This paper addresses the challenge of automating high-stakes meta-review generation, a critical task in academic peer review that involves synthesizing conflicting evaluations and deriving consensus. The authors argue that current Large Language Model (LLM)-based methods for this task are underdeveloped and susceptible to cognitive biases like the anchoring effect and conformity bias, hindering their ability to effectively handle disagreements. To overcome these limitations, the paper introduces the Cognitive Alignment Framework (CAF), a novel dual-process architecture inspired by Kahneman's dual-process theory of human cognition. CAF employs a three-step cognitive pipeline: review initialisation, incremental integration, and cognitive alignment. Empirical validation on the PeerSum dataset demonstrates that CAF outperforms existing LLM-based methods in terms of sentiment and content consistency.