Pith. sign in

REVIEW 4 major objections 5 minor 48 references

Unified multimodal LLMs can follow complex spatial and logical instructions by generating an explicit reasoning chain, a grounded bounding-box layout, and then the image — with large benchmark gains over prior layout-based MLLMs.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

T0 review · deepseek-v4-flash

2026-08-01 21:02 UTC pith:AIR7VGQY

load-bearing objection Solid systems paper with a real evaluation-entanglement problem in its headline numbers; the core pipeline is credible, but the 65% gain over PlanGen needs independent scoring on final images before I trust the magnitude. the 4 major comments →

arxiv 2607.16409 v1 pith:AIR7VGQY submitted 2026-07-17 cs.CV cs.AIcs.LG

Think, Plan, Paint: Layout-Aware Reasoning for Controllable Image Generation in Unified Models

Junhao Liu , Jian-Wei Zhang , Tao Huang , Miles Yang , Zhao Zhong , Liefeng Bo This is my paper
classification cs.CV cs.AIcs.LG
keywords multimodal large language modelscontrollable image generationlayout planningspatial reasoningchain-of-thoughtreinforcement learningtext-to-image generationunified multi-modal models
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper argues that unified multimodal LLMs fail at complex spatial and logical instructions because they treat layout as an auxiliary control signal rather than as a shared language between reasoning and pixels. ATLAS restructures generation into a human-like Think, Plan, Paint loop: a layout-aware chain-of-thought decomposes the prompt, a grounded bounding-box plan makes the arrangement explicit and editable, and reinforcement learning aligns the final image to the plan. On T2I-CompBench and a new 500-prompt ATLAS-Reasoning benchmark, the authors report an average 65.31% improvement over prior layout-based unified MLLMs, with the 80B variant roughly matching closed-source generators. The same layout interface also handles instruction-guided editing and visual grounding, making layout a unified control surface rather than a one-way generation signal.

Core claim

ATLAS establishes that promoting layout from a control signal to a cross-modal bridge unlocks the reasoning strengths of text to structure the visual modality. The model first generates an explicit reasoning chain (Think), then a sequence of label-bounding-box pairs in a shared coordinate-token vocabulary (Plan), then denoises image latents conditioned on the full prompt-reasoning-layout sequence (Paint). Shared Positional Tokens give text, planning, and vision a common spatial vocabulary, while Flow-GRPO-based layout alignment with five rewards (position, semantic alignment, region counting, negative-region suppression, quality) enforces plan-to-image fidelity. The authors claim this yields

What carries the argument

The central object is the layout as a shared representation, instantiated as Shared Positional Tokens: a vocabulary of 1,000 x- and y-coordinate tokens that encode each grounded object as a label followed by a four-token bounding box (two corners). This label-box format is the unified spatial interface across Think, Plan, Paint, editing, and grounding. The Think phase produces a Layout-Aware Chain-of-Thought that reasons about object semantics, counts, attributes, and spatial relations before any coordinates are emitted. The Plan phase autoregressively generates the grounded label-box sequence. The Paint phase runs a Transfusion-style backbone (interleaving auto-regressive text tokens with d

Load-bearing premise

The headline numbers rest on evaluation by a single MLLM judge that is also the teacher model that synthesized the training layouts and reasoning chains; if that model family has systematic spatial blind spots or stylistic preferences, part of the measured improvement may reflect agreement with its own biases rather than human-verifiable fidelity.

What would settle it

Rescore the ATLAS-Reasoning and T2I-CompBench spatial subsets with human raters (or a panel of judges from different model families) and compare ATLAS-7B against PlanGen. If the ~65% average improvement collapses to noise or reverses, the central claim that explicit Think–Plan–Paint reasoning causes large spatial gains is not robust. A cheaper pre-check: rerun the Layout Shift Test with a detector that has no linguistic grounding (e.g., a pure box-regression detector) and see whether the Hit Rate gap between ATLAS and PlanGen persists.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • The same Think–Plan–Paint recipe improves both 7B and 80B unified MLLMs, suggesting the gains scale with model size rather than being a small-model artifact.
  • Because layout is a shared interface, users can inspect, edit, add, or delete bounding boxes between Plan and Paint, turning ambiguous instructions into interactive control.
  • Scores on DPG-Bench and GenEval improve on attribute binding and counting, indicating that explicit layout planning helps general prompt following, not only spatial prompts.
  • The new ATLAS-Reasoning benchmark gives the community a 500-prompt testbed for evaluating multi-step spatial and logical instructions in generation.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the judge and teacher models share systematic spatial blind spots, a fraction of the reported gains may be self-agreement; a human-rated or multi-judge replication would reveal the true fidelity gap.
  • The layout-as-bridge idea is not tied to bounding boxes; extending Shared Positional Tokens to masks, keypoints, or depth could carry the same reasoning-to-pixels benefit to finer-grained control.
  • A testable offshoot: apply only the RL layout-alignment stage to an existing layout pipeline (without Think/Plan) and measure the gain; this would isolate how much of the improvement comes from alignment versus from explicit reasoning.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper proposes ATLAS, a unified multimodal large language model framework that generates images through a three-stage 'Think, Plan, Paint' process. A layout representation, expressed with shared positional tokens, connects the reasoning stage, the layout-planning stage, and the image-synthesis stage. Training uses Qwen3-VL-235B-A22B to synthesize layouts and reasoning chains, followed by supervised joint training and Flow-GRPO-based layout alignment. ATLAS is instantiated at 7B and 80B scales on BAGEL and Hunyuan Image 3.0. The central claims are state-of-the-art generation among MLLMs, large gains over PlanGen (65.31% in the abstract, 56.59% for ATLAS-7B in §4.1), gains over the base models on spatial tasks (23.06%), a new ATLAS-Reasoning benchmark, and support for layout-based editing and grounding.

