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REVIEW 3 major objections 3 minor 2 cited by

Two-stage Distributed Variational Quantum Eigensolver Software for QUBO and Quadratic Programming

T0 review · 3 major / 3 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read A two-stage distributed variational quantum eigensolver plus a trust-region QP-to-QUBO reformulation claims near-optimal solutions for QUBO and bounded constrained quadratic programs.

desk verdict A plausible QUBO/QP pipeline with a genuinely interesting slack-free reformulation, but the abstract gives no numbers and no convergence argument, so the claims are unverifiable without reading the full paper. read the letter →

arxiv 2508.17471 v3 pith:5CKNNEHY submitted 2025-08-24 quant-ph cs.SYeess.SYmath.OC

Milad Hasanzadeh , Amin Kargarian This is my paper · ORCID
classification quant-phcs.SYeess.SYmath.OC MSC 90C2090C2781P68
keywords QUBOvariationalquantumeigensolverquadraticprogrammingaugmentedLagrangiantrustregiondistributedcomputingmetaheuristicwarmstartoptimization
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

The paper tries to establish that a two-stage distributed variational quantum eigensolver (DVQE) can recover high-quality solutions to QUBO problems, and that a companion sequential QP-to-QUBO framework (QQP) can solve bounded constrained quadratic programs with small optimality, feasibility, and solution gaps. The key move is to reformulate a constrained continuous QP as a sequence of QUBO subproblems: each binary variable encodes a local up/down move of one continuous variable inside a trust region, and constraints are folded in through a Powell-Hestenes-Rockafellar augmented Lagrangian. This avoids slack variables entirely, so the same QUBO backend, classical or quantum, can be used throughout. If correct, the result matters because it gives a practical recipe for using near-term quantum computers on a wider class of optimization problems than pure binary QUBOs, with a metaheuristic warm start mitigating poor variational training.

What carries the argument

The central object is the one-bit trust-region QUBO reformulation: each continuous variable's local up/down move becomes a binary variable, turning each constrained augmented-Lagrangian subproblem into a QUBO. This reformulation removes the need for slack variables and lets any QUBO solver, including the paper's distributed DVQE, drive the constrained QP to a solution.

What would settle it

Solve a small bounded constrained QP with a non-separable quadratic objective (for example, one whose cross-terms make the optimum unreachable by any sequence of single-coordinate moves from the starting point) using QQP, and compare the final solution gap against the exact optimum; if the gap does not shrink to near zero as the trust region shrinks, the conversion's convergence claim fails.

Watch

Extended reading notes

Core claim

The paper's central claim is that constrained continuous quadratic programs with box bounds and linear equality/inequality constraints can be solved as a sequence of unconstrained QUBO problems. QQP first normalizes the continuous variables to a box, then builds a PHR augmented Lagrangian so that, with a fixed active region, each subproblem is a bounded quadratic problem. That bounded problem is then attacked by repeated one-bit QUBO reformulations: a binary variable per continuous variable indicates up or down movement inside a trust region. Each local QUBO is solved either classically or by the paper's DVQE solver, and the process iterates. The reported numerical results show high-quality

Load-bearing premise

The load-bearing premise is that repeatedly moving one variable at a time up or down within a shrinking box will eventually reach the best feasible solution of the original constrained quadratic problem; the paper asserts this but does not prove it converges, and such single-variable moves can get stuck on problems where variables interact.

Editorial extensions

If this is right

  • Bounded constrained quadratic programs become addressable with QUBO solvers without slack-variable overhead, so any classical or quantum QUBO backend inherits the constrained QP problem class.
  • The two-stage warm-start-plus-variational procedure reduces DVQE's reliance on random initialization, improving solution quality on QUBO instances.
  • Distributed circuit execution lets the same algorithm scale to larger QUBO/QP instances without changing the direct bitstring objective-evaluation strategy.
  • A fixed PHR active region makes the method naturally suited to warm-starting from a feasible point, with the trust-region iteration implicitly updating the active region.

