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REVIEW 4 major objections 5 minor 2 cited by

MMMG: a Comprehensive and Reliable Evaluation Suite for Multitask Multimodal Generation

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read A benchmark claims automated grading of multimodal generation matches human judges 94.3 percent of the time.

desk verdict Solid benchmark with an in-sample agreement headline; deserves review but needs out-of-sample validation. read the letter →

arxiv 2505.17613 v1 pith:QGDCLHVK submitted 2025-05-23 cs.AI cs.CLcs.CV

Jihan Yao , Yushi Hu , Yujie Yi , Bin Han , Shangbin Feng , Guang Yang , Bingbing Wen , Ranjay Krishna
show 4 more authors
Lucy Lu Wang Yulia Tsvetkov Noah A. Smith Banghua Zhu
This is my paper · ORCID
classification cs.AIcs.CLcs.CV
keywords multimodalgenerationbenchmarkautomatedevaluationhumanalignmentinterleavedimage-textaudio-textimageaudioinstructionfollowing
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

MMMG is a benchmark for multimodal generation built on a specific bet: that automated evaluation of images, audio, and interleaved text-image or text-audio outputs can be made nearly as trustworthy as human evaluation, without expensive annotation, if tasks are chosen carefully. The paper claims 94.3% average agreement between the best automated method per task and human annotators across 37 tasks, based on 674 instructions and 1,886 evaluation questions. If that holds, model rankings in multimodal generation no longer need to rely on costly human studies or on unvalidated model-as-a-judge scores. The paper also benchmarks 24 models and finds the strongest image generator still weak at multimodal reasoning and interleaved generation, with audio generation showing much headroom.

What carries the argument

The load-bearing mechanism is the per-task evaluation pipeline, with the method for each task chosen to maximize human agreement. For image tasks, manually designed VQA prompts push a vision-language model to reason step by step, answer as multiple choice, or reject negative prompts. For audio, CLAP similarity against reference audio, program checks for tempo and silence, and Whisper/WavLM-based transcript or speaker checks are used. Programmatic checks, such as pixel-level border verification, cover objectively verifiable tasks. The paper's methodological move is selecting one method per task based on validation-set human agreement and then applying it to all models.

What would settle it

Take a fresh set of instructions and generations from models not among the original 24, score them with the paper's per-task methods and thresholds, and collect the same three-annotator majority judgments; if human-model agreement falls substantially below 0.943, the reported alignment is specific to the validation set rather than a general property of the suite.

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Extended reading notes

Core claim

The central claim is that a benchmark built from verifiable tasks and tasks with a large generation-evaluation gap can be evaluated automatically with high human agreement. Across image, audio, interleaved image-text, and interleaved audio-text generation, the per-task evaluation method that best agrees with human raters averages 0.943 agreement, while inter-annotator agreement averages 0.971. The suite contains 49 tasks (29 newly developed) and 937 instructions. Benchmarking 24 models with these methods shows GPT Image leading image generation at 78.3% accuracy but scoring only 13.1% on interleaved math and code reasoning, while the best sound and music models reach 48.7% and 41.9% accuracy.

Load-bearing premise

For every task, the evaluation method and numeric thresholds that best matched human ratings on a validation sample will keep matching human ratings on the full benchmark and on outputs from models and instructions not seen during selection.

Editorial extensions

If this is right

  • Automated rankings on MMMG can be updated cheaply as new multimodal models appear, without rerunning human studies.
  • The benchmark's fine-grained 49-task breakdown lets a developer see whether a model fails at counting, spatial reasoning, text rendering, or audio-level control.
  • The reported gaps, such as GPT Image reaching 78.3% on image tasks but 13.1% on interleaved math and code, suggest instruction-following and multimodal reasoning are separate capabilities that need separate benchmarks.
  • MMMG's 0.857 Spearman correlation with a human-preference leaderboard indicates that evaluator alignment can matter more than instruction distribution matching.