Significance. If the headline numbers are trustworthy, this is a substantial contribution to controllable image generation in unified MLLMs. The architecture is well motivated: explicit layout tokens give the user a transparent, editable interface, while the Think phase provides a reasoning scaffold. The empirical structure is unusually strong for this area: four benchmark families, component ablation (Table 4), paired bootstrap significance testing (Appendix D.2), and two independent-ish checks (rule-based layout scoring in Table 2 and GroundingDINO detection in Table 3). The scaling behavior from 7B to 80B is also a useful datapoint. However, the flagship generation scores are judged by Qwen3-VL-235B-A22B, the same model family used to synthesize the training data. The significance is therefore conditional on an external, human or at least out-of-family validation of the main claims. With that validation supplied, the paper would be a clear advance; without it, the quantitative claims are not yet convincing.

major comments (4)
  1. [§4.1, §3.3.1, Appendix E] The main T2I-CompBench and ATLAS-Reasoning numbers in Table 1 are evaluated by Qwen3-VL-235B-A22B, and §3.3.1 states that the same model family generated the training layout annotations and reasoning chains. This creates a teacher/judge overlap. The abstract's '65.31% improvement' and the 91.21 ATLAS-Reasoning score may partly reflect the judge agreeing with its own preferred spatial/attribute patterns rather than human-visible fidelity. The mitigating experiments do not fully close the gap: Table 2 scores layouts, not final images, and Table 3 covers only a narrow relocation task. Please provide a human re-scoring study on a random subset of T2I-CompBench and ATLAS-Reasoning prompts, report agreement between Qwen3-VL and human raters, and preferably repeat the evaluation with an independent judge (e.g., a different MLLM family or rule-based detectors on the final images). This is testab
  2. [Appendix D.3, Eq. (3)] The layout-position reward r_pos is computed from object boxes 'estimated from the generated image', but the detector is never named or specified. This is a reproducibility gap and also a bias concern: if the same open-vocabulary detector family is used not only to compute r_pos during RL training but also implicitly shapes the evaluation, the reward and the final metric can share systematic errors. Please specify the detector, its checkpoint and prompting, the box-matching algorithm (semantic label matching, IoU thresholds), and the reward weight w_pos. State explicitly whether any component of this detector appears in the evaluation pipeline.
  3. [§4.1, ATLAS-Reasoning benchmark] ATLAS-Reasoning is introduced as a new benchmark with 500 prompts, but the paper gives no details about how the prompts were generated, filtered, or validated, and it is scored only with the same Qwen3-VL judge. A self-constructed benchmark with an aligned judge makes the 91.21 versus 25.43 gain hard to interpret. Please release the prompt set, describe the construction process, and provide at least one external judge or human evaluation with agreement statistics. Also clarify whether any of these 500 prompts, or near-duplicates, appear in the training mixture used to supervise ATLAS; leakage would inflate the reported advantage.
  4. [Abstract and §4.1] The abstract reports an 'average 65.31% improvement,' while §4.1 states that ATLAS-7B surpasses PlanGen by 56.59% on average. Recomputing from Table 1, 65.31% is the aggregate relative gain for ATLAS-80B (mean absolute increase divided by PlanGen's mean), and 56.59% is the analogous number for ATLAS-7B. These are different quantities. Please state the per-model numbers explicitly, define the aggregation formula, and include confidence intervals from the paired bootstrap described in Appendix D.2 (which currently only reports significance at α=0.01). Also, the comparison class 'existing layout-based unified MLLMs' appears to contain PlanGen only; please make that explicit or expand the baseline set.
minor comments (5)
  1. [Table 2] The rule-based clause checker is a key independent validation, but no implementation details are given. Please describe how the checker parses spatial relations, how it verifies each clause, and whether it will be released.
  2. [Appendix E] The scoring rubrics in Appendix E use 1–4 or 1–5 scales. Please state how these are normalized to 0–100 for Table 1, and whether all categories use the same normalization.
  3. [Figure 7] Layout diversity is only shown qualitatively. A quantitative metric, e.g., average pairwise bounding-box IoU or box-entropy, would strengthen the claim that the plan does not collapse to fixed templates.
  4. [§3.3.1] The human-verification paragraph reports 95.3% grounding accuracy and 93.5% reasoning accuracy but does not say how many authors assessed each item or whether there was independent/disjoint annotation. Inter-author agreement would be useful.
  5. [General] Please include a statement on whether the models and benchmarks will be released. This is standard for reproducibility of a new framework and a new benchmark.

