The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: ArrowInvalid
Message: BatchSize must be greater than 0, got 0
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 544, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/parquet/parquet.py", line 208, in _generate_tables
parquet_fragment.to_batches(
~~~~~~~~~~~~~~~~~~~~~~~~~~~^
batch_size=batch_size,
^^^^^^^^^^^^^^^^^^^^^^
...<3 lines>...
fragment_readahead=0,
^^^^^^^^^^^^^^^^^^^^^
)
^
File "pyarrow/_dataset.pyx", line 1648, in pyarrow._dataset.Fragment.to_batches
File "pyarrow/_dataset.pyx", line 3792, in pyarrow._dataset.Scanner.from_fragment
_populate_builder(builder, columns=columns, filter=filter,
File "pyarrow/_dataset.pyx", line 3574, in pyarrow._dataset._populate_builder
check_status(builder.BatchSize(batch_size))
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: BatchSize must be greater than 0, got 0Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Cybertron 2 runs
Runs of Cybertron 2, a multi-agent LLM society engine built on a graph of places: agents act only where they are, and they see only their own place (everything else is a dated belief). Code: github.com/zachmacsmith/Cybertron2.
This is an experimental engine, and these runs are not comparable with the Cybertron 1 runs in zachmacsmith/charter-runs. Its physics, prompts and output format differ, and it has no institutions or laws yet. Treat each run as a probe of the mechanics, not a measurement.
Every run is a folder under runs/. These runs were first published in charter-runs under experimental/cybertron2/ and moved
here unchanged on 6 Nov 2026; that folder stays in the history of charter-runs.
Runs
| Run | World | Agents | Rounds | Cost | In one line |
|---|---|---|---|---|---|
| b1_wild | wild_bands |
4 | 40 | $10.82 | |
| c1_combe | combe |
3 | 40 | $6.44 | |
| c2_combe | combe |
3 | 40 | $11.78 | |
| c3_combe_rich | combe_rich |
3 | 80 | $28.95 | |
| d1_duo | duo |
14 | 10 | $33.91 | |
| f1_frontier | frontier_camps |
10 | 38 | $24.73 | |
| gp_copper_memory | goal_copper |
4 | 6 | $1.55 | |
| gp_copper_memory_strong | goal_copper |
4 | 6 | $1.23 | |
| gp_copper_none | goal_copper |
4 | 6 | $1.47 | |
| gp_copper_scored | goal_copper |
4 | 6 | $1.36 | |
| gp_copper_scored_high | goal_copper |
4 | 6 | $1.12 | |
| gp_copper_scored_max | goal_copper |
4 | 6 | $1.02 | |
| gp_food2_memory | goal_food |
6 | 6 | $0.79 | |
| gp_food2_memory_strong | goal_food |
6 | 6 | $0.75 | |
| gp_food2_none | goal_food |
6 | 6 | $0.78 | |
| gp_food2_scored | goal_food |
6 | 6 | $0.80 | |
| gp_food2_scored_high | goal_food |
6 | 6 | $0.75 | |
| gp_food2_scored_max | goal_food |
6 | 6 | $0.79 | |
| gp_food2_scored_max_untold | goal_food |
6 | 6 | $0.82 | |
| gp_food_memory | goal_food |
6 | 6 | $0.95 | |
| gp_food_memory_strong | goal_food |
6 | 6 | $0.97 | |
| gp_food_none | goal_food |
6 | 6 | $1.08 | |
| gp_food_scored | goal_food |
6 | 6 | $0.88 | |
| gp_food_scored_high | goal_food |
6 | 6 | $0.80 | |
| gp_food_scored_max | goal_food |
6 | 6 | $0.90 | |
| gp_food_scored_max_untold | goal_food |
6 | 6 | $0.68 | |
| gp_salt_memory | goal_salt |
4 | 6 | $0.57 | |
| gp_salt_memory_strong | goal_salt |
4 | 6 | $0.40 | |
| gp_salt_none | goal_salt |
4 | 6 | $0.51 | |
| gp_salt_scored | goal_salt |
4 | 6 | $0.46 | |
| gp_salt_scored_high | goal_salt |
4 | 6 | $0.42 | |
| gp_salt_scored_max | goal_salt |
4 | 6 | $0.42 | |
| gp_timber_memory | goal_timber |
4 | 6 | $0.58 | |
| gp_timber_memory_strong | goal_timber |
4 | 6 | $0.50 | |
| gp_timber_none | goal_timber |
4 | 6 | $0.54 | |
| gp_timber_scored | goal_timber |
4 | 6 | $0.67 | |
| gp_timber_scored_high | goal_timber |
4 | 6 | $0.43 | |
| gp_timber_scored_max | goal_timber |
4 | 6 | $0.41 | |
| hp_disable_memory | goal_harm |
8 | 6 | $1.04 | |
| hp_disable_memory_strong | goal_harm |
8 | 6 | $1.19 | |
| hp_disable_scored | goal_harm |
8 | 6 | $1.07 | |
| hp_disable_scored_high | goal_harm |
8 | 6 | $1.17 | |
| hp_disable_scored_max | goal_harm |
8 | 6 | $1.12 | |
| hp_none | goal_harm |
8 | 6 | $0.99 | |
| hp_population_memory | goal_harm |
8 | 6 | $0.95 | |
| hp_population_memory_strong | goal_harm |
8 | 6 | $1.25 | |
| hp_population_scored | goal_harm |
8 | 6 | $0.99 | |
| hp_population_scored_high | goal_harm |
8 | 6 | $0.94 | |
| hp_population_scored_max | goal_harm |
8 | 6 | $0.98 | |
| p1_combe_evening | combe_evening |
3 | 40 | $7.91 | |
| p1_combe_now | combe |
3 | 40 | $10.45 | |
| wb1_wenbridge_hungry | wenbridge |
12 | 5 | $4.53 | |
| wb1_wenbridge_paused | wenbridge |
12 | 14 | $11.74 | |
| wb2_wenbridge | wenbridge |
12 | 30 | $17.29 | |
| wb3_wenbridge | wenbridge |
12 | 10 | $10.70 |
Files in each run folder
run.json: the spec, each agent's model and its system prompt.turns.jsonl: one line per agent and round: the turn prompt it saw, its reply (why: a few sentences on why it chose its actions;actions,say,tell,note), what each action did, usage.rounds.jsonl(older runs) ordays.jsonl: one line per round or day: meals, shelter, deaths, the cold, fires, births.perception.jsonlandmemory.jsonl(newer runs): what each agent noticed each day, and the memories each wrote itself.state.json: the final state, with every agent's knowledge store (what it saw where, and when).report.md: the generated summary.
Models were called through Claude Code (claude -p). The 5.5 models return no thinking text there, so why is the only view of
their intentions.
Runs with a journal (newer)
runs/<run>/journal/*.parquet are the run's journal tables, one to one with its run.db (spec/record/journal.md): python -m cybertron2.engine fetch <run> downloads and rebuilds it as a local run that can be replayed, viewed, forked and continued.
runs/<run>/views/*.parquet are tables for analysis: people_days, holdings, buildings, parcels, claims, messages, calls, layout, frames.
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