Reading between the lines

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

  • The paper does not state this, but the per-task method selection and thresholds were tuned on the same human judgments used to measure agreement, so a held-out validation split could reveal optimistic bias in the 94.3% figure.
  • If evaluator alignment outweighs instruction distribution, benchmark designers could generate synthetic instructions freely and focus annotation budget on evaluator validation.
  • The same verifiable-task design could extend to video generation, where currently no comparable human-aligned automated benchmark exists.
  • The dependence on proprietary graders means benchmark scores may shift as those graders update, so reproducing the benchmark requires caching model outputs.
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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 / 5 minor

Summary. The paper introduces MMMG, a benchmark and automated evaluation suite for multimodal generation covering image, audio, interleaved image-text, and interleaved audio-text tasks. It comprises 49 tasks (29 new), 937 instructions, and four modality combinations, with each task assigned a program-, model-, or hybrid-based evaluation pipeline. The authors report a human-model agreement of 94.3% based on human evaluation of 674 instructions and 1886 evaluation questions, and benchmark 24 generation models, finding that GPT-Image leads image generation but lags on multimodal reasoning and interleaved generation. Code and data are released publicly.

Significance. If the reliability claim holds, MMMG would be a valuable contribution: it is substantially broader than existing benchmarks (Table 1), it combines verifiable program checks with model-based evaluation for hard generation tasks, and it ships a large human-annotation study (1886 questions, three annotations per item, 97.1% inter-annotator agreement). The public release and the careful design of VLM prompts (e.g., negative prompts, multiple-choice formats, chain-of-thought) are praiseworthy. However, the headline 94.3% agreement is computed by selecting, per task, the evaluator with the highest human agreement on the same validation data used to report the number, with thresholds also tuned on those judgments. This makes the central reliability claim an in-sample best fit rather than a predictive estimate, and it is the main load-bearing weakness of the paper.

major comments (4)
  1. [§5.1 and Table 8] The reported 0.943 agreement is the mean of per-task maximum agreement over the candidate evaluators (GPT-4O, GEMINI2.5, QWEN2.5-VL for images; CLAPScore-audio, CLAPScore-text, GEMINI2.5 for audio; WavLM, Wav2Vec for speech), where the maximum is taken on the same 674 instructions that are then used to report the agreement. This is an in-sample selection; it does not estimate the agreement of a fixed evaluation protocol. The paper should report a held-out estimate, e.g., leave-one-task-out or a split of instructions, and should also report the agreement of a pre-specified default method (e.g., the average across evaluators or the method chosen by a fixed rule). Without such an estimate, the 94.3% headline and the subsequent claim that MMMG is 'highly aligned with human evaluation' are not yet established for unseen instructions and model outputs.
  2. [§4 and Appendix C.3] The thresholds used in the audio evaluation (CLAP similarity threshold 0.68 for ESC-50, 0.62 for OpenMIC-2018, and WavLM speaker-similarity threshold 0.86) are described as 'empirical optimal' and were tuned on the same human judgments that define the agreement numbers. This additional selection on the validation set further inflates the reported agreement. The authors should either tune thresholds on a separate development split and evaluate on a held-out split, or report the sensitivity of task-level agreement to these thresholds; otherwise the reported 92.6% audio agreement and the WavLM-based speech results may be optimistically biased.
  3. [§3.1 and §5.1] Human validation covers only 37 of the 49 tasks; for the remaining 12 tasks the reliability of the selected metric is assumed rather than demonstrated. The paper justifies this by saying that verifiable instructions do not require human validation, but several of the unvalidated tasks (e.g., Border Fill, Region Fill, Music Tempo, Music Intensity) rely on programmatic checks with hand-set thresholds (e.g., the 15% color-deviation threshold and the BPM/intensity rules in Appendix C.3). The manuscript should explicitly list which tasks lack human validation and provide a rationale or a small-scale sanity check for those program-based metrics, or restrict the reliability claim to the 37 validated tasks.
  4. [§5.3, Table 3] The correlation with Chatbot Arena is computed on only 7 models. While Spearman's 0.857 is suggestive, the small n makes the estimate fragile and no confidence interval or significance test is reported. The claim that MMMG 'provides reliable model rankings' would be strengthened by reporting a confidence interval or bootstrap estimate, and by acknowledging the small-sample caveat in the text.
minor comments (5)
  1. [Abstract and §5.1] The phrase 'average agreement of 94.3%' should be qualified as 'average best human-model agreement after per-task method selection' to avoid implying a single pre-specified protocol achieves this value.
  2. [§2, Table 1] The table's use of symbols (⊷, /volume-down, T+⊷, T+/volume-down) is not intuitive; a legend or explicit expansion in the caption would improve readability.
  3. [§5.1] The sentence 'The average inter-annotator agreement remains as high as 0.971 with the worst case being 0.917' is slightly ambiguous; clarify whether the worst-case is across tasks or across annotator pairs.
  4. [Table 8] The per-task agreement and correlation values are presented without standard errors or confidence intervals; adding a small-sample caveat would help readers interpret differences among evaluators (e.g., GPT-4O vs GEMINI2.5).
  5. [Appendix C.3] The description of the solid-color-fill program mentions a 'relative deviation exceeds 15%' threshold, but the exact comparison metric (e.g., Euclidean distance in RGB or relative to the reference color) is not defined; please specify the formula.

