MaLiang-Harness: A Programmable Path to Image and Video Generation

0:00Welcome to the Daily Papercast. Today, we're diving into a paper from the Hugging Face Daily Paper List of September 30, 2026, which has 189 upvotes. The title of the paper is Maliang Harnas, a programmable path to image and video generation. It is authored by Haoyu Zhao and Zihau Zhang with Haoyu Zhao as the corresponding author affiliated with the National University of Singapore. Sounds intriguing. Let's jump right into the introduction. Sure. So, the introduction starts with a quote from Saul LeWitt. The idea becomes a machine that makes the art. This sets the stage for the paper, which explores the concept of leveraging executable programs to generate images and videos. Executable programs meaning code that, when run, creates these visual outputs, right? Exactly. Executable programs offer explicit control over how images and videos are constructed.

1:02But merely having runnable code isn't enough. The generated content must meet specific visual requirements like composition, appearance, or motion. The paper identifies this challenge as the program to visual or P2V, GAP. Got it. So the P2V GAP is essentially the difference between what the code should achieve visually and what it actually produces? Yes. To address this, the authors introduce Maliang Harnas, a unified framework designed to organize multimodal, large language model, or MLLM, driven visual generation into a process of construction, inspection, and revision. What are the key features of Maliang Harnas? Maliang Harnas incorporates several mechanisms, the persistent executable generation or PEG state, the traceable generation process or TGP, and revision-aware editing and verification or REV. These mechanisms help coordinate planning, execution, and visual feedback across different rendering backends.

2:04And how do these mechanisms function together? The PEG state ensures that program and task context are preserved across revisions. The TGP connects program edits to their rendered visual evidence, and the REV supports the restoration of earlier program states while verifying revisions before completion. So the PEG state maintains the overall program, TGP provides traceability, and Revan Shur's continuous improvement and verification? Precisely. Collectively, these mechanisms ensure that Maliang Harnas supports a persistent and verifiable visual generation process. The system is capable of refining visual outputs iteratively by examining the rendered evidence and making targeted revisions. What are the practical implications of using Maliang Harnas? It allows for explicit control over visual content through code. This means that creative intent expressed through an executable program can be effectively translated into a visual form, with progress and quality improvements documented at each

3:06revision. Right. And the paper also evaluates the framework extensively. Indeed, they evaluate 11 powerful closed source MLLMs on Maliang EyeBench and 4 on Maliang VBench, focusing on generation success, visual quality, and computational cost. Notably, GPT-6 Astra achieves a 100% generation success rate on both benchmarks. That's impressive. It sounds like a significant step forward in visual content generation. Definitely. It also highlights a mismatch between general capability scores and actual visual generation performance among models. This framework provides a systematic way to study and improve how these models translate executable code into visual results. So Maliang Harnas is not just about generating visual content, but also about refining and verifying it to meet specific requirements accurately. Exactly. And that's the end of the introduction section.

4:08Let's dive into the Method section to understand how Maliang Harnas actually works. Sure. The Method section explains how Maliang Harnas organizes image and video creation to a structured process. The core of the system is made up of three interlinked components, the persistent executable generation, traceable generation process, and revision aware editing and verification. Got it. Let's start with the persistent executable generation or PEG state. What does it entail? The PEG state maintains the program and task context across all revisions. Essentially, it stores the visual program, any associated assets, the spatial and temporal dynamics, and the current plan along with task requirements. So it acts as a reference point, ensuring consistency throughout the project's lifecycle. How does it maintain this state? Exactly. For each revision, the PEG state holds a snapshot that includes the executable visual program, its assets, spatial composition, the generation context, and the revision index.