Circularity Check

2 steps flagged

Headline gains are judged by Qwen3-VL, the same model that generated the training layouts/reasoning, so the flagship numbers are partly self-agreement; independent rule-based/GroundingDINO checks keep this from being fully circular.

specific steps
  1. fitted input called prediction [§3.3.1 (Data Construction) and §4.1 (RQ1 Evaluation)]
    "We employ Qwen3-VL-235B-A22B [3] to analyze both the text prompt T and the target image I_tgt. ... We prompt Qwen3-VL-235B-A22B to act as a spatial reasoner: given T and the annotated L, it generates a reasoning chain R ... Specifically, we use the prompts provided by T2I-CompBench and employ Qwen3-VL-235B-A22B [3] to assess generation quality."

    ATLAS is trained to imitate Qwen3-VL's layout annotations and reasoning chains (the Think/Plan supervision), and the same model is then used to score the generated images for the headline T2I-CompBench and ATLAS-Reasoning results. The model is therefore fit to reproduce Qwen3-VL's spatial judgments, and the evaluation measures agreement with that same judge rather than an independent ground truth. The claimed 65.31%/56.59% gains are partly self-consistency with the teacher. The rule-based checker (Table 2) and GroundingDINO (Table 3) are independent and mitigate, but they do not cover the main image-generation columns.

  2. other [§4.1 (ATLAS-Reasoning benchmark)]
    "To evaluate the reasoning capability of our CoT mechanism, we construct a challenging dataset comprising 500 complex prompts. ... For ATLAS-Reasoning, we follow the method for evaluating spatial relations in T2I-CompBench."

    The new ATLAS-Reasoning benchmark is introduced in the same paper, its 500 prompts are not independently human-validated, and its scores come from the same Qwen3-VL judge that synthesized the training reasoning/layout data. The large ATLAS-Reasoning gap (ATLAS-80B 91.21 vs PlanGen 25.43) may therefore reflect how closely ATLAS mirrors the teacher's reasoning preferences rather than an independent measure of complex-instruction following.

full rationale

The paper's core derivation — a Think/Plan/Paint loop trained with joint LM+diffusion losses and Flow-GRPO — is not circular: the training objectives (Eq. 1) and rewards (Eqs. 2–7) are defined from the prompt, layout, and image, not from the evaluation scores, and the ablations (Table 4) are genuine removals of components. The base models are used as backbones, not as evidence for ATLAS's claims. The main circularity-adjacent concern is evaluation entanglement: Qwen3-VL-235B-A22B generates the layout/reasoning supervision (§3.3.1) and also judges the headline T2I-CompBench and ATLAS-Reasoning scores (§4.1, Appendix E). This makes the flagship numbers partly a measure of self-consistency with the teacher. The paper partly mitigates this with a rule-based clause checker (Table 2), GroundingDINO (Table 3), and human audits of training data (95.3%/93.5%), which are independent and show the same trends, so the central claim is not forced by construction. However, those checks do not cover the image-generation columns that support the 65.31%/56.59% headline, and the Limitations section (Appendix B) does not flag this entanglement. Score 4 reflects partial self-reference in the evaluation, not a definitional equivalence.

Axiom & Free-Parameter Ledger

5 free parameters · 6 axioms · 2 invented entities

The central claims rest on: (i) a representational choice (boxes carry all spatial semantics), (ii) a data pipeline whose teacher (Qwen3-VL) later serves as evaluation judge, (iii) an RL stage whose reward detector is unnamed, and (iv) several undisclosed tuned weights (λ, w_*, τ, data mixture). Visible hyperparameters (batch sizes, LRs, Flow-GRPO settings) are disclosed. No physics-like free constants or physically invented entities beyond the positional-token vocabulary and the self-built benchmark.