Circularity Check

2 steps flagged · score 6.0 of 10

The 94.3% human-agreement headline is an in-sample best over per-task evaluators and tuned thresholds, so the central reliability claim is partially circular.

  1. fitted input called prediction [Section 5.1 (Alignment with Human Judges)]
    "MMMG demonstrates high human alignment, with average best human-model agreement for image, audio, interleaved image-text, and interleaved audio-text being 0.948, 0.926, 0.956 and 0.910 respectively, calculated by selecting the method achieving the highest agreement per task and averaging across tasks."

    The headline 94.3% agreement is not the agreement of a pre-specified evaluation protocol but the average of per-task maxima over candidate evaluators. The same human judgments are used both to select the best method per task and to compute the reported agreement, so the number is an in-sample optimum by construction. Any evaluator that happened to agree well on this validation set is chosen, mechanically raising the reported alignment. The benchmark then adopts these selected methods for all model rankings, so the reliability claim does not estimate out-of-sample agreement for unseen instructions or generations. No held-out split or comparison against the average or median evaluator is reported.

  2. fitted input called prediction [Sections 3.2 and 4 (Audio Model; Experiment Settings)]
    "WAVLM [Chen et al., 2022] is employed for speaker similarity verification with an empirical optimal threshold of 0.86. ... Following the optimal configurations identified in empirical studies, we calculate the average CLAPScoreaudio with the 10 most similar reference audio samples The threshold is 0.68 for ESC-50 and 0.62 for OpenMIC-2018."

    The audio thresholds are described as 'empirical optimal' and are selected following 'empirical studies,' then the same human-agreement numbers in Table 8 are reported with these fitted thresholds in place. Because threshold selection is optimized against the same human judgments used to measure agreement, the audio component of the 94.3% headline is a fitted maximum rather than a held-out evaluation. The paper does not report a separate validation split for threshold selection or confidence intervals around the chosen thresholds, so this part of the alignment claim reduces to a post-hoc fit.

full rationale

The central reliability claim of MMMG rests on the 0.943 average human agreement, but the paper states explicitly that this is 'calculated by selecting the method achieving the highest agreement per task and averaging across tasks' (Section 5.1). Selecting the best per-task method and tuning audio thresholds on the same human judgments makes the headline an in-sample maximum, not a prediction for a fixed evaluation protocol. This is the clearest circular step: the benchmark's reliability metric is defined as the best observed agreement after optimizing over evaluators, and then the same value is presented as evidence that the benchmark is highly aligned with humans. The external Chatbot Arena correlation (Spearman 0.857, Table 3) provides independent evidence for MMMG's usefulness on image ranking, which prevents a higher score, but it covers only 7 image models and does not validate the 94.3% figure or the audio/interleaved tasks. The paper also human-validates only 37 of 49 tasks (Section 5.1), treating the remaining 12 as objectively verifiable programs; Appendix A acknowledges limited task coverage, but this is a scope limitation rather than a circular step. Overall, the benchmark construction and model rankings have substantial independent content, yet the headline alignment number itself reduces by construction to a fitted selection over methods and thresholds.

