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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VideoScienceBench

A benchmark for evaluating video understanding and scientific reasoning in vision-language models. Each example pairs a textual description of an experiment (what is shown) with the correct scientific explanation (expected phenomenon).

Dataset Summary

Attribute	Value
Examples	160
Domains	Physics, Chemistry
Format	JSONL (prompt + expected phenomenon + vid)

Data Creation Pipeline

Each researcher selects two or more scientific concepts and references relevant educational materials or videos to design a prompt. Prompts undergo peer and model review, followed by model-based quality checking, before being finalized for dataset inclusion.

Dataset Structure

Each line is a JSON object with:

Field	Type	Description
keywords	list[str]	Relevant scientific concepts
field	str	Scientific discipline (e.g., Physics)
prompt	str	Textual description of what is shown in the video/experiment
expected phenomenon	str	The correct scientific explanation
vid	str	Video identifier

Example

{
  "keywords": ["Buoyancy", "Gas Laws", "Pressure"],
  "field": "Physics",
  "prompt": "A sealed plastic bottle is filled with water containing a floating eyedropper with an air bubble inside. A person squeezes the sides of the bottle.",
  "expected phenomenon": "The eyedropper immediately sinks when the bottle is squeezed, then rises again when released, as increased pressure compresses the air bubble, reducing buoyancy.",
  "vid": "98"
}

Usage

from datasets import load_dataset

dataset = load_dataset("lmgame/VideoScienceBench")
# Access the test split (configured in the dataset card)
data = dataset["test"]

License

MIT

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Total file size:

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