ResearchResearch paperLarge Language Models · Multimodal Models · Robotics & Embodied AI1 source · Oct 20, 2026

Near-Duplicate Image Selection Transfers Preferences in Vision-Language Models

Subliminal learning is the phenomenon in which a model transmits its preferences to another model through data that do not explicitly express them.

As models increasingly curate training data for other models, we ask whether this transfer can arise from choosing among near-duplicate images while keeping the source photographs and human-written captions fixed.
For each photograph, a curator selects one of eight near-duplicate variants by caption likelihood, without target-specific prompts.
Students trained on target-preferring curators' selections show higher mean target-versus-alternatives log-likelihood ratios in an image-free readout than students trained on neutral selections.
First-generation students also pass the owl preference to a second generation through captions without explicit owl mentions in LLaVA-1.5 and Qwen3-VL.

[1]Near-Duplicate Image Selection Transfers Preferences in Vision-Language Models
arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 09:51 AM
Subliminal learning is the phenomenon in which a model transmits its preferences to another model through data that do not explicitly express them.
As models increasingly curate training data for other models, we ask whether this transfer can arise from choosing among near-duplicate images while keeping the source photographs and human-written captions fixed.

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