Researchers at Google Research and Technion published a study demonstrating that frontier large language models often fail to recall facts they have already encoded in their parameters. ^[1] Researchers addressed the challenge of detecting whether modern Korean poetry is human-authored or generated by Large Language Models (LLMs). ^[2] Experiments showed that frontier models like GPT-5 and Gemini-3 encode 95-98% of tested facts, indicating that recall rather than encoding is the primary bottleneck for factual accuracy. ^[3] Scaling the Gemma3 model from 1 billion to 27 billion parameters decreased encoding failures from 85% to 23%, but simultaneously increased the share of recall failures to 40% without thinking. ^[4] The study found that providing models with extra computational effort, such as inference-time thinking, successfully retrieves 40-65% of the encoded facts that models initially fail to directly recall. ^[5] Expert evaluation on GPT-5.2 preferred feature-guided poems over the unconstrained baseline for generation tasks. ^[6] The classifier attained an average AUC-ROC of 83.60 in zero-shot out-of-distribution detection across seven unseen LLMs. ^[7] This performance represented an absolute gain of 7.76 AUC points and a 10.23% relative improvement over KatFishNet, the strongest baseline in the comparison. ^[8]