "synergy Jacobian" intended to balance gradient conflicts. The central empirical claim is that CAO sets new performance benchmarks across all six contradictory alignment objectives while remaining stable and scalable.

Significance. If the claims were supported, the benchmark and CAO would be a useful step toward measuring and optimizing trade-offs in T2I alignment, and the paper's emphasis on contradictory objectives is timely. However, the current manuscript does not substantiate the headline claim: the evaluation is circular, the method is not fully specified, and no code, dataset, checkpoints, or standard baselines are provided. The paper also contains several internal inconsistencies in the dataset statistics and in the definition of the synergy Jacobian. The topic is valuable, but the evidence presented is not sufficient for acceptance.

major comments (4)
  1. [§5 and §6] The headline claim that CAO sets new performance benchmarks across all six contradictory objectives is not supported by the evaluation. Section 5 defines the per-axiom losses L_faith, L_artistic, L_emotion, L_originality, L_verifiability, and L_cultural, and Section 6 reports percentage impact figures for DPO and DPO-CAO computed on those same loss functions. Since the CAO training objective (Sections 4.1–4.3) is essentially a weighted sum of these losses, the reported gains are partially self-fulfilling. An independent evaluation—for example, human preference judgments, established T2I alignment benchmarks, or held-out reward models not used in training—is required to support the claim.
  2. [§4.5 vs. Appendix G] The Synergy Jacobian is defined inconsistently across the paper. Section 4.5 defines J_S = ∂S(I)/∂θ and gives the parameter update Δθ = η∇L − αJ_S, treating J_S as a gradient vector. Appendix G defines J_S as a p×A matrix of ∂f_a/∂θ_j and gives Δθ = −η J_S ∇S. These are different mathematical objects with different dimensions and opposite sign conventions. The paper must present a single consistent definition and show explicitly how J_S is computed and used in the optimizer.
  3. [§3.1 vs. Appendix B] The dataset statistics are internally inconsistent. Section 3.1 reports 50,000 VLM-flagged images, 10,000 images discarded during manual review, a final 40,000-image dataset, and a 5,000-image subset annotated by all 10 annotators. Appendix B says each annotator is assigned 5,500 images with a 500-image overlap between adjacent annotators, and the FAQ states that agreement was computed over 500 samples. These numbers cannot all be true, so the paper needs a single consistent account of the dataset size, overlap design, and annotator agreement computation.
  4. [§6 and Fig. 11] The empirical results are not reproducible as reported. No code, dataset, model checkpoints, prompt lists, evaluation scripts, or random seed details are provided, and no comparisons against standard T2I alignment baselines or existing benchmarks are included. The percentage-impact numbers in Section 6 and Figure 11 therefore cannot be verified by the community. The authors should release the YinYangAlign dataset and the CAO training/evaluation code, or explicitly mark the reported numbers as illustrative rather than benchmark results.
minor comments (4)
  1. [§1 and §6] There are typographical errors: "plarforms" in the introduction and "Directed Preference Optimization" in Section 6; the latter should be "Direct Preference Optimization."
  2. [§4.1, §5.1, §5.3] The symbol α is overloaded: it denotes the per-axiom mixing parameter in §4.1, the AFS weight in §5.1, and the TEMN weight in §5.3. Using distinct symbols would improve readability.
  3. [Fig. 11] The numeric values printed in Figure 11 are not clearly keyed to the bar colors and legend. For example, the caption states CAO achieves +44% Cultural Sensitivity, while the printed value list contains both +44% and +45% without indicating which corresponds to DPO and which to CAO. The figure and caption should be reconciled.
  4. [§7] Research Question 2 in Section 7 asks about "DPO and CPO" but the paper's method is called CAO; this typo should be corrected.

Circularity Check

2 steps flagged · score 8.0 of 10

CAO's headline SOTA is measured on the same per-axiom losses it optimizes; no independent evaluation supports the six-objective benchmark claim.