Assumptions & free parameters 5 free parameters · 3 assumptions · 0 invented entities

The central claim of human alignment rests on domain assumptions about human judgments as ground truth, the transferability of metric correlation from the validation set to the full benchmark, and the objectivity of programmatic checks. The headline agreement is a per-task best fit, and four numeric thresholds (CLAP 0.68, CLAP 0.62, WavLM 0.86, color deviation 15%) are tuned or hand-set without a reported held-out validation.

free parameters (5)
  • CLAP threshold for ESC-50 sound evaluation = 0.68
    Described as 'threshold is 0.68 for ESC-50' in Appendix C.3 and chosen as part of optimal configurations identified in empirical human alignment studies.
  • CLAP threshold for OpenMIC-2018 music evaluation = 0.62
    Same appendix: 'threshold is 0.68 for ESC-50 and 0.62 for OpenMIC-2018'; fitted to maximize agreement with human judgments.
  • WavLM speaker similarity threshold = 0.86
    Section 3.2: 'WAVLM is employed for speaker similarity verification with an empirical optimal threshold of 0.86'.
  • Number of nearest reference audio clips for CLAPScore = 10
    Appendix C.3: 'we calculate the average CLAPScore_audio with the 10 most similar reference audio samples'; selected as an optimal configuration.
  • Color deviation threshold for solid color fill program = 15% relative RGB deviation
    Appendix C.3: 'if the relative deviation exceeds 15% ... evaluation returns zero'; a hand-set heuristic in the programmatic evaluator.
assumptions (3)
  • domain assumption Human majority-vote judgments are the gold standard for evaluating multimodal generation quality.
    Section 5.1 uses 20 graduate student annotators with majority vote as ground truth for all human alignment numbers.
  • domain assumption The selected automatic metrics (VLM answers, CLAP similarity, SSIM/DreamSim, program checks) are valid proxies for the specific underlying capability when they correlate with humans on the validation sample.
    Section 3.2 and Appendix C.3 select evaluators based on human agreement; the assumption is that this correlation transfers to unseen instructions and models.
  • domain assumption Tasks labeled verifiable (e.g., border color, transcript word replacement) can be evaluated by programs without human validation.
    Section 3.2 states 'For verifiable instructions, human alignment validation is unnecessary'; this assumes the program checks are objective and correct.

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Cite this review

Pith. "Pith review of MMMG: a Comprehensive and Reliable Evaluation Suite for Multitask Multimodal Generation." pith.science (2026). https://pith.science/paper/QGDCLHVK

@misc{pith2026250517613,
  author       = {Pith},
  title        = {Pith review of: MMMG: a Comprehensive and Reliable Evaluation Suite for Multitask Multimodal Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QGDCLHVK}},
  note         = {Machine review of arXiv:2505.17613}
}
read the original abstract

Automatically evaluating multimodal generation presents a significant challenge, as automated metrics often struggle to align reliably with human evaluation, especially for complex tasks that involve multiple modalities. To address this, we present MMMG, a comprehensive and human-aligned benchmark for multimodal generation across 4 modality combinations (image, audio, interleaved text and image, interleaved text and audio), with a focus on tasks that present significant challenges for generation models, while still enabling reliable automatic evaluation through a combination of models and programs. MMMG encompasses 49 tasks (including 29 newly developed ones), each with a carefully designed evaluation pipeline, and 937 instructions to systematically assess reasoning, controllability, and other key capabilities of multimodal generation models. Extensive validation demonstrates that MMMG is highly aligned with human evaluation, achieving an average agreement of 94.3%. Benchmarking results on 24 multimodal generation models reveal that even though the state-of-the-art model, GPT Image, achieves 78.3% accuracy for image generation, it falls short on multimodal reasoning and interleaved generation. Furthermore, results suggest considerable headroom for improvement in audio generation, highlighting an important direction for future research.

