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cs.CL2026
When Text and Numbers Disagree: Evidence Arbitration in Large Language Models
Mattia Carletti, Edward Phillips, Fredrik K. Gustafsson +5
Large language models (LLMs) are increasingly used in settings where textual summaries, numerical observations, and external tool outputs may provide conflicting evidence. We study…
cs.CL2026
Measuring Epistemic Resilience of LLMs Under Misleading Medical Context
Hongjian Zhou, Xinyu Zou, Jinge Wu +19
Large language models (LLMs) now reach expert-level scores on medical licensing exams, encouraging the assumption that high scores imply safe medical judgment while patients increa…