SEW: Self-Evolving Agentic Workflows for Automated Code Generation

Han Zhou; Jinyuan Fang; Siwei Liu; Yingxu Wang; Zaiqiao Meng

arxiv: 2505.18646 · v2 · submitted 2025-05-24 · 💻 cs.SE · cs.AI· cs.CL

SEW: Self-Evolving Agentic Workflows for Automated Code Generation

Siwei Liu , Jinyuan Fang , Han Zhou , Yingxu Wang , Zaiqiao Meng This is my paper

Pith reviewed 2026-05-19 13:16 UTC · model grok-4.3

classification 💻 cs.SE cs.AIcs.CL

keywords self-evolving workflowsagentic workflowsautomated code generationmulti-agent systemsLLM agentsworkflow optimizationself-improvement

0 comments

The pith

Self-evolving workflows let LLMs automatically design and optimize multi-agent code generation systems.

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

The paper introduces SEW as a way to let large language models create and improve their own multi-agent workflows for coding tasks without relying on human-designed structures. By using a self-evolution process, the system generates different agent setups, tests them on coding problems, and refines the better ones over time. This addresses the limitation of fixed, hand-crafted workflows that cannot easily adjust to new or varied coding challenges. If successful, it means AI coding assistants could become more flexible and effective at handling complex software development tasks by learning optimal collaboration patterns on their own.

Core claim

SEW is a novel self-evolving framework that automatically generates and optimizes multi-agent workflows for code generation, achieving up to 12% improvement on LiveCodeBench compared to using the backbone LLM alone, while also exploring optimal text representations for encoding workflow information.

What carries the argument

The self-evolution loop that iteratively generates, evaluates, and refines agentic workflow representations encoded as text.

If this is right

Workflows adapt automatically to different types of coding problems without manual intervention.
Performance gains of up to 12% on challenging benchmarks like LiveCodeBench over base LLM usage.
Insights into the best ways to represent workflow details using plain text descriptions.
Reduced need for expert human design in building multi-agent coding systems.

Where Pith is reading between the lines

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

This approach could be applied to agentic workflows in non-coding domains such as mathematical reasoning or scientific discovery.
Longer evolution cycles might uncover more sophisticated agent topologies that humans have not yet considered.
Evaluation on a wider variety of real-world software projects would test if the gains hold outside controlled benchmarks.

Load-bearing premise

The self-evolution process will reliably converge on effective workflows that generalize across coding problems instead of overfitting to particular benchmarks.

What would settle it

Running the evolved workflows on a completely new coding benchmark or real-world project set and finding no improvement or even worse performance than the base model.

Figures

Figures reproduced from arXiv: 2505.18646 by Han Zhou, Jinyuan Fang, Siwei Liu, Yingxu Wang, Zaiqiao Meng.

**Figure 2.** Figure 2: The overall framework of SEW. The process begins with [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: Illustration of the Direct Evolution and Hyper Evolution of SEW. We use green, yellow and blue boxes to [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 4.** Figure 4: A workflow represented by the BPMN and the CoRE schemes, respectively. BPMN: This graphical standard is well-established in business process modeling and widely recognized for its ability to clearly depict the order of tasks and their dependencies. CoRE: CoRE integrates natural language programming, pseudo-code, and flow-based programming, and is a strong candidate for agentic workflows. It allows workfl… view at source ↗

**Figure 5.** Figure 5: Performance comparison of Code Rewriting and Task Parsing Workflows under different agent evolution [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

read the original abstract

Large Language Models (LLMs) have demonstrated effectiveness in code generation tasks. To enable LLMs to address more complex coding challenges, existing research has focused on crafting multi-agent systems with agentic workflows, where complex coding tasks are decomposed into sub-tasks, assigned to specialized agents. Despite their effectiveness, current approaches heavily rely on hand-crafted agentic workflows, with both agent topologies and prompts manually designed, which limits their ability to automatically adapt to different types of coding problems. To address these limitations and enable automated workflow design, we propose \textbf{S}elf-\textbf{E}volving \textbf{W}orkflow (\textbf{SEW}), a novel self-evolving framework that automatically generates and optimises multi-agent workflows. Extensive experiments on three coding benchmark datasets, including the challenging LiveCodeBench, demonstrate that our SEW can automatically design agentic workflows and optimise them through self-evolution, bringing up to 12\% improvement on LiveCodeBench compared to using the backbone LLM only. Furthermore, by investigating different representation schemes of workflow, we provide insights into the optimal way to encode workflow information with text.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