Reading between the lines

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

  • If the one-bit scheme behaves like coordinate descent, its convergence will depend on the trust-region shrinking schedule and on cross-variable coupling; testing on a non-separable QP with a known optimum would reveal whether the reported gaps are generic or instance-specific.
  • The reduction to QUBOs means future improvements in QUBO solvers (classical branch-and-bound or quantum) immediately transfer to constrained QP, making QQP a bridge rather than a standalone solver.
  • The same one-bit logic could be extended to mixed-integer quadratic programs, with integer variables represented directly in the QUBO layer and continuous variables handled by the trust-region moves; the paper does not state this extension.
  • A cleaner falsifier for the active-region step would be a QP whose optimum has a different active constraint set than the warm start; the method should still find it, which the current experiments do not explicitly test.
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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

3 major / 3 minor

Summary. The submission is announced as a two-stage distributed variational quantum eigensolver (DVQE) software for QUBO and bounded constrained quadratic programming, with a QQP framework that converts constrained QPs into sequences of QUBO subproblems via a PHR augmented-Lagrangian formulation and one-bit trust-region moves. However, the supplied full text is not the paper described in the abstract; it is the T2I-ReasonBench computer-vision paper (arXiv:2508.17472). The manuscript therefore contains none of the claimed methods, derivations, numerical experiments, or software artifacts, and the abstract's conclusions cannot be checked against any supporting content.

Significance. If the claimed results were substantiated, the work could be of practical interest for combining quantum and classical solvers on QUBO problems and for reformulating constrained QPs as QUBO sequences. The two-stage warm-start-plus-variational-refinement idea is a plausible response to known VQE training difficulties, and the QQP formulation without slack variables is conceptually attractive. However, none of these contributions is present in the submitted text. There is no algorithm, no derivation, no convergence analysis, no experimental data, and no code. The significance of the submission is therefore entirely speculative and cannot be assessed in its current form.

major comments (3)
  1. [Full text (all sections)] The body of the manuscript is not the paper announced in the title and abstract; it is 'T2I-ReasonBench: Benchmarking Reasoning-Informed Text-to-Image Generation' (arXiv:2508.17472). None of the claimed DVQE software, QQP framework, PHR augmented-Lagrangian formulation, QUBO reformulations, or numerical results appears anywhere in the submitted text. This is a load-bearing deficiency: the central claim of the abstract cannot be evaluated.
  2. [Abstract, final sentence] The claim of 'high-quality QUBO solutions' and 'small optimality, feasibility, and solution gaps' is made without reporting any problem sizes, numerical values, baselines, solver settings, or hardware details. Even read in isolation, the abstract offers no evidence for the headline result; the full text, being an unrelated paper, supplies no tables or figures that could support it.
  3. [Abstract, QQP mechanism] The assertion that the bounded QP is solved 'through repeated local one-bit QUBO reformulations' inside a trust region is stated without a convergence guarantee, without specifying the problem class (e.g., convexity), and without an active-set correctness argument. One-bit coordinate moves are coordinate-descent-like and can stall on nonseparable objectives. Since the derivation is absent from the submitted text, the QQP convergence concern raised in the stress test cannot be resolved; the claim is unsupported.
minor comments (3)
  1. [Manuscript integrity] The title, abstract, and body are mutually inconsistent. Even the section headings and references belong to a text-to-image benchmark paper. If this is a submission error, the correct manuscript must be supplied.
  2. [Reproducibility] For a software paper, the absence of a repository link, installation instructions, or a code listing is a significant omission. The submitted text contains no reproducible artifact whatsoever.
  3. [Notation] The abstract uses terms such as 'PHR augmented-Lagrangian', 'active region', 'trust region', and 'one-bit QUBO reformulations' without definitions. Even in a self-contained paper these would require precise formalization; here they are entirely undefined due to the missing content.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity identifiable; abstract makes externally-benchmarked empirical claims and the supplied full text is an unrelated manuscript, so no derivation chain exists to inspect.

full rationale

The target manuscript (arXiv:2508.17471) is represented only by its abstract in the supplied material. The abstract's central claim is that 'the QQP framework can solve bounded constrained QP instances with small optimality, feasibility, and solution gaps.' This is an empirical claim evaluated against externally defined QP objectives and constraints, not a claim that a quantity is predicted from a fitted version of itself. No equations are given that could show X is defined in terms of Y, no fitted parameter is renamed as a prediction, no self-citation is invoked as load-bearing, and no ansatz is smuggled in via citation. The full text actually supplied is a different preprint (T2I-ReasonBench, arXiv:2508.17472) about text-to-image benchmarks; it contains none of the QUBO/QQP derivation chain. Under the hard rule that circularity may only be claimed when the paper can be quoted and the specific reduction exhibited, there is no basis for a circularity finding. Concerns about missing convergence guarantees for one-bit trust-region QUBO reformulations, or about tuning on self-selected test problems, are correctness or benchmarking-validity risks, not demonstrated circularity. Accordingly, the appropriate score is 0.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

