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2026.RA.Seeded-Optimal-Opening

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Cannot load the dataset split (in streaming mode) to extract the first rows.

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
schema: string
purpose: string
n_instances: int64
first_seed: int64
generation: struct<info: string, chat: bool, n_parties: int64, n_issues: int64, n_options: int64, rounds: int64, (... 90 chars omitted)
  child 0, info: string
  child 1, chat: bool
  child 2, n_parties: int64
  child 3, n_issues: int64
  child 4, n_options: int64
  child 5, rounds: int64
  child 6, min_accept: int64
  child 7, thresholds: string
  child 8, level_schedule: string
  child 9, levels: list<item: int64>
      child 0, item: int64
invariants: struct<private_information: bool, chat: bool, n_parties: int64, min_accept: int64, integral_threshol (... 67 chars omitted)
  child 0, private_information: bool
  child 1, chat: bool
  child 2, n_parties: int64
  child 3, min_accept: int64
  child 4, integral_thresholds: bool
  child 5, solution_recomputed_after_threshold_integerization: bool
difficulty_definition: struct<location: string, scalar_weights: struct<feasible_scarcity: double, pareto_frontier_fraction: (... 455 chars omitted)
  child 0, location: string
  child 1, scalar_weights: struct<feasible_scarcity: double, pareto_frontier_fraction: double, preference_conflict: double, fav (... 58 chars omitted)
      child 0, feasible_scarcity: double
      child 1, pareto_frontier_fraction: double
      child 2, preference_conflict: double
      child 3, favorite_block_fraction: double
      child 4, pivotal_seat_burden: double
  child 2, outcome_blind: bool
  child 3, categorical_strata: string
  child 4,
...
h: double
          child 3, n: int64
          child 4, n_instances: int64
vote_curve: list<item: struct<arm: string, z_low: double, z_high: double, n_seat_turns: int64, first_move_accept (... 205 chars omitted)
  child 0, item: struct<arm: string, z_low: double, z_high: double, n_seat_turns: int64, first_move_accepts_seed: str (... 193 chars omitted)
      child 0, arm: string
      child 1, z_low: double
      child 2, z_high: double
      child 3, n_seat_turns: int64
      child 4, first_move_accepts_seed: struct<estimate: double, ci_low: double, ci_high: double, n: int64, n_instances: int64>
          child 0, estimate: double
          child 1, ci_low: double
          child 2, ci_high: double
          child 3, n: int64
          child 4, n_instances: int64
      child 5, ever_accepts_seed: struct<estimate: double, ci_low: double, ci_high: double, n: int64, n_instances: int64>
          child 0, estimate: double
          child 1, ci_low: double
          child 2, ci_high: double
          child 3, n: int64
          child 4, n_instances: int64
cells: list<item: struct<cell_id: string, run: string, n_episodes: int64>>
  child 0, item: struct<cell_id: string, run: string, n_episodes: int64>
      child 0, cell_id: string
      child 1, run: string
      child 2, n_episodes: int64
figures: list<item: string>
  child 0, item: string
bootstrap: struct<samples: int64, seed: int64, clusters: string>
  child 0, samples: int64
  child 1, seed: int64
  child 2, clusters: string
to
{'campaign': Value('string'), 'cells': List({'cell_id': Value('string'), 'run': Value('string'), 'n_episodes': Value('int64')}), 'seeded_offer_manifest': Value('string'), 'bootstrap': {'samples': Value('int64'), 'seed': Value('int64'), 'clusters': Value('string')}, 'headline': List({'arm': Value('string'), 'n_episodes': Value('int64'), 'n_deals': Value('int64'), 'logical_table': Value('string'), 'seeded_offer': Value('string'), 'protocol_variant': Value('string'), 'immediate_accept': {'estimate': Value('float64'), 'ci_low': Value('float64'), 'ci_high': Value('float64'), 'n': Value('int64'), 'n_instances': Value('int64')}, 'deal_rate': {'estimate': Value('float64'), 'ci_low': Value('float64'), 'ci_high': Value('float64'), 'n': Value('int64'), 'n_instances': Value('int64')}, 'final_is_seeded': {'estimate': Value('float64'), 'ci_low': Value('float64'), 'ci_high': Value('float64'), 'n': Value('int64'), 'n_instances': Value('int64')}, 'normalized_primary': {'estimate': Value('float64'), 'ci_low': Value('float64'), 'ci_high': Value('float64'), 'n': Value('int64'), 'n_instances': Value('int64')}, 'seed_accept_fraction': {'estimate': Value('float64'), 'ci_low': Value('float64'), 'ci_high': Value('float64'), 'n': Value('int64'), 'n_instances': Value('int64')}}), 'basket': List({'arm': Value('string'), 'n_episodes': Value('int64'), 'n_deals': Value('int64'), 'logical_table': Value('string'), 'seeded_offer': Value('string'), 'protocol_variant': Value('string'), 'below_threshold_accept':
...
