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Living clinical guidelines continuously drift, yet pairing vectorless hierarchical retrieval with explicit temporal verification enables models to outperform ASCO's own assistant on multi-hop oncology reasoning without embedding drift.
Standard privacy redactions induce unpredictable failures in LLM reasoning across enterprise and clinical data, causing models to break down on record selection and predicate interpretation rather than computational execution.
Critique-aware training can boost LLM agent reliability by over 10% in complex, stateful environments, transforming how we approach tool-calling tasks.