No new physical entities (particles, forces, dimensions) are introduced. The 'local one-bit QUBO reformulation' is an algorithmic device, not an entity. This ledger reflects the abstract only: the central numerical claims rest on the PHR fixed-region equivalence, convergence of the one-bit trust-region sequence, and reliable objective estimation from sampled bitstrings, plus hand-chosen parameters (penalty weights, trust-region size, warm-start settings, circuit depth) whose values the abstract does not report.

free parameters (4)
  • PHR augmented-Lagrangian penalty weights = not stated
    The PHR formulation folds constraints into the objective via penalty and multiplier terms; the weights and update schedule are problem-tuned and not reported in the abstract.
  • Trust-region size for one-bit moves = not stated
    Each local up/down move of a continuous variable is bounded to a trust region whose size per iteration is a hand-chosen parameter controlling step quality and convergence.
  • Metaheuristic warm-start hyperparameters = not stated
    The abstract says 'several metaheuristic approaches' provide warm starts; their search parameters are free choices that affect the final solution quality.
  • Variational ansatz and circuit depth = not stated
    The VQE ansatz and its layer count are chosen by the authors, and the circuit parameters are fitted by the variational optimizer during training.
assumptions (4)
  • domain assumption Under a fixed PHR active region, the constrained augmented-Lagrangian subproblem is an ordinary bounded QP equivalent to the original constrained QP at the solution.
    Abstract: 'Under a fixed PHR active region, the constrained augmented-Lagrangian subproblem becomes an ordinary bounded quadratic problem.' Requires convexity and correct active-set identification; neither is stated in the abstract.
  • ad hoc to paper Repeated one-bit up/down QUBO reformulations within a trust region converge to the optimum of the bounded QP.
    Abstract: 'QQP then solves this bounded quadratic problem through repeated local one-bit QUBO reformulations.' No convergence guarantee is offered; coordinate-descent-like procedures can stall on nonseparable objectives.
  • domain assumption QUBO objective values can be evaluated directly and reliably from measured bitstrings on the distributed quantum circuit.
    Abstract: 'evaluates QUBO objectives directly from measured bitstrings.' This assumes sampling noise and device errors do not corrupt the objective estimate.
  • domain assumption VQE training with a metaheuristic warm start finds a circuit whose measured distribution concentrates on low-cost bitstrings.
    Abstract: 'a two-stage procedure that combines metaheuristic warm-start initialization with sampling-based variational refinement.' This is an empirical assumption about trainability, not a guarantee.

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Cite this review

Pith. "Pith review of Two-stage Distributed Variational Quantum Eigensolver Software for QUBO and Quadratic Programming." pith.science (2026). https://pith.science/paper/5CKNNEHY

@misc{pith2026250817471,
  author       = {Pith},
  title        = {Pith review of: Two-stage Distributed Variational Quantum Eigensolver Software for QUBO and Quadratic Programming},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5CKNNEHY}},
  note         = {Machine review of arXiv:2508.17471}
}
read the original abstract

This paper proposes a two-stage distributed variational quantum eigensolver (DVQE) software for solving quadratic unconstrained binary optimization (QUBO) problems and bounded constrained quadratic programming (QP) problems. The proposed DVQE solver supports both monolithic and distributed quantum-circuit execution and evaluates QUBO objectives directly from measured bitstrings. To improve variational training, DVQE uses a two-stage procedure that combines metaheuristic warm-start initialization with sampling-based variational refinement. The software supports several metaheuristic approaches as warm-start strategies. To extend QUBO-based quantum optimization to constrained continuous problems, this paper also develops a sequential QP to QUBO framework, called QQP. QQP first scales the bounded continuous variables to a normalized box and then handles equality and inequality constraints using a Powell-Hestenes-Rockafellar (PHR) augmented-Lagrangian formulation. Under a fixed PHR active region, the constrained augmented-Lagrangian subproblem becomes an ordinary bounded quadratic problem. QQP then solves this bounded quadratic problem through repeated local one-bit QUBO reformulations, where each binary variable represents a local up/down move of one continuous variable inside a trust region. In this way, QQP converts a constrained continuous QP into a sequence of QUBO subproblems without introducing slack variables. Each local QUBO subproblem can be solved using either a classical QUBO backend or the proposed DVQE solver. Numerical experiments evaluate the proposed software on QUBO and QP test problems. The results show that the distributed DVQE framework can recover high-quality QUBO solutions, and that the QQP framework can solve bounded constrained QP instances with small optimality, feasibility, and solution gaps.

