cs.CVOct 17, 2026
SaveDesign Anything, Design at Scale
Organizations: Academy of Arts & Design, Tsinghua University · Beijing Institute of Fashion Technology · Senseable City Lab, Massachusetts Institute of Technology · DAMO Academy, Alibaba Group
Abstract
As multimodal resources are transformed into media outputs, agentic AI is expected to stay aligned with design intent over long horizons. Designers follow briefs specifying goals, constraints, and deliverables, with later work building on earlier decisions. Recent work reuses design experience across tasks, yet alignment among artifacts within one project remains challenging. Agents therefore need to preserve and apply this intent across domains and as projects grow. We introduce Design Anything, a graph-based harness that preserves design intent outside the model and binds deliverables to the workflow steps that produce them through an intent-execution graph. A task contract retains constraints and decisions, selected designs become explicit inputs to later generations, and a deterministic controller uses bindings and execution records to check output types and counts, identify outstanding deliverables, and guide subsequent turns. We also introduce DesignAnything-Bench, with 228 tasks across 19 design domains, including 38 project families at scales of 6, 14, and 24 artifacts. With the LLM held fixed, Design Anything satisfies 85.0% and 92.5% of the brief's factual constraints with DeepSeek V4.1 Flash High and GPT-5.6 Sol High, respectively, exceeding the strongest same-model baselines by 15.4% and 14.4%. These gains reach 25.1% and 23.0% on 24-artifact tasks. Under each model, Design Anything leads or ties in constraint satisfaction in 18 of 19 domains. Professional designers also prefer its complete deliverable sets over those of same-model baselines in a blind evaluation.
Figures & tables
Figure 1: Design Anything outperforms the best same-model baseline at every scale. Colored bars show the strongest baseline and purple segments the gain of Design Anything; labels give its rate and lead in percentage points.
Figure 2: Design anything across twelve design domains. Each group shows artifacts from a single run of Design Anything with GPT-5.6 Sol High, using the same configuration for every domain, without skills written for a particular design domain or task-specific hints.
Figure 4: DesignAnything-Bench contains 228 tasks across 19 domains. In (b), the inner ring shows task conditions; the outer ring shows requested artifact counts. Only matched-scale tasks reuse the same 38 families across scales; each other task is an independent project at one scale. Artifact count measures scale.
| Requirement fulfillment | Consistency | Design quality | Human | |||||||
| System | Delivery completion | Constraint satisfaction | Reference fidelity | Identity consistency | Set consistency | Technical quality | Aesthetics | Text rendering | Domain criteria | Designer preference |
| (a) DeepSeek V4.1 Flash High | ||||||||||
| Design Anything | 96.6 | 85.0 | 75.6 | 84.5 | 87.9 | 88.4 | 87.1 | 93.0 | 92.0 | 60.7 |
| OpenDesign | 95.2 | 69.6 | 55.0 | 75.2 | 88.3 | 85.1 | 82.3 | 80.6 | 85.5 | 21.8 |
| JarvisHub | 87.6 | 68.0 | 49.6 | 68.5 | 65.4 | 80.9 | 80.2 | 84.7 | 84.2 | 49.1 |
| (b) GPT-5.6 Sol High | ||||||||||
Table 1: Results on DesignAnything-Bench (228 tasks). Fulfillment, identity consistency, and designer preference are percentages; other scores use a 100-point scale. Higher is better; bold marks the within-model best. Design quality is weighted by delivery completion; designer preference uses Bradley–Terry estimates (Section 3.5 ).
Figure 5: Design Anything leads or ties in 18 of 19 domains per model. Cells show constraint satisfaction (%) on 12 tasks per domain; bold marks the within-model best. Parentheses give applicable-constraint counts. DS and GPT denote DeepSeek V4.1 Flash High and GPT-5.6 Sol High.
Figure 6: Same-task comparison across five configurations. Red markers highlight selected content errors; slashed circles indicate items not credited by the evaluator.
Figure 7: Design Anything retains its constraint-satisfaction advantage from 6 to 24 artifacts. Panels show (a) delivery completion and (b) constraint satisfaction on 38 matched families. Solid lines with filled markers denote DS; dashed lines with hollow markers denote GPT.
