TaxaWalk: Species Recognition as a Walk Down the Tree of Life

Biodiversity has long been organized through a hierarchical system of biological classification. This hierarchy is closely related to visual recognition from broad to fine-grained taxonomic distinctions, narrowing the space of possible identities at each rank. Yet existing methods either classify species directly or predict each taxonomic rank independently, without allowing coarse-rank decisions to guide finer-grained identification. We propose a different perspective, modelling identification as a traversal of the taxonomy rather than a classification over its leaves. We introduce TaxaWalk, a hierarchical taxonomic model that autoregressively predicts an organism's lineage, one rank at a time, conditioned on preceding ranks. This formulation enables expert-conditioned inference where a biologist can specify a taxon at any rank, and the model completes the remaining lineage consistently. We further introduce an adaptive training objective that focuses learning on underperforming ranks, improving fine-grained recognition without allowing easier, coarse-grained predictions to dominate training. TaxaWalk sets a new state of the art in zero-shot open-vocabulary recognition across multiple biodiversity benchmarks, while supporting expert conditioning and zero-shot novelty detection. All code and models will be released upon acceptance.