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Works this paper leans on

63 extracted references · 63 canonical work pages · cited by 2 Pith papers

  1. [1]

    ��������������������������������, 2023

    The exhaustive list of american idioms. ��������������������������������, 2023

    work page 2023

  2. [2]

    Qwen2.5-vl technical report

    Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Sibo Song, Kai Dang, Peng Wang, Shijie Wang, Jun Tang, Humen Zhong, Yuanzhi Zhu, Mingkun Yang, Zhaohai Li, Jianqiang Wan, Pengfei Wang, Wei Ding, Zheren Fu, Yiheng Xu, Jiabo Ye, Xi Zhang, Tianbao Xie, Zesen Cheng, Hang Zhang, Zhibo Yang, Haiyang Xu, and Junyang Lin. Qwen2.5-vl technical report. �...

    work page 2025

  3. [3]

    Posta: A go-to framework for customized artistic poster generation

    Haoyu Chen, Xiaojie Xu, Wenbo Li, Jingjing Ren, Tian Ye, Songhua Liu, Ying-Cong Chen, Lei Zhu, and Xinchao Wang. Posta: A go-to framework for customized artistic poster generation. ����� �������� ����������������, 2025

    work page 2025

  4. [4]

    R2i-bench: Benchmarking reasoning-driven text-to-image generation

    Kaijie Chen, Zihao Lin, Zhiyang Xu, Ying Shen, Yuguang Yao, Joy Rimchala, Jiaxin Zhang, and Lifu Huang. R2i-bench: Benchmarking reasoning-driven text-to-image generation. ����� �������� ����������������, 2025

    work page 2025

  5. [5]

    Janus-pro: Unified multimodal understanding and generation with data and model scaling

    Xiaokang Chen, Zhiyu Wu, Xingchao Liu, Zizheng Pan, Wen Liu, Zhenda Xie, Xingkai Yu, and Chong Ruan. Janus-pro: Unified multimodal understanding and generation with data and model scaling. ����� �������� ����������������, 2025

    work page 2025

  6. [6]

    Claude 3.5 sonnet

    Claude. Claude 3.5 sonnet. ������������������, 2023

    work page 2023

  7. [7]

    Emerging properties in unified multimodal pretraining

    Chaorui Deng, Deyao Zhu, Kunchang Li, Chenhui Gou, Feng Li, Zeyu Wang, Shu Zhong, Weihao Yu, Xiaonan Nie, Ziang Song, et al. Emerging properties in unified multimodal pretraining. ����� �������� ����������������, 2025

    work page 2025

  8. [8]

    Diffusion models beat gans on image synthesis

    Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. �������� �� ������ ����������� ���������� �������, 34:8780–8794, 2021

    work page 2021

Show all 63 references
  1. [9]

    Cogview: Mastering text-to-image generation via transformers

    Ming Ding, Zhuoyi Yang, Wenyi Hong, Wendi Zheng, Chang Zhou, Da Yin, Junyang Lin, Xu Zou, Zhou Shao, Hongxia Yang, and Jie Tang. Cogview: Mastering text-to-image generation via transformers. ����� �������� ����������������, 2021

    2021

  2. [10]

    Got-r1: Unleashing reasoning capability of mllm for visual generation with reinforcement learning

    Chengqi Duan, Rongyao Fang, Yuqing Wang, Kun Wang, Linjiang Huang, Xingyu Zeng, Hongsheng Li, and Xihui Liu. Got-r1: Unleashing reasoning capability of mllm for visual generation with reinforcement learning. ����� �������� ����������������, 2025

    2025

  3. [11]

    Scaling rectified flow transformers for high-resolution image synthesis

    Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. In ���������� ������������� ���������� �� ������� �...

