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TOPIC · ENTITY #18370

multimodal benchmarks

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2

RESEARCH · 1 SOURCE · arXiv cs.AI

AlignPoison paper: decoding-aligned prefixes enable targeted poisoning of generative recommenders

arXiv:2610.14487v1 (AlignPoison) studies conditional targeted poisoning of autoregressive generative recommendation models under a realistic black-box threat model. The authors show that injecting under 1% synthetic interactions can substantially increase promotion of malicious targets, identify short context-aligned prefixes that amplify attacks, and present AlignPoison, a black-box method that discovers such prefixes and yields up to a 16.3 percentage-point ASR lift on unimodal and multimodal benchmarks.

7.0

RESEARCH · 1 SOURCE · arXiv cs.AI

SRCoD: structure-response co-calibration for item-level diagnosis of multimodal benchmarks

This paper (arXiv:2610.14542v1) introduces SRCoD, a structure–response co-calibrated framework that estimates sample-wise multimodal information structure and uses it as a soft structural anchor in modality-conditioned item-response models to produce calibrated item-level diagnostics and attributions. Experiments using benchmark-refinement and human-labeled validations show SRCoD outperforms prior benchmark-diagnosis methods in detecting modality dependence and shortcut items.

6.0