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cloudcatcher2
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VCBench

Tasks:
Question Answering
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math
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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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Overview

VCBench provides a standardized framework for evaluating vision-language models. This document outlines the procedures for both standard evaluation and GPT-assisted evaluation of your model's outputs.

1. Standard Evaluation

1.1 Output Format Requirements

Models must produce outputs in JSONL format with the following structure:

{"id": <int>, "pred_answer": "<answer_letter>"}
{"id": <int>, "pred_answer": "<answer_letter>"}
...

Example File (submit.jsonl):

{"id": 1, "pred_answer": "A"}
{"id": 2, "pred_answer": "B"}
{"id": 3, "pred_answer": "C"}

1.2 Evaluation Procedure

  1. Ensure your predictions file follows the specified format
  2. Run the evaluation script:
    python evaluate_vcbench.py -p ./path/to/predictions.jsonl -g ./path/to/VCBench_with_answer.json

VCBench_with_answer.json is the ground truth file which can be downloaded from here.

2. GPT-Assisted Evaluation

2.1 Output Format Requirements

For natural language responses, use this JSONL format:

{"id": <int>, "pred_answer": "<natural_language_response>"}
{"id": <int>, "pred_answer": "<natural_language_response>"}
...

Example File (nl_predictions.jsonl):

{"id": 1, "pred_answer": "The correct answer is A"}
{"id": 2, "pred_answer": "After careful analysis, option B appears correct"}
{"id": 3, "pred_answer": "C is the right choice"}

2.2 Environment Setup

Set your Dashscope API key:

export DASHSCOPE_KEY="your_api_key_here"

2.3 Evaluation Procedure 

python evaluate_vcbench_by_gpt.py -p ./path/to/nl_predictions.jsonl -g ./path/to/VCBench_with_answer.json

3. Expected Output

Both evaluation scripts will provide:

  • Overall accuracy percentage
  • Per-question-type accuracy breakdown
  • Progress updates during evaluation

Citation

BibTeX:

@misc{wong2025vcbench
  author    = {Zhikai Wang and Jiashuo Sun and Wenqi Zhang and Zhiqiang Hu and Xin Li and Fan Wang and Deli Zhao},
  title     = {Benchmarking Multimodal Mathematical Reasoning with Explicit Visual Dependency},
  year      = {2025},
  eprint    = {2504.18589},
  archivePrefix = {arxiv},
  primaryClass  = {cs.CV},
  url       = {https://arxiv.org/abs/2504.18589}
}

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