proxy
Datasets
All datasets matching “proxy”Proxy3D-SpaceSpan-318K
SpaceSpan Dataset
SpaceSpan is a large-scale dataset curated for the training and evaluation of 3D vision-language models (VLMs), specifically introduced in the paper Proxy3D: Efficient 3D Representations for Vision-Language Models via Semantic Clustering and Alignment.
Project Page | GitHub Repository
Dataset Description
The SpaceSpan dataset is designed to help VLMs develop spatial intelligence through 3D proxy representations. It incorporates heterogeneous visual… See the full description on the dataset page: https://huggingface.co/datasets/Spacewanderer8263/Proxy3D-SpaceSpan-318K.proxy-listproxy-logs-ShareGPTAll the proxy logs I could find (lmk if there are more), converted to ShareGPT so it's all formatted the same way in one dataset. I didn't .strip() any of the turns, I only used ftfy.fix_text() on them, and skipped any empty turns.
The original sample response is moved to be the final turn of conversations. If the last turn in the original sample prompt was a model turn, it is assumed that it was for doing prefill. So I moved this turn to response_prefill.
sample["response_prefill"] and… See the full description on the dataset page: https://huggingface.co/datasets/PJMixers-Dev/proxy-logs-ShareGPT.greater-london-tx-proxy-rat-path-gain
Greater London Per-Transmitter-Proxy, Per-RAT Simulated Path-Gain Dataset
Short display name: Greater London Tx-Proxy × RAT Path-GainChinese name: 大伦敦逐发射代理、逐 RAT 模拟路径增益数据集Release: v9 final release (COMPLETE)
City-scale propagation, one transmitter proxy at a time.
This release turns Greater London into a queryable radio-propagation dataset:
449,437,201,731 simulated path-gain relations connect 22,678 computed
transmitter-proxy hypotheses with 171,549,960 receiver faces across… See the full description on the dataset page: https://huggingface.co/datasets/EEzim/greater-london-tx-proxy-rat-path-gain.ProxyCoT-HotpotQAThis is the HotpotQA data that we used in our ProxyCoT project (https://aclanthology.org/2026.acl-long.1917/), and it is based on long-context reasoning (32K-128K tokens).
HotpotQA here is a new version originally from https://aclanthology.org/2026.acl-long.1917/ with extended contexts.
For more details on the context extension, refer to the ProxyCoT paper.
To use our dataset, please follow the code below.
train_samples = load_dataset("oaimli/proxycot-hotpotqa", split="train")
dev_samples =… See the full description on the dataset page: https://huggingface.co/datasets/oaimli/ProxyCoT-HotpotQA.ProxyCoT-SciTrekThis is the SciTrek data that we used in the ProxyCoT project, and it is based on long-context reasoning (32K-128K tokens).
SciTrek is originally from https://arxiv.org/abs/2509.21028.
To use our dataset, please follow the code below.
train_samples = load_dataset("oaimli/proxycot-scitrek", split="train")
dev_samples = load_dataset("oaimli/proxycot-scitrek", split="val")
test_samples = load_dataset("oaimli/proxycot-scitrek", split="test")
for sample in train_samples:
question =… See the full description on the dataset page: https://huggingface.co/datasets/oaimli/ProxyCoT-SciTrek.