SEW automates multi-agent workflow design for code gen via self-evolution and shows gains on LiveCodeBench, but the evolution details and generalization checks are too thin to trust the improvements yet.

read the letter

The main point is that SEW generates and then iteratively improves its own multi-agent setups for code tasks instead of depending on fixed human designs. That shift from manual topologies to an automatic loop is the actual novelty here, and the paper backs it with tests on three benchmarks including LiveCodeBench where they report up to 12% better results than the base LLM alone. They also compare a few ways to represent workflows as text and note which encodings seem more effective for the evolution process. Those pieces are useful for anyone trying to reduce hand-tuning in agent systems for software engineering. The soft spot is exactly the one the stress-test flags. The abstract and available description give no concrete account of the mutation steps, the fitness function, early stopping, or any split that keeps the evolution from seeing the final test problems. Without that, the reported gains could easily come from repeated exposure to the same benchmark distributions rather than discovering genuinely better workflows. The experiments section would need to show held-out problems, statistical tests, and clear controls before the central claim holds up. This paper is for people working on LLM agents for coding who want to explore adaptive rather than static multi-agent structures. A reader in that niche could pull the representation insights and the basic empirical pattern, but anyone needing reproducible methods or strong generalization evidence will find it preliminary. It should go to peer review because the idea of self-evolving workflows is worth testing properly and the current results, limited as they are, give a starting point for that discussion.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces SEW (Self-Evolving Workflow), a framework that automatically generates and optimizes multi-agent workflows for LLM-based code generation tasks. It claims that self-evolution enables adaptation to different coding problems and yields up to 12% improvement on LiveCodeBench relative to the backbone LLM alone, while also comparing alternative text-based representations of workflows across three benchmarks.

Significance. If the self-evolution mechanism produces workflows that generalize beyond the training distribution rather than overfitting to benchmark-specific patterns, the work would meaningfully reduce manual engineering of agent topologies and prompts in code-generation systems. The empirical results on LiveCodeBench and the analysis of representation schemes provide a concrete starting point for automated workflow discovery, though the absence of mechanistic details limits immediate impact.

major comments (2)

[Section 3] Section 3 (Method): The self-evolution loop is described at a high level but supplies no explicit definition of the mutation operators, selection mechanism, fitness function, or early-stopping rule. Without these, it is impossible to determine whether the reported gains arise from genuine workflow optimization or from repeated exposure to the LiveCodeBench distribution during evolution.
[Section 4] Section 4 (Experiments), Table 2: The 12% improvement on LiveCodeBench is presented without reporting standard deviation across runs, number of independent trials, or statistical significance tests. In addition, no held-out problem set or cross-benchmark generalization experiment is described, leaving the overfitting concern unaddressed.

minor comments (2)

[Figure 1] Figure 1: The workflow diagram would be clearer if the arrows indicating the self-evolution feedback loop were labeled with the specific operations performed at each step.
[Section 2] Section 2 (Related Work): The discussion of prior multi-agent code-generation systems could include a brief comparison table of hand-crafted versus learned workflow approaches to better situate the contribution.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment point by point below and indicate the changes planned for the revised version.

read point-by-point responses

Referee: [Section 3] Section 3 (Method): The self-evolution loop is described at a high level but supplies no explicit definition of the mutation operators, selection mechanism, fitness function, or early-stopping rule. Without these, it is impossible to determine whether the reported gains arise from genuine workflow optimization or from repeated exposure to the LiveCodeBench distribution during evolution.

