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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 11 new columns ({'model', 'reinflations', 'budget', 'n_turns', 'policy', 'seed', 'out_tok', 'n_recall', 'n_correct', 'peak_tok', 'wall_s'}) and 4 missing columns ({'tok_per_correct', 'arm', 'setting', 'ablation'}).
This happened while the csv dataset builder was generating data using
hf://datasets/AbteeXAILabs/foveance-benchmark/results/by_seed.csv (at revision 0f49b2f64140b7a95da6fd364db7735d350f553c), ['hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results/ablations.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results/by_seed.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results/greedy_gap.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results/pareto.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results/per_turn.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results/summary.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_baselines/llama_trajectory.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_baselines/single_shot.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_replay/codec_compare.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_replay/codec_compare_bytes.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_replay/codec_compare_bytes_summary.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_replay/codec_compare_summary.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_replay/codec_fullstack.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_replay/codec_longbench.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_replay/codec_longbench_bydomain.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_replay/codec_overhead.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_replay/codec_paper.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_replay/codec_ratio.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_replay/codec_ruler.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_replay/codec_ruler_bytask.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_replay/codec_scaling.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_replay/codec_separation.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_replay/codec_transport.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
model: string
seed: int64
policy: string
budget: int64
accuracy: double
in_tok: int64
out_tok: int64
peak_tok: int64
wall_s: double
n_turns: int64
n_recall: int64
n_correct: int64
reinflations: int64
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1738
to
{'ablation': Value('string'), 'setting': Value('string'), 'arm': Value('string'), 'accuracy': Value('float64'), 'in_tok': Value('float64'), 'tok_per_correct': Value('float64')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1839, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 11 new columns ({'model', 'reinflations', 'budget', 'n_turns', 'policy', 'seed', 'out_tok', 'n_recall', 'n_correct', 'peak_tok', 'wall_s'}) and 4 missing columns ({'tok_per_correct', 'arm', 'setting', 'ablation'}).
This happened while the csv dataset builder was generating data using
hf://datasets/AbteeXAILabs/foveance-benchmark/results/by_seed.csv (at revision 0f49b2f64140b7a95da6fd364db7735d350f553c), ['hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results/ablations.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results/by_seed.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results/greedy_gap.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results/pareto.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results/per_turn.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results/summary.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_baselines/llama_trajectory.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_baselines/single_shot.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_replay/codec_compare.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_replay/codec_compare_bytes.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_replay/codec_compare_bytes_summary.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_replay/codec_compare_summary.