miniCTX
Collection
miniCTX: Neural Theorem Proving with (Long-)Contexts (ICLR 2025 Oral) • 8 items • Updated • 2
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 failedNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
An entry in the miniCTX dataset consists of the theorem statement, preceding file contents, and metadata information. For example, given the following theorem s_eq_pow_two in context:
import Mathlib.Data.Real.Basic
/-!
# Square function
We define the squaring function `s : ℝ → ℝ` to be `s x := x * x`.
-/
def s (x : ℝ) : ℝ := x * x
lemma s_eq_pow_two {x : ℝ} : s x = x ^ 2 := by
rw [s, pow_two]
The problem is formatted in JSON as follows:
{
# Preceding file content
"srcContext": "import Mathlib.Data.Real.Basic\n\n/-!\n# Square function\nWe define the squaring function `s : ℝ → ℝ` to be `s x := x * x`.\n-/\n\ndef s (x : ℝ) : ℝ := x * x\n\n",
# Theorem statement
"theoremStatement": "lemma s_eq_pow_two {x : ℝ} : s x = x ^ 2",
# Fully qualified theorem name
"theoremName": "s_eq_pow_two",
# Temporal metadata
"fileCreated": {"commit":"(git commit)", "date":"(date the commit is updated)"},
"theoremCreated": {"commit":"(git commit)", "date":"(date the commit is updated)"},
# Source metadata
"file": "MyProject/Square.lean",
"module": "MyProject.Square",
"positionMetadata": {
# Line number the theorem is on
"lineInFile": 10,
# Number of tokens before the theorem
"tokenPositionInFile": 152,
# Number of premises (definitions, theorems) before the theorem
"theoremPositionInFile": 1
},
# Dependency metadata
"dependencyMetadata": {
# Number of definitions or lemmas defined in this file that the theorem uses
"inFilePremises": true,
"numInFilePremises": 1,
# Number of definitions or lemmas defined in this repository that the theorem uses (including in-file ones)
"repositoryPremises": true
"numRepositoryPremises": 1,
# Number of total premises (in file, repository, or otherwise)
"numPremises": 2,
# Modules imported in the current file
"importedModules": ["Mathlib.Data.Real.Basic", ...]
},
# Proof metadata
"proofMetadata": {
"hasProof": true,
"proof": "by\n rw [s, pow_two]",
"proofType": "tactic",
"proofLengthLines": 2,
"proofLengthTokens": 20
}
}
In addition to individual entries, we also provide the link and git commit version of each split for evaluation:
Please cite:
@article{hu2024minictx,
title={miniCTX: Neural Theorem Proving with (Long-) Contexts},
author={Hu, Jiewen and Zhu, Thomas and Welleck, Sean},
journal={arXiv preprint arXiv:2408.03350},
year={2024}
}