Authors: We agree that the current description in Section 3 is at a high level and that explicit definitions of the mutation operators, selection mechanism, fitness function, and early-stopping rule would improve clarity and reproducibility. In the revised manuscript we will expand this section to supply these definitions along with pseudocode for the self-evolution loop. This addition will make it possible to verify that performance improvements result from the optimization process rather than repeated exposure to the benchmark distribution. revision: yes
Referee: [Section 4] Section 4 (Experiments), Table 2: The 12% improvement on LiveCodeBench is presented without reporting standard deviation across runs, number of independent trials, or statistical significance tests. In addition, no held-out problem set or cross-benchmark generalization experiment is described, leaving the overfitting concern unaddressed.

Authors: We acknowledge that the results in Table 2 would be strengthened by reporting standard deviations, the number of independent trials, and statistical significance tests. We will update the experimental section and Table 2 accordingly in the revision. Regarding the overfitting concern, the current experiments already evaluate the evolved workflows on three distinct benchmarks. To further address generalization, we will add a held-out problem set analysis and a cross-benchmark transfer experiment in the revised version. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical framework with independent experimental validation

full rationale

The paper describes an empirical self-evolving framework for generating and optimizing multi-agent workflows for code generation. No equations, fitted parameters, or first-principles derivations are present that reduce reported improvements to definitional tautologies or self-citations. The 12% gain on LiveCodeBench is presented as an experimental outcome from applying the SEW process to benchmarks, with no load-bearing step that equates the result to its inputs by construction. Self-citations to prior agentic workflow literature are standard and not used to justify uniqueness theorems or forbid alternatives within the paper itself. The derivation chain remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no explicit free parameters, axioms, or invented entities are stated.

pith-pipeline@v0.9.0 · 5735 in / 936 out tokens · 47877 ms · 2026-05-19T13:16:58.735831+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

SEW ... automatically generates and optimises multi-agent workflows ... Direct Evolution (DE) operator F(·) and the Hyper Evolution (HE) operator H(·) ... mutation prompt Tmut ... hyper-mutation prompt Thmut
IndisputableMonolith/Foundation/BranchSelection.lean branch_selection unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

five different representation schemes ... BPMN, CoRE, python, YAML and pseudo-code ... LSR and GSR

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

AgentCo-op: Retrieval-Based Synthesis of Interoperable Multi-Agent Workflows
cs.AI 2026-05 unverdicted novelty 6.0

AgentCo-op retrieves and assembles existing agents and tools into interoperable workflows for open-world scientific tasks, showing effectiveness in genomics case studies and competitive benchmark results with lower costs.
Do Self-Evolving Agents Forget? Capability Degradation and Preservation in Lifelong LLM Agent Adaptation
cs.AI 2026-05 unverdicted novelty 6.0

Self-evolving LLM agents exhibit capability erosion under continual adaptation, which Capability-Preserving Evolution mitigates by raising retained simple-task performance from 41.8% to 52.8% in workflow evolution und...
GoodServe: Towards High-Goodput Serving of Agentic LLM Inferences over Heterogeneous Resources
cs.DC 2026-05 unverdicted novelty 4.0

GoodServe proposes a predict-and-rectify routing system for agentic LLM inferences on heterogeneous GPUs that improves goodput by up to 27.4%.

Reference graph

Works this paper leans on

30 extracted references · 30 canonical work pages · cited by 3 Pith papers

[1]

Understand the Workflow: Here is the detailed workflow: [Detailed work- flow]

work page
[2]

Identify Agent Roles: Based on the workflow, determine the distinct roles and responsibilities of each agent in- volved

work page
[3]

• Objectives: The specific goals the agent is expected to achieve

Generate Agent-Specific Prompts: For each identified agent, craft a clear and concise prompt that includes: • Agent Role: A brief description of the agent’s function within the workflow. • Objectives: The specific goals the agent is expected to achieve. • Inputs: The information or data the agent will receive. • Outputs: The expected results or actions th...

work page
[4]

Review the Workflow Template: [De- tailed workflow template]

work page
[5]