Figure 8: Design Anything (GPT-5.6 Sol High) on RIVER COMMONS with 6, 14, and 24 requested artifacts. Stacks denote documents; play icons denote videos. Counts give credited artifacts.
| Configuration | Delivery (%) | Runs | Constraint (%) | Gen. requests | Cost ($) † |
|---|---|---|---|---|---|
| Stop at model end | 73.9 | 6 | 57.5 | 18.6 | 0.48 |
| Generic prompt | 96.9 (+23.0) | 0 | 75.3 (+17.8) | 36.1 | 1.30 |
| Complete (Design Anything) | 96.1 (+22.1) | 0 | 90.4 (+32.9) | 24.1 | 0.43 |
Table 2: Delivery-loop study on 19 paired 24-artifact tasks (DeepSeek V4.1 Flash High). Parentheses give gains over Stop at model end; “Runs ” counts runs below half delivery. Requests and costs are per-run means; † cost uses LLM list prices on 17 paired tasks with complete telemetry.
Figure 9: The two Design Anything configurations have the highest estimated designer preference. (a) Bradley–Terry estimates with 95% task-and-designer bootstrap intervals (1,019 judgments, 185 tasks, 12 designers). (b) Pairwise choices with ties; counts judgments. DA denotes Design Anything; the bold last row compares its DeepSeek and GPT configurations.
Appendix figures & tables30 assets
Supplementary material from the paper’s appendix.
Appendix
| Measure | Compared against | Definition | Scored by |
| Requirement fulfillment | |||
| Delivery completion | Brief: categories, quantities | Requested files delivered, capped per category | Classifier, count |
| Constraint satisfaction | Brief: factual constraints | Factual constraints satisfied in every targeted artifact (Eq. 7 ) | Criterion check |
| Reference fidelity | Client’s reference assets | Output–reference pairs depicting the same object | Calibrated judge |
| Cross-artifact consistency | |||
| Identity consistency | The run’s anchor artifact | Output–anchor pairs depicting the same object | Calibrated judge |
Table 3: Measures of DesignAnything-Bench. The measures assess fulfillment of the brief, agreement with the client’s materials or the run’s own outputs, design quality, and designer preference between systems on the same brief.
| Comparison | Tasks | Held fixed | Varied factor |
|---|---|---|---|
| DeepSeek group (Table 1 a) | 228 | DeepSeek V4.1 Flash High for every agent and sub-agent; gpt-image-2.5-sunburst image generation | Harness and agent: Design Anything, OpenDesign, JarvisHub |
| GPT group (Table 1 b) | 228 | GPT-5.6 Sol High; gpt-image-2.5-sunburst image generation | Harness and agent: Design Anything, Codex |
| Project scale (Section 3.3.2 ) | Project family, background, and inputs; system | Requested artifacts: 6, 14, 24; process artifacts | |
| Reference coverage (Appendix C.5 ) | 228 | DeepSeek V4.1 Flash High; within JarvisHub, the harness | Reference coverage (observed, not assigned) |
| Delivery loop (Section 3.4 ) | 19 | Design Anything on DeepSeek; 24-artifact tasks, one per domain | Delivery loop and other channels for tracking the task |
| Planning interface (Appendix D.2 ) | 8 | Design Anything; other channels for tracking the task | Planning tools: structured, text, none |
Table 4: Controlled comparison matrix. The final column states the comparison of interest; the rows include both interventions and observational analyses. The main model groups share briefs except on three tasks of one book project family (Appendix B.1 ) and use a common evaluator. The delivery-loop study uses the same briefs and evaluator across its configurations; the planning and state-summary studies use separate tasks and harness versions (Appendix D ).
| Setting | Recorded configuration |
|---|---|
| Harness version | DeepSeek V4.1 Flash High runs: one controller version. GPT-5.6 Sol High runs: a later version of the same code that adds the retries listed under Controller limits and updates image analysis and image repair. |
| Agent and role models | Every role (orchestrator, builder, verifier, summarizer, and specialist helpers), text-generation nodes, and image analysis use the configuration’s LLM. High in the configuration names denotes the reasoning setting: DeepSeek V4.1 Flash runs at its default high reasoning, with reasoning disabled for image analysis; GPT-5.6 Sol receives reasoning effort high for the main agent and specialist helpers. |
| Generation backends | Images: gpt-image-2.5-sunburst, medium quality, 2K. Video: a third-party video model. |
| Skills | General-purpose skills bundled with the product; none written for a particular design domain |
| Other tools | Workspace reading and editing, contract and workflow construction, synchronous and batched execution, on-demand image analysis, clarifying questions, web search, memory, and delegation to helper agents |
| Context policy | Tool results are elided beyond 50% of the context window (1M tokens for DeepSeek V4.1 Flash High, 272k for GPT-5.6 Sol High); history is summarized beyond 85%, keeping the last six steps; the state summary is always on |
Table 5: Recorded runtime configuration of Design Anything. Settings apply to both configurations unless noted.