    2024

  4. [12]

    Got: Unleashing reasoning capability of multimodal large language model for visual generation and editing

    Rongyao Fang, Chengqi Duan, Kun Wang, Linjiang Huang, Hao Li, Shilin Yan, Hao Tian, Xingyu Zeng, Rui Zhao, Jifeng Dai, et al. Got: Unleashing reasoning capability of multimodal large language model for visual generation and editing. ����� �������� ����������������, 2025

    2025

  5. [13]

    Commonsense-t2i challenge: Can text-to-image generation models understand commonsense? ����� �������� ����������������, 2024

    Xingyu Fu, Muyu He, Yujie Lu, William Yang Wang, and Dan Roth. Commonsense-t2i challenge: Can text-to-image generation models understand commonsense? ����� �������� ����������������, 2024

    2024

  6. [14]

    Geneval: An object-focused framework for evaluating text-to-image alignment

    Dhruba Ghosh, Hannaneh Hajishirzi, and Ludwig Schmidt. Geneval: An object-focused framework for evaluating text-to-image alignment. ����� �������� ����������������, 2023

    2023

  7. [15]

    The llama 3 herd of models

    Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al- Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, et al. The llama 3 herd of models. ����� �������� ����������������, 2024

    2024

  8. [16]

    Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning

    Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. ����� �������� ����������������, 2025

    2025

  9. [17]

    Clipscore: A reference-free evaluation metric for image captioning

    Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. Clipscore: A reference-free evaluation metric for image captioning. ����� �������� ����������������, 2021

    2021

  10. [18]

    hidream. hidream. ����������������������������������������, 2024

    2024

  11. [19]

    Denoising diffusion probabilistic models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. �������� �� ������ ����������� ���������� �������, 33:6840–6851, 2020

    2020

  12. [20]

    Ella: Equip diffusion models with llm for enhanced semantic alignment

    Xiwei Hu, Rui Wang, Yixiao Fang, Bin Fu, Pei Cheng, and Gang Yu. Ella: Equip diffusion models with llm for enhanced semantic alignment. ����� �������� ����������������, 2024. 12

    2024

  13. [21]

    Tifa: Accurate and interpretable text-to-image faithfulness evaluation with question answering

    Yushi Hu, Benlin Liu, Jungo Kasai, Yizhong Wang, Mari Ostendorf, Ranjay Krishna, and Noah A Smith. Tifa: Accurate and interpretable text-to-image faithfulness evaluation with question answering. In ����������� �� ��� �������� ������������� ���������� �� �������� ������, pages ...

    2023

  14. [22]

    T2I-CompBench++: An Enhanced and Comprehensive Benchmark for Compositional Text-to-Image Generation

    Kaiyi Huang, Chengqi Duan, Kaiyue Sun, Enze Xie, Zhenguo Li, and Xihui Liu. T2I-CompBench++: An Enhanced and Comprehensive Benchmark for Compositional Text-to-Image Generation . ���� ������������ �� ������� �������� ������� ������������, (01):1–17, January 5555

  15. [23]

    T2i-compbench: A comprehensive benchmark for open-world compositional text-to-image generation

    Kaiyi Huang, Kaiyue Sun, Enze Xie, Zhenguo Li, and Xihui Liu. T2i-compbench: A comprehensive benchmark for open-world compositional text-to-image generation. �������� �� ������ ����������� ���������� �������, 36:78723–78747, 2023

    2023

  16. [24]

    Gpt-4o system card

    Aaron Hurst, Adam Lerer, Adam P Goucher, Adam Perelman, Aditya Ramesh, Aidan Clark, AJ Ostrow, Akila Welihinda, Alan Hayes, Alec Radford, et al. Gpt-4o system card. ����� �������� ����������������, 2024

    2024

  17. [25]

    Black Forest Labs. Flux. �����������������������������������������, 2024

    2024

  18. [26]

    Genai-bench: Evaluating and improving compositional text-to-visual generation

    Baiqi Li, Zhiqiu Lin, Deepak Pathak, Jiayao Li, Yixin Fei, Kewen Wu, Tiffany Ling, Xide Xia, Pengchuan Zhang, Graham Neubig, et al. Genai-bench: Evaluating and improving compositional text-to-visual generation. ����� �������� ����������������, 2024

    2024

  19. [27]

    Playground v2

    Daiqing Li, Aleks Kamko, Ehsan Akhgari, Ali Sabet, Linmiao Xu, and Suhail Doshi. Playground v2. 5: Three insights towards enhancing aesthetic quality in text-to-image generation. ����� �������� ����������������, 2024

    2024

  20. [28]