Analyze the Dataset Description: [Dataset description]

work page
[6]

• Steps and Sequence: Outline each step of the workflow and specify the order of execution

Design the Agent Workflow: Based on the above information, develop a comprehensive Agent Workflow that in- cludes: • Inputs and Outputs: Define the types of input data and the ex- pected output results. • Steps and Sequence: Outline each step of the workflow and specify the order of execution. • Agent Roles and Responsibilities: Describe the role and duti...

work page 2024
[7]

Comprehensively summarize the given programming task for the subsequent code generation

Task Parsing Agent: "You are a task pars- ing agent. Comprehensively summarize the given programming task for the subsequent code generation. You will NOT return any- thing except for the task summary. { TASK PROMPT}

work page
[8]

Your task is to write Python code according to the sum- mary parsed by your colleague

Code Generation Agent: "You are a proficient Python programmer. Your task is to write Python code according to the sum- mary parsed by your colleague. You will be given the problem description followed by the summary. You will NOT return anything except for the program." ⊕ { Output from Task Parsing Agent} Code Rewriting Workflow:

work page
[9]

Your task is to write Python code according to the summary parsed by your colleague

Code Generation Agent: "You are a pro- ficient Python programmer. Your task is to write Python code according to the summary parsed by your colleague. You will be given the problem description followed by the sum- mary. You will NOT return anything except for the program."⊕{ TASK PROMPT}

work page
[10]

Code Reviewer Agent: "You are a criti- cal python code reviewer. You are tasked to label generated codes with 1 or 0, where 1 indicates that this code satisfies the require- ments and can pass the sample test, while 0 indicates that this code doesn’t satisfies the requirements and will fail the sample test. You will be given the Problem Description follow...

work page
[11]

You just generated some codes that cannot pass the sample test

Code Rewriting Agent: "You are a profi- cient Python programmer tasked with coding solutions based on given problem specifica- tions. You just generated some codes that cannot pass the sample test. You role is to regenerate python code that strictly adheres to the specifications, ensuring it reads input from standard input (stdin) and writes out- put to s...

work page
[12]

Task Parsing Agent: " **Genre: Science Fiction** **Setting/Condition: A Floating City Above a Dying Earth** **Creative Writing Prompt:** In the year 2145, humanity has retreated to a sprawling floating city known as Aetheris, suspended high above the ravaged surface of a dying Earth. The city is powered by advanced tech- nology that harnesses the energy o...

work page
[13]

"" Returns the square of the given number

Code Generation Agent: "When faced with a mutation question like the one you’ve provided, individuals who excel in creative thinking typically approach it in several ways: 1). Understanding the Problem: They start by thoroughly understanding the existing code and its purpose. In this case, the code reads a number of test cases and computes the square of e...

work page
[14]

Your task is to explore the fu- sion of these elements and generate innova- tive concepts that transcend their traditional boundaries

Code Generation Agent: "Certainly! Here’s a mutator prompt designed to inspire creativity and innovation: — **Mutator Prompt: ¨The Elemental Fusion¨** Imagine a world where the four classi- cal elements ˘2014Earth, Water, Air, and Fire˘2014can be combined in unconventional ways to create new entities, phenomena, or experiences. Your task is to explore the...

work page
[15]

Describe how they interact and what new properties or entities emerge from their fusion

**Elemental Combinations:** Choose two or more elements from Earth, Water, Air, and Fire. Describe how they interact and what new properties or entities emerge from their fusion

work page
[16]

What challenges or benefits do they bring?

**Conceptual Expansion:** For each combination, think about how these new enti- ties could influence the environment, society, or technology. What challenges or benefits do they bring?

work page
[17]

This could include art, architecture, environmental solutions, or even new forms of entertainment

**Unconventional Applications:** Con- sider how these fusions could be applied in real-world scenarios. This could include art, architecture, environmental solutions, or even new forms of entertainment

work page
[18]

How does it look? What colors, shapes, or movements define it?