| System | Delivery | Constraints | Reference | Identity | Set items |
|---|---|---|---|---|---|
| Design Anything (DS) | 2809/2908 | 767/902 | 1063/1407 | 554/656 | 606 |
| OpenDesign (DS) | 2768/2908 | 616/885 | 768/1396 | 467/621 | 585 |
| JarvisHub (DS) | 2547/2908 | 608/894 | 658/(1127–1326) | 411/600 | 502 |
| Design Anything (GPT) | 2830/2908 | 834/902 | 1122/1414 | 598/663 | 614 |
| Codex (GPT) | 2853/2908 | 699/895 | 1016/1425 | 498/661 | 550 |
Table 6: Counts underlying Table 1 . Columns count delivered/requested items (delivery completion), passed/applicable constraints (constraint satisfaction), same/eligible pairs against supplied reference assets (reference fidelity), same/eligible pairs within the run (identity consistency), and scored rubric items (set consistency). JarvisHub’s reference-fidelity denominator is a range: 1,326 counts the judge’s abstentions as mismatches and 1,127 excludes them. The last column is a coverage count, not a simple pooled-mean denominator.
| Evaluation measures | Supplementary outcomes | |||||
| System | Delivery | Constraints | Identity | Set | P/F/U (all) | P/F/U (final) |
| 6 artifacts (38 tasks) | ||||||
| Design Anything (DS) | 96.5 | 86.4 | 87.7 | 93.6 | 27/10/1 | 27/10/1 |
| OpenDesign (DS) | 95.6 | 70.6 | 78.1 | 86.9 | 22/14/2 | 22/14/2 |
| JarvisHub (DS) | 93.4 | 76.3 | 76.9 | 75.0 | 21/15/2 | 21/15/2 |
| Design Anything (GPT) | 97.4 | 97.4 | 97.0 | 94.9 | 31/7/0 | 31/7/0 |
Table 7: Design Anything leads same-model baselines in constraint satisfaction in every task condition. Shaded rows are Design Anything; bold marks the best delivery, constraint, and identity score within each LLM group and condition. These scores are percentages; set consistency uses a 100-point scale. Supplementary P/F/U counts denote task passes/failures/unmeasurable outcomes for all or final deliverables (Appendix C.3 ); they are distinct from constraint satisfaction. DS: DeepSeek V4.1 Flash High; GPT: GPT-5.6 Sol High.
| Pair | All tasks | 6 art. | 14 art. | 24 art. | Growth |
|---|---|---|---|---|---|
| Design Anything (DeepSeek) JarvisHub | 17.0 [12.4, 22.2] | 10.0 [0.3, 19.6] | 20.1 [7.2, 34.9] | 31.3 [18.6, 45.8] | 21.3 [3.8, 40.0] |
| Design Anything (DeepSeek) OpenDesign | 15.4 [9.4, 21.5] | 15.7 [2.1, 30.8] | 23.3 [9.9, 35.5] | 25.1 [12.3, 38.4] | 9.4 [ , 24.0] |
| Design Anything (GPT) Codex | 14.4 [9.3, 19.3] | 12.3 [6.0, 20.0] | 17.9 [10.2, 26.4] | 23.0 [9.0, 36.4] | 10.6 [ , 24.7] |
Table 8: Paired constraint-satisfaction gaps with family-clustered 95% bootstrap intervals (points). Growth is the gap at 24 artifacts minus the gap at 6.
