Open-weights chemistry reasoning model + verifiable reward functions from FutureHouse's ether0 (arXiv 2506.17238). Use to score model-generated chemistry outputs (SMILES validity, molecular completion, synthesis reasoning) against ground truth, or to run the open-weights ether0 model itself for chemistry reasoning. Also useful for visualizing molecules and reactions from SMILES.
Multilingual reading comprehension across text and speech modalities. It probes a model's ability to understand spoken or written passages in 39 languages and answer multiple-choice questions based on them. Use when the user wants to benchmark on 2M-Belebele, or asks about evaluating this task. Reports accuracy.
Evaluates the capability of neural radiance field models to perform real-time, high-fidelity novel view synthesis on large-scale indoor scenes using 360° panoramic imagery. It probes the trade-off between rendering quality, computational efficiency, and geometric awareness in complex, unbounded indoor environments. Use when the user wants to benchmark on 360Roam Dataset, or asks about evaluating this task. Reports PSNR.
Evaluates a unified vision-language model's ability to ground natural language instructions to 3D objects and 2D regions, as well as answer 3D visual questions. It probes spatial reasoning, cross-modal alignment, and robustness to different 3D input representations (mesh-sampled vs. sensor RGB-D point clouds). Use when the user wants to benchmark on SR3D, NR3D, ScanRefer, RefCOCO, RefCOCO+, RefCOCOg, ScanQA, SQA3D, or asks about evaluating this task. Reports top-1 accuracy (Acc@25/50/75).
Evaluates 3D point cloud networks on visual object affordance understanding. It probes the model's ability to predict point-wise probabilistic scores for 18 affordance classes given full, partial, or rotated 3D shapes. Use when the user wants to benchmark on 3D AffordanceNet, or asks about evaluating this task. Reports mAP.
Evaluates the accuracy and realism of 3D face reconstruction and generation from single 2D images. Probes shape reconstruction fidelity against ground truth meshes, texture identity preservation across novel poses, and the diversity of synthesized 3D faces. Use when the user wants to benchmark on NoW Benchmark, REALY 3D Benchmark, or asks about evaluating this task. Reports Per-vertex error (mm).
Evaluates network intrusion detection systems on identifying malicious traffic flows in IoT and general network environments. It probes the model's ability to handle severe class imbalance, dynamic graph topologies, and both known and unknown attack patterns using binary and multi-class classification tasks. Use when the user wants to benchmark on CIC-ToN-IoT, CIC-BoT-IoT, EdgeIIoT, NF-UNSW-NB15-v2, NF-CSE-CIC-IDS2018-v2, or asks about evaluating this task. Reports F1-score.
Evaluates whether Vision-Language Models (VLMs) and 3D LLMs genuinely understand 3D spatial reasoning or merely exploit 2D visual priors by rendering point clouds into images. It probes capabilities like object captioning, scene question-answering, and situation understanding across single-view, multi-view, and oracle-viewpoint settings. Use when the user wants to benchmark on 3D MM-Vet, ObjaverseXL-LVIS Caption, ScanQA, SQA3D, or asks about evaluating this task. Reports LLM-eval, EM.