The dataset viewer is not available for this split.
Error code: FeaturesError
Exception: BadZipFile
Message: zipfiles that span multiple disks are not supported
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 228, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3496, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2257, in _head
return next(iter(self.iter(batch_size=n)))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2461, in iter
for key, example in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1974, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 503, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 350, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 113, in _generate_tables
for file_idx, file in enumerate(itertools.chain.from_iterable(files)):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/utils/track.py", line 49, in __iter__
for x in self.generator(*self.args):
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 1350, in _iter_from_urlpaths
if xisfile(urlpath, download_config=download_config):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 733, in xisfile
fs, *_ = url_to_fs(path, **storage_options)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/fsspec/core.py", line 395, in url_to_fs
fs = filesystem(protocol, **inkwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/fsspec/registry.py", line 293, in filesystem
return cls(**storage_options)
^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/fsspec/spec.py", line 80, in __call__
obj = super().__call__(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/fsspec/implementations/zip.py", line 62, in __init__
self.zip = zipfile.ZipFile(
^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1354, in __init__
self._RealGetContents()
File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1417, in _RealGetContents
endrec = _EndRecData(fp)
^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/zipfile/__init__.py", line 311, in _EndRecData
return _EndRecData64(fpin, -sizeEndCentDir, endrec)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/zipfile/__init__.py", line 257, in _EndRecData64
raise BadZipFile("zipfiles that span multiple disks are not supported")
zipfile.BadZipFile: zipfiles that span multiple disks are not supportedNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
TeleEgo:
Benchmarking Egocentric AI Assistants in the Wild
π’ NoteοΌThis project is still under active development, and the benchmark will be continuously updated.
π Introduction
TeleEgo is a comprehensive omni benchmark designed for multi-person, multi-scene, multi-task, and multimodal long-term memory reasoning in egocentric video streams. It reflects realistic personal assistant scenarios where continuous egocentric video data is collected across hours or even days, requiring models to maintain and reason over memory, understanding, and cross-memory reasoning. Omni here means that TeleEgo covers the full spectrum of roles, scenes, tasks, modalities, and memory horizons, offering all-round evaluation for egocentric AI assistants.
TeleEgo provides:
- π§ Omni-scale, diverse egocentric data from 5 roles across 4 daily scenarios.
- π€ Multi-modal annotations: video, narration, and speech transcripts.
- β Fine-grained QA benchmark: 3 cognitive dimensions, 12 subcategories.
π Dataset Overview
- Participants: 5 (balanced gender)
- Scenarios:
- Work & Study
- Lifestyle & Routines
- Social Activities
- Outings & Culture
- Recording: 3 days/participant (~14.4 hours each)
- Modalities:
- Egocentric video streams
- Speech & conversations
- Narration and event descriptions
π§ͺ Benchmark Tasks
TeleEgo-QA evaluates models along three main dimensions:
Memory
- Short-term / Long-term / Ultra-long Memory
- Entity Tracking
- Temporal Comparison & Interval
Understanding
- Causal Understanding
- Intent Inference
- Multi-step Reasoning
- Cross-modal Understanding
Cross-Memory Reasoning
- Cross-temporal Causality
- Cross-entity Relation
- Temporal Chain Understanding
Each QA instance includes:
- Question type: Single-choice, Multi-choice, Binary, Open-ended
π Citation
If you find our TeleEgo in your research, please cite:
@misc{yan2025teleegobenchmarkingegocentricai,
title={TeleEgo: Benchmarking Egocentric AI Assistants in the Wild},
author={Jiaqi Yan and Ruilong Ren and Jingren Liu and Shuning Xu and Ling Wang and Yiheng Wang and Yun Wang and Long Zhang and Xiangyu Chen and Changzhi Sun and Jixiang Luo and Dell Zhang and Hao Sun and Chi Zhang and Xuelong Li},
year={2025},
eprint={2510.23981},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2510.23981},
}
πͺͺ License
This project is licensed under the MIT License. Dataset usage is restricted under a research-only license.
π¬ Contact
If you have any questions, please feel free to reach out: [email protected].
β¨ TeleEgo is an Omni benchmark, a step toward building personalized AI assistants with true long-term memory, reasoning and decision-making in real-world wearable scenarios. β¨
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