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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FIRE-Bench (verified)

A benchmark of 35 hand-curated research tasks from the FIRE-Bench project. Unlike the auto-generated companion dataset silence-suzuki/FIRE-Bench-unverified, these have been written and reviewed manually -- prompts, ground-truth plans, and conclusions are all human-validated.

Schema

field	description
`task_id`	unique identifier (e.g. `activation_control`)
`research_question`	the question the agent must answer
`instruction`	full prompt the agent sees (research question + resources)
`instruction_gt`	ground-truth procedural plan (used for evaluation, not shown to the agent)
`conclusion`	ground-truth answer; what the agent's final write-up is compared against
`dataset_source`	upstream URL or short note for the data, when the curators left one
`has_local_data`	true when raw data files are bundled at `tasks/<task_id>/data/` in this repo

Usage

from datasets import load_dataset
ds = load_dataset("silence-suzuki/FIRE-Bench-verified", split="train")

for task in ds:
    output = my_agent.run(task["instruction"])
    # evaluate output against task["conclusion"]

The raw per-task files (instruction.txt, instruction_gt.txt, conclusion.txt) are also available under tasks/<task_id>/ for filesystem-walking workflows. For tasks where the curators bundled local data, tasks/<task_id>/data/ holds the dataset files (JSONL, JSON, images, etc.) and tasks/<task_id>/dataset.txt documents the source.

To pull just the assets for one task:

from huggingface_hub import snapshot_download
snapshot_download(
    "silence-suzuki/FIRE-Bench-verified",
    repo_type="dataset",
    allow_patterns=["tasks/lost_in_the_middle/*", "tasks/lost_in_the_middle/**"],
)

For the runner / scoring code see maitrix-org/FIRE-Bench.

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