## 3 Methods
```
1:Define AttributeEncoder(input_dim, latent_dim, hidden_dim):
2:net \(\leftarrow\) Sequential(Linear(input_dim, hidden_dim), ReLU(0), Linear(hidden_dim, hidden_dim), ReLU(0))
3:fc_mu, fc_logvar \(\leftarrow\) Linear(hidden_dim, latent_dim), Linear(hidden_dim, latent_dim)
4:forward(x): h \(\leftarrow\) net(x); return (fc_mu(h), fc_logvar(h))
5:end Define
6:Define SpecificationDecoder(latent_dim, hidden_dim):
7:net \(\leftarrow\) Sequential(Linear(latent_dim, hidden_dim), ReLU(), Linear(hidden_dim, hidden_dim), ReLU(0))
8:size_fc, weight_fc, speed_fc, motor_fc \(\leftarrow\) Linear(...),...
9:forward(z): h \(\leftarrow\) net(z); size = ReLU(size_fc(h))\(\times\)100; weight=ReLU(weight_fc(h))\(\times\)50
10:speed = ReLU(speed_fc(h)); motor_probs = Softmax(motor_fc(h))
11:return {size, weight, battery, autonomy_type, drive_type, payload, sensors, PC, navigation_system, spraying_type, spray_system, terrain, safety}
12:end Define
13:Define MMF_VAE(input_dim, latent_dim, hidden_dim):
14:encoder \(\leftarrow\) AttributeEncoder(input_dim, latent_dim, hidden_dim)
15:decoder \(\leftarrow\) SpecificationDecoder(latent_dim, hidden_dim)
16:reparameterize(mu, logvar): std = exp(0.5\(\times\)logvar); eps = randn_like(std); return mu + eps \(\times\) std
17:forward(x): (mu, logvar) = encoder(x); z = reparameterize(mu, logvar); specs = decoder(z)
18:return (specs, mu, logvar)
19:end Define
20:mmf_vae_model \(\leftarrow\) MMF_VAE(input_dim, latent_dim, hidden_dim)
21://Train via Adam and domain-specific feedback to get final model
22://e.g., for epoch in 1.num_epochs: forward pass, VAE + feedback loss, backprop
23://After training, save mmf_vae_model for LLM integration
24:return mmf_vae_model //The trained model
```  
**Algorithm 1**Development of MMF-VAE Model  
Algorithm 2 illustrates how the trained MMF-VAE model file integrates with a conversational LLM like ChatGPT/Ollama. The MMF-VAE is loaded into a loop that listens for user prompts. When a user requests a new design, a random latent vector is sampled and decoded and then passed back as suggested specifications. If the user provides additional feedback, these textual constraints are translated into numeric adjustments that refine the latent space, generating an updated set of specifications. Throughout this process, the LLM mediates the user interaction, delivering suggestions, explanations, and refined outputs in a dialog-based fashion. The session concludes once the user has reached satisfactory fumigation robot attributes. ```
Input: mmf_vae_model: a trained MMF-VAE model file LLM_service: large language models (e.g., ChatGPT/Ollama) user_prompt: User prompts/instructions Output: Generate an output with targeted or desired requirements
```  
```
1:Enverse: Interactive design refinements via LLM based on MMF-VAE outputs
```
2:Pseudocode:
3:Load mmf_vae_model
4:While user is active:
5:prompt \(\leftarrow\) GET_INPUT_FROM_USER()
6:if prompt requests new design:
7:z_random \(\leftarrow\) SAMPLE_LATENT_VECTOR()
8:generated_specs \(\leftarrow\) mmf_vae_model.decoder(z_random)
9:SEND_TO_LLM(LLM_service, \"Suggested Parameters: \"+STR(generated_specs))
10:elseif prompt provides feedback:
11:feedback_vector \(\leftarrow\) CONVERT_TO_CONSTRAINTS(prompt)
12:z_refined \(\leftarrow\) APPLY_FEEDBACK_TO_LATENT(mmf_vae_model, feedback_vector)
13:refined_specs \(\leftarrow\) mmf_vae_model.decoder(z_refined)
14:SEND_TO_LLM(LLM_service, \"Refined Parameters: \"+STR(refined_specs))
15:endif
16:endwhile
17:return//End interactive session ```  
**Algorithm 2**Integrating MMF-VAE with LLM Model  
Figure 1:2 The framework of the proposed Machine-Learned Multimodal and Feedback-Based VAE model.