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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
entry_type: string
display_name: string
total_score: double
recall: double
success_rate: double
avg_steps: double
step_score: double
submission_date: string
vs
team_name: string
team_id: int64
best_total_score: double
best_recall: double
best_success_rate: double
best_avg_steps: double
best_step_score: double
best_submit_time: string
best_record_id: int64
latest_total_score: double
latest_recall: double
latest_success_rate: double
latest_avg_steps: double
latest_step_score: double
latest_submit_time: string
latest_record_id: int64
latest_is_best: bool
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4379, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2661, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2839, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 580, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              entry_type: string
              display_name: string
              total_score: double
              recall: double
              success_rate: double
              avg_steps: double
              step_score: double
              submission_date: string
              vs
              team_name: string
              team_id: int64
              best_total_score: double
              best_recall: double
              best_success_rate: double
              best_avg_steps: double
              best_step_score: double
              best_submit_time: string
              best_record_id: int64
              latest_total_score: double
              latest_recall: double
              latest_success_rate: double
              latest_avg_steps: double
              latest_step_score: double
              latest_submit_time: string
              latest_record_id: int64
              latest_is_best: bool

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BC-Plus A Leaderboard Results

This dataset stores public evaluation artifacts for XIR Competition 1170 BC-Plus A leaderboard.

  • leaderboard-public.csv: public A leaderboard, one best result per team plus baselines.
  • leaderboard-best-latest.csv: each team's best result and latest submission result side by side.
  • submission-results-history.csv: append-only submission history.
  • baseline-results.csv: baseline rows.

Public files omit retriever repository identifiers. Internal evaluation files under /mnt/bn/search-tiktok-nas-au/yuqizhou/competition retain the private repo mapping for maintenance.

Scoring:

total_score = 0.4 * Recall + 0.4 * Success_Rate + 0.2 * (1 - Avg_Steps / 50) * 100

A榜 uses Qwen3.5-4B as the agent model with search and get_document tools, up to 20 tool steps. Step Score continues to use 50 as its normalization denominator.

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