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The paper introduces Open-Endedness Bench (OEB), a benchmark-agnostic methodology that evaluates AI agents' epistemic process by compiling execution logs into epistemic event graphs and scoring how well claims are supported by executed experiments, finding that only 16-29% of claimed agent improvements are real.
This paper introduces PEEL (Protocols for Epistemically Engaged Literacy in AI), a framework combining deterministic text analysis via Voyant Tools with LLM interpretation via Claude, grounded in Peircean semiotics, to expose systematic distortions in AI-generated research summaries and promote epistemic accountability.