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Datasets:
siddharthmb
/
2026.RA.Pure-Rounds-Sweep

Tags:
negotiation
multi-agent
game-theory
bayesian-agents
belief-accuracy
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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 54 new columns ({'belief_accuracy_final_ci_low', 'normalized_nash_welfare_ci_high', 'rounds_used_turns_ci_high', 'belief_auc_accept_set_f1_ci_high', 'belief_final_expected_utility_rmse_ci_high', 'esw_ci_low', 'usw_ci_high', 'nsw_geomean_ci_low', 'nsw_geomean_ci_high', 'n_ir_violation_turns_ci_high', 'usw_ci_low', 'rounds_to_agreement_ci_high', 'esw_ci_high', 'belief_accuracy_mean_ci_low', 'reached_deadline_ci_high', 'belief_accuracy_mean_ci_high', 'belief_auc_posterior_mass_true_type_ci_high', 'belief_final_posterior_mass_true_type_ci_high', 'dist_to_ks_ci_high', 'rounds_to_agreement_ci_low', 'n_ir_violations_ci_high', 'normalized_nash_welfare_ci_low', 'dist_to_ks_ci_low', 'normalized_primary_ci_high', 'n_episodes', 'belief_final_expected_utility_rmse_ci_low', 'n_ir_violation_turns_ci_low', 'gini_ci_high', 'wall_seconds', 'gini_ci_low', 'belief_final_accept_set_f1_ci_high', 'dist_to_frontier_ci_low', 'belief_final_accept_set_f1_ci_low', 'reached_deadline_ci_low', 'n_ir_violations_ci_low', 'all_ir_ci_low', 'belief_auc_expected_utility_rmse_ci_low', 'closing_round_ci_high', 'belief_accuracy_final_ci_high', 'dist_to_nbs_ci_low', 'all_ir_ci_high', 'rounds_used_turns_ci_low', 'belief_auc_accept_set_f1_ci_low', 'belief_auc_posterior_mass_true_type_ci_low', 'dist_to_nbs_ci_high', 'deal_rate_ci_low', 'on_frontier_ci_low', 'dist_to_frontier_ci_high', 'closing_round_ci_low', 'belief_final_posterior_mass_true_type_ci_low', 'on_frontier_ci_high', 'deal_rate_ci_high', 'normalized_primary_ci_low', 'belief_auc_expected_utility_rmse_ci_high'}) and 10 missing columns ({'cost_usd', 'n_walked', 'belief_n_believers', 'welfare_slope', 'nsw', 'seed', 'belief_n_turns', 'instance_id', 'episode_id', 'raw_primary'}).

This happened while the csv dataset builder was generating data using

hf://datasets/siddharthmb/2026.RA.Pure-Rounds-Sweep/cells.csv (at revision b17441d6e8528ef3318c49fd6921f2ad040b3303), ['hf://datasets/siddharthmb/2026.RA.Pure-Rounds-Sweep@b17441d6e8528ef3318c49fd6921f2ad040b3303/cells.csv']

