Neural Catalog: Scaling Species Recognition with Catalog of Life-Augmented Generation

Scaling fine-grained recognition from curated benchmarks (200 species) to real-world inventories (11,202 species) costs vision-language models more than 30 points of accuracy, exposing a structural limitation: discriminating among thousands of visually similar candidates requires reasoning over relative differences rather than absolute matching. We introduce CoLAG (Catalogue of Life Augmented Generation), a training-free system built on a catalogue of 11,202 species indexed as discriminative summaries rather than raw articles. CoLAG pairs a multimodal retrieval stage with neighborhood-subtracted embeddings that amplify discriminative dimensions by suppressing features shared among confusable species, and a comparative reasoning stage that presents a multimodal LLM with retrieved candidates, discriminative descriptions, and calibrated retrieval scores for joint evaluation. Across five bird benchmarks, marine species, and Pok\'emon, CoLAG adds $35.1$ points on average over direct prompting across six MLLM backbones, and $10.0$ points over independent scoring.