  1. self definitional [Sections 4.1-4.3 and 5]
    "For each axiom a, CAO defines a loss function fa(I) that blends two competing sub-objectives, Lp(I) and Lq(I), via a mixing parameter αa: fa(I) = αa Lp(I) + (1 − αa) Lq(I). ... Concretely, each fa(I) enters a Bradley-Terry style preference: P a ij = exp(fa(Ii)) / (exp(fa(Ii)) + exp(fa(Ij))). Meanwhile, a combined preference over S(I) expresses the global tradeoff."

    The CAO training objective is built directly from the per-axiom functions f_a and the synergy aggregator S(I)=Σ ωa f_a(I). Section 5 then defines those same f_a components as the evaluation losses: Lfaith, Lartistic, Lemotion, Loriginality, Lverifiability, and Lcultural. Thus the objective being optimized and the metrics used to claim 'new performance benchmarks across all six contradictory alignment objectives' are the same functions by construction. Training a model to increase Bradley-Terry likelihoods over f_a and S will move those exact scalar proxies, so any measured improvement on them is partly a tautology rather than an independent verification.

  2. fitted input called prediction [Section 6, Figure 11]
    "The impact of this single-axiom optimization on the other five objectives was measured in terms of percentage changes compared to a baseline. ... Comparison of Alignment Impacts: ... CAO achieves a more balanced alignment with controlled tradeoffs (e.g., +10% Artistic Freedom and +44% Cultural Sensitivity)."

    Section 6 reports percentage improvements for DPO and DPO-CAO on 'Artistic Freedom,' 'Faithfulness to Prompt,' 'Emotional Impact,' 'Originality,' 'Cultural Sensitivity,' and 'Verifiability' without specifying any evaluation instrument other than the Section 5 loss functions. The paper provides no human evaluation, no established external T2I benchmark, and no held-out metric definition for these percentages. Therefore the claimed CAO advantage over single-axiom DPO is a report of how the optimized training objectives themselves moved, not a prediction validated on independent ground truth. The absence of error bars, held-out prompt details, or code further prevents any independent check of the reported numbers.

full rationale

The paper's central claim — that CAO 'sets new performance benchmarks across all six contradictory alignment objectives' — is not supported by any instrument independent of the objective being optimized. Section 4 defines CAO's loss as local Bradley-Terry preferences over per-axiom losses f_a and a global synergy preference over S(I)=Σ ωa f_a(I). Section 5 then defines the six f_a components: Lfaith (Sinkhorn-VAE Wasserstein distance), Lartistic (style/content CLIP distances), Lemotion (emotion-classifier intensity), Loriginality (CLIP-retrieval divergence), Lverifiability (Google Image Search cosine similarity), and Lcultural (LLM-generated sub-prompt CLIP matching). Section 6 reports percentage improvements on 'Artistic Freedom,' 'Faithfulness,' etc., with no statement that these percentages come from a different, held-out, human-judged, or established benchmark. Consequently, the reported CAO advantage is largely a statement about the training loss itself: a method trained to increase the Bradley-Terry likelihood of f_a and S will move those same scalar proxies. This is a self-referential evaluation, not a verified external prediction. The benchmark dataset's human/VLM annotation pipeline (Section 3) is separate and gives the benchmark some independent content, but the headline SOTA claim is not validated against it in the paper. Internal inconsistencies (e.g., the Synergy Jacobian defined as ∂S/∂θ in Section 4.5 with Δθ = η∇L − αJ_S, versus a matrix of ∂f_a/∂θ with Δθ = −ηJ_S∇S in Appendix G) further prevent verification, but those are correctness and reproducibility concerns rather than circularity issues.

Assumptions & free parameters 7 free parameters · 6 assumptions · 2 invented entities

The central claims rest on a large stack of unverified modeling choices: per-axiom loss proxies (VAE Wasserstein, CLIP retrieval, Google Image Search, LLM-generated sub-prompts), VLM-plus-human annotation, and a family of tuned weights. None of these are validated against independent human preference data, and several are explicitly tuned on a validation set that is then used to report success. This makes the empirical contribution hard to separate from parameter fitting.