Figures

Figures reproduced from arXiv: 2505.17613 by the authors.

Figure 1
Figure 1. Examples of tasks and their evaluation metrics in [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Benchmark results of multimodal generation models on [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Two prevalent failure cases observed in interleaved image-text generation tasks for [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (50 more)
Figure 4
Figure 4. Figure 4: Human annotation interface for instrument inclusion task. Typically, an inference will [PITH_FULL_IMAGE:figures/full_fig_p023_4.png]
Figure 5
Figure 5. Figure 5: Examples for the task: Object Inclusion Object Exclusion Instruction: Generate an image of a birthday party. Do not include cakes in the image. Good Example: IDEOGRAM 2 Evaluation: ✔ Bad Example: DALLE 3 Evaluation: ✘ Analysis: It fails to exclude cakes in the con￾text…
Figure 6
Figure 6. Figure 6: Examples for the task: Object Exclusion 28 [PITH_FULL_IMAGE:figures/full_fig_p028_6.png]
Figure 7
Figure 7. Figure 7: Examples for the task: Object Count Object Reasoning Instruction: Generate an image of a race track with 3 clearly visible race cars, evenly spaced and not overlapping. The race cars should be of regular size and common shapes. Good Example: IMAGEN 3 Evaluation: ✔ Bad …
Figure 8
Figure 8. Figure 8: Examples for the task: Object Reasoning 29 [PITH_FULL_IMAGE:figures/full_fig_p029_8.png]
Figure 9
Figure 9. Figure 9: Examples for the task: Object Attribution [PITH_FULL_IMAGE:figures/full_fig_p030_9.png]
Figure 10
Figure 10. Figure 10: Examples for the task: Comparison Relation [PITH_FULL_IMAGE:figures/full_fig_p030_10.png]
Figure 11
Figure 11. Figure 11: Examples for the task: Universal Relation [PITH_FULL_IMAGE:figures/full_fig_p031_11.png]
Figure 12
Figure 12. Figure 12: Examples for the task: Absolute Spatial Relation [PITH_FULL_IMAGE:figures/full_fig_p032_12.png]
Figure 13
Figure 13. Figure 13: Examples for the task: Relative Spatial Relation [PITH_FULL_IMAGE:figures/full_fig_p033_13.png]
Figure 14
Figure 14. Figure 14: Examples for the task: Region Fill 33 [PITH_FULL_IMAGE:figures/full_fig_p033_14.png]
Figure 15
Figure 15. Figure 15: Examples for the task: Border Fill 34 [PITH_FULL_IMAGE:figures/full_fig_p034_15.png]
Figure 16
Figure 16. Figure 16: Examples for the task: Single Text Rendering [PITH_FULL_IMAGE:figures/full_fig_p035_16.png]
Figure 17
Figure 17. Figure 17: Examples for the task: Double Text Rendering [PITH_FULL_IMAGE:figures/full_fig_p036_17.png]
Figure 18
Figure 18. Figure 18: Examples for the task: Multi-Lingual Text Rendering [PITH_FULL_IMAGE:figures/full_fig_p037_18.png]
Figure 19
Figure 19. Figure 19: Examples for the task: Semantic Consistency [PITH_FULL_IMAGE:figures/full_fig_p038_19.png]
Figure 20
Figure 20. Figure 20: Examples for the task: Multi-Angle Consistency 39 [PITH_FULL_IMAGE:figures/full_fig_p039_20.png]
Figure 21
Figure 21. Figure 21: Examples for the task: Multi-View Consistency [PITH_FULL_IMAGE:figures/full_fig_p040_21.png]
Figure 22
Figure 22. Figure 22: Examples for the task: Composition Consistency [PITH_FULL_IMAGE:figures/full_fig_p041_22.png]
Figure 23
Figure 23. Figure 23: Examples for the task: Decomposition Consistency [PITH_FULL_IMAGE:figures/full_fig_p042_23.png]
Figure 24
Figure 24. Figure 24: Examples for the task: Interleaved Image Editing - Object Adding [PITH_FULL_IMAGE:figures/full_fig_p043_24.png]
Figure 25
Figure 25. Figure 25: Examples for the task: Interleaved Image Editing - Color Modifying [PITH_FULL_IMAGE:figures/full_fig_p044_25.png]
Figure 26
Figure 26. Figure 26: Examples for the task: Image Editing - Text Editing [PITH_FULL_IMAGE:figures/full_fig_p045_26.png]
Figure 27
Figure 27. Figure 27: Examples for the task: Image Editing - Object Adding [PITH_FULL_IMAGE:figures/full_fig_p046_27.png]