free parameters (5)
  • RL reward weights = undisclosed
    Eq. (2) defines r = w_pos·r_pos + w_sem·r_sem + w_cnt·r_cnt + w_neg·r_neg + w_qua·r_qua; the weights are tuned but no values are reported, so the RL objective cannot be reproduced.
  • Loss weight λ = undisclosed ('following base model strategies')
    Eq. (1): L = L_txt + λ·L_diff; λ is not given, only that it follows base-model training strategy.
  • τ in semantic-alignment reward = undisclosed
    Eq. (5): r_sem = σ(τ·cos(E_text(T), E_image(I))); temperature not reported.
  • Coordinate-token granularity K=1000 = 1000 per axis
    Section 3.1.2 fixes q(u)=min(⌊1000u⌋,999); a hand-chosen discretization that bounds achievable layout precision and is not derived.
  • Data mixture ratios = undisclosed
    Section 3.3.1 lists five data formats mixed into 5M samples but gives no proportions; mixture balance is a tuned quantity affecting downstream scores.
axioms (6)
  • domain assumption Bounding boxes adequately represent spatial constraints for reasoning, planning, generation, and editing.
    The entire shared representation is label-box pairs (Sec 3.1.2); the authors flag this limitation in App. B ('bounding boxes are only one possible way to represent layouts').
  • domain assumption Qwen3-VL-235B-A22B annotations (layouts L, reasoning R) are correct-enough supervision.
    Sec 3.3.1 builds the training set from Qwen3-VL outputs; human audit covers only 500 layouts (95.3% grounding) and 200 reasoning chains (93.5%) out of 5M samples.
  • domain assumption MLLM judge scores (Qwen3-VL) reflect human-perceived compositional correctness.
    All T2I-CompBench and ATLAS-Reasoning numbers in Tables 1–3 use Qwen3-VL as judge (Sec 4.1, App E); no human evaluation of final images is reported.
  • domain assumption The detector computing r_pos in RL is a valid measure of layout adherence.
    Eq. (3) and App D.3 say detected objects are matched with input layout boxes, but the detector is never named; if the RL reward detector equals the evaluation detector, training directly optimizes the eval metric.
  • domain assumption The rule-based clause checker (Table 2) is a fair independent measure.
    No description of the rule-based checker or its relation templates is given, so its independence and strictness cannot be audited.
  • domain assumption Base backbones BAGEL and Hunyuan Image 3.0 are capable Transfusion-style unified models.
    ATLAS inherits generation quality from these published backbones (Sec 3.1.1); the 80B backbone is the authors' own Hunyuan Image 3.0 technical report [5].
invented entities (2)
  • Shared Positional Tokens (V_pos) independent evidence
    purpose: A 1000-token-per-axis vocabulary encoding normalized coordinates so bounding boxes become model-native tokens usable uniformly across Think, Plan, Paint, editing, and grounding (Sec 3.1.2, Fig 3).
    Falsifiable handles exist: layout-planning scores (Table 2), layout-shift HR@0.5 (Table 3), and attention maps (Fig 10); the token scheme itself is a closed-vocabulary design, not a physical entity.
  • ATLAS-Reasoning benchmark (500 prompts) independent evidence
    purpose: A self-constructed evaluation set for multi-step spatial and logical instructions (counts, attribute binding, entangled relations), introduced in Sec 4.1.
    Scores on it (e.g., ATLAS-80B 91.21 vs PlanGen 25.43) are falsifiable in principle, but the prompt set is not released and construction details are not given, so independent verification awaits artifacts.

pith-pipeline@v1.3.0-alltime-deepseek · 16559 in / 24500 out tokens · 200409 ms · 2026-08-01T21:02:43.102700+00:00 · methodology

0 comments
read the original abstract

Unified Multimodal Large Language Models (MLLMs) offer a promising paradigm for unifying visual understanding and generation, yet they still struggle to follow complex spatial instructions and logical constraints in controllable image generation. To address this gap, we present ATLAS, a unified framework that equips MLLMs with a human-like "Think, Plan, and Paint" paradigm. We adopt layout as the shared representation that connects the three stages, enabling the model to reason about spatial requirements, plan explicit object arrangements, and render the final image. We further improve plan-to-image fidelity with reinforcement-learning-based layout alignment. We instantiate ATLAS at 7B and 80B scales, achieving state-of-the-art performance among MLLMs on image generation benchmarks and an average 65.31% improvement over existing layout-based unified MLLMs. On spatially related tasks, ATLAS obtains an average 23.06% gain over the base models. Through the same layout interface, ATLAS also supports instruction-guided editing and multimodal grounding. We further introduce ATLAS-Reasoning, a benchmark for evaluating generation under complex spatial instructions.

Figures

Figures reproduced from arXiv: 2607.16409 by Jian-Wei Zhang, Junhao Liu, Liefeng Bo, Miles Yang, Tao Huang, Zhao Zhong.