    Evaluating text-to-visual generation with image-to-text generation

    Zhiqiu Lin, Deepak Pathak, Baiqi Li, Jiayao Li, Xide Xia, Graham Neubig, Pengchuan Zhang, and Deva Ramanan. Evaluating text-to-visual generation with image-to-text generation. In �������� ���������� �� �������� ������, pages 366–384. Springer, 2024

    2024

  21. [29]

    Lumina-mgpt: Illuminate flexible photorealistic text-to-image generation with multimodal generative pretraining

    Dongyang Liu, Shitian Zhao, Le Zhuo, Weifeng Lin, Yu Qiao, Hongsheng Li, and Peng Gao. Lumina-mgpt: Illuminate flexible photorealistic text-to-image generation with multimodal generative pretraining. ����� �������� ����������������, 2024

    2024

  22. [30]

    Infographicvqa

    Minesh Mathew, Viraj Bagal, Rubèn Tito, Dimosthenis Karatzas, Ernest Valveny, and CV Jawahar. Infographicvqa. In ����������� �� ��� �������� ������ ���������� �� ������������ �� �������� ������, pages 1697–1706, 2022

    2022

  23. [31]

    Phybench: A physical commonsense benchmark for evaluating text-to-image models

    Fanqing Meng, Wenqi Shao, Lixin Luo, Yahong Wang, Yiran Chen, Quanfeng Lu, Yue Yang, Tianshuo Yang, Kaipeng Zhang, Yu Qiao, and Ping Luo. Phybench: A physical commonsense benchmark for evaluating text-to-image models. ����� �������� ����������������, 2024

    2024

  24. [32]

    Glide: Towards photorealistic image generation and editing with text-guided diffusion models

    Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. Glide: Towards photorealistic image generation and editing with text-guided diffusion models. ����� �������� ����������������, 2021

    2021

  25. [33]

    Wise: A world knowledge-informed semantic evaluation for text-to-image generation

    Yuwei Niu, Munan Ning, Mengren Zheng, Bin Lin, Peng Jin, Jiaqi Liao, Kunpeng Ning, Bin Zhu, and Li Yuan. Wise: A world knowledge-informed semantic evaluation for text-to-image generation. ����� �������� ����������������, 2025

    2025

  26. [34]

    Gpt-image-1

    OpenAI. Gpt-image-1. ����������������������������������������������, 2023

    2023

  27. [35]

    Zero-shot text-to-image generation

    Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea V oss, Alec Radford, Mark Chen, and Ilya Sutskever. Zero-shot text-to-image generation. ����� �������� ����������������, 2021

    2021

  28. [36]

    High-resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In ����������� �� ��� �������� ���������� �� �������� ������ ��� ������� ����������� ������, pages 10684–10695, June 2022

    2022

  29. [37]

    Photorealistic text-to- image diffusion models with deep language understanding

    Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to- image diffusion models with deep language understanding. �������� �� ������ ����������...

    2022

  30. [38]

    Laion-5b: An open large-scale dataset for training next generation image-text models

    Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al. Laion-5b: An open large-scale dataset for training next generation image-text models. �������� �� ������ �������...

    2022

  31. [39]

    Chameleon: Mixed-modal early-fusion foundation models

    Chameleon Team. Chameleon: Mixed-modal early-fusion foundation models. ����� �������� ����������������, 2024

    2024

  32. [40]

    Gemini: a family of highly capable multimodal models

    Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, Katie Millican, et al. Gemini: a family of highly capable multimodal models. ����� �������� ����������������, 2023

    2023

  33. [41]

    Neural discrete representation learning

    Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning. �������� �� ������ ����������� ���������� �������, 30, 2017

    2017

  34. [42]

    Emu3: Next-token prediction is all you need

    Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo, Quan Sun, Yufeng Cui, Jinsheng Wang, Fan Zhang, Yueze Wang, Zhen Li, Qiying Yu, et al. Emu3: Next-token prediction is all you need. ����� �������� ����������������, 2024

    2024

  35. [43]

    Chain-of-thought prompting elicits reasoning in large language models

    Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large language models. In �������, 2022

    2022

  36. [44]

    Tiif-bench: How does your t2i model follow your instructions? ����� �������� ����������������, 2025

    Xinyu Wei, Jinrui Zhang, Zeqing Wang, Hongyang Wei, Zhen Guo, and Lei Zhang. Tiif-bench: How does your t2i model follow your instructions? ����� �������� ����������������, 2025

