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VeriLoop-E2-Evaluation-Evidence

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Languages:
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Tags:
veriloop-e2
evaluation
benchmarks
reproducibility
code-generation
mathematical-reasoning
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
schema: string
benchmark: string
task_number: string
task_id: string
expected_trials: int64
recorded_trials: int64
resolved_count: int64
pass_count: int64
fail_count: int64
result_provenance: string
time_semantics: string
normalized_sequence_interval_seconds: int64
public_safe: bool
trial_results: list<item: struct<trial_index: int64, trial_id: string, trial_uid: string, official_resolved: bool,  (... 117 chars omitted)
  child 0, item: struct<trial_index: int64, trial_id: string, trial_uid: string, official_resolved: bool, official_pa (... 105 chars omitted)
      child 0, trial_index: int64
      child 1, trial_id: string
      child 2, trial_uid: string
      child 3, official_resolved: bool
      child 4, official_pass: bool
      child 5, official_reward: double
      child 6, normalized_sequence_time_utc: timestamp[s]
      child 7, result_provenance: string
trial_count: int64
normalized_sequence_finished_at_utc: timestamp[s]
mean_binary_reward: double
normalized_sequence_started_at_utc: timestamp[s]
to
{'schema': Value('string'), 'benchmark': Value('string'), 'task_number': Value('string'), 'task_id': Value('string'), 'trial_count': Value('int64'), 'resolved_count': Value('int64'), 'pass_count': Value('int64'), 'fail_count': Value('int64'), 'mean_binary_reward': Value('float64'), 'result_provenance': Value('string'), 'normalized_sequence_started_at_utc': Value('timestamp[s]'), 'normalized_sequence_finished_at_utc': Value('timestamp[s]'), 'normalized_sequence_interval_seconds': Value('int64'), 'trial_results': List({'trial_index': Value('int64'), 'trial_id': Value('string'), 'trial_uid': Value('string'), 'official_resolved': Value('bool'), 'official_pass': Value('bool'), 'official_reward': Value('float64'), 'normalized_sequence_time_utc': Value('timestamp[s]'), 'result_provenance': Value('string')})}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                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 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              schema: string
              benchmark: string
              task_number: string
              task_id: string
              expected_trials: int64
              recorded_trials: int64
              resolved_count: int64
              pass_count: int64
              fail_count: int64
              result_provenance: string
              time_semantics: string
              normalized_sequence_interval_seconds: int64
              public_safe: bool
              trial_results: list<item: struct<trial_index: int64, trial_id: string, trial_uid: string, official_resolved: bool,  (... 117 chars omitted)
                child 0, item: struct<trial_index: int64, trial_id: string, trial_uid: string, official_resolved: bool, official_pa (... 105 chars omitted)
                    child 0, trial_index: int64
                    child 1, trial_id: string
                    child 2, trial_uid: string
                    child 3, official_resolved: bool
                    child 4, official_pass: bool
                    child 5, official_reward: double
                    child 6, normalized_sequence_time_utc: timestamp[s]
                    child 7, result_provenance: string
              trial_count: int64
              normalized_sequence_finished_at_utc: timestamp[s]
              mean_binary_reward: double
              normalized_sequence_started_at_utc: timestamp[s]
              to
              {'schema': Value('string'), 'benchmark': Value('string'), 'task_number': Value('string'), 'task_id': Value('string'), 'trial_count': Value('int64'), 'resolved_count': Value('int64'), 'pass_count': Value('int64'), 'fail_count': Value('int64'), 'mean_binary_reward': Value('float64'), 'result_provenance': Value('string'), 'normalized_sequence_started_at_utc': Value('timestamp[s]'), 'normalized_sequence_finished_at_utc': Value('timestamp[s]'), 'normalized_sequence_interval_seconds': Value('int64'), 'trial_results': List({'trial_index': Value('int64'), 'trial_id': Value('string'), 'trial_uid': Value('string'), 'official_resolved': Value('bool'), 'official_pass': Value('bool'), 'official_reward': Value('float64'), 'normalized_sequence_time_utc': Value('timestamp[s]'), 'result_provenance': Value('string')})}
              because column names don't match

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VeriLoop E2 Evaluation Evidence

Public evaluation evidence for VeriLoop E2 across nine code, agentic, mathematical, and scientific reasoning benchmarks.

This dataset repository is the canonical public evidence layer for the reported benchmark results of VeriLoop E2, a post-trained model based on Qwen 3.8-27B. It is designed to separate headline benchmark reporting from the underlying auditable artifacts required to inspect, reproduce, and verify those results.

The repository contains benchmark-level summaries together with the corresponding public evaluation artifacts, manifests, configuration records, and integrity hashes. Raw evidence is preserved as released; benchmark summaries are derived from those frozen artifacts rather than edited independently.

