Dual-Format Scientific Publishing: Optimizing Knowledge Transfer for Human and AI Cognition

We propose a fundamental paradigm shift in scientific communication: the systematic creation of
dual-format publications optimized separately for human and artificial intelligence readers. This is not
about AI-generated versus human-generated content, nor about co-authorship models, but rather about
recognizing that humans and AIs possess radically different cognitive architectures that demand
distinct presentation strategies. While human readers require narrative structure, visual aids, and
progressive contextualization within ~20-40 page constraints, AI systems can process hundreds of
pages of dense, cross-referenced data in seconds. We argue that maintaining a single-format approach
artificially limits both human accessibility (through unnecessary complexity) and AI utility (through
forced simplification). Dual-format publishing preserves deep knowledge in AI-optimized repositories
while making science maximally accessible to humans through AI-mediated interaction. This approach
represents not mere formatting preference but a recognition of AI as a fundamentally new class of
scientific reader requiring purpose-built communication protocols. We demonstrate that this
transformation is not speculative but immediately implementable with existing technology, and we
provide concrete frameworks for adoption across scientific disciplines.