free parameters (7)
  • AFS weights alpha, beta, gamma = alpha=0.5, beta=0.3, gamma=0.2
    Set by empirical tuning in Section 5.1 to balance style, abstraction, and content difference; not derived.
  • Emotion-neutrality trade-off weights = alpha=0.3, beta=0.7
    Section 5.3 states these are chosen empirically.
  • Per-axiom mixing parameter alpha_a = alpha_a(0)=0.5, dynamically adjusted
    Section D.1: initialized to 0.5 and adjusted based on relative magnitudes of Lp and Lq; a fitted trade-off per axiom.
  • Global synergy weights omega_a = initialized 1/6, tuned on validation
    Section D.1: omega_a are fine-tuned based on validation metrics, so the reported trade-off balance is partly fitted to the evaluation set.
  • Regularization coefficients tau_a = tau_init=1e-3, range 1e-4 to 1e-2
    Section D.1 and E.1: chosen by hyperparameter sweeps; no ablation on held-out generalization.
  • Global synergy scaling lambda = lambda=0.7
    Section D.2: set to 0.7 based on validation performance.
  • SCCM normalization bounds = SCCM_min=0.70, SCCM_max=0.90
    Section 5.5: predefined from a validation dataset; these choices directly affect cultural sensitivity scores.
assumptions (6)
  • domain assumption DPO and the Bradley-Terry preference framework apply to T2I generations as reward signals
    Basis for P^a_ij in Section 4.2; no validation that the f_a scores behave like Bradley-Terry rewards.
  • domain assumption Per-axiom losses are valid proxies for human alignment concepts
    Section 5 defines faithfulness, originality, verifiability, and cultural sensitivity via VAE/CLIP/Google Search/LLM sub-prompts; no human correlation study for these metrics.
  • domain assumption VLM consensus and human kappa validate chosen/rejected labels
    Section 3.1 relies on GPT-4o and LLaVA scores greater than or equal to 3 and a reported kappa of 0.83, but the scoring scales are subjective and only a subset was double-annotated.
  • domain assumption A weighted sum of conflicting objectives traces a useful Pareto frontier
    Standard scalarization does not recover all non-convex Pareto frontiers, and the paper does not justify its use here.
  • domain assumption Heavy-tailed self-regularization theory transfers from LLMs to T2I diffusion models
    Section 7 applies Weighted Alpha to T2I without empirical calibration; results lack confidence intervals.
  • ad hoc to paper Data filtering rules do not bias the benchmark
    Selection thresholds (VLM agreement greater than or equal to 3, discarding about 10k images) are chosen for annotation convenience; no analysis of the discarded-image distribution is provided.
invented entities (2)
  • Synergy Jacobian J_S
    purpose: Gradient modulation device intended to balance objective updates in CAO
    Defined in Section 4.5 and Appendix G, but the update rule is inconsistent (Delta_theta = eta grad L minus alpha J_S versus Delta_theta = -eta J_S grad S), and no ablation isolates its effect.
  • Simulated Cultural Context Matching (SCCM)
    purpose: Metric that generates LLM sub-prompts and measures image alignment to cultural context
    Introduced in Section 5.5; normalization bounds chosen from validation data and no external cultural-sensitivity benchmark demonstrates validity.

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Cite this review

Pith. "Pith review of YINYANG-ALIGN: Benchmarking Contradictory Objectives and Proposing Multi-Objective Optimization based DPO for Text-to-Image Alignment." pith.science (2026). https://pith.science/paper/72VRMDZB

@misc{pith2026250203512,
  author       = {Pith},
  title        = {Pith review of: YINYANG-ALIGN: Benchmarking Contradictory Objectives and Proposing Multi-Objective Optimization based DPO for Text-to-Image Alignment},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/72VRMDZB}},
  note         = {Machine review of arXiv:2502.03512}
}
read the original abstract

Precise alignment in Text-to-Image (T2I) systems is crucial to ensure that generated visuals not only accurately encapsulate user intents but also conform to stringent ethical and aesthetic benchmarks. Incidents like the Google Gemini fiasco, where misaligned outputs triggered significant public backlash, underscore the critical need for robust alignment mechanisms. In contrast, Large Language Models (LLMs) have achieved notable success in alignment. Building on these advancements, researchers are eager to apply similar alignment techniques, such as Direct Preference Optimization (DPO), to T2I systems to enhance image generation fidelity and reliability. We present YinYangAlign, an advanced benchmarking framework that systematically quantifies the alignment fidelity of T2I systems, addressing six fundamental and inherently contradictory design objectives. Each pair represents fundamental tensions in image generation, such as balancing adherence to user prompts with creative modifications or maintaining diversity alongside visual coherence. YinYangAlign includes detailed axiom datasets featuring human prompts, aligned (chosen) responses, misaligned (rejected) AI-generated outputs, and explanations of the underlying contradictions.