5:12For example, if a user introduces a new edit, the PEG state checks and validates this before committing it as a new revision. OK. And how does this interlink with visual outputs like images or videos? Each time the program is rendered, the PEG state creates a visual output for a specified content time, thus maintaining a tight link between code changes and their visual results. An image is a static snapshot while a video is obtained by sampling the temporal behaviors in the program. That makes sense. Let's move on to the traceable generation process, or TGP. How does that work? The TGP documents how the visual content is built and changed. Essentially, each operation executed during visual generation is recorded with its inputs, inputs, and source, and resulting revisions. This traceability ensures that any visual change can be traced back to the specific program edit that caused it. Does this mean you can pinpoint exactly where an error or unexpected visual element was

6:12introduced? Precisely. If a visual discrepancy arises, TGP allows developers to review the operation history, to identify and correct the specific part of the program responsible. This approach is particularly useful for debugging and refining visual outputs. Sounds like a powerful way to maintain control over the generation process. How about the third mechanism? Revision aware editing and verification? Or Rev? Rev closes the loop by associating each edit with visual requirements and verification steps. It uses the evidence collected during TGP to ensure each new revision meets all predefined criteria before it's finalized and delivered. So it's a final checkpoint that makes sure the generated content aligns with the initial goals. Exactly. When an edit is proposed, Rev validates the edit by reviewing both historical and current visual evidence to ensure that requirements are consistently met. Tasks are only marked as complete when all specified checks and export conditions are

7:15satisfied. Wow, that seems thorough. Does the paper explain how these components interact during an actual visual generation task? Yes, it does. The paper offers detailed examples and pseudocode to illustrate the PEG, TGP, and Rev mechanisms in action. For instance, they describe a botanical portrait image generated by the system. The process includes initializing the canvas, drawing multiple layers of foliage, and detailing a woman's portrait, all traced and verified through the harness. That seems illustrative. How does Maliang harness handle rendering and non-deterministic behaviors during this process? The rendering is handled through different backends like Canvas, SVG, or 3.js, allowing expressive visual content through a shared interface. Non-deterministic behaviors are managed by associating each rendered observation with the PEG revision evaluated by the renderer, capturing actual outputs, sampled timestamps,

8:16and specific crop specifications. And what about the iterative improvements? How does the system ensure that each iteration gets closer to the desired outcome? Each iteration is assessed against specific visual requirements. If the criteria are not met, the system uses visual feedback to guide further revisions, ensuring any discrepancies are addressed in subsequent iterations. This iterative approach allows for continuous refinement of the visual output, moving it closer to the desired result. The harness also seems to have a structured method for dealing with visual discrepancies. Can you explain how it differentiates between different types of visual feedback? Sure. Visual discrepancies are categorized based on the type of requirement they fail to meet, be it spatial arrangement, color accuracy, or temporal dynamics in the case of videos. The system then uses this categorized feedback to make targeted corrections in the visual program.

9:16Does the paper provide any quantitative results on how effective this approach is? Yes, it does. The authors evaluate the framework on two benchmarks, Maliang iBench for images and Maliang vBench for videos. They report metrics like generation success, visual quality, and computational cost using multiple MLLMs. For instance, GPT-6 Astra achieves a 100% generation success rate on both benchmarks with 96% of image tasks and about 77% of video tasks meeting all quality thresholds. Intriguing. That suggests the framework is quite robust across different models. Indeed, it shows substantial differences in generation success and visual quality across models, highlighting the framework's capability to expose and address these variations. So Maliang harness not only helps generate the visual content, but also ensures that the final output meets the desired criteria through meticulous refinement and verification.

10:20Exactly, and this concludes the methods section of our discussion. Let's now delve into the experiments and results to see how Maliang harness performs in action. The paper evaluates the framework using two benchmarks, Maliang iBench for images and Maliang vBench for videos. These benchmarks measure generation success, visual quality, and computational cost across several models. Interesting. What models were tested? And how extensive was the evaluation? They evaluated 11 multimodal large language models, or MLLMs, including some from the GPT and deep-seek families, on Maliang iBench. For videos, they tested four models on Maliang vBench, including GPT-6 Astra, GPT-5. GPT-6 Soul, deep-seek v4.1 Flash, and chemiK2.6. And how were these evaluations carried out? The evaluation covered two main aspects, computational cost and generation quality.

