# TerraIncognita: A Dynamic Benchmark for Species Discovery Using Frontier Models

arXiv: https://arxiv.org/abs/2506.03182 · HTML: https://mlsanity.com/paper/2506.03182/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 36 references that are in the map.
- First posted on arXiv 29 May 2025.

## Builds on (most cited first, 16 of 16)

- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [Energy-based Out-of-distribution Detection](https://mlsanity.com/paper/2010.03759/) (2020)
- [Deep Anomaly Detection with Outlier Exposure](https://mlsanity.com/paper/1812.04606/) (2018)
- [Towards Open Set Deep Networks](https://mlsanity.com/paper/1511.06233/) (2015)
- [Open-Set Recognition: a Good Closed-Set Classifier is All You Need?](https://mlsanity.com/paper/2110.06207/) (2021)
- [Benchmarking Representation Learning for Natural World Image Collections](https://mlsanity.com/paper/2103.16483/) (2021)
- [BioCLIP: A Vision Foundation Model for the Tree of Life](https://mlsanity.com/paper/2311.18803/) (2023)
- [Feed Two Birds with One Scone: Exploiting Wild Data for Both Out-of-Distribution Generalization and Detection](https://mlsanity.com/paper/2306.09158/) (2023)
- [A Step Towards Worldwide Biodiversity Assessment: The BIOSCAN-1M Insect Dataset](https://mlsanity.com/paper/2307.10455/) (2023)
- [BIOSCAN-5M: A Multimodal Dataset for Insect Biodiversity](https://mlsanity.com/paper/2406.12723/) (2024)
- [BioTrove: A Large Curated Image Dataset Enabling AI for Biodiversity](https://mlsanity.com/paper/2406.17720/) (2024)
- [CLIBD: Bridging Vision and Genomics for Biodiversity Monitoring at Scale](https://mlsanity.com/paper/2405.17537/) (2024)
- [Open-Set Recognition in the Age of Vision-Language Models](https://mlsanity.com/paper/2403.16528/) (2024)
- [Out-of-Distribution Detection via Deep Multi-Comprehension Ensemble](https://mlsanity.com/paper/2403.16260/) (2024)
- [Out-of-distribution detection algorithms for robust insect classification](https://mlsanity.com/paper/2305.01823/) (2023)
- [Long-Tailed Out-of-Distribution Detection via Normalized Outlier Distribution Adaptation](https://mlsanity.com/paper/2410.20807/) (2024)

## Built on by (most cited first, 1 of 1)

- [Taxonomy-Aware Representation Alignment for Hierarchical Visual Recognition with Large Multimodal Models](https://mlsanity.com/paper/2603.00431/) (2026)

## Abstract (arXiv)

The rapid global loss of biodiversity, particularly among insects, represents an urgent ecological crisis. Current methods for insect species discovery are manual, slow, and severely constrained by taxonomic expertise, hindering timely conservation actions. We introduce TerraIncognita, a dynamic benchmark designed to evaluate state-of-the-art multimodal models for the challenging problem of identifying unknown, potentially undescribed insect species from image data. Our benchmark dataset combines a mix of expertly annotated images of insect species likely known to frontier AI models, and images of rare and poorly known species, for which few/no publicly available images exist. These images were collected from underexplored biodiversity hotspots, realistically mimicking open-world discovery scenarios faced by ecologists. The benchmark assesses models' proficiency in hierarchical taxonomic classification, their capability to detect and abstain from out-of-distribution (OOD) samples representing novel species, and their ability to generate explanations aligned with expert taxonomic knowledge. Notably, top-performing models achieve over 90% F1 at the Order level on known species, but drop below 2% at the Species level, highlighting the sharp difficulty gradient from coarse to fine taxonomic prediction (Order rightarrow Family rightarrow Genus rightarrow Species). TerraIncognita will be updated regularly, and by committing to quarterly dataset expansions (of both known and novel species), will provide an evolving platform for longitudinal benchmarking of frontier AI methods. All TerraIncognita data, results, and future updates are available hrefhttps://baskargroup.github.io/TerraIncognita/here.