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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FORGE: Fine-grained Multimodal Evaluation for Manufacturing Scenarios

🌐 Website | 📑 Paper | 💻 Code | 🤗 Dataset

Quick Start

from datasets import load_dataset

ds = load_dataset("AI4Manufacturing/forge", "task1_three_view", split="train")
print(ds[0].keys())
ds[0]["test_image"]  # PIL Image

Configs

Core Tasks

Config	Cases	Task	Modality
`task1_image`	451	Wrong model detection (MCQ)	Photo
`task1_three_view`	496	Wrong model detection (letter)	Three-View
`task2_three_view`	830	Anomaly classification (normal + defect type)	Three-View
`task3_image`	857	Extra/wrong part detection (MCQ)	Photo
`task3_three_view`	309	Extra/wrong part detection (letter)	Three-View
`task3_missing_part_image`	240	Missing part identification (MCQ)	Photo
`task3_missing_part_three_view`	137	Missing part identification (MCQ)	Three-View

Grounding Ablation (Single-Image)

Config	Cases	Description
`grounding_task_a_zero_shot`	500	Coord → Letter, zero-shot
`grounding_task_a_icl_within`	500	Coord → Letter, ICL (same image)
`grounding_task_a_icl_outside`	500	Coord → Letter, ICL (cross image)
`grounding_task_b_zero_shot`	500	Letter → Coord, zero-shot
`grounding_task_b_icl_within`	500	Letter → Coord, ICL (same image)
`grounding_task_b_icl_outside`	500	Letter → Coord, ICL (cross image)

Grounding Ablation (Cross-Image)

Config	Cases	Description
`grounding_cross_letter_to_letter`	513	Match parts by letter across images
`grounding_cross_coord_to_coord`	513	Match parts by coordinate across images

Total: 6,846 cases across 15 configs

Data Fields

Each row is self-contained with all images embedded. Unused image slots hold a 1x1 placeholder. Use n_normal_refs / n_icl_examples to know how many are real.

Task 1/3 Image -- test_image, grounding_image, assembly_name, assembly_description, error_case, ref_image_0..4, icl_ori_image_0..2, icl_grounding_image_0..2, n_normal_refs, n_icl_examples

Task 1/3 Three-View -- test_image, gt_parts (JSON), query_description, scenario_name, error_case, ref_image_0..4, icl_image_0..2, icl_gt_letters (JSON), n_normal_refs, n_icl_examples

Task 2 Three-View -- test_image, defect_type, is_normal, component_type, component_description, ref_image_0..4, icl_image_0..2, icl_metadata (JSON), n_normal_refs, n_icl_examples

Missing Part -- test_image, assembly_name, assembly_description, choices_text, gt_letter, gt_answer, mcq_mapping (JSON), ref_image_0..4, icl_image_0..2, icl_gt_letters (JSON), n_normal_refs, n_icl_examples

Grounding (single) -- test_image, target_coord (JSON), target_letter, choices (JSON), gt_choice_letter, icl_image_0..2, icl_metadata (JSON), n_icl_examples

Grounding (cross) -- ref_image, test_image, ref_hint, ref_hint_coord (JSON), test_choices (JSON), test_mcq_options (JSON), gt_answer

Evaluation Code

See the FORGE GitHub repo for the full evaluation toolkit supporting OpenRouter, OpenAI, Anthropic, Google, and vLLM backends.

Citation

@misc{jian2026forge,
      title={FORGE:Fine-grained Multimodal Evaluation for Manufacturing Scenarios}, 
      author={Xiangru Jian and Hao Xu and Wei Pang and Xinjian Zhao and Chengyu Tao and Qixin Zhang and Xikun Zhang and Chao Zhang and Guanzhi Deng and Alex Xue and Juan Du and Tianshu Yu and Garth Tarr and Linqi Song and Qiuzhuang Sun and Dacheng Tao},
      year={2026},
      eprint={2604.07413},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2604.07413}, 
}

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Paper for AI4Manufacturing/forge

FORGE:Fine-grained Multimodal Evaluation for Manufacturing Scenarios

Paper • 2604.07413 • Published Apr 8 • 98