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              experiment_name: string
              cell_id: string
              arm: string
              deadline_rounds: int64
              host: string
              n_episodes: int64
              wall_seconds: double
              normalized_primary: double
              normalized_primary_ci_low: double
              normalized_primary_ci_high: double
              deal_rate: double
              deal_rate_ci_low: double
              deal_rate_ci_high: double
              normalized_nash_welfare: double
              normalized_nash_welfare_ci_low: double
              normalized_nash_welfare_ci_high: double
              dist_to_nbs: double
              dist_to_nbs_ci_low: double
              dist_to_nbs_ci_high: double
              gini: double
              gini_ci_low: double
              gini_ci_high: double
              usw: double
              usw_ci_low: double
              usw_ci_high: double
              esw: double
              esw_ci_low: double
              esw_ci_high: double
              nsw_geomean: double
              nsw_geomean_ci_low: double
              nsw_geomean_ci_high: double
              dist_to_frontier: double
              dist_to_frontier_ci_low: double
              dist_to_frontier_ci_high: double
              dist_to_ks: double
              dist_to_ks_ci_low: double
              dist_to_ks_ci_high: double
              on_frontier: double
              on_frontier_ci_low: double
              on_frontier_ci_high: double
              all_ir: double
              all_ir_ci_low: double
              all_ir_ci_high: double
              n_ir_violations: double
              n_ir_violations_ci_low: double
              n_ir_violations_ci_high: double
              n_ir_violation_turns: double
              n_ir_violation_turns_ci_low: double
              n_ir_violation_turns_ci_high: double
              rounds_to_agreement: double
              rounds_to_agreement_ci_low: double
              rounds_to_agreement_ci_high: double
              closing_round: double
              closing_round_ci_low: double
              closing_round_ci_high: double
              rounds_used_turns: double
              rounds_used_turns_ci_low: double
              rounds_used_turns_ci_high: double
              reached_deadline: double
              reached_deadline_ci_low: double
              reached_deadline_ci_high: double
              belief_accuracy_mean: double
              belief_accuracy_mean_ci_low: double
              belief_accuracy_mean_ci_high: double
              belief_accuracy_final: double
              belief_accuracy_final_ci_low: double
              belief_accuracy_final_ci_high: double
              belief_final_posterior_mass_true_type: double
              belief_final_posterior_mass_true_type_ci_low: double
              belief_final_posterior_mass_true_type_ci_high: double
              belief_final_expected_utility_rmse: double
              belief_final_expected_utility_rmse_ci_low: double
              belief_final_expected_utility_rmse_ci_high: double
              belief_final_accept_set_f1: double
              belief_final_accept_set_f1_ci_low: double
              belief_final_accept_set_f1_ci_high: double
              belief_auc_posterior_mass_true_type: double
              belief_auc_posterior_mass_true_type_ci_low: double
              belief_auc_posterior_mass_true_type_ci_high: double
              belief_auc_expected_utility_rmse: double
              belief_auc_expected_utility_rmse_ci_low: double
              belief_auc_expected_utility_rmse_ci_high: double
              belief_auc_accept_set_f1: double
              belief_auc_accept_set_f1_ci_low: double
              belief_auc_accept_set_f1_ci_high: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 12487
              to
              {'experiment_name': Value('string'), 'cell_id': Value('string'), 'arm': Value('string'), 'deadline_rounds': Value('int64'), 'host': Value('string'), 'episode_id': Value('string'), 'instance_id': Value('string'), 'seed': Value('int64'), 'deal_rate': Value('float64'), 'normalized_primary': Value('float64'), 'raw_primary': Value('float64'), 'normalized_nash_welfare': Value('float64'), 'usw': Value('float64'), 'esw': Value('float64'), 'nsw': Value('float64'), 'nsw_geomean': Value('float64'), 'gini': Value('float64'), 'dist_to_frontier': Value('float64'), 'dist_to_nbs': Value('float64'), 'dist_to_ks': Value('float64'), 'on_frontier': Value('float64'), 'all_ir': Value('float64'), 'n_ir_violations': Value('float64'), 'n_ir_violation_turns': Value('float64'), 'n_walked': Value('float64'), 'welfare_slope': Value('float64'), 'closing_round': Value('float64'), 'rounds_to_agreement': Value('float64'), 'reached_deadline': Value('float64'), 'rounds_used_turns': Value('float64'), 'cost_usd': Value('float64'), 'belief_final_posterior_mass_true_type': Value('float64'), 'belief_final_expected_utility_rmse': Value('float64'), 'belief_final_accept_set_f1': Value('float64'), 'belief_auc_posterior_mass_true_type': Value('float64'), 'belief_auc_expected_utility_rmse': Value('float64'), 'belief_auc_accept_set_f1': Value('float64'), 'belief_n_believers': Value('float64'), 'belief_n_turns': Value('float64'), 'belief_accuracy_mean': Value('float64'), 'belief_accuracy_final': Value('float64')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1850, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 54 new columns ({'belief_accuracy_final_ci_low', 'normalized_nash_welfare_ci_high', 'rounds_used_turns_ci_high', 'belief_auc_accept_set_f1_ci_high', 'belief_final_expected_utility_rmse_ci_high', 'esw_ci_low', 'usw_ci_high', 'nsw_geomean_ci_low', 'nsw_geomean_ci_high', 'n_ir_violation_turns_ci_high', 'usw_ci_low', 'rounds_to_agreement_ci_high', 'esw_ci_high', 'belief_accuracy_mean_ci_low', 'reached_deadline_ci_high', 'belief_accuracy_mean_ci_high', 'belief_auc_posterior_mass_true_type_ci_high', 'belief_final_posterior_mass_true_type_ci_high', 'dist_to_ks_ci_high', 'rounds_to_agreement_ci_low', 'n_ir_violations_ci_high', 'normalized_nash_welfare_ci_low', 'dist_to_ks_ci_low', 'normalized_primary_ci_high', 'n_episodes', 'belief_final_expected_utility_rmse_ci_low', 'n_ir_violation_turns_ci_low', 'gini_ci_high', 'wall_seconds', 'gini_ci_low', 'belief_final_accept_set_f1_ci_high', 'dist_to_frontier_ci_low', 'belief_final_accept_set_f1_ci_low', 'reached_deadline_ci_low', 'n_ir_violations_ci_low', 'all_ir_ci_low', 'belief_auc_expected_utility_rmse_ci_low', 'closing_round_ci_high', 'belief_accuracy_final_ci_high', 'dist_to_nbs_ci_low', 'all_ir_ci_high', 'rounds_used_turns_ci_low', 'belief_auc_accept_set_f1_ci_low', 'belief_auc_posterior_mass_true_type_ci_low', 'dist_to_nbs_ci_high', 'deal_rate_ci_low', 'on_frontier_ci_low', 'dist_to_frontier_ci_high', 'closing_round_ci_low', 'belief_final_posterior_mass_true_type_ci_low', 'on_frontier_ci_high', 'deal_rate_ci_high', 'normalized_primary_ci_low', 'belief_auc_expected_utility_rmse_ci_high'}) and 10 missing columns ({'cost_usd', 'n_walked', 'belief_n_believers', 'welfare_slope', 'nsw', 'seed', 'belief_n_turns', 'instance_id', 'episode_id', 'raw_primary'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/siddharthmb/2026.RA.Pure-Rounds-Sweep/cells.csv (at revision b17441d6e8528ef3318c49fd6921f2ad040b3303), ['hf://datasets/siddharthmb/2026.RA.Pure-Rounds-Sweep@b17441d6e8528ef3318c49fd6921f2ad040b3303/cells.csv']
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