**Visual Representation:** If possible, sketch or describe a visual representation of your fused element or concept. How does it look? What colors, shapes, or movements define it?

work page
[19]

**Narrative Element:** Create a short story or scenario that features your new en- tity in action. How does it interact with the world around it? What conflicts or resolu- tions arise from its presence? — **Example:** • **Combination:** Fire + Water • **New Entity:** Steam Elementals • **Description:** These beings are com- posed of swirling steam, capabl...

work page
[20]

Code Reviewer Agent:"

work page
[21]

Problem Description: In a world where creativity knows no bounds, we seek to de- velop a mutator prompt that inspires individ- uals to break free from conventional think- ing and explore innovative mutations in their ideas, projects, or artistic expressions. The goal is to create a prompt that encourages users to think divergently, combining unre- lated c...

work page
[22]

time travel \

Generated Code: 1```python 2import random 3 4def g e n e r a t e _ m u t a t o r _ p r o m p t () : 5# Lists of unconventional concepts , actions , and mediums 6concepts = [ 7\" time travel \" , \" bioluminescence \" , \" quantum entanglement \" , 8\" dreamscapes \" , \" mythical creatures \" , \" urban legends \" , 9\" chaos theory \" , \" synesthesia \"...

work page
[23]

**Concepts, Actions, and Mediums**: The code defines three lists containing un- conventional concepts, actions, and artistic mediums

Explanation of the Code: i. **Concepts, Actions, and Mediums**: The code defines three lists containing un- conventional concepts, actions, and artistic mediums. These lists are designed to inspire creativity by combining elements that are typically not associated with each other. ii. **Random Selection**: The ‘ran- dom.choice()‘ function is used to selec...

work page
[24]

Your task is to create a Python program that generates a unique piece of art or a story based on a set of unconventional in- puts

Code Rewriting Agent:" **Mutator Prompt: ¨The Infinite Canvas¨** **Problem Description:** Imagine a world where the laws of physics and logic are mere suggestions, and creativity reigns supreme. Your task is to create a Python program that generates a unique piece of art or a story based on a set of unconventional in- puts. These inputs can be anything fr...

work page
[25]

This flexibility will allow for a broader spectrum of creative outputs

**Input Handling:** Ensure that the program can accept a diverse range of in- puts, including strings, lists, and even user- generated content. This flexibility will allow for a broader spectrum of creative outputs

work page
[26]

This could involve using random selec- tions from lists of words, colors, or themes

**Randomization:** Incorporate ele- ments of randomness to ensure that each ex- ecution of the program yields a different re- sult. This could involve using random selec- tions from lists of words, colors, or themes

work page
[27]

The goal is to cre- ate something that feels alive and dynamic

**Artistic Generation:** Depending on the chosen output type (art or story), utilize libraries such as ‘matplotlib‘ for visual art or ‘nltk‘ for text generation. The goal is to cre- ate something that feels alive and dynamic

work page
[28]

**User Interaction:** Consider adding an interactive component where users can tweak parameters or provide additional in- puts mid-execution, allowing them to steer the creative process

work page
[29]

For art, this could mean creating a colorful canvas; for stories, it could involve formatting the text in a captivating way

**Output Presentation:** Design the out- put to be visually appealing or narratively engaging. For art, this could mean creating a colorful canvas; for stories, it could involve formatting the text in a captivating way

work page
[30]

dream \

**Documentation:** Ensure that the code is well-commented and documented, explaining the purpose of each function and how users can modify inputs to explore dif- ferent creative avenues. — **Example Python Code:** Here’s a simple example to get you started with the concept: 1 2```python 3import random 4import matplotlib . pyplot as plt 5import numpy as np...

work page

[1] [1]

Understand the Workflow: Here is the detailed workflow: [Detailed work- flow]

work page

[2] [2]

Identify Agent Roles: Based on the workflow, determine the distinct roles and responsibilities of each agent in- volved

work page

[3] [3]

• Objectives: The specific goals the agent is expected to achieve

Generate Agent-Specific Prompts: For each identified agent, craft a clear and concise prompt that includes: • Agent Role: A brief description of the agent’s function within the workflow. • Objectives: The specific goals the agent is expected to achieve. • Inputs: The information or data the agent will receive. • Outputs: The expected results or actions th...