11:24Computational cost included metrics like generation time, model calls, and token usage. Generation quality was judged by prompt alignment, aesthetics, composition, and motion coherence for video tasks. Did you give us an example of the results from Maliang iBench? Sure. On Maliang iBench, GPT-6 Astra achieved a 100% generation success rate, with 48 out of 50 images meeting all quality criteria. In comparison, models like deep-seek v4 by 1 Flash had a 12% success rate, while generating only six qualifying images according to the set benchmarks. That's quite a contrast. It seems like there's a significant performance gap among these models. Indeed. This indicates that although some models have high general scores, they may not perform as well in specific visual generation tasks. GPT-6 Astra's outstanding performance highlights its ability to meet both computational and

12:24quality criteria effectively. And what about in terms of computational cost? How do these models compare? GPT-6 Astra also excelled in computation and computational efficiency. It required 3.70 minutes per qualifying image, which was competitive considering it had the highest success rate. In contrast, deep-seek v4 pro took 48.12 minutes per qualifying image, even though it only had an 18% generation success rate. That's very telling. How about the video tasks on Maliang vBench? Does the trend continue? Yes. The trend largely continues. GPT-6 Astra maintained a 100% generation success rate for videos with 10 out of 13 successful generations meeting all quality criteria. GPT-5.6 saw also performed well, but it achieved a lower success rate of 53.8% with only 5 out of 7 successful videos meeting all criteria.

13:27What kind of visual quality metrics were considered for videos? For videos, the metrics included prompt alignment, aesthetics, composition, and motion coherence. For instance, GPT-6 Astra achieved high marks across all these categories, particularly excelling in motion coherence, which was a challenging criterion for many models. Were there any examples or case studies provided in the paper to illustrate these results? Yes. They included qualitative examples that showcased the differences in visual realization across models. For example, in a task involving a pixel art scene with a kitten and a star, GPT-6 Astra produced a highly detailed and consistent animation while other models like Kimi struggled with scene details and motion sequences. That's quite illustrative. How did they ensure the visual outputs actually match the expected quality? The quality of the visual outputs was subject to a thorough review process where each frame or sampled video segment was evaluated against predefined criteria.

14:30This ensured that every aspect of the visual output from spatial arrangements to temporal behaviors met the specified requirements. What kind of visual feedback mechanisms did they use to refine the outputs? The system uses visual feedback to guide iterative improvements. For instance, discrepancies in spatial composition, misalignments, or unintended color variations are fed back into the system. This information then helps in refining and generating subsequent revisions that are closer to the desired outcome. So it's a continuous cycle of generation, feedback, and refinement? Exactly. This iterative approach ensures high fidelity in the final visual outputs. Each revision incorporates critical feedback, making the next iteration better than the previous one. In summary, the experiments and results show that Maliang harness is highly effective in translating executable programs into high quality visual content. Precisely. And this brings us to the end of the experiment section.

15:34Let's explore the related work section. To see how Maliang harness fits into the broader landscape of image and video generation technologies. The related work section offers a comprehensive overview of existing visual generation approaches and their limitations, laying the groundwork for understanding the significance of Maliang harness. Interesting. What types of models and techniques has the paper discussed? The paper covers various foundational models for image and video generation, including diffusion models, flow-based models, and their extensions into video content. For instance, diffusion models like those proposed by Ho in 2020 generate images through learned denoising processes, while latent diffusion models reduce the computational cost of high-resolution synthesis. I'm familiar with diffusion models, but how do flow-based models differ in their approach? Flow-based models, like those described by Lippmann in 2022, focus on learning continuous

16:34generative dynamics. These models extend visual synthesis capabilities by providing a framework that can adapt to temporal content, making them relevant for both images and videos. Are there specific models that extend these techniques to videos? Yes, the paper mentions video diffusion models and stable video diffusion, which have been designed to handle the synthesis of temporal content. These models incorporate mechanisms for explicit conditioning, such as spatial guidance through input features like edges and depth. That sounds quite detailed. How does Maliang harness distinguish itself from these models? Maliang harness focuses on the representation through which visual content is constructed and revised. Unlike implicit synthesis techniques, it maintains an executable artwork state, a unified interface for programmable image and video generation. This approach allows for transparent and inspecable creation processes, facilitating continuous refinement and verification.

