Datasets:
The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
return check_status(status)
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Column(/gb_discrepancies/[]/gb/fans_cai_replay/[]/[]) changed from string to number in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 101, in _split_generators
pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 304, in _generate_tables
batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
examples = [ujson_loads(line) for line in original_batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 66, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
MCR Final-2026 Engine Correctness Test Set
A correctness test dataset for Chinese Standard Mahjong (MCR, 国标麻将) engine and judge implementers, built from the 12,288 official IJCAI-2026 Mahjong AI Competition Final Stage-2 games (Botzone, contest finished 2026-07-09; 512 duplicate walls x 24 seat/wind permutations, 4 finalist bots, zero protocol errors).
The product is simple: any new MCR judge/engine (e.g. a JAX implementation) must reproduce these games exactly — every deal, every draw, every claim resolution, every win/fan decision, and the final four-seat score of all 12,288 games. If it does, it is protocol- and rules-equivalent to the official judge on ~590k decision points. The games were played by four independent, strong, mutually adversarial policies, so the stream exercises claim priority, kong edge cases, robbed kongs, 8-fan boundary wins and monster hands far better than random play does.
Files
| file | contents |
|---|---|
mcr_final2026_full.jsonl.gz |
all 12,288 games, one JSON record per line (~14 MB) |
mcr_final2026_golden.jsonl |
221 curated edge-case games (uncompressed, browsable) — every record also appears in the full file |
validate_engine.py |
reference validator: dataset self-test + engine harness (pure stdlib) |
TAGS_SUMMARY.json |
per-tag counts, request-grammar census, judge-vs-MahjongGB scan details, golden game ids |
LICENSE |
usage note (competition game logs, research use) |
Status at publication: validate_engine.py --self-test passes 12,288/12,288 games;
the built-in demo engine reproduces 221/221 golden games through the engine harness.
Record schema (one game per line)
{
"game_id": "6a4eba831ba515095a1f155b", // Botzone match id
"srand": 2143175072, // judge RNG seed that generated the wall
"quan": 0, // prevalent wind 0-3 (0=E); 3072 games each
"players": ["player152/player2", ...], // seat 0..3 -> "user/bot" (metadata only)
"wall": ["W4","F3", ... 136 tiles], // full ordered wall — see deal order below
"turns": [ // the complete lockstep protocol stream
{
"request": {"0": "2 T8", "1": "3 0 DRAW", ...}, // judge -> each seat (verbatim)
"display": {"action": "DRAW", "player": 0, "tile": "T8",
"canHu": [-3,-4,-4,-4], "tileCnt": [20,21,21,21]},
"responses": {"0": "PLAY T9", "1": "PASS", "2": "PASS", "3": "PASS"}
},
// ... terminal turn: display.action == "HU" (fan/fanCnt/score) or "HUANG",
// no request/responses on the terminal turn
],
"expected": {
"ending": "ron", // "zimo" | "ron" | "draw" (draw = 荒庄)
"winner": 2, // seat, or null for draw
"discarder": 3, // seat that dealt in (ron only; for qianggang = the BUGANG player)
"qianggang": false, // true = robbed kong (HU immediately after BUGANG)
"fan": [["清龙",16,1],["平和",2,1], ...], // [name, value, count]
"fan_total": 20,
"scores": [-8,-8,44,-28] // final duplicate-format game scores, sum = 0
},
"tags": ["ron","multi_claim", ...],
"known_discrepancy": { ... } // only on gb_discrepancy / gb_fan_diff games (see below)
}
Tiles: W1-W9 characters (万), B1-B9 dots (饼), T1-T9 bamboo (条), F1-F4 winds
(东南西北), J1-J3 dragons (中发白). No flowers. Seat s has seat wind s; seat 0 is
always the dealer (first to draw) regardless of quan, which is the prevalent wind
(uniform 0-3 across the 24 permutations of each wall).
Wall / deal / draw convention (reproduce this or nothing matches)
Each seat owns a private 34-tile wall segment: seat s draws from
wall[34*s .. 34*s+33], consumed from the back (wall[34*s+33] first).
The deal gives each seat the last 13 tiles of its segment in back-to-front order:
hand[s][i] == wall[34*s + 33 - i]. The dealer's first draw is wall[20], i.e.
wall[34*s + 20] for s = 0. Kong replacement draws come from the drawer's own
segment, same order — there is no separate dead wall. tileCnt[s] in every display is
the number of tiles remaining in seat s's segment. The game is a draw (HUANG, 荒庄)
when the seat that would draw next has an empty segment. All 12,288 games were verified
to obey this convention exactly. srand is informative (the judge's C srand seed) —
you never need to re-derive the wall from it; the wall is given explicitly.