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_replay/codec_fullstack.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_replay/codec_longbench.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_replay/codec_longbench_bydomain.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_replay/codec_overhead.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_replay/codec_paper.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_replay/codec_ratio.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_replay/codec_ruler.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_replay/codec_ruler_bytask.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_replay/codec_scaling.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_replay/codec_separation.csv', 'hf://datasets/AbteeXAILabs/foveance-benchmark@0f49b2f64140b7a95da6fd364db7735d350f553c/results_replay/codec_transport.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
ablation string | setting string | arm string | accuracy float64 | in_tok float64 | tok_per_correct float64 |
|---|---|---|---|---|---|
drift | drift=0.0,name_target=True | reactive_afm | 0.9815 | 63,313 | 2,389.2 |
drift | drift=0.0,name_target=True | foveance | 0.9815 | 63,313 | 2,389.2 |
drift | drift=0.3,name_target=True | reactive_afm | 0.9815 | 63,260.2 | 2,387.2 |
drift | drift=0.3,name_target=True | foveance | 0.9815 | 63,441.3 | 2,394 |
drift | drift=0.6,name_target=True | reactive_afm | 0.9815 | 63,263.2 | 2,387.3 |
drift | drift=0.6,name_target=True | foveance | 0.9815 | 63,484.8 | 2,395.7 |
drift | drift=0.9,name_target=True | reactive_afm | 0.963 | 63,266.7 | 2,433.3 |
drift | drift=0.9,name_target=True | foveance | 0.963 | 63,742 | 2,451.6 |
drift | drift=0.0,name_target=False | reactive_afm | 0.9938 | 63,483.7 | 2,365.9 |
drift | drift=0.0,name_target=False | foveance | 0.9938 | 63,483.7 | 2,365.9 |
drift | drift=0.3,name_target=False | reactive_afm | 0.9938 | 63,526.2 | 2,367.4 |
drift | drift=0.3,name_target=False | foveance | 1 | 63,647.5 | 2,357.3 |
drift | drift=0.6,name_target=False | reactive_afm | 0.9877 | 63,437.2 | 2,378.9 |
drift | drift=0.6,name_target=False | foveance | 0.9938 | 63,689.8 | 2,373.5 |
drift | drift=0.9,name_target=False | reactive_afm | 0.9877 | 63,322.7 | 2,374.6 |
drift | drift=0.9,name_target=False | foveance | 0.9938 | 63,616.3 | 2,370.8 |
predictor | heuristic | foveance | 1 | 63,730.3 | 2,360.4 |
predictor | learned | foveance | 0.9815 | 63,835.2 | 2,408.9 |
retrieve | retrieve_on | foveance | 1 | 56,919 | 2,108.1 |
retrieve | retrieve_off | foveance | 1 | 56,919 | 2,108.1 |
fidelity_cost | fidelity_cost_on | foveance | 1 | 63,730.3 | 2,360.4 |
fidelity_cost | fidelity_cost_off | foveance | 1 | 56,919 | 2,108.1 |
null | null | null | 1 | 8,259 | null |
null | null | null | 1 | 8,259 | null |
null | null | null | 1 | 8,259 | null |
null | null | null | 1 | 8,259 | null |
null | null | null | 1 | 8,259 | null |
null | null | null | 0.666667 | 7,207 | null |
null | null | null | 0.666667 | 7,213 | null |
null | null | null | 0.666667 | 7,209 | null |
null | null | null | 0.666667 | 7,213 | null |
null | null | null | 0.666667 | 7,206 | null |
null | null | null | 1 | 3,091 | null |
null | null | null | 1 | 3,091 | null |
null | null | null | 1 | 3,011 | null |
null | null | null | 1 | 3,091 | null |
null | null | null | 1 | 3,091 | null |
null | null | null | 1 | 3,091 | null |
null | null | null | 1 | 3,091 | null |
null | null | null | 1 | 3,011 | null |
null | null | null | 1 | 3,091 | null |
null | null | null | 1 | 3,091 | null |
null | null | null | 1 | 8,259 | null |
null | null | null | 1 | 8,259 | null |
null | null | null | 1 | 8,259 | null |
null | null | null | 1 | 8,259 | null |
null | null | null | 1 | 8,259 | null |
null | null | null | 0.666667 | 7,207 | null |
null | null | null | 0.666667 | 7,213 | null |
null | null | null | 0.666667 | 7,209 | null |
null | null | null | 0.666667 | 7,213 | null |
null | null | null | 0.666667 | 7,206 | null |
null | null | null | 1 | 5,219 | null |
null | null | null | 1 | 5,219 | null |
null | null | null | 1 | 5,139 | null |