work page

[4] [4]

Review the Workflow Template: [De- tailed workflow template]

work page

[5] [5]

Analyze the Dataset Description: [Dataset description]

work page

[6] [6]

• Steps and Sequence: Outline each step of the workflow and specify the order of execution

Design the Agent Workflow: Based on the above information, develop a comprehensive Agent Workflow that in- cludes: • Inputs and Outputs: Define the types of input data and the ex- pected output results. • Steps and Sequence: Outline each step of the workflow and specify the order of execution. • Agent Roles and Responsibilities: Describe the role and duti...

work page 2024

[7] [7]

Comprehensively summarize the given programming task for the subsequent code generation

Task Parsing Agent: "You are a task pars- ing agent. Comprehensively summarize the given programming task for the subsequent code generation. You will NOT return any- thing except for the task summary. { TASK PROMPT}

work page

[8] [8]

Your task is to write Python code according to the sum- mary parsed by your colleague

Code Generation Agent: "You are a proficient Python programmer. Your task is to write Python code according to the sum- mary parsed by your colleague. You will be given the problem description followed by the summary. You will NOT return anything except for the program." ⊕ { Output from Task Parsing Agent} Code Rewriting Workflow:

work page

[9] [9]

Your task is to write Python code according to the summary parsed by your colleague

Code Generation Agent: "You are a pro- ficient Python programmer. Your task is to write Python code according to the summary parsed by your colleague. You will be given the problem description followed by the sum- mary. You will NOT return anything except for the program."⊕{ TASK PROMPT}

work page

[10] [10]

Code Reviewer Agent: "You are a criti- cal python code reviewer. You are tasked to label generated codes with 1 or 0, where 1 indicates that this code satisfies the require- ments and can pass the sample test, while 0 indicates that this code doesn’t satisfies the requirements and will fail the sample test. You will be given the Problem Description follow...

work page

[11] [11]

You just generated some codes that cannot pass the sample test

Code Rewriting Agent: "You are a profi- cient Python programmer tasked with coding solutions based on given problem specifica- tions. You just generated some codes that cannot pass the sample test. You role is to regenerate python code that strictly adheres to the specifications, ensuring it reads input from standard input (stdin) and writes out- put to s...

work page

[12] [12]

Task Parsing Agent: " **Genre: Science Fiction** **Setting/Condition: A Floating City Above a Dying Earth** **Creative Writing Prompt:** In the year 2145, humanity has retreated to a sprawling floating city known as Aetheris, suspended high above the ravaged surface of a dying Earth. The city is powered by advanced tech- nology that harnesses the energy o...

work page

[13] [13]

"" Returns the square of the given number

Code Generation Agent: "When faced with a mutation question like the one you’ve provided, individuals who excel in creative thinking typically approach it in several ways: 1). Understanding the Problem: They start by thoroughly understanding the existing code and its purpose. In this case, the code reads a number of test cases and computes the square of e...

work page

[14] [14]

Your task is to explore the fu- sion of these elements and generate innova- tive concepts that transcend their traditional boundaries

Code Generation Agent: "Certainly! Here’s a mutator prompt designed to inspire creativity and innovation: — **Mutator Prompt: ¨The Elemental Fusion¨** Imagine a world where the four classi- cal elements ˘2014Earth, Water, Air, and Fire˘2014can be combined in unconventional ways to create new entities, phenomena, or experiences. Your task is to explore the...

work page

[15] [15]

Describe how they interact and what new properties or entities emerge from their fusion

**Elemental Combinations:** Choose two or more elements from Earth, Water, Air, and Fire. Describe how they interact and what new properties or entities emerge from their fusion

work page

[16] [16]

What challenges or benefits do they bring?