Figure 28
Figure 28. Figure 28: Examples for the task: Image Editing - Object Removing [PITH_FULL_IMAGE:figures/full_fig_p047_28.png]
Figure 29
Figure 29. Figure 29: Examples for the task: Image Editing - Object Modifying [PITH_FULL_IMAGE:figures/full_fig_p048_29.png]
Figure 30
Figure 30. Figure 30: Examples for the task: Self Count 49 [PITH_FULL_IMAGE:figures/full_fig_p049_30.png]
Figure 31
Figure 31. Figure 31: Examples for the task: Self Color Recognition [PITH_FULL_IMAGE:figures/full_fig_p050_31.png]
Figure 32
Figure 32. Figure 32: Examples for the task: Self Size Recognition [PITH_FULL_IMAGE:figures/full_fig_p051_32.png]
Figure 33
Figure 33. Figure 33: Examples for the task: Self OCR 52 [PITH_FULL_IMAGE:figures/full_fig_p052_33.png]
Figure 34
Figure 34. Figure 34: Examples for the task: Self Relative Spatial Relation Recognition [PITH_FULL_IMAGE:figures/full_fig_p053_34.png]
Figure 35
Figure 35. Figure 35: Examples for the task: Self Absolute Spatial Relation Recognition [PITH_FULL_IMAGE:figures/full_fig_p054_35.png]
Figure 36
Figure 36. Figure 36: Examples for the task: Interleaved Math Reasoning [PITH_FULL_IMAGE:figures/full_fig_p055_36.png]
Figure 37
Figure 37. Figure 37: Examples for the task: Interleaved Code Reasoning [PITH_FULL_IMAGE:figures/full_fig_p056_37.png]
Figure 38
Figure 38. Figure 38: Examples for the task: Image-Text Order 57 [PITH_FULL_IMAGE:figures/full_fig_p057_38.png]
Figure 39
Figure 39. Figure 39: Examples for the task: Sound Begin-End Sound Positional Inclusion Instruction: Create an audio of a city street, ensuring a police car siren is included in the first half. Good Example: AUDIOGEN Audio Description: an audio of a city street with the first 3 seconds inc…
Figure 40
Figure 40. Figure 40: Examples for the task: Sound Positional Inclusion [PITH_FULL_IMAGE:figures/full_fig_p058_40.png]
Figure 41
Figure 41. Figure 41: Examples for the task: Sound Reasoning Sound Silence Instruction: Create an audio that begins with a loud car horn, followed by a long silence, and concludes with a distant siren. Good Example: MakeAnAudio2 Audio Description: an audio with a loud car horn in the begin…
Figure 42
Figure 42. Figure 42: Examples for the task: Sound Silence 58 [PITH_FULL_IMAGE:figures/full_fig_p058_42.png]
Figure 43
Figure 43. Figure 43: Examples for the task: Music Instrument Inclusion [PITH_FULL_IMAGE:figures/full_fig_p059_43.png]
Figure 44
Figure 44. Figure 44: Examples for the task: Music Instrument Exclusion [PITH_FULL_IMAGE:figures/full_fig_p059_44.png]
Figure 45
Figure 45. Figure 45: Examples for the task: Music Intensity Music Tempo Instruction: Create a laid-back lo-fi hip-hop beat at 100 BPM. Good Example: MusicGen Audio Description: an audio of a hip-hop beat at approximately 102 BPM. Evaluation: ✔ Bad Example: AUDIOLDM2 Audio Description: an …
Figure 46
Figure 46. Figure 46: Examples for the task: Music Tempo 59 [PITH_FULL_IMAGE:figures/full_fig_p059_46.png]
Figure 47
Figure 47. Figure 47: Examples for the task: Speaker Voice Attribution [PITH_FULL_IMAGE:figures/full_fig_p060_47.png]
Figure 48
Figure 48. Figure 48: Examples for the task: Multi-Lingual Speech [PITH_FULL_IMAGE:figures/full_fig_p060_48.png]
Figure 49
Figure 49. Figure 49: Examples for the task: Speaker Voice Replication [PITH_FULL_IMAGE:figures/full_fig_p060_49.png]
Figure 50
Figure 50. Figure 50: Examples for the task: Transcript Generation [PITH_FULL_IMAGE:figures/full_fig_p061_50.png]
Figure 51
Figure 51. Figure 51: Examples for the task: Transcript Editing [PITH_FULL_IMAGE:figures/full_fig_p061_51.png]
Figure 52
Figure 52. Figure 52: Examples for the task: Speech Conversation [PITH_FULL_IMAGE:figures/full_fig_p062_52.png]
Figure 53
Figure 53. Figure 53: Examples for the task: Audio-Text Order 63 [PITH_FULL_IMAGE:figures/full_fig_p063_53.png]

