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egora-benchmarks

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egora
lora
fine-tuning
rotation-retention-law
knowledge-retention
benchmark
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Error code:   StreamingRowsError
Exception:    ValueError
Message:      Expected object or value
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 236, 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: Column() changed from object to number in row 0
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 464, in __iter__
                  yield from self.ex_iterable
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 363, in __iter__
                  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 250, in _generate_tables
                  batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/utils/json.py", line 90, in json_encode_fields_in_json_lines
                  examples = [ujson_loads(line) for line in original_batch.splitlines()]
                              ^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/utils/json.py", line 20, in ujson_loads
                  return pd.io.json.ujson_loads(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
              ValueError: Expected object or value

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EgoRA Benchmark Results

Comprehensive benchmark results for EgoRA (Entropy-Governed Orthogonality Regularization for Adaptation) across multiple model scales, domains, and architectures.

πŸ“¦ Package: egora on PyPI πŸ’» Code: ArsSocratica/EgoRA on GitHub πŸ“„ Paper: arXiv:2602.05192 πŸ”– DOI: 10.5281/zenodo.19398709

Dataset Structure

llama-3.2-1b/, llama-3.2-3b/, llama-3.1-8b/

Fine-tuning results across 3 model scales, 2 domains (Alpaca general, Medical), 4 methods:

  • Baseline LoRA β€” standard low-rank adaptation
  • DoRA β€” weight-decomposed low-rank adaptation
  • EgoRA eΒ² β€” with entropy-squared governor
  • EgoRA adaptive v2 β€” with adaptive entropy governor

Each config contains:

  • *_results.json β€” benchmark scores (MMLU, TruthfulQA, HellaSwag, Winogrande, MedQA, MedMCQA)
  • history.json β€” per-step training curves (loss, penalty, Ξ», entropy)
  • summary.json β€” final metrics summary
  • rotation_analysis_*.json β€” per-head rotation geometry

threshold-analysis/

Rotation-Retention Law validation:

  • golden_ratio_k.json β€” k-value (Ξ”M/ΞΈΜ„) per condition
  • dimensionality_threshold.json β€” ΞΈ_crit = arcsin(1/√d_head)
  • cross_architecture_threshold.json β€” cross-architecture analysis
  • phase_transition.json β€” phase transition at ΞΈΜ„ β‰ˆ 5Β°

cross-modal/

Cross-modal experiments (Mistral-7B, Phi-3 Mini): rotation geometry, benchmarks, knowledge maps.

figures/

Publication-ready figures.

Key Results

Scale Method ΞΈΜ„ (Β°) MMLU Ξ” (pp) Damaged Heads
1B Baseline LoRA 6.89 βˆ’3.11 51.2%
1B EgoRA 2.33 βˆ’1.04 2.4%
3B Baseline LoRA 6.09 βˆ’4.61 49.4%
3B EgoRA 2.07 βˆ’1.89 1.3%
8B Baseline LoRA 7.00 βˆ’3.56 56.1%
8B EgoRA 2.57 βˆ’0.56 3.8%

Rotation-Retention Law: ΞΈΜ„ > 5Β° β†’ MMLU loss > 3pp; ΞΈΜ„ < 3Β° β†’ MMLU loss < 2pp.

Citation 

@article{dillerop2026egora,
  title={EgoRA: Entropy-Governed Orthogonality Regularization for Adaptation},
  author={Dillerop, Mark},
  journal={arXiv preprint arXiv:2602.05192},
  year={2026}
}

License

AGPL-3.0 with Academic Additional Permission (Section 7). Academic use is free with citation. Commercial use requires a separate license. Contact: mark@dillerop.com
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