Counterfactual neighborhood risk fields: evidence-grounded selective safety for vision-language models
Read the original at arxiv.org→arXiv:2610.20679v1 Announce Type: new Abstract: Representation-based safety defenses for vision-language models (VLMs) often learn to separate harmful from benign inputs, but separation alone does not establish what...
Original headline: "Counterfactual Neighborhood Risk Fields: Evidence-Grounded Selective Safety for Vision-Language Models"
Coverage timeline
- Oct 16, 04:00 UTC arXiv cs.AI lead source Counterfactual Neighborhood Risk Fields: Evidence-Grounded Selective Safety for Vision-Language Models