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Reference graph

Works this paper leans on

29 extracted references · 29 canonical work pages · cited by 2 Pith papers

  1. [1]

    Identify when visual content would be beneficial or requested

    work page

  2. [2]

    Detailed image generation or editing prompt here

    Insert an image generation/editing placeholder using the following format: <image_start><image_prompt="Detailed image generation or editing prompt here."><image_ref=[reference identifiers]><image_end>

    work page

  3. [3]

    The post-processing system replaces this placeholder with an image created or edited based on your instructions

    work page

  4. [4]

    When crafting image prompts, follow these guidelines: For image prompts: • Provide detailed, specific descriptions (15-30 words) for optimal results

    Naturally incorporate references to the generated or edited image in your ongoing conversation. When crafting image prompts, follow these guidelines: For image prompts: • Provide detailed, specific descriptions (15-30 words) for optimal results. • Include artistic styles (photorealistic, cartoon, watercolor, etc.) or style transfers. • Specify key objects...

    work page

  5. [5]

    Image generation (no reference): <image_ref=[]>

    work page

  6. [6]

    Example:<image_ref=[0]>references the first provided image

    Editing user-provided images: Format: <image_ref=[i]> where i is the index of the provided image (indices starting at 0). Example:<image_ref=[0]>references the first provided image. Multiple images example: <image_ref=[0,2]> references the first and third provided images

    work page

  7. [7]

    Example:<image_ref=[#3]>references the fourth generated image

    Editing previously generated images: Format: <image_ref=[#N]>, where N is the sequential number of previ- ously generated images (starting from 0). Example:<image_ref=[#3]>references the fourth generated image. Multiple images example: <image_ref=[#0,#2]> references the first and third generated images. Important:Use only one reference type within each pl...

    work page

  8. [8]

    Identify when audio content would be beneficial or requested

    work page

Show all 29 references
  1. [9]

    sound" OR

    Insert an audio generation placeholder using the format: <audio_start><audio_type="sound" OR "speech" OR "music"><audio_text="Text to be spoken here."><audio_style="Descriptive text here." OR audio reference ID><audio_end>

  2. [10]

    The post-processing system replaces this placeholder with generated audio based on your specifications

  3. [11]

    sound","speech

    Naturally incorporate references to the generated audio in your ongoing conversation. When crafting audio prompts, follow these guidelines: Audio Type: • Must be exactly one of:"sound","speech", or"music". •"speech": For human speech. •"sound": For environmental sounds or effe...

