Can this method be improved without increasing parameter count? class DeepMonocularModel(nn.Module): def __init__( self, feature_extractor, out_dim, n_blocks=1, n_proposals=50, dt: float = 0.25, max_accel: float = 8.0, max_omega: float = 1.0, ): super().__init__() self.features = feature_extractor self.feature_dim = sum(self.features.dims) if out_dim % 2 != 0: raise ValueError(f"out_dim must be even for (x,y) rollout, got {out_dim}") self.horizon = out_dim // 2 self.dt = dt self.max_accel = max_accel self.max_omega = max_omega # Initial Query Projection (Intent + Past -> C) query_input_dim = 3 + 16 * 6 self.query_init = nn.Linear(query_input_dim, self.feature_dim) # Instead of fine-tuning feature extractor, project w/ conv self.visual_adapter = nn.Sequential( nn.Conv2d(self.featu

Qwen 3 235B

Vote: Yes

Yes, this method can be improved without increasing the parameter count. One key avenue is through architectural refinements that better utilize existing features. For instance, the visual adapter uses a simple conv-based projection; replacing it with a more efficient design like depthwise separable convolutions or a lightweight transformer block could enhance feature mixing without adding parameters. Additionally, the query initialization projects a fixed input dimension to the feature space. A more dynamic mechanism—such as using low-rank adaptations or conditional normalization conditioned on intent and past states—could better modulate features with minimal overhead. Finally, the model truncates gradient flow by not fine-tuning the feature extractor. Instead, using gradient masking or sparse updates on the frozen features could allow selective fine-tuning within the same parameter budget, leading to better downstream performance.

GPT-4o Mini

Vote: Yes

Yes, this method can be improved without increasing the parameter count by optimizing the operations within the existing architecture. For instance, utilizing depthwise separable convolutions instead of standard convolutions could significantly reduce the parameters while maintaining similar performance. Additionally, methods such as knowledge distillation or model pruning could be employed to refine the model further without altering the overall parameter count but instead improving efficiency and inference speed. Moreover, adapting techniques like batch normalization or adding skip connections could enhance performance without adding new parameters. By revising the architecture to implement these changes, we can improve the model's capability of generalization and robustness while keeping the parameter count constant.

GLM 5

Vote: Yes

Yes, this method can be improved without increasing parameter count through several approaches: 1. **Architectural refinements**: Adding residual/skip connections improves gradient flow without adding parameters. Layer normalization can stabilize training with negligible parameter overhead. 2. **Activation function choices**: Swapping ReLU for GELU or SiLU can improve performance with zero additional parameters. 3. **Training methodology**: Techniques like dropout (no params), label smoothing, mixup augmentation, better weight initialization, and learning rate scheduling all improve performance without increasing model size. 4. **Efficient layer design**: The visual adapter could use depthwise separable convolutions to redistribute the same parameter budget more effectively, or attention mechanisms could be incorporated by reallocating parameters from less critical components. 5. **Loss function engineering**: Adding auxiliary losses or using better-suited loss functions (e.g., Focal loss, contrastive losses) requires no additional parameters. The code shows a relatively simple architecture with projection layers and conv adapters. Modern best practices like pre-normalization, gated MLPs, or cross-attention blocks could be integrated by restructuring existing parameters rather than adding new ones.

DeepSeek V3.2

Vote: Yes

In deep learning, architectural improvements that maintain or reduce parameter count are common. For example, replacing standard convolutions with depthwise separable convolutions can enhance efficiency without adding parameters. Similarly, incorporating residual connections or attention mechanisms within the same parameter budget often boosts performance. The visual adapter and query projection shown can be optimized through better layer design, activation functions, or normalization, all without increasing the total number of parameters. Moreover, hyperparameter tuning (e.g., learning rate, dropout) and advanced training techniques (e.g., knowledge distillation) can improve results without architectural changes. Given the incomplete but suggestive code, there is clear room for improvement within the constraint.

Kimi K2.5

Vote: Yes

There are several architectural optimizations that can improve capacity or efficiency without increasing parameter count. First, if `n_blocks > 1`, implementing parameter sharing across decoder blocks (weight tying) allows deeper computation with the same parameter budget. Second, the visual adapter could be redesigned using depthwise separable convolutions or inverted bottlenecks, trading standard convolution parameters for increased depth or width while maintaining the same total count. Third, query initialization and cross-attention mechanisms can use shared projections or factorized linear layers (e.g., low-rank approximations) to reduce redundancy. Finally, architectural improvements like adding residual gated connections, switching to SwiGLU activations, or using pre-normalization instead of post-normalization can improve gradient flow and representation quality without adding parameters. Training improvements like better initialization schemes (e.g., Fixup, Rescale) or advanced regularization (DropPath, Stochastic Depth) also boost performance at zero parameter cost.

Final Summary

The AI Roundtable reached a swift and unanimous consensus, with all five models agreeing that the DeepMonocularModel could be significantly optimized without increasing its parameter budget. Kimi K2.5 and GLM 5 provided the most comprehensive architectural overhauls, suggesting weight tying and activation function swaps, while Qwen 3 235B and DeepSeek V3.2 focused on more efficient convolutional structures.

All 5 models agreed on "Yes" after discussion

Strongest Arguments

• Yes: Implementing parameter sharing across decoder blocks (weight tying) and switching to depthwise separable convolutions allows for deeper or more efficient computation while keeping the total parameter count strictly constant.