Dynamic Latent Routing jointly learns discrete latent codes, routing policies, and model parameters via dynamic search to match or exceed supervised fine-tuning by 6.6 points on average in low-data settings across four datasets and six models.
InInternational Conference on Ma- chine Learning, ICML 2022, 17-23 July 2022, Balti- more, Maryland, USA, volume 162 ofProceedings of Machine Learning Research, pages 20827–20840
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Orientation information is recoverable from MLLM visual encoder embeddings via linear regression, contradicting the hypothesis that failures originate in the encoders.
RCS learns projections on LVLM internal representations to produce contrastive scores that separate malicious jailbreaks from benign inputs, with MCD and KCD variants claiming SOTA generalization to unseen attacks.
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Dynamic Latent Routing
Dynamic Latent Routing jointly learns discrete latent codes, routing policies, and model parameters via dynamic search to match or exceed supervised fine-tuning by 6.6 points on average in low-data settings across four datasets and six models.
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Why MLLMs Struggle to Determine Object Orientations
Orientation information is recoverable from MLLM visual encoder embeddings via linear regression, contradicting the hypothesis that failures originate in the encoders.
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Rethinking Jailbreak Detection of Large Vision Language Models with Representational Contrastive Scoring
RCS learns projections on LVLM internal representations to produce contrastive scores that separate malicious jailbreaks from benign inputs, with MCD and KCD variants claiming SOTA generalization to unseen attacks.