Vision-language-action (VLA) models are emerging as embodied foundation models for robotic manipulation, but their deployment introduces a new unlearning challenge: removing unsafe, spurious, or privacy-sensitive behaviors without degrading perception, language grounding, and action control. In OpenVLA-style policies, behavior is produced through a fused visual encoder, a cross-modal projector, and a language backbone that predicts tokenized robot actions, so undesirable knowledge can be distributed across perception, alignment, and reasoning/action layers rather than confined to a single module. Consequently, partial unlearning applied only to the vision stack or only to the language backbone is often insufficient, while conventional unlearning baselines designed for standalone vision or language models may leave residual forgetting or incur unnecessary utility loss in embodied settings. We propose VLA-Forget, a hybrid unlearning framework that combines ratio-aware selective editing for perception and cross-modal specificity with layer-selective reasoning/action unlearning for utility-preserving forgetting. VLA-Forget jointly optimizes three objectives: targeted forgetting, perceptual preservation, and reasoning retention, through staged updates over the visual encoder, projector, and upper action-generating transformer blocks. Across forget-set behavior probes and retain-task evaluations, VLA-Forget improves forgetting efficacy by 10%, preserves perceptual specificity by 22%, retains reasoning and task success by 9%, and reduces post-quantization recovery by 55% relative to strong unlearning baselines.

People intuitively ask natural language dialogue systems for advice on nutrition and dietary guidelines, but systems based on prompted text generation are susceptible to fabricating details, which could be hazardous to non-specialist users. The FoodBench-QA shared task grounds answers in knowledge bases with linked ontologies, in order to evaluate and mitigate fabrication of nutrition information. Our system treats nutrient estimation and entity linking not as a generative problem (predicting numbers from scratch), but as a retrieval problem. We operate on the hypothesis that for structured data like food composition, finding a “real” recipe that is 95% similar is more likely to approximate the correct values than letting the language model fabricate values from sparse context. Our system performed well on food safety labeling from recipe ingredients alone, and it did not benefit from the additional information of recipe titles. In the NER and NEL tasks, our system handled the recipe-focused FCD corpus well, but suffered from poor recall on scientific abstracts and the artificial dataset. These results show the importance of basing information retrieval and question answering in data that is well-matched to the target data.