This work introduces paired prompts that repeat the same diagnostic evidence verbatim while changing the causal target, and shows that the hidden state contains linearly decodable information about whether diagnostic evidence favors, challenges, or fails to address the causal target.
CDEP Agent is presented, an auditable LLM-agent framework that tests this mismatch directly by linking CDEP candidates detected from meteorological reanalysis to real-world hazard and impact evidence across sources with different spatial scales, temporal resolutions, and reporting conventions.
CARE is introduced, a reference-conditioned controller that separates program synthesis from experiment selection and attains the lowest normalized regret, the highest normalized best-so-far AUC, and the highest Top-1% Success@15 among the evaluated methods.
Guanyu Liu, Weiyi Kong, Chao Tang et al.· 1 citation
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