Addressing the Hidden-Profile Problem in Multi-Agent Systems

Multi-agent systems often struggle with "hidden-profile" scenarios, where individual agents possess incomplete, private information that must be synthesized to reach a correct global conclusion. Standard communication protocols often lead to confirmation bias or the premature convergence on incorrect solutions because agents fail to effectively share or weigh their unique, non-overlapping data. The Consilience framework addresses this by implementing a communication control mechanism that treats agent interactions as a statistical calibration problem.

Conformal Calibration for Communication Control

At the heart of Consilience is the use of conformal calibration to regulate what and when agents communicate. Instead of allowing agents to broadcast all information—which can lead to noise and cognitive overload—or restricting communication arbitrarily, the framework uses a calibration layer to assess the reliability and necessity of information exchange.

By applying conformal prediction techniques, the system ensures that the information shared by an agent is statistically likely to be relevant to the collective goal. This process effectively filters out redundant or misleading signals that typically derail multi-agent reasoning. The framework forces agents to maintain a degree of uncertainty until sufficient evidence is aggregated, preventing the group from locking into a consensus based on incomplete or biased initial observations.

Impact on Reasoning and Convergence

The paper demonstrates that by controlling the flow of information through this calibrated approach, agents achieve higher accuracy in complex decision-making tasks. The framework provides a formal mechanism to balance the trade-off between communication overhead and reasoning depth. By ensuring that agents only communicate when the potential for information gain exceeds a calibrated threshold, the system avoids the common pitfalls of groupthink and information cascade, leading to more robust, data-driven outcomes in distributed reasoning environments.