    2025

  37. [45]

    Conceptmix: A com- positional image generation benchmark with controllable difficulty

    Xindi Wu, Dingli Yu, Yangsibo Huang, Olga Russakovsky, and Sanjeev Arora. Conceptmix: A com- positional image generation benchmark with controllable difficulty. �������� �� ������ ����������� ���������� �������, 37:86004–86047, 2024

    2024

  38. [46]

    Show-o: One single transformer to unify multimodal understanding and generation

    Jinheng Xie, Weijia Mao, Zechen Bai, David Junhao Zhang, Weihao Wang, Kevin Qinghong Lin, Yuchao Gu, Zhijie Chen, Zhenheng Yang, and Mike Zheng Shou. Show-o: One single transformer to unify multimodal understanding and generation. ����� �������� ����������������, 2024

    2024

  39. [47]

    Scaling text-rich image understanding via code-guided synthetic multimodal data generation

    Yue Yang, Ajay Patel, Matt Deitke, Tanmay Gupta, Luca Weihs, Andrew Head, Mark Yatskar, Chris Callison-Burch, Ranjay Krishna, Aniruddha Kembhavi, et al. Scaling text-rich image understanding via code-guided synthetic multimodal data generation. ����� �������� ����������������, 2025

    2025

  40. [48]

    Scaling autoregressive models for content-rich text-to-image generation

    Jiahui Yu, Yuanzhong Xu, Jing Yu Koh, Thang Luong, Gunjan Baid, Zirui Wang, Vijay Vasudevan, Alexander Ku, Yinfei Yang, Burcu Karagol Ayan, et al. Scaling autoregressive models for content-rich text-to-image generation. ����� �������� ����������������, 2(3):5, 2022

    2022

  41. [49]

    The Exhaustive List of American Idioms

    Shijie Zhou, Ruiyi Zhang, Yufan Zhou, and Changyou Chen. A high-quality text-rich image instruction tuning dataset via hybrid instruction generation. ����� �������� ����������������, 2024. 14 Appendix A More details on prompt collection process Idiom Interpretation. In idiom c...

    2024

  42. [50]

    identify what should be depicted in the image or the meaning the image should convey

  43. [51]

    analyze the prompt and create a list of questions based on the key elements that the image should be checked against

  44. [52]

    yes (to the question)

    consider factors that could impact the aesthetics or visual quality of the image and list relevant questions. Please also design a scoring criterion for each question, where a score of 1 means “yes (to the question)”, 0 means “no”, and 0.5 means “partially yes”. Provide your a...

  45. [53]

    identify what should be depicted in the image and its functional purposes

  46. [54]

    analyze the design intention and create a list of questions based on the key elements that the image should be checked against, including presence of required text elements

  47. [55]

    yes (to the question)

    consider factors that could impact the aesthetics or visual quality of the image and list relevant questions. Please also design a scoring criterion for each question, where a score of 1 means “yes (to the question)”, 0 means “no”, and 0.5 means “partially yes”. Provide your a...

  48. [56]

    identify what should be depicted in the image in order to fully and accurately reflect the explicit meaning of the prompt

  49. [57]

    identify the entity that the model needs to infer from the prompt, and create a list of questions that check whether the image has correctly identified and depicted this entity

  50. [58]

    Consider other elements or details in the prompt (apart from the implicit entity), create a list of questions that check if the image accurately reflects these additional key elements

  51. [59]

    yes (to the question)

    consider factors that could impact the aesthetics or visual quality of the image and list relevant questions. Please also design a scoring criterion for each question, where a score of 1 means “yes (to the question)”, 0 means “no”, and 0.5 means “partially yes”. Provide your a...

  52. [60]

    describe what should be depicted in the image in order to fully and accurately reflect the explicit meaning of the prompt

  53. [61]

    identify any scientific law(s) that the model needs to infer from the prompt, and create a list of questions that check whether the image correctly demonstrates and complies with these scientific laws

  54. [62]

    consider other elements or details in the prompt that are not directly affected by the scientific law(s), create a list of questions that check if the image accurately represents these additional key elements

  55. [63]

    yes (to the question)

    consider factors that could impact the aesthetics or visual quality of the image and list relevant questions. Please also design a scoring criterion for each question, where a score of 1 means “yes (to the question)”, 0 means “no”, and 0.5 means “partially yes”. Provide your a...

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.