| System | All tasks | 6 art. | 14 art. | 24 art. |
|---|---|---|---|---|
| Design Anything (DS) | 86.2 (12) | 87.5 (2) | 83.7 (3) | 85.7 (0) |
| OpenDesign (DS) | 70.3 (9) | 70.6 (0) | 61.1 (6) | 61.0 (1) |
| JarvisHub (DS) | 71.1 (39) | 79.5 (6) | 64.0 (5) | 58.8 (12) |
| Design Anything (GPT) | 92.7 (2) | 98.0 (1) | 94.2 (0) | 91.9 (0) |
| Codex (GPT) | 78.5 (5) | 85.1 (0) | 76.3 (0) | 68.9 (0) |
Table 9: Constraint satisfaction (%) without constraints on wholly undelivered categories. Constraints none of whose target categories contains a delivered file are removed from numerator and denominator; parentheses give how many were removed. Reported rates, which count them as unsatisfied, are in Tables 1 and 7 .
| Identity consistency | Reference fidelity | |||||
| System | Abstained | Counted | Excluded | Abstained | Counted | Excluded |
| Design Anything (DS) | 41/656 | 84.5 | 90.1 | 137/1407 | 75.6 | 83.7 |
| OpenDesign (DS) | 93/621 | 75.2 | 88.4 | 351/1396 | 55.0 | 73.5 |
| JarvisHub (DS) | 38/600 | 68.5 | 73.1 | 199/1326 | 49.6 | 58.4 |
| Design Anything (GPT) | 23/663 | 90.2 | 93.4 | 113/1414 | 79.3 | 86.2 |
| Codex (GPT) | 97/661 | 75.3 | 88.3 | 195/1425 | 71.3 | 82.6 |
Table 10: Judged image-pair measures with judge abstentions counted as mismatches (reported) and excluded. Abstention counts are attributed to the system’s own evidence; for reference fidelity, abstentions attributed to the external reference are excluded in both columns.
| System | Targets per constraint | Per-file coverage | Constraint satisfaction |
|---|---|---|---|
| Design Anything (DS) | 2.21 / 3.38 / 5.76 | 87.9 / 89.2 / 97.0 | 86.4 / 82.1 / 85.7 |
| OpenDesign (DS) | 2.32 / 3.42 / 5.84 | 74.7 / 76.4 / 75.3 | 70.6 / 58.7 / 60.6 |
| JarvisHub (DS) | 2.14 / 3.29 / 5.30 | 90.2 / 86.3 / 85.5 | 76.3 / 61.9 / 54.4 |
| Design Anything (GPT) | 2.21 / 3.47 / 5.68 | 99.1 / 98.3 / 97.9 | 97.4 / 94.2 / 91.9 |
| Codex (GPT) | 2.31 / 3.37 / 5.92 | 87.3 / 88.6 / 86.1 | 85.1 / 76.3 / 68.9 |
Table 11: Constraint satisfaction decomposed by target file. Columns give mean target files per constraint, per-file constraint coverage (%), and constraint satisfaction (%) at 6 / 14 / 24 artifacts.
Figure 10: High per-file coverage can coexist with unmet constraints across a set. Curves compare five configurations on 38 matched families, showing successful factual constraint–target checks as a percentage of evaluated pairs at each scale. Filled markers and solid lines denote DeepSeek V4.1 Flash High; hollow markers and dashed lines denote GPT-5.6 Sol High. A constraint passes only if every eligible target satisfies it.
Figure 11: Design Anything uses references more often; reference use is associated with identity consistency within JarvisHub. Panel (a) shows the share of image generations with at least one reference input, pooled over each condition’s tasks, for Design Anything and JarvisHub on DeepSeek V4.1 Flash High. Breadth and Reference denote breadth and reference-material tasks. Panel (b) plots JarvisHub’s reference coverage against identity consistency, with one point per task with identity pairs (Spearman , ).
| Condition | DeepSeek V4.1 Flash High | GPT-5.6 Sol High |
|---|---|---|
| 6 artifacts | 1.36 | 5.59 |
| 14 artifacts | 6.58 | 16.57 |
| 24 artifacts | 9.06 | 18.38 |
| Breadth | 4.22 | 16.42 |
| Reference materials | 2.37 | 12.34 |
| All | 4.04 | 14.63 |
Table 12: Median LLM input tokens per task for Design Anything (millions). The GPT-5.6 Sol High column counts the orchestrating agent only.