Figures

Figures reproduced from arXiv: 2502.03512 by the authors.

Figure 1
Figure 1. The figure illustrates six core trade-offs (e.g., [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Illustrative examples of all six contradictory alignment axioms, with each row highlighting specific [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Illustrative example of aligning T2I models with Faithfulness to Prompt vs. Artistic Freedom. The [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figures from the paper (13 more)
Figure 4
Figure 4. Figure 4: Annotation Agreement Heatmap: The VLM column represents the kappa score indicating the average agreement between GPT-4o and LLaVA across all axioms. Columns (H1–H10) correspond to the kappa scores measuring the agreement between each specific human annotator and the co…
Figure 5
Figure 5. Figure 5: Visualization of error loss surface tension for six axiom pairs in YinYang alignment. Each pair highlights [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: A modular breakdown of the CAO loss. (A) Local per-axiom preferences, (B) global synergy preference, (C) axiom-specific regularizers. Three error loss surfaces from the ablation study demonstrate the progressive impact of incorporating components of the YinYang alignme…
Figure 7
Figure 7. Figure 7: Visualization of optimization paths and gradient dynamics with and without the Synergy Jacobian. [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: Visualization of optimization paths and cost landscapes with and without Sinkhorn regularization. The [PITH_FULL_IMAGE:figures/full_fig_p011_8.png]
Figure 9
Figure 9. Figure 9: A comparative visualization of the density dis￾tributions of the Alpha values for three models: Stable Diffusion 3.5, DPO, and CAO. The X-axis represents the Alpha values, while the Z-axis denotes the density. Peaks at 3.34 for Stable Diffusion 3.5, 4.82 for DPO, and 4…
Figure 10
Figure 10. Figure 10: Impact of Training DPO with Individual Axioms on Others: A Comparative Evaluation. The plots illustrate the impact of training DPO to optimize a single axiom on the other alignment objectives. Each subplot corresponds to one axiom, with percentage changes in performan…
Figure 11
Figure 11. Figure 11: Comparison of Alignment Impacts: The plot illustrates the effect of training with DPO versus CAO [PITH_FULL_IMAGE:figures/full_fig_p017_11.png]
Figure 12
Figure 12. Figure 12: This interface allows users to dynamically [PITH_FULL_IMAGE:figures/full_fig_p019_12.png]
Figure 13
Figure 13. Figure 13: A Comprehensive Visual Depiction of Trade-offs Between Alignment Axioms Across Prompts and [PITH_FULL_IMAGE:figures/full_fig_p020_13.png]
Figure 14
Figure 14. Figure 14: A Comprehensive Visual Depiction of Alignment Trade-offs for [PITH_FULL_IMAGE:figures/full_fig_p021_14.png]
Figure 15
Figure 15. Figure 15: Weight-Objective Heatmap: Visualizing the [PITH_FULL_IMAGE:figures/full_fig_p066_15.png]
Figure 16
Figure 16. Figure 16: Error Surfaces for Vanilla DPO (Left) and CAO (Right). The smooth surface of DPO contrasts with the [PITH_FULL_IMAGE:figures/full_fig_p072_16.png]

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Reference graph

Works this paper leans on

56 extracted references · 47 canonical work pages

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    A regularization term to prevent overfitting to any single objective. ✽ How does YinYangAlign handle annotation challenges? ➠ YinYangAlign combines automated annotations using Vision-Language Models (VLMs) like GPT-4o and LLaV A with rigorous human verification. A consensus filtering mechanism ensures reliability, with a high inter-annotator agreement sco...

