Seeing but Not Thinking: Routing Distraction in Multimodal Mixture-of-Experts

1. 1
   Understand the 'Seeing but Not Thinking' Phenomenon

   Recognize that strong perceptual accuracy in your Multimodal MoE does not guarantee robust reasoning. Your model might 'see' correctly but fail to 'think' or reason about the content, even if individual experts can solve similar problems.
2. 2
   Identify Routing Distraction as the Root Cause

   Pinpoint 'routing distraction' within your MoE architecture. This occurs when the router misdirects or distracts experts, preventing the correct information flow for higher-level reasoning tasks. Examine expert activation patterns for misrouting.
3. 3
   Evaluate Reasoning Capabilities Beyond Perception

   Design specific evaluation metrics and benchmarks that rigorously test reasoning abilities, not just perceptual accuracy. Create test cases where the visual input is clear, but the required output demands complex inference or logical deduction from the MoE model.
4. 4
   Implement Robust Routing Mechanisms

   Focus on developing and integrating more sophisticated routing mechanisms. Experiment with routing strategies that are less prone to distraction and can effectively channel perceived information to the most relevant experts for reasoning tasks. Consider attention-based routing or hierarchical routing strategies.
5. 5
   Analyze Internal MoE Dynamics

   Conduct in-depth analysis of your MoE model's internal dynamics during reasoning tasks. Visualize expert activation, information flow between experts, and how routing decisions evolve. This can reveal why certain experts are activated or ignored, leading to distraction.

import torch
import torch.nn as nn

class SimpleMoERouter(nn.Module):
    def __init__(self, input_dim, num_experts):
        super().__init__()
        self.gate = nn.Linear(input_dim, num_experts)

    def forward(self, x):
        # x: input features (batch_size, input_dim)
        gate_logits = self.gate(x)
        # A basic router might use argmax or top-k, but this is where 'distraction' can happen.
        # For 'Seeing but Not Thinking', this simple gate might misdirect.
        selected_expert_indices = torch.argmax(gate_logits, dim=1)
        return selected_expert_indices, gate_logits

# Example usage:
input_data = torch.randn(4, 128) # batch_size, input_dim
router = SimpleMoERouter(128, 8) # input_dim, num_experts

expert_choices, raw_logits = router(input_data)
print(f"Selected experts per sample: {expert_choices}")
print(f"Raw gate logits: {raw_logits}")

# Challenge: How to ensure `expert_choices` lead to correct reasoning, not just perception?

• Paperarxiv.org