| System | Technical | Aesthetics | Text | Domain |
|---|---|---|---|---|
| Design Anything (DS) | 88.4 | 87.1 | 93.0 | 92.0 |
| Design Anything (GPT) | 88.8 | 88.5 | 93.8 | 92.5 |
| OpenDesign (DS) | 85.1 | 82.3 | 80.6 | 85.5 |
| JarvisHub (DS) | 80.9 | 80.2 | 84.7 | 84.2 |
| Codex (GPT) | 88.9 | 87.9 | 94.3 | 90.8 |
Table 13: Design quality (100-point scale). Scores combine four image-checklist dimensions with project delivery coverage (Equation 8 ).
| Mechanism | Stop at model end | Generic prompt | Complete |
|---|---|---|---|
| After the model stops | End run | Fixed prompt to keep working | Delivery loop (Section 2.4 ) |
| Contract and planning tools | Removed | Removed | Retained |
| Task and progress reminders | Removed | Removed | Retained |
| Automatic verifier | Disabled | Disabled | Enabled |
| Batched asynchronous execution | Removed | Removed | Retained |
Table 14: Delivery-loop configurations. The first two configurations differ only in a generic reply after stopping within the reduced harness. Complete restores the full set of mechanisms.
| Measurement | Stop at model end | Generic prompt | Complete |
|---|---|---|---|
| Agent steps | 31.2 | 79.7 | 36.4 |
| Input tokens (M) | 16.9 | 41.8 | 17.9 |
| LLM cost (USD) | 0.48 | 1.30 | 0.43 |
| Generation requests | 18.3 | 37.1 | 23.8 |
| Delivery completion (%) | 72.1 | 96.8 | 96.1 |
Table 15: Delivery-loop study: mean cost per run on 17 paired tasks. Two Generic prompt runs reached the wall-clock limit without step or token counts and are excluded with their pairs. Input tokens (millions) and LLM cost include sub-agents; cost uses list prices.
Figure 12: Enlarged outputs for Figure 6 : DeepSeek V4.1 Flash High. Each block shows one configuration’s 14 requested artifacts in deliverable order, with the markers of Figure 6 .
Figure 13: Enlarged outputs for Figure 6 : GPT-5.6 Sol High. The layout follows Figure 12 .
Figure 14: Recorded cross-modal dependencies in the LUMA project. This DeepSeek V4.1 Flash High task requests 24 artifacts in nine numbered groups. Selected real outputs show how one run reuses the chosen Continuum direction board across design boards, video, and a presentation; orange arrows mark recorded input connections. The bottom panel redraws an excerpt of the workflow graph that the agent built in this run: the 18 recorded connections that take the selected Continuum board as an input; text prompts, provided references, and the other 61 connections are omitted.
| Figure | Domain | Artifacts |
|---|---|---|
| 15 | Fashion (first project) | 24 |
| 16 | Game art | 24 |
| 17 | Brand identity | 24 |
| 18 | Jewelry | 24 |
| 19 | Landscape design | 24 |
| 20 | Photography | 24 |
Table 16: Gallery index. Domains and requested artifact counts for thirteen recorded projects, selected for display.
Figure 15: A 24-artifact fashion project (I). Each block shows, from top to bottom, the workflow graph the agent built during the run, its delivered image boards in deliverable order, and up to three video frames and three presentation pages. Projects were selected for display.
Figure 16: A 24-artifact game-art project (II). The layout follows Figure 15 .
Figure 17: A 24-artifact brand-identity project (III). The layout follows Figure 15 .
Figure 18: A 24-artifact jewelry project (IV). The layout follows Figure 15 .
Figure 19: A 24-artifact landscape-design project (V). The layout follows Figure 15 .
Figure 20: A 24-artifact photography project (VI). The layout follows Figure 15 .
Figure 21: A second 24-artifact fashion project (VII). The layout follows Figure 15 .
Figure 22: A 24-artifact packaging project (VIII). The layout follows Figure 15 .
Figure 23: A 24-artifact UI/UX project (IX). The layout follows Figure 15 and includes three direction boards.
Figure 24: Two 14-artifact projects (X). The upper and lower blocks show exhibition design and landscape design, respectively. Both follow the layout of Figure 15 .
Figure 25: Two 6-artifact projects (XI). The upper and lower blocks show furniture and product design, respectively. Both follow the layout of Figure 15 .
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