The dataset viewer is not available for this split.
Error code: FeaturesError
Exception: ArrowInvalid
Message: JSON parse error: Invalid value. in row 0
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 280, in _generate_tables
df = pandas_read_json(f)
^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 34, in pandas_read_json
return pd.read_json(path_or_buf, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 815, in read_json
return json_reader.read()
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 1014, in read
obj = self._get_object_parser(self.data)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 1040, in _get_object_parser
obj = FrameParser(json, **kwargs).parse()
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 1176, in parse
self._parse()
File "/usr/local/lib/python3.12/site-packages/pandas/io/json/_json.py", line 1392, in _parse
ujson_loads(json, precise_float=self.precise_float), dtype=None
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 247, 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 4196, 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 2533, in _head
return next(iter(self.iter(batch_size=n)))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2711, in iter
for key, pa_table in ex_iterable.iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2249, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, 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 283, in _generate_tables
raise e
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 246, in _generate_tables
pa_table = paj.read_json(
^^^^^^^^^^^^^^
File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Aurora Nano v0.1 is a research preview intended for experimentation, benchmarking, and evaluation. Real-world benchmark results are currently being collected and future releases may differ substantially from the current implementation.
PLEASE SEE GITHUB FOR FULL PROJECT SOURCE USE QUICKSTART.MD FOR THE GUIDE
Aurora Nano v0.1 — Research Preview
Aurora Nano is a lightweight runtime and evaluation framework designed to improve consistency, contradiction handling, escalation decisions, and structured reasoning for small language models.
The project explores a simple idea:
Can a small model paired with a deterministic runtime perform more reliably than the model alone?
Aurora Nano separates language generation from decision structure. Models are used for extraction and interpretation, while the Aurora runtime provides deterministic processing for contradiction detection, routing, confidence assessment, policy-aware handling, and auditability.
Key Features
- Lightweight runtime for small language models (3B–7B class)
- Contradiction and conflict detection
- Structured claim extraction workflows
- JSON validation and repair utilities
- Benchmark and evaluation framework
- Model-only vs runtime+model comparison tooling
- Android and local-device testing support
- Compute and latency audit tools
- Reproducible synthetic benchmark suites
Repository Contents
- Aurora Runtime v0.1
- 25-ticket smoke benchmark
- 250-ticket synthetic benchmark suite
- Evaluation and scoring pipelines
- Android/Termux testing kit
- Documentation and research reports
Current Status
Aurora Nano v0.1 is a research preview.
The repository currently focuses on methodology, benchmarking, and evaluation infrastructure. Synthetic benchmark validation has been completed. Real-world benchmarking of open-weight models is ongoing.
Design Philosophy
Aurora Nano is not intended to replace language models.
Instead, it investigates whether deterministic runtime systems can improve reliability, consistency, and decision quality while preserving the efficiency of smaller models.
Input ↓ Claim Extraction ↓ Aurora Runtime ↓ Routing & Analysis ↓ Decision & Audit Trail
Roadmap
Near-term validation targets include:
- Qwen3 4B
- Llama 3.2 3B
- Gemma 3 4B
- Phi-4 Mini
Future development will focus on real-world benchmarking, extraction quality, contradiction handling, and runtime-assisted small-model performance.
License
Released under the Apache License 2.0.
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