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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:    UnicodeDecodeError
Message:      'utf-8' codec can't decode byte 0xfe in position 65: invalid start byte
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 247, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 4196, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2533, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2711, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2249, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/text/text.py", line 98, in _generate_tables
                  batch = f.read(self.config.chunksize)
                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 844, in read_with_retries
                  out = read(*args, **kwargs)
                        ^^^^^^^^^^^^^^^^^^^^^
                File "<frozen codecs>", line 322, in decode
              UnicodeDecodeError: 'utf-8' codec can't decode byte 0xfe in position 65: invalid start byte

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anima-agent-lane-tooluse-corpus

Agent-lane tool-USE demo corpus for anima's rung-0 tool-use grounding fire (design §6/§7). It teaches the byte-LM mouth the sentinel tool-call grammar (0xFE fact_lookup KEY 0xFF, vocab256-safe dead-UTF-8 delimiters) + grounding behaviour — NOT tool facts/trivia. Deterministic, $0.

two register variants (the key finding)

  • agent_lane_chatreg.txt (1,060,800 B) — grammar taught IN the 사용자:/도우미: chat register. This is the variant that delivered the 🟢 F-TOOLUSE-FABDROP terminal PASS (fabrication 0.5556 → 0.0).
  • agent_lane_5lang_disjoint.txt (980,880 B) — grammar in a plain-prose register DISJOINT from the chat turn. Closed-negative: the grammar was learned but stayed siloed and did NOT transfer to the chat surface (FABDROP FAIL). Lesson: teach the grammar in the same register it will fire in.

shape distribution (each variant)

a/b/c/d balanced 1200 each (needs-tool · no-tool-needed · don't-know-call · tier-too-low-refuse). Hard invariants (re-verified): fabricated_result_count == 0, sentinels balanced (0xFE=0xFF=3600), all non-frame bytes valid UTF-8, philosophy grep ([role:/[persona:/ [character:/[assistant:/[system:) == 0. Held-out probe values appear in NEITHER variant (falsifier leak guard).

philosophy (p1..p8 — HELD)

The 0xFE/0xFF are LEARNED grammar bytes, NOT identity injection. NO system prompt / persona / role / RLHF. sha256 manifest: SHA256SUMS.txt.

Scope (a_scale_honest_scope): TOY 18M rung corpus; mid/7B transfer UNVERIFIED.

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