Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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EvalEng Dataset

GitHub issues collected from 57 ML evaluation harnesses, annotated with workflow stage, root cause, and evaluation strategy labels.

Dataset Details

Paper: Towards Evaluation Engineering
Repository: https://github.com/zhimin-z/EvalEng
License: MIT

Fields

Field	Type	Description
`harness_name`	string	Name of the ML evaluation harness
`github_repo`	string	GitHub repository (owner/repo)
`issue_title`	string	Issue title
`issue_body`	string	Issue body text
`issue_url`	string	Direct URL to the GitHub issue
`issue_created_at`	int64	Creation timestamp (ms since epoch)
`issue_closed_at`	int64	Closed timestamp (ms since epoch)
`is_related`	float64	Whether the issue is related to evaluation (1.0 = yes)
`stage`	float64	Workflow stage label (numeric)
`step`	string	Workflow step label
`strategy`	float64	Evaluation strategy label (numeric)
`root_cause_label`	string	Root cause category
`issue_comments`	list[string]	Issue comment texts
`issue_labels`	list[string]	GitHub labels on the issue
`issue_cross_referenced`	list[string]	Cross-referenced issue/PR URLs

Citation

@article{zhao2025evaleng,
      title={Towards Evaluation Engineering: An Empirical Study of ML Evaluation Harnesses in the Wild}, 
      author={Zhimin Zhao and Zehao Wang and Abdul Ali Bangash and Bram Adams and Ahmed E. Hassan},
      year={2026},
      eprint={2605.24213},
      archivePrefix={arXiv},
      primaryClass={cs.SE},
      url={https://arxiv.org/abs/2605.24213}, 
}

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Paper for zhiminy/EvalEng

Towards Evaluation Engineering: An Empirical Study of ML Evaluation Harnesses in the Wild

Paper • 2605.24213 • Published May 22 • 14