null | null | null | 1 | 5,219 | null |
null | null | null | 1 | 5,219 | null |
null | null | null | 1 | 5,219 | null |
null | null | null | 1 | 5,219 | null |
null | null | null | 1 | 5,159 | null |
null | null | null | 1 | 5,219 | null |
null | null | null | 1 | 5,219 | null |
null | null | null | 1 | 8,259 | null |
null | null | null | 1 | 8,259 | null |
null | null | null | 1 | 8,259 | null |
null | null | null | 1 | 8,259 | null |
null | null | null | 1 | 8,259 | null |
null | null | null | 0.666667 | 7,207 | null |
null | null | null | 0.666667 | 7,213 | null |
null | null | null | 0.666667 | 7,209 | null |
null | null | null | 0.666667 | 7,213 | null |
null | null | null | 0.666667 | 7,206 | null |
null | null | null | 1 | 7,347 | null |
null | null | null | 1 | 7,347 | null |
null | null | null | 0.666667 | 7,307 | null |
null | null | null | 1 | 7,347 | null |
null | null | null | 1 | 7,347 | null |
null | null | null | 1 | 7,347 | null |
null | null | null | 1 | 7,347 | null |
null | null | null | 0.666667 | 7,307 | null |
null | null | null | 1 | 7,347 | null |
null | null | null | 1 | 7,347 | null |
null | null | null | 1 | 9,894 | null |
null | null | null | 1 | 9,895 | null |
null | null | null | 1 | 9,890 | null |
null | null | null | 1 | 9,922 | null |
null | null | null | 1 | 9,873 | null |
null | null | null | 0.666667 | 8,619 | null |
null | null | null | 0.666667 | 8,627 | null |
null | null | null | 0.666667 | 8,611 | null |
null | null | null | 0.666667 | 8,647 | null |
null | null | null | 0.666667 | 8,601 | null |
null | null | null | 1 | 3,603 | null |
null | null | null | 1 | 3,606 | null |
null | null | null | 1 | 3,518 | null |
null | null | null | 1 | 3,610 | null |
null | null | null | 1 | 3,599 | null |
null | null | null | 1 | 3,603 | null |
null | null | null | 1 | 3,605 | null |
null | null | null | 1 | 3,513 | null |
End of preview.
Foveance — real-model benchmark results
Evidence files for Foveance, an anticipatory
context-allocation layer for long-horizon LLM agents that cuts your LLM token bill by 60%+
without changing your code or your answers (pip install foveance, npx foveance-proxy).
Every number in the project's README and report traces to the CSVs in this dataset; nothing is
hand-entered.
What was measured
Six policies (full replay, recency, budget-aware truncation, uniform allocation, reactive AFM, and foveance) on a buried-fact recall agent loop, run on three real models via Ollama (gemma2:2b, llama3.2:1b, qwen2.5:1.5b), three token budgets (400/700/1200), five seeds each — 270 rows total — plus a single-shot head-to-head that includes real LLMLingua-2, a greedy-gap measurement of the allocator against the exact dynamic-programming optimum, and a drift ablation.
Headline: at the tight budget every relevance-blind baseline drops the buried fact (recency 0.67, truncation 0.00, uniform 0.00 on two of three models, LLMLingua-2 0.00–0.33) while foveance matches full-replay accuracy at roughly a third of full replay's input tokens (62–64% fewer) on all three models.
Files
| File | Contents |
|---|---|
report.md |
Human-readable benchmark report with reproduction commands |
results/by_seed.csv |
270 per-seed rows: policy x model x budget x seed |
results/summary.csv |
Aggregates with 95% bootstrap CIs |
results/headline.json |
Headline numbers consumed by the README |
results/ablations.csv |
Drift / predictor / retrieve / fidelity-cost ablations |
results/greedy_gap.csv |
Index policy vs exact DP vs LP bound (800 measurements) |
results/pareto.csv, results/per_turn.csv |
Budget-sweep frontier and per-turn traces |
results_baselines/single_shot.csv |
Head-to-head recall probe incl. real LLMLingua-2 |
results_baselines/llama_trajectory.csv |
Full agent-loop comparison on llama3.2:1b |
plots/*.png |
Figures generated from the CSVs |
Reproduce
pip install "foveance[bench]"
git clone https://github.com/aimaghsoodi/foveance && cd foveance
bash scripts/run_everything.sh # real models via Ollama
# or offline: bash scripts/run_offline_demo.sh
Apache-2.0. Author: Abtin Maghsoodi.
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