Figure 1
Figure 1. Figure 1: Our ATLAS framework generates images following a human-like [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the ATLAS framework. Given a text prompt, ATLAS [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: ATLAS represents each grounded object with a semantic label followed by four dedicated [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative comparison of text-to-image generation. ATLAS better follows complex [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative comparison of layout planning. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Additional editing examples using ATLAS. [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Layout diversity under different random seeds. For the same counting prompts, ATLAS [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Handling ambiguous spatial language. For prompts such as “a cat and a dog around a tree,” [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Interactive plan editing interface. Users can adjust, add, or delete intermediate bounding [PITH_FULL_IMAGE:figures/full_fig_p019_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Attention analysis between layout planning and image generation. For each selected [PITH_FULL_IMAGE:figures/full_fig_p020_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Example of physical reasoning in layout planning. Given a prompt implying that a cup [PITH_FULL_IMAGE:figures/full_fig_p021_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Interactive interface for ATLAS. The interface streams the model’s thinking process [PITH_FULL_IMAGE:figures/full_fig_p022_12.png] view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

48 extracted references · 37 linked inside Pith

  1. [1]

    Flamingo: a visual language model for few-shot learning, 2022

    Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katie Millican, Malcolm Reynolds, Roman Ring, Eliza Ruther- ford, Serkan Cabi, Tengda Han, Zhitao Gong, Sina Samangooei, Marianne Monteiro, Ja- cob Menick, Sebastian Borgeaud, Andrew Brock, Aida Nematzadeh, Sahand Sharifzadeh, Mikolaj Binkow...

    Pith/arXiv arXiv 2022

  2. [2]

    Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond, 2023

    Jinze Bai, Shuai Bai, Shusheng Yang, Shijie Wang, Sinan Tan, Peng Wang, Junyang Lin, Chang Zhou, and Jingren Zhou. Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond, 2023. URLhttps://arxiv.org/abs/2308.12966

    Pith/arXiv arXiv 2023

  3. [3]

    Qwen3-vl technical report, 2025

    Shuai Bai, Yuxuan Cai, Ruizhe Chen, Keqin Chen, Xionghui Chen, Zesen Cheng, Lianghao Deng, Wei Ding, Chang Gao, Chunjiang Ge, Wenbin Ge, Zhifang Guo, Qidong Huang, Jie Huang, Fei Huang, Binyuan Hui, Shutong Jiang, Zhaohai Li, Mingsheng Li, Mei Li, Kaixin Li, Zicheng Lin, Junyang Lin, Xuejing Liu, Jiawei Liu, Chenglong Liu, Yang Liu, Dayiheng Liu, Shixuan ...

    Pith/arXiv arXiv 2025

  4. [4]

    Tim Brooks, Aleksander Holynski, and Alexei A. Efros. Instructpix2pix: Learning to follow image editing instructions, 2023. URLhttps://arxiv.org/abs/2211.09800

    Pith/arXiv arXiv 2023

  5. [5]

    Hunyuanimage 3.0 technical report, 2025

    Siyu Cao, Hangting Chen, Peng Chen, Yiji Cheng, Yutao Cui, Xinchi Deng, Ying Dong, Kipper Gong, Tianpeng Gu, Xiusen Gu, Tiankai Hang, Duojun Huang, Jie Jiang, Zhengkai Jiang, Weijie Kong, Changlin Li, Donghao Li, Junzhe Li, Xin Li, Yang Li, Zhenxi Li, Zhimin Li, Jiaxin Lin, Linus, Lucaz Liu, Shu Liu, Songtao Liu, Yu Liu, Yuhong Liu, Yanxin Long, Fanbin Lu...

    Pith/arXiv arXiv 2025

  6. [6]

    Pixart- α: Fast training of diffusion transformer for photorealistic text-to-image synthesis, 2023

    Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Yue Wu, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, and Zhenguo Li. Pixart- α: Fast training of diffusion transformer for photorealistic text-to-image synthesis, 2023. 10

    2023

  7. [7]

    Training-free layout control with cross-attention guidance

    Minghao Chen, Iro Laina, and Andrea Vedaldi. Training-free layout control with cross-attention guidance. InProceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pages 5343–5353, January 2024

    2024

  8. [8]

    Janus-pro: Unified multimodal understanding and generation with data and model scaling.arXiv preprint arXiv:2501.17811, 2025

    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.arXiv preprint arXiv:2501.17811, 2025

    Pith/arXiv arXiv 2025

  9. [9]

    Visual programming for text-to-image generation and evaluation, 2023

    Jaemin Cho, Abhay Zala, and Mohit Bansal. Visual programming for text-to-image generation and evaluation, 2023. URLhttps://arxiv.org/abs/2305.15328

    Pith/arXiv arXiv 2023

  10. [10]

    Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities, 2025

    Gheorghe Comanici and et al. Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities, 2025. URL https: //arxiv.org/abs/2507.06261

    Pith/arXiv arXiv 2025

  11. [11]

    Emerging properties in unified multimodal pretraining, 2025

    Chaorui Deng, Deyao Zhu, Kunchang Li, Chenhui Gou, Feng Li, Zeyu Wang, Shu Zhong, Weihao Yu, Xiaonan Nie, Ziang Song, Guang Shi, and Haoqi Fan. Emerging properties in unified multimodal pretraining, 2025. URL https://arxiv.org/abs/2505.14683. Licensed under the Apache License 2.0

