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This work introduces Selective Posterior Margin Regularization (SPMR), which preserves the Forward objective and converts this disagreement into a graded update on the clean classifier and transfers to estimated transitions, human annotations, architectural changes, and stronger Forward recipes.
ADAM-Bench (Auditing Dialogue Assertions with Multimodal Evidence), a benchmark for paper-grounded hallucinations in scientific dialogue, is introduced and two tasks are defined: hallucination detection and minimal evidence set localization.
AI reviewers can now produce many specific criticisms, but more criticism is not necessarily a better review. A review may miss a consequential weakness or retain an allegation that available evidence does not support. These failures require opposite corrections, yet generation-oriented systems and aggregate measures o...
Large language models (LLMs) and large multimodal models (LMMs) are increasingly applied in scientific dialogue, but it remains unclear whether they can reliably ground specific dialogue statements to paper-based evidence. A central challenge is paper-grounded hallucination under a paper-as-truth setting: statements th...
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