To distinguish these two phenomena, researchers must move beyond measuring the frequency of a specific output and instead analyze the pathways taken to reach that output. Consensus driven by training data bias often exhibits low logical entropy, meaning different models or agents reach the same conclusion using identical linguistic patterns or statistical shortcuts.
Mathematically, one can utilize Information Theory to measure the mutual information between different agents. If multiple agents arrive at a conclusion through wildly different reasoning chains (high divergence in latent space) but converge on the same truth, it suggests a robust consensus. Conversely, if the reasoning steps and the final outputs are highly correlated, it indicates a shared prior bias from the dataset.
Structurally, one can implement Cross-Verification Frameworks. By presenting agents with perturbed or counterfactual prompts that test the logical foundations of a claim, you can see if they maintain the consensus through sound logic or if they revert to a superficial pattern matching. If an agent can defend a conclusion via multiple, non-overlapping logical paths, it is likely a product of reasoning rather than simple data memorization.
