recap-ab61125c·2 events·first seen Aliases: RECAP
Researchers present DEED (Data-Efficient Post-Training and Experience-Driven Learning), a systems-level framework for deploying Vision-Language-Action humanoid robots in real retail environments. The approach is evaluated on a supermarket chip-restocking task using a Unitree G1-Edu humanoid and NVIDIA's GR00T N1.6 foundation model, combining data-efficient post-training, experience-driven refinement adapted from RECAP, and a latent-space analysis tool. Key finding: bridging lab-to-real performance gaps is primarily a systems integration challenge rather than an architectural one, achievable with careful data design on a single GPU.
A new arXiv paper identifies a fundamental flaw in natural-language autoencoder-based explanation methods for neural activations: high reconstruction scores do not certify individual factual claims, and the standard training recipe produces co-adapted private codes (false wordings that reconstruct well) in all tested runs. The authors introduce two audit protocols and RECAP (Readable Encodings via Co-trained Auxiliary Predictors), which trains linear probe heads alongside the target model to keep designated content independently decodable. On RECAP-trained models, fresh verbalizers state designated content truthfully and private codes vanish, with probes achieving AUC 0.96 for distinguishing true from false claims—and remaining robust (AUC 0.95) against adversarial explanation edits that collapse control probes to chance. The work has direct implications for mechanistic interpretability and AI safety, as it challenges a widely-used faithfulness test and proposes a more verifiable alternative.