17:36So Maliang harness essentially brings a higher level of control and transparency to the generation process, right? Exactly. By structuring generation tasks through persistent executable generation, traceable generation process, and revision aware editing and verification, it addresses the limitations of implicit synthesis approaches by making every stage of creation traceable and modifiable. How about earlier work on executable representations? Does the paper discuss any specific studies in this area? Yes, there are several prior studies that connect language models with executable visual representations. For instance, VisProg generates modular programs for visual reasoning and image editing, exposing intermediate results as inspecable rationales. Design2Code focuses on translating web page screenshots into renderable implementations, and BlenderAlchemy combines vision-based editing with state evaluation to align with the user's design intent.

18:36Are these approaches similar to what Maliang harness proposes? We share similarities in terms of connecting language models with executable visual representations, but Maliang harness extends this concept further. It organizes image and video creation around a persistent artwork state across various rendering backends, integrating systematic planning, execution, and revision processes. Fascinating. Does the paper also touch upon agent harnesses and their role in programmable generation? Yes, indeed. It mentions several studies on agent harnesses, which frame code as infrastructure for reasoning, action, and stateful execution. For instance, code as agent harness provides an account of code functioning as an agent, while show harness links visual language model decisions to executable actions in robot embodiments. How does Maliang harness fit into this context? Maliang harness aligns with the principles of these agent harness studies by managing executable

19:38artwork through interfaces and feedback. It connects visual construction to its edit history and revision specific evidence, enabling a structured generation process that is both transparent and traceable. Are there any key takeaways on how these comparisons underline Maliang harnesses innovative aspects? By juxtaposing Maliang harness with existing models, the paper illustrates how its approach to persistent state management and systematic feedback loops offers unique advantages. It bridges the gap between intended visual outcomes and executable program revisions, a step toward more controllable and high fidelity visual generation. That makes sense. The ability to iteratively refine and verify visual outputs while maintaining a transparent creation process is indeed a significant step forward. Exactly. That brings us to the end of the related work section. We're coming to the end of our discussion on Maliang harness, but before we wrap up, Ashley,

20:40can you summarize the key contributions and takeaways from this paper? Of course. The paper presents Maliang harness, a unified framework for programmable image and video generation that addresses the program to visual or P2V gap. The framework's core mechanisms are the persistent executable generation or PIG state, the traceable generation process or TGP and revision aware editing and verification or RIV. So it's designed to provide consistent control and transparency during the generation process, right? Exactly. PIG maintains the program in task context across revisions, TGP links edits to visual evidence, and rev ensures revisions meet all predefined criteria. These components together ensure continuous improvement and traceable generation processes. And its practical implications are significant, aren't they? Maliang harness enables explicit control over visual content generation, allowing creative intent to be effectively translated into visual form.

21:43It systematically refines outputs by examining rendered evidence and making targeted corrections. How is this framework evaluated in the paper? The paper evaluates Maliang harness on two benchmarks, Maliang eye bench for images, and Maliang v-bench for videos. The results show substantial differences in generation success and visual quality among models, with GPT-6 Astra achieving the highest success rate and quality performance on both benchmarks. What does this tell us about the effectiveness of Maliang harness? It demonstrates that Maliang harness is highly effective in bridging the gap between executable programs and high quality visual content. Furthermore, it highlights the importance of direct evaluation in understanding model performance in specific tasks. Well, that's a wrap for today's episode. Thanks for the insightful discussion, Ashley. Thank you, Evan. And thanks to our listeners for tuning in. We hope you found today's episode both informative and engaging.

22:44Join us again next time on Daily Papercast, where we'll continue exploring fascinating research papers from the cutting edge of AI and visual generation. Goodbye for now, and we look forward to having you with us in our next episode. Goodbye, everyone.