Protocol stream: requests, displays, responses
turns[k] is one judge step: request is the exact per-seat request string the judge
sent (Botzone Chinese-Standard-Mahjong "simple interaction" format), display is the
judge's public event record, responses is what the four bots answered. Complete
request-grammar census over all 12,288 games (counts in TAGS_SUMMARY.json):
0 <seat> <quan> game start (own seat wind, prevalent wind)
1 0 0 0 0 <t1> ... <t13> deal (the four 0s are flower counts, always 0)
2 <tile> your own draw
3 <p> DRAW player p drew (tile hidden)
3 <p> PLAY <tile> player p discarded <tile>
3 <p> PENG <tile> player p pengs the last discard AND discards <tile>
3 <p> CHI <mid> <tile> player p chis the last discard (run's middle tile =
<mid>) AND discards <tile>
3 <p> GANG AMBIGUOUS: melded kong of the last discard, OR a
concealed kong (AnGang) if p just drew — the AnGang
tile is never in the request (only in the display)
3 <p> BUGANG <tile> player p promotes an exposed peng of <tile> to a kong
Legal responses: PASS, PLAY <t>, PENG <t-to-discard>, CHI <mid> <t-to-discard>,
GANG (meld last discard), GANG <t> (concealed kong, after own draw), BUGANG <t>,
HU.
The four classic replay traps (all fixed in this dataset's semantics)
These are real bugs found in a published replay harness for this log format. An engine or log consumer that gets any of them wrong diverges within a few games:
- PENG/CHI events embed the claimer's follow-up discard.
{"action":"PENG", "player":2,"tile":"W4"}means seat 2 pengs the live discard and then discards W4. The claimed tile is NOT in the event — it is the last live discard. For CHI,tileCHIis the run's middle tile andtileis again the follow-up discard. Dropping these embedded discards causes silent state drift and later crashes. GANGis ambiguous and must be discriminated by live-discard tracking. AGANGwhile an unclaimed discard is on the table is a melded kong of that discard; aGANGwith no live discard (i.e. right after the player's own draw) is a concealed kong (AnGang) whose tile appears only in the display, never in the request.BUGANGis a first-class event (promoted kong). It kills the live discard, offers every other seat a robbed-kong (qianggang) HU claim, and on PASS the kong player draws a replacement tile.- Terminal labeling: a ron is the winner's claim on the live discard (or on the
BUGANG tile — qianggang); a zimo is a win on the winner's own pending draw. The
expectedblock encodes the corrected labels:ending/winner/discarder/qianggang.
Rules the games exercise (and your engine must implement)
- Minimum 8 fan to win (MCR): the judge only granted HU with
fan_total >= 8(flowerless Botzone MCR scoring;fanlists each 番种 as name/value/count exactly as the judge scored it;fan_total == sum(value*cnt)holds on every win). - Claim priority: HU > PENG/GANG > CHI. On multiple HU claims for the same tile the seat nearest downstream (counter-clockwise) of the discarder wins. CHI is only legal from the left neighbour. Verified on every multi-claim turn in the corpus (1,622 multi-claim games, 140 with competing HU/claim priority on the same tile).
- Scoring (duplicate format, per game, sums to 0):
- zimo: winner
+3*(8+fan), each other seat-(8+fan) - ron: winner
+(3*8+fan), discarder-(8+fan), bystanders-8(qianggang: the BUGANG player is the discarder) - draw (荒庄): all 0.
- zimo: winner
canHuper-step oracle: every display carriescanHu[4]; a value>= 0is the exact fan count that seat could win with on the current tile event; negative values are judge-internal codes (-4: not applicable / own event, -3: cannot win). Verified: whenever a game ends, the winning event'scanHu[winner]equals the finalfanCnt. Your engine can check its fan calculator at every single step against this field.
Golden subset & edge-case tags
mcr_final2026_golden.jsonl — 221 games chosen deterministically (lowest game_id per
tag; all games of the rare tags). A game carries all tags that apply to it.