    Pith/arXiv arXiv 2025

  12. [12]

    Scaling rectified flow transformers for high-resolution image synthesis, 2024

    Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas M¨uller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, Dustin Podell, Tim Dockhorn, Zion English, Kyle Lacey, Alex Goodwin, Yannik Marek, and Robin Rombach. Scaling rectified flow transformers for high-resolution image synthesis, 2024. URL https://arxiv.org/abs/ 2403.03206

    Pith/arXiv arXiv 2024

  13. [13]

    Layoutgpt: Compositional visual planning and generation with large language models, 2023

    Weixi Feng, Wanrong Zhu, Tsu jui Fu, Varun Jampani, Arjun Akula, Xuehai He, Sugato Basu, Xin Eric Wang, and William Yang Wang. Layoutgpt: Compositional visual planning and generation with large language models, 2023. URL https://arxiv.org/abs/2305.15393

    Pith/arXiv arXiv 2023

  14. [14]

    Guiding instruction-based image editing via multimodal large language models, 2024

    Tsu-Jui Fu, Wenze Hu, Xianzhi Du, William Yang Wang, Yinfei Yang, and Zhe Gan. Guiding instruction-based image editing via multimodal large language models, 2024. URL https: //arxiv.org/abs/2309.17102

    Pith/arXiv arXiv 2024

  15. [15]

    Seed-x: Multimodal models with unified multi-granularity comprehension and generation, 2025

    Yuying Ge, Sijie Zhao, Jinguo Zhu, Yixiao Ge, Kun Yi, Lin Song, Chen Li, Xiaohan Ding, and Ying Shan. Seed-x: Multimodal models with unified multi-granularity comprehension and generation, 2025. URLhttps://arxiv.org/abs/2404.14396

    Pith/arXiv arXiv 2025

  16. [16]

    Thinkmorph: Emergent properties in multimodal interleaved chain-of-thought reasoning, 2026

    Jiawei Gu, Yunzhuo Hao, Huichen Will Wang, Linjie Li, Michael Qizhe Shieh, Yejin Choi, Ranjay Krishna, and Yu Cheng. Thinkmorph: Emergent properties in multimodal interleaved chain-of-thought reasoning, 2026. URLhttps://arxiv.org/abs/2510.27492

    arXiv 2026

  17. [17]

    Gldesigner: Leveraging multi-modal llms as designer for enhanced aesthetic text glyph layouts, 2025

    Junwen He, Yifan Wang, Lijun Wang, Huchuan Lu, Jun-Yan He, Chenyang Li, Hanyuan Chen, Jin-Peng Lan, Bin Luo, and Yifeng Geng. Gldesigner: Leveraging multi-modal llms as designer for enhanced aesthetic text glyph layouts, 2025. URL https://arxiv.org/abs/ 2411.11435

    Pith/arXiv arXiv 2025

  18. [18]

    Plangen: Towards unified layout planning and image genera- tion in auto-regressive vision language models

    Runze He, Bo Cheng, Yuhang Ma, Qingxiang Jia, Shanyuan Liu, Ao Ma, Xiaoyu Wu, Liebucha Wu, Dawei Leng, and Yuhui Yin. Plangen: Towards unified layout planning and image genera- tion in auto-regressive vision language models. InProceedings of the IEEE/CVF International Conference on Computer Vision, pages 18143–18154, 2025

    2025

  19. [19]

    Prompt-to-prompt image editing with cross attention control, 2022

    Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Prompt-to-prompt image editing with cross attention control, 2022. URL https://arxiv. org/abs/2208.01626

    Pith/arXiv arXiv 2022

  20. [20]

    T2i- compbench++: An enhanced and comprehensive benchmark for compositional text-to-image generation, 2025

    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, 2025. URLhttps://arxiv.org/abs/2307.06350

    Pith/arXiv arXiv 2025

  21. [21]

    Gpt-4o system card.arXiv preprint arXiv:2410.21276, 2024

    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.arXiv preprint arXiv:2410.21276, 2024. 11

    Pith/arXiv arXiv 2024

  22. [22]

    FLUX.2: Frontier Visual Intelligence

    Black Forest Labs. FLUX.2: Frontier Visual Intelligence. https://bfl.ai/blog/flux-2, 2025

    2025

  23. [23]

    Evaluating object hallucination in large vision-language models

    Yifan Li, Yifan Du, Kun Zhou, Jinpeng Wang, Xin Zhao, and Ji-Rong Wen. Evaluating object hallucination in large vision-language models. InThe 2023 Conference on Empirical Methods in Natural Language Processing, 2023. URL https://openreview.net/forum? id=xozJw0kZXF

    2023

  24. [24]