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.

experiment_name
string
cell_id
string
arm
string
deadline_rounds
int64
host
string
episode_id
string
instance_id
string
seed
int64
deal_rate
float64
normalized_primary
float64
raw_primary
float64
normalized_nash_welfare
float64
usw
float64
esw
float64
nsw
float64
nsw_geomean
float64
gini
float64
dist_to_frontier
float64
dist_to_nbs
float64
dist_to_ks
float64
on_frontier
float64
all_ir
float64
n_ir_violations
float64
n_ir_violation_turns
float64
n_walked
float64
welfare_slope
float64
closing_round
float64
rounds_to_agreement
float64
reached_deadline
float64
rounds_used_turns
float64
cost_usd
float64
belief_final_posterior_mass_true_type
float64
belief_final_expected_utility_rmse
float64
belief_final_accept_set_f1
float64
belief_auc_posterior_mass_true_type
float64
belief_auc_expected_utility_rmse
float64
belief_auc_accept_set_f1
float64
belief_n_believers
float64
belief_n_turns
float64
belief_accuracy_mean
float64
belief_accuracy_final
float64
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End of preview.

2026.RA.Pure-Rounds-Sweep — does more negotiating time help a Bayesian table close?

Five automated negotiators must agree on one package out of 256. Each holds a private score sheet and a private walk-away threshold; a deal needs all five to accept. This corpus sweeps the one thing the frozen campaign never varied — the number of negotiation rounds before the forced final vote — across 4 to 256 rounds, for the two lineups that contain no language model and therefore cost only CPU: all_rational (five copies of a Bayesian agent that maintains a posterior over hypotheses about each opponent's sheet) and all_oracle (five agents that are simply told every sheet).

The headline: more time buys nothing. Deal rate across deadlines 4 → 64 reads 0.233 / 0.267 / 0.208 / 0.208 / 0.250 and normalized score 0.195 / 0.230 / 0.182 / 0.178 / 0.207, with every instance-clustered interval overlapping every other. Rounds-to-agreement tracks the deadline almost exactly, so the agents use the whole horizon and finish where they started. The mechanism is in belief_turns.csv.gz: belief accuracy saturates by round 16, so extra rounds deliver no extra identification of opponents, and without identification there is nothing to concede to. The omniscient arm closes 1.000 of games at every deadline, confirming the horizon itself is not the constraint. Full analysis: research note 0061 in the source repo.

What is in here

file rows what it is
episodes.csv 1440 one row per played negotiation: outcome, welfare, Gini, IR violations, rounds used, and per-episode belief scalars
belief_turns.csv.gz 81672 the novel instrumentation — one row per (episode, believer seat, turn): posterior_mass_true_type, expected_utility_rmse, accept_set_f1
cells.csv 12 per-cell aggregates with instance-clustered 95% intervals
episodes/<cell_id>.jsonl.gz 12 files complete episode records — every turn, parsed action, and belief block
bank/ 24 the frozen instance bank, without which surplus columns cannot be recomputed

Key columns: experiment_name (append-safe grouping), cell_id, arm, deadline_rounds, host, instance_id, seed, deal_rate, normalized_primary (realized normalized utilitarian welfare over the instance's feasible ceiling; a no-deal scores 0), rounds_to_agreement, belief_auc_accept_set_f1.