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    Neutrality: Balances generating emotionally evocative images with unbiased representation

    Emotional Impact vs. Neutrality: Balances generating emotionally evocative images with unbiased representation

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    Artistic Freedom: Maintains photorealism while allowing artistic stylization when appropriate

    Visual Realism vs. Artistic Freedom: Maintains photorealism while allowing artistic stylization when appropriate

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    Referentiality: Promotes unique outputs while avoiding style plagiarism

    Originality vs. Referentiality: Promotes unique outputs while avoiding style plagiarism

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    Artistic Freedom: Preserves respectful cultural representations while fostering artistic freedom

    Cultural Sensitivity vs. Artistic Freedom: Preserves respectful cultural representations while fostering artistic freedom. These were selected based on their prevalence in real-world applications and their alignment with academic and ethical considerations in AI image generati...

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    Faithfulness to Prompt: Evaluate how well the image adheres to the user’s prompt

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    Artistic Freedom: Assess if the image introduces creative or artistic elements that deviate from, enhance, or reinterpret the original prompt

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    Output Format: Faithfulness Score (1-5), Artistic Freedom Score (1-5), Observations (Text)

    Identify if artistic freedom significantly compromises faithfulness to the prompt. Output Format: Faithfulness Score (1-5), Artistic Freedom Score (1-5), Observations (Text). Emotional Impact vs. Neutrality Given the textual description (prompt) and an image, evaluate the alig...

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    Emotional Impact: Evaluate whether the image conveys specific emotions as implied by the prompt

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    Neutrality: Assess if the image avoids strong emotional biases and maintains an impartial tone

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    Output Format: Emotional Impact Score (1-5), Neutrality Score (1-5), Observations (Text)

    Identify if the emotional intensity compromises the neutrality required by the prompt. Output Format: Emotional Impact Score (1-5), Neutrality Score (1-5), Observations (Text). Visual Realism vs. Artistic Freedom Given the textual description (prompt) and an image, evaluate th...

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    Visual Realism: Evaluate how accurately the image replicates real-world visuals, including details, textures, and proportions

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    Artistic Freedom: Assess if the image introduces artistic or creative elements that deviate from strict realism

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    Output Format: Realism Score (1-5), Artistic Freedom Score (1-5), Observations (Text)

    Identify if artistic freedom compromises the visual realism implied or required by the prompt. Output Format: Realism Score (1-5), Artistic Freedom Score (1-5), Observations (Text). Table 1: Instructions for evaluating alignment across six key axioms in Text-to-Image generatio...

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    See Table 1

    Preliminary Annotation by Vision- Language Models (VLMs): • Annotate all generated images using two VLMs: GPT-4 and LLaV A. See Table 1. • Evaluate each image for the six alignment ax- ioms (e.g., Emotional Impact, Visual Real- ism). • Retain images where both VLMs give a high...

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    Each annotator is assigned 5,500 images to ensure comprehensive coverage of the dataset

    Human Annotation Process: • Engage 10 human annotators for manual evaluation. Each annotator is assigned 5,500 images to ensure comprehensive coverage of the dataset. • Include a 500-image overlap between adja- cent annotators to calculate inter-annotator agreement and ensure ...

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    – Improper color rendering or other significant quality issues

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    Final Dataset: • The final YinYang dataset consists of 40K high-quality datapoints, carefully selected and annotated for the six alignment axioms. • This dataset will be released for research pur- poses, enabling studies in Text-to-Image align- ment and related areas. This sel...

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    Local Axiom-Wise Loss Functions: Each align- ment axiom (e.g., Faithfulness to Prompt vs. Artis- tic Freedom) is assigned a specific loss function that balances two competing sub-objectives: fa(I) = αaLp(I) + (1 − αa)Lq(I), where: • Lp(I) and Lq(I) represent the sub-objectives...

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    • ωa represents the priority or weight assigned to each axiom a

    Global Synergy Aggregator: To reconcile multi- ple axioms, a global synergy function S(I) aggre- gates the local losses: S(I) = AX a=1 ωafa(I), where: • A is the total number of axioms (e.g., A = 6 for the YinYang framework). • ωa represents the priority or weight assigned to ...

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    For example, increasing Artis- tic Freedom may reduce Faithfulness to Prompt, but Pareto efficiency ensures that these trade-offs are optimized globally

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Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.