    Gligen: Open-set grounded text-to-image generation

    Yuheng Li, Haotian Liu, Qingyang Wu, Fangzhou Mu, Jianwei Yang, Jianfeng Gao, Chunyuan Li, and Yong Jae Lee. Gligen: Open-set grounded text-to-image generation. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 22511–22521, 2023

    2023

  25. [25]

    Mixture-of-transformers: A sparse and scalable architecture for multi-modal foundation models, 2025

    Weixin Liang, Lili Yu, Liang Luo, Srinivasan Iyer, Ning Dong, Chunting Zhou, Gargi Ghosh, Mike Lewis, Wen tau Yih, Luke Zettlemoyer, and Xi Victoria Lin. Mixture-of-transformers: A sparse and scalable architecture for multi-modal foundation models, 2025. URL https: //arxiv.org/abs/2411.04996

    Pith/arXiv arXiv 2025

  26. [26]

    Lumina-mgpt: Illuminate flexible photorealistic text-to-image generation with multimodal generative pretraining, 2025

    Dongyang Liu, Shitian Zhao, Le Zhuo, Weifeng Lin, Yi Xin, Xinyue Li, Qi Qin, Yu Qiao, Hongsheng Li, and Peng Gao. Lumina-mgpt: Illuminate flexible photorealistic text-to-image generation with multimodal generative pretraining, 2025. URL https://arxiv.org/abs/ 2408.02657

    Pith/arXiv arXiv 2025

  27. [27]

    Visual instruction tuning, 2023

    Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning, 2023. URLhttps://arxiv.org/abs/2304.08485

    Pith/arXiv arXiv 2023

  28. [28]

    Flow-grpo: Training flow matching models via online rl, 2025

    Jie Liu, Gongye Liu, Jiajun Liang, Yangguang Li, Jiaheng Liu, Xintao Wang, Pengfei Wan, Di Zhang, and Wanli Ouyang. Flow-grpo: Training flow matching models via online rl, 2025. URLhttps://arxiv.org/abs/2505.05470

    Pith/arXiv arXiv 2025

  29. [29]

    Grounding dino: Marrying dino with grounded pre-training for open-set object detection.arXiv preprint arXiv:2303.05499, 2023

    Shilong Liu, Zhaoyang Zeng, Tianhe Ren, Feng Li, Hao Zhang, Jie Yang, Chunyuan Li, Jianwei Yang, Hang Su, Jun Zhu, et al. Grounding dino: Marrying dino with grounded pre-training for open-set object detection.arXiv preprint arXiv:2303.05499, 2023

    Pith/arXiv arXiv 2023

  30. [30]

    Deepseek-vl: Towards real-world vision-language understanding, 2024

    Haoyu Lu, Wen Liu, Bo Zhang, Bingxuan Wang, Kai Dong, Bo Liu, Jingxiang Sun, Tongzheng Ren, Zhuoshu Li, Hao Yang, Yaofeng Sun, Chengqi Deng, Hanwei Xu, Zhenda Xie, and Chong Ruan. Deepseek-vl: Towards real-world vision-language understanding, 2024. URL https://arxiv.org/abs/2403.05525

    Pith/arXiv arXiv 2024

  31. [31]

    Chameleon: Plug-and-play compositional reasoning with large language models.Advances in Neural Information Processing Systems, 36:43447–43478, 2023

    Pan Lu, Baolin Peng, Hao Cheng, Michel Galley, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, and Jianfeng Gao. Chameleon: Plug-and-play compositional reasoning with large language models.Advances in Neural Information Processing Systems, 36:43447–43478, 2023

    2023

  32. [32]

    T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models, 2023

    Chong Mou, Xintao Wang, Liangbin Xie, Yanze Wu, Jian Zhang, Zhongang Qi, Ying Shan, and Xiaohu Qie. T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models, 2023. URLhttps://arxiv.org/abs/2302.08453

    Pith/arXiv arXiv 2023

  33. [33]

    Sdxl: Improving latent diffusion models for high-resolution image synthesis, 2023

    Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas M¨uller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion models for high-resolution image synthesis, 2023. URLhttps://arxiv.org/abs/2307.01952

    Pith/arXiv arXiv 2023

  34. [34]

    Outrageously large neural networks: The sparsely-gated mixture-of-experts layer, 2017

    Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer, 2017. URLhttps://arxiv.org/abs/1701.06538

    Pith/arXiv arXiv 2017

  35. [35]

    Emu: Generative pretraining in multimodality,

    Quan Sun, Qiying Yu, Yufeng Cui, Fan Zhang, Xiaosong Zhang, Yueze Wang, Hongcheng Gao, Jingjing Liu, Tiejun Huang, and Xinlong Wang. Emu: Generative pretraining in multimodality,

  36. [36]

    Qwen2-vl: Enhancing vision- language model’s perception of the world at any resolution, 2024

    Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, Yang Fan, Kai Dang, Mengfei Du, Xuancheng Ren, Rui Men, Dayiheng Liu, Chang Zhou, Jingren Zhou, and Junyang Lin. Qwen2-vl: Enhancing vision- language model’s perception of the world at any resolution, 2024. URL https://arxiv.org/ abs/2409....