| tag | full set | golden | meaning |
|---|---|---|---|
ron |
8,436 | 109 | win on another seat's discard |
zimo |
3,652 | 92 | self-drawn win |
draw |
200 | 20 | 荒庄, wall exhausted, all scores 0 |
fan8_boundary |
1,778 | 36 | win at exactly the 8-fan legal minimum |
multi_claim |
1,622 | 57 | ≥2 seats claimed the same tile (priority resolution) |
multi_hu |
140 | 20 | ≥2 competing HU claims on one tile |
bugang |
1,204 | 54 | promoted kong occurred |
minggang |
939 | 25 | melded kong of a discard occurred |
angang |
519 | 23 | concealed kong occurred |
qianggang |
28 | 28 (all) | robbed-kong win (HU right after BUGANG) |
fan_qiangganghu |
28 | 28 (all) | 抢杠和 scored in the fan list |
fan_gangshangkaihua |
46 | 8 | 杠上开花 (win on kong replacement tile) |
fan_last_tile_zimo |
9 | 5 | 妙手回春 (last wall tile, self-drawn) |
fan_last_tile_ron |
4 | 4 (all) | 海底捞月 (win on the final discard) |
big_fan |
20 | 10 | fan_total ≥ 48 (max in corpus: 93) |
gb_discrepancy |
20 | 20 (all) | KNOWN-DISCREPANCY, see below |
gb_fan_diff |
98 | 20 | KNOWN-DISCREPANCY (milder), see below |
KNOWN-DISCREPANCY cases (judge vs cai-style MahjongGB replay) — do not "fix" them
Every HU ending was re-scored with the community python MahjongGB (PyMahjongGB) fan
calculator by replaying hands/melds through the widely-copied "cai" FeatureAgent (the
encoder used by most Botzone MCR RL/imitation codebases). Result over 12,088 wins:
| status | games | meaning |
|---|---|---|
agree |
11,970 | replayed MahjongGB fan == judge fan, fan by fan |
cai_replay_fan_diff |
98 | cai-style replay scores lower but still ≥8 fan |
cai_replay_below_8fan_gate |
20 | cai-style replay scores the judge-accepted zimo below 8 fan — a cai-gated engine would have forbidden a legal win |
All 118 disagreements have the same root cause, in the replay layer, not in
MahjongGB: the cai FeatureAgent computes is4thTile = shownTiles[winTile] == 4, and
the just-drawn tile is never counted in shownTiles, so 和绝张 (Last Of Its Kind,
4 fan) can never fire on a self-drawn win. With is4thTile corrected to
shownTiles + (1 if self-drawn) == 4, python MahjongGB matches the judge on all
12,088 wins. The 118 games are tagged (gb_discrepancy = the 20 gate-relevant ones,
gb_fan_diff = the 98 milder ones) and carry a known_discrepancy field with both fan
lists side by side. The official Botzone judge is the ground truth for engine
acceptance. If your engine embeds a cai-style feature/legality layer, these are
exactly the games where you will diverge — handle them consciously rather than
discovering them as flaky test failures.
Validating your engine
# 1. dataset self-test (no engine needed; re-checks every game against the
# reference replay semantics: wall order, per-step hand/meld legality, claim
# priority, terminal classification, exact score arithmetic):
python3 validate_engine.py --self-test mcr_final2026_full.jsonl.gz --jobs 16
# -> self-test: 12288/12288 games PASS
# 2. interface smoke test with the built-in replay engine:
python3 validate_engine.py --demo mcr_final2026_golden.jsonl
# 3. your engine (start with the golden subset, then run the full set):
python3 validate_engine.py --engine mymodule:MyEngine mcr_final2026_golden.jsonl
python3 validate_engine.py --engine mymodule:MyEngine mcr_final2026_full.jsonl.gz
Your engine implements two methods (see the docstring in validate_engine.py):
reset(wall, quan, srand) -> turn and step(responses) -> turn, where a turn is
{"requests": {seat: str}, "display": {...}}. The validator feeds the logged responses
and requires byte-exact request strings and display events (--loose compares only
action/player/tile/tileCHI/hand/fan/fanCnt/score). Acceptance = 12,288/12,288 games
reproduced.
Provenance & stats
- IJCAI-2026 Mahjong AI Competition, Final Stage 2 (Botzone contest
6a4eb8c71ba515095a1f1417, finished 2026-07-09). 512 duplicate walls x 24 seat/wind permutations; finalists: kong/shiro, moyu/kdens3, QiuQiuR/丘丘人, player152/player2. - 12,088 HU endings + 200 荒庄 draws; zero ERROR/TLE/invalid-move events anywhere in
the corpus (all per-turn verdicts
OK) — every game is a clean, complete protocol trace. - Harvested from the public Botzone match archive; bot response
time/memory/debugfields were stripped, everything else is verbatim.
License / usage
Game logs of a public AI competition (Botzone platform), redistributed for research and engineering use: engine testing, rules verification, imitation-learning research. The bot policies that produced the moves remain the property of their authors; no bot code or model weights are included. If you use this dataset in a publication, please cite the IJCAI-2026 Mahjong AI Competition and link this dataset card.
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