feature-discovery

This paper proposes a thinking-learning interaction model for autonomous robots, enabling them to adaptively discover new features, expand output categories, update learning models, and reconstruct action routines in open environments. Experimental results demonstrate significant improvements in recognition accuracy, category formation, and action efficiency.

This paper proposes an operational criterion for interpretable text representations based on inter-annotator agreement and label disentanglement, and introduces LLM-assisted Feature Discovery (LFD), a method that uses cross-LLM agreement screening and residual predictive gain to select clear, label-disentangled features. Experiments show LFD matches predictive performance while producing more interpretable features, validated by human audits.