  4. [12]

    Your task is to judge if the given image accurately follows the ground- truth pattern. Analyze step by step and end your answer with “Yes

    The model-generated pattern must state the pattern clearly without ambiguity. For example, a 3*3 grid of circles with some circles filled is ambiguous. 2. Make sure the overall structure, the position and situation of each element are accurate. Specifically, the situation of e...

    2019

  5. [13]

    The first image will show the ’2’ face, the second will show the ’3’ face, the third will show the ’4’ face, and the final im- age will show the ’5’ face

    Five pips: Evaluation:✔ Bad Example:GEMINIIMAGE Response: I will generate four images show- ing individual faces of a standard six-sided die. The first image will show the ’2’ face, the second will show the ’3’ face, the third will show the ’4’ face, and the final im- age will...

  6. [15]

    60 degrees to the right:

  7. [16]

    30 degrees to the left:

  8. [17]

    Generating an image of the floral patterned seat cushion rotated 60 degrees to the right, revealing much of its right side and significantly obscuring the left

    60 degrees to the left: Bad Example:GEMINIIMAGE Response: Generating an image of the floral patterned seat cushion rotated 30 degrees to the right, showing more of its right side and slightly obscuring the left. Generating an image of the floral patterned seat cushion rotated ...

  9. [18]

    15 degrees to the right:

  10. [19]

    60 degrees to the left:

  11. [20]

    30 degrees to the right:

  12. [21]

    45 degrees to the left: Bad Example:GEMINI2.5 + GPT IMAGE Response: Okay, here are the four images of the kitchen from the specified angles, using your provided image as the frontal view ref- erence. First, here is the kitchen viewed from 15 degrees to the right: Next, the kit...

  13. [22]

    Empty Fruit Bowl with an Orange Added:

  14. [23]

    Fruit Bowl with an Orange and a Banana Added:

  15. [24]

    chair”: “[color]

    Fruit Bowl with an Orange, a Banana, and a Kiwi Added: These images will illustrate the gradual addi- tion of each fruit into the bowl. Evaluation:✔ Bad Example:GEMINIIMAGE Response: Image 1: An empty, clear glass fruit bowl sits on a light wooden surface, ready to be filled. ...

  16. [25]

    A”, “D”]).. Good Example:GEMINI2.5 + IMAGEN3 Response: [“C

    In which quadrant is the cutting board located? A. bottom left B. bottom right C. top left D. top right. Respond with ONLY a list of the corresponding letter choices (e.g., [“A”, “D”]).. Good Example:GEMINI2.5 + IMAGEN3 Response: [“C”, “B”] Evaluation:✔ Bad Example:GEMINI2.5 +...

  17. [26]

    #AF7E57" d=

    <path fill="#AF7E57" d="M6 18.562..."/>: This is the main shape of the face, filled with a tan color ( #AF7E57). It’s a large, somewhat pear-shaped element forming the head

  18. [27]

    #DD2E44" d=

    <path fill="#DD2E44" d="M30.961 10c..."/>: This path, filled with red (#DD2E44), forms the main part of a hat sitting on top of the head

  19. [28]

    #DD2E44" d=

    <path fill="#DD2E44" d="M18 30.651c..."/>: A red shape at the bottom of the face, forming a smiling mouth

  20. [29]

    #D1D3D4" d=

    <path fill="#D1D3D4" d="M10 25c0.../> and <path fill="#D1D3D4" d="M26.003 25c0..."/> : Two light gray shapes at the very bottom, possibly representing parts of a beard, chin, or stylized feet if it were a full figure. Given the context, they are likely part of the lower face o...

  21. [30]

    Ensure each speaker maintains a consistent voice throughout the conversation

    James’s final speech must contain exactly two uses of the word “design”. Ensure each speaker maintains a consistent voice throughout the conversation. Good Example:GEMINI2.5 + VOICELDM Conversation Transcript: James: I’ve been fascinated by the potential of kinetic pavements i...

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.