    Pith/arXiv arXiv 2024

  37. [37]

    Genartist: Multimodal llm as an agent for unified image generation and editing, 2024

    Zhenyu Wang, Aoxue Li, Zhenguo Li, and Xihui Liu. Genartist: Multimodal llm as an agent for unified image generation and editing, 2024. URLhttps://arxiv.org/abs/2407.05600

    Pith/arXiv arXiv 2024

  38. [38]

    Boxdiff: Text-to-image synthesis with training-free box-constrained diffu- sion

    Jinheng Xie, Yuexiang Li, Yawen Huang, Haozhe Liu, Wentian Zhang, Yefeng Zheng, and Mike Zheng Shou. Boxdiff: Text-to-image synthesis with training-free box-constrained diffu- sion. InProceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 7452–7461, October 2023

    2023

  39. [39]

    Show-o: One single transformer to unify multimodal understanding and generation, 2025

    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, 2025. URL https://arxiv. org/abs/2408.12528

    Pith/arXiv arXiv 2025

  40. [40]

    Show-o2: Improved native unified multimodal models.arXiv preprint arXiv:2506.15564, 2025

    Jinheng Xie, Zhenheng Yang, and Mike Zheng Shou. Show-o2: Improved native unified multimodal models.arXiv preprint arXiv:2506.15564, 2025

    Pith/arXiv arXiv 2025

  41. [41]

    Imgedit: A unified image editing dataset and benchmark.arXiv preprint arXiv:2505.20275, 2025

    Yang Ye, Xianyi He, Zongjian Li, Bin Lin, Shenghai Yuan, Zhiyuan Yan, Bohan Hou, and Li Yuan. Imgedit: A unified image editing dataset and benchmark.arXiv preprint arXiv:2505.20275, 2025

    Pith/arXiv arXiv 2025

  42. [42]

    Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi, 2024

    Xiang Yue, Yuansheng Ni, Kai Zhang, Tianyu Zheng, Ruoqi Liu, Ge Zhang, Samuel Stevens, Dongfu Jiang, Weiming Ren, Yuxuan Sun, Cong Wei, Botao Yu, Ruibin Yuan, Renliang Sun, Ming Yin, Boyuan Zheng, Zhenzhu Yang, Yibo Liu, Wenhao Huang, Huan Sun, Yu Su, and Wenhu Chen. Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for exp...

    Pith/arXiv arXiv 2024

  43. [43]

    Creatilayout: Siamese multimodal diffusion transformer for creative layout-to-image generation.arXiv preprint arXiv:2412.03859, 2024

    Hui Zhang, Dexiang Hong, Tingwei Gao, Yitong Wang, Jie Shao, Xinglong Wu, Zuxuan Wu, and Yu-Gang Jiang. Creatilayout: Siamese multimodal diffusion transformer for creative layout-to-image generation.arXiv preprint arXiv:2412.03859, 2024

    Pith/arXiv arXiv 2024

  44. [44]

    Adding conditional control to text-to-image diffusion models, 2023

    Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models, 2023. URLhttps://arxiv.org/abs/2302.05543

    Pith/arXiv arXiv 2023

  45. [45]

    Layercraft: Enhancing text-to-image generation with cot reasoning and layered object integration, 2025

    Yuyao Zhang, Jinghao Li, and Yu-Wing Tai. Layercraft: Enhancing text-to-image generation with cot reasoning and layered object integration, 2025. URL https://arxiv.org/abs/ 2504.00010

    arXiv 2025

  46. [46]

    Divide and conquer: Language models can plan and self-correct for compositional text-to-image generation

    Wang Zhenyu, Xie Enze, Li Aoxue, Wang Zhongdao, Liu Xihui, and Li Zhenguo. Divide and conquer: Language models can plan and self-correct for compositional text-to-image generation. arXiv preprint arXiv:2401.15688, 2024

    Pith/arXiv arXiv 2024

  47. [47]

    [Prompt Text]

    Chunting Zhou, Lili Yu, Arun Babu, Kushal Tirumala, Michihiro Yasunaga, Leonid Shamis, Jacob Kahn, Xuezhe Ma, Luke Zettlemoyer, and Omer Levy. Transfusion: Predict the next token and diffuse images with one multi-modal model.arXiv preprint arXiv:2408.11039, 2024. 13 A LLM Usage Statements We used LLMs to polish our writing during paper preparation. B Limi...

    Pith/arXiv arXiv 2024

  48. [2024]

    URLhttps://arxiv.org/abs/2307.05222

    Pith/arXiv arXiv