The real move isn't inference optimization—it's making LLMs actually reliable at scale. Papers on table-reading errors, uncertainty quantification, and agent evaluation signal that the field is finally hitting the wall where raw capability doesn't matter if the model hallucinates specs or misreads your schema. The DevOps/enterprise tooling noise (PR gates, root cause analysis, documentation copilots) is just noise until agents stop confidently lying—and these papers suggest that's the actual bottleneck teams will hit in 2025, not speed. Watch the uncertainty and feedback-loop research; ignore the "add AI to X" SaaS churn until someone solves faithful reasoning under real constraints.