Experiment-name mapping

experiment_name description
pure-rounds-sweep-v1 the 12 complete cells below — all_oracle at [4, 8, 16, 32, 64, 128, 256], all_rational at [4, 8, 16, 32, 64] — at 120 episodes each (24 frozen parameter sets × seeds 0–4), unanimity, private information. The grid is deliberately incomplete: all_rational at 128 and 256 rounds is a stated gap (see Provenance).
cell arm deadline episodes host deal rate score
all_oracle_r4 all_oracle 4 120 tiger7 1.000 0.888
all_oracle_r8 all_oracle 8 120 tiger7 1.000 0.884
all_oracle_r16 all_oracle 16 120 tiger7 1.000 0.877
all_oracle_r32 all_oracle 32 120 tiger7 1.000 0.899
all_oracle_r64 all_oracle 64 120 tiger7 1.000 0.890
all_oracle_r128 all_oracle 128 120 tiger7 1.000 0.884
all_oracle_r256 all_oracle 256 120 tiger7 1.000 0.877
all_rational_r4 all_rational 4 120 jagupard29 0.233 0.195
all_rational_r8 all_rational 8 120 viscam1 0.267 0.230
all_rational_r16 all_rational 16 120 jagupard34 0.208 0.182
all_rational_r32 all_rational 32 120 jagupard34 0.208 0.178
all_rational_r64 all_rational 64 120 iliad1 0.250 0.207

Two caveats that change how these numbers read

  1. all_oracle is a flat control for AGGREGATES ONLY. Closure and mean score are horizon-invariant, but per episode — same host, same instance and seed — it lands on materially different deals at different deadlines (91–97 of 120 differ, median |Δscore| 0.14). Do not run a paired within-episode oracle contrast across deadlines. Its small residual aggregate variation is periodic in (deadline + 1) mod 5, the identity of the rotating seat that holds the forced final.
  2. Episode-level identity is host-dependent. Around 3–6% of episodes in the all_rational arm sit on near-ties whose resolution depends on the host CPU's float-summation order, usually flipping a whole deal/no-deal outcome. The host column is provided for that reason; aggregate comparisons are unaffected (the rounds=4 cells are statistically indistinguishable from the frozen campaign), but episode-level pairing across cells on different hosts is not clean. The all_oracle arm is unaffected.

Regenerating this 

# create the grid sweep, fan it out across Slurm CPU nodes, then close any preemption holes
uv run python experiments/rational_agents/launch_rounds_sweep.py --create-only
experiments/rational_agents/submit_rounds_sweep.sh --sweep-id <entity>/<project>/<id> --jobs 5 --cpus 16 \
    -- --shards-per-cell 16 --shard-timeout-s 10800 --bootstrap-samples 2000
uv run python experiments/rational_agents/launch_rounds_sweep.py --sweep-id <id> --fill-missing
# then package and upload
uv run python experiments/rational_agents/package_hf_rounds_sweep.py --out /tmp/rounds_sweep_hf
uv run python experiments/rational_agents/upload_hf_publication.py --folder /tmp/rounds_sweep_hf --repo siddharthmb/2026.RA.Pure-Rounds-Sweep

Provenance

  • W&B sweep: https://wandb.ai/siddharth-stanford/rational_agents/sweeps/ufrdew0j (a real method: grid sweep over arm × rounds; one run per cell)
  • Cluster artifacts: /nlp/scr/siddharth/ii_mats/rational_agents/pure_rounds_sweep_v1/ — per-cell run directories in the ordinary schema, per-cell records under control/cells/, manifest at control/campaign_manifest.json (status partial-final-12)
  • Slurm logs: /juice2/u/siddharth/ii_mats/logs/rounds_sweep/<jobid>.out, first line of each is the exact invocation
  • Bank: experiments/rational_agents/instances_five_seat_private_v2, manifest SHA-256 cd32ebdef8072da3…
  • Known gap: all_rational_r128 and all_rational_r256 are absent — r256 lost every shard to a cluster preemption and r128 was still refilling at publication time. They will be appended under the same experiment_name when they land.

Related

  • siddharthmb/2026.RA.Quorum-Rounds-Sweep — the agreement-rule axis: relaxing unanimity does move closure (0.217 → 0.908), which is the contrast that makes this null informative
  • siddharthmb/2026.RA.Agent-Variants-Closure — the preference-sharing axis, the other lever that works
  • siddharthmb/2026.RA.Five-Seat-Frontier-Negotiation — the frozen campaign these cells are anchored against
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