Benchmark Results

Benchmark VeriLoop E2
SWE-bench Pro 76.20%
DeepSWE v1.1 64.60%
SWE-Marathon v1.1 45.00%
Terminal-Bench 2.1 88.80%
Terminal-Bench 3.0 29.70%
Terminal-Bench 4.0 37.90%
AIME 2026 98.30%
GPQA Diamond 93.94%
MathArena Apex 2025 89.60%

Scope

The nine published evaluations cover four complementary capability regimes:

  • Software engineering: SWE-bench Pro, DeepSWE v1.1, and SWE-Marathon v1.1.
  • Agentic terminal interaction: Terminal-Bench 2.1, Terminal-Bench 3.0, and Terminal-Bench 4.0.
  • Mathematical reasoning: AIME 2026 and MathArena Apex 2025.
  • Scientific reasoning: GPQA Diamond.

This repository is an evidence repository, not a replacement for the original benchmark implementations. Benchmark definitions, task data, graders, and licensing remain governed by their respective upstream projects.

Repository Structure 

VeriLoop-E2-Evaluation-Evidence/
├── README.md
├── MANIFEST.json
├── SHA256SUMS
├── swe-bench-pro/
│   ├── summary.json
│   ├── results/
│   ├── logs/
│   ├── config/
│   └── SHA256SUMS
├── deep-swe-v1.1/
│   ├── summary.json
│   ├── results/
│   ├── logs/
│   ├── config/
│   └── SHA256SUMS
├── swe-marathon-v1.1/
│   ├── summary.json
│   ├── results/
│   ├── logs/
│   ├── config/
│   └── SHA256SUMS
├── terminal-bench-2.1/
│   ├── summary.json
│   ├── results/
│   ├── logs/
│   ├── config/
│   └── SHA256SUMS
├── terminal-bench-3.0/
│   ├── summary.json
│   ├── results/
│   ├── logs/
│   ├── config/
│   └── SHA256SUMS
├── terminal-bench-4.0/
│   ├── summary.json
│   ├── results/
│   ├── logs/
│   ├── config/
│   └── SHA256SUMS
├── aime-2026/
│   ├── summary.json
│   ├── results/
│   ├── logs/
│   ├── config/
│   └── SHA256SUMS
├── gpqa-diamond/
│   ├── summary.json
│   ├── results/
│   ├── logs/
│   ├── config/
│   └── SHA256SUMS
└── matharena-apex-2025/
    ├── summary.json
    ├── results/
    ├── logs/
    ├── config/
    └── SHA256SUMS

Evidence Model

Each benchmark directory is organized around the same provenance contract:

  1. summary.json records the released aggregate result and benchmark identity.
  2. results/ contains the public task- or run-level evaluation outputs retained for audit.
  3. logs/ contains the corresponding public execution or grading records where release is permitted.
  4. config/ records the evaluation configuration required to interpret the run.
  5. SHA256SUMS binds the released files to immutable content hashes.

The repository-level MANIFEST.json binds the benchmark set, model identity, release version, and evidence locations into one machine-readable index.

Integrity and Reproducibility

The publication policy for this repository is intentionally strict:

  • Aggregate scores must be derivable from frozen evaluation artifacts.
  • Released raw artifacts are not rewritten to match a headline score.
  • Benchmark identity and version are recorded explicitly.
  • Public files are bound by cryptographic hashes.
  • Any redaction required for licensing, privacy, or infrastructure security is performed without altering the reported evaluation outcome.
  • Corrections are versioned rather than silently replacing previously published evidence.
  • The evidence repository and the model repository remain logically separate so that model weights, model-card reporting, and benchmark evidence can evolve without conflating their provenance.

Hugging Face Eval Results

The VeriLoop E2 model repository uses Hugging Face's structured evaluation-results format under:

.eval_results/

Each supported benchmark entry points back to the corresponding evidence in this dataset repository. This provides two complementary publication layers:

Model repository → structured score registration and Hub presentation

Evaluation evidence dataset → auditable benchmark artifacts and provenance

Model

Model: VeriLoop E2
Base model: Qwen 3.8-27B
Release type: Post-training
Evaluation evidence: This repository
Model repository: xxxx
Technical report: xxxx

Citation

If you use VeriLoop E2 or the evaluation evidence released here, please cite the accompanying technical report and this dataset release.

@misc{wang2026veriloop_e2,
  title        = {VeriLoop E2},
  author       = {Libo Wang},
  year         = {2026},
  howpublished = {Hugging Face},
  note         = {Model and public evaluation evidence}
}

License and Upstream Benchmarks

This repository distributes evaluation evidence produced for VeriLoop E2. It does not relicense upstream benchmark datasets, tasks, graders, or third-party artifacts. Files derived from upstream benchmarks remain subject to their original licenses and terms. Model-specific evidence and repository metadata are released under the terms stated in this repository.

VeriLoop E2 — public results backed by inspectable evidence.
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