**Conceptual Expansion:** For each combination, think about how these new enti- ties could influence the environment, society, or technology. What challenges or benefits do they bring?

work page

[17] [17]

This could include art, architecture, environmental solutions, or even new forms of entertainment

**Unconventional Applications:** Con- sider how these fusions could be applied in real-world scenarios. This could include art, architecture, environmental solutions, or even new forms of entertainment

work page

[18] [18]

How does it look? What colors, shapes, or movements define it?

**Visual Representation:** If possible, sketch or describe a visual representation of your fused element or concept. How does it look? What colors, shapes, or movements define it?

work page

[19] [19]

**Narrative Element:** Create a short story or scenario that features your new en- tity in action. How does it interact with the world around it? What conflicts or resolu- tions arise from its presence? — **Example:** • **Combination:** Fire + Water • **New Entity:** Steam Elementals • **Description:** These beings are com- posed of swirling steam, capabl...

work page

[20] [20]

Code Reviewer Agent:"

work page

[21] [21]

Problem Description: In a world where creativity knows no bounds, we seek to de- velop a mutator prompt that inspires individ- uals to break free from conventional think- ing and explore innovative mutations in their ideas, projects, or artistic expressions. The goal is to create a prompt that encourages users to think divergently, combining unre- lated c...

work page

[22] [22]

time travel \

Generated Code: 1```python 2import random 3 4def g e n e r a t e _ m u t a t o r _ p r o m p t () : 5# Lists of unconventional concepts , actions , and mediums 6concepts = [ 7\" time travel \" , \" bioluminescence \" , \" quantum entanglement \" , 8\" dreamscapes \" , \" mythical creatures \" , \" urban legends \" , 9\" chaos theory \" , \" synesthesia \"...

work page

[23] [23]

**Concepts, Actions, and Mediums**: The code defines three lists containing un- conventional concepts, actions, and artistic mediums

Explanation of the Code: i. **Concepts, Actions, and Mediums**: The code defines three lists containing un- conventional concepts, actions, and artistic mediums. These lists are designed to inspire creativity by combining elements that are typically not associated with each other. ii. **Random Selection**: The ‘ran- dom.choice()‘ function is used to selec...

work page

[24] [24]

Your task is to create a Python program that generates a unique piece of art or a story based on a set of unconventional in- puts

Code Rewriting Agent:" **Mutator Prompt: ¨The Infinite Canvas¨** **Problem Description:** Imagine a world where the laws of physics and logic are mere suggestions, and creativity reigns supreme. Your task is to create a Python program that generates a unique piece of art or a story based on a set of unconventional in- puts. These inputs can be anything fr...

work page

[25] [25]

This flexibility will allow for a broader spectrum of creative outputs

**Input Handling:** Ensure that the program can accept a diverse range of in- puts, including strings, lists, and even user- generated content. This flexibility will allow for a broader spectrum of creative outputs

work page

[26] [26]

This could involve using random selec- tions from lists of words, colors, or themes

**Randomization:** Incorporate ele- ments of randomness to ensure that each ex- ecution of the program yields a different re- sult. This could involve using random selec- tions from lists of words, colors, or themes

work page

[27] [27]

The goal is to cre- ate something that feels alive and dynamic

**Artistic Generation:** Depending on the chosen output type (art or story), utilize libraries such as ‘matplotlib‘ for visual art or ‘nltk‘ for text generation. The goal is to cre- ate something that feels alive and dynamic

work page

[28] [28]

**User Interaction:** Consider adding an interactive component where users can tweak parameters or provide additional in- puts mid-execution, allowing them to steer the creative process

work page

[29] [29]

For art, this could mean creating a colorful canvas; for stories, it could involve formatting the text in a captivating way

**Output Presentation:** Design the out- put to be visually appealing or narratively engaging. For art, this could mean creating a colorful canvas; for stories, it could involve formatting the text in a captivating way

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**Documentation:** Ensure that the code is well-commented and documented, explaining the purpose of each function and how users can modify inputs to explore dif- ferent creative avenues. — **Example Python Code:** Here’s a simple example to get you started with the concept: 1 2```python 3import random 4import matplotlib . pyplot as plt 5import numpy as np...

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