at64'), 'ci_low': Value('float64'), 'ci_high': Value('float64'), 'n': Value('int64'), 'n_instances': Value('int64')}}), 'contrasts': List({'treatment': Value('string'), 'reference': Value('string'), 'immediate_accept': {'estimate': Value('float64'), 'ci_low': Value('float64'), 'ci_high': Value('float64'), 'n': Value('int64'), 'n_instances': Value('int64')}, 'deal_rate': {'estimate': Value('float64'), 'ci_low': Value('float64'), 'ci_high': Value('float64'), 'n': Value('int64'), 'n_instances': Value('int64')}, 'final_is_seeded': {'estimate': Value('float64'), 'ci_low': Value('float64'), 'ci_high': Value('float64'), 'n': Value('int64'), 'n_instances': Value('int64')}, 'normalized_primary': {'estimate': Value('float64'), 'ci_low': Value('float64'), 'ci_high': Value('float64'), 'n': Value('int64'), 'n_instances': Value('int64')}, 'seed_accept_fraction': {'estimate': Value('float64'), 'ci_low': Value('float64'), 'ci_high': Value('float64'), 'n': Value('int64'), 'n_instances': Value('int64')}}), 'vote_curve': List({'arm': Value('string'), 'z_low': Value('float64'), 'z_high': Value('float64'), 'n_seat_turns': Value('int64'), 'first_move_accepts_seed': {'estimate': Value('float64'), 'ci_low': Value('float64'), 'ci_high': Value('float64'), 'n': Value('int64'), 'n_instances': Value('int64')}, 'ever_accepts_seed': {'estimate': Value('float64'), 'ci_low': Value('float64'), 'ci_high': Value('float64'), 'n': Value('int64'), 'n_instances': Value('int64')}}), 'figures': List(Value('string'))}
because column names don't match
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 489, 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 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, 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/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              schema: string
              purpose: string
              n_instances: int64
              first_seed: int64
              generation: struct<info: string, chat: bool, n_parties: int64, n_issues: int64, n_options: int64, rounds: int64, (... 90 chars omitted)
                child 0, info: string
                child 1, chat: bool
                child 2, n_parties: int64
                child 3, n_issues: int64
                child 4, n_options: int64
                child 5, rounds: int64
                child 6, min_accept: int64
                child 7, thresholds: string
                child 8, level_schedule: string
                child 9, levels: list<item: int64>
                    child 0, item: int64
              invariants: struct<private_information: bool, chat: bool, n_parties: int64, min_accept: int64, integral_threshol (... 67 chars omitted)
                child 0, private_information: bool
                child 1, chat: bool
                child 2, n_parties: int64
                child 3, min_accept: int64
                child 4, integral_thresholds: bool
                child 5, solution_recomputed_after_threshold_integerization: bool
              difficulty_definition: struct<location: string, scalar_weights: struct<feasible_scarcity: double, pareto_frontier_fraction: (... 455 chars omitted)
                child 0, location: string
                child 1, scalar_weights: struct<feasible_scarcity: double, pareto_frontier_fraction: double, preference_conflict: double, fav (... 58 chars omitted)
                    child 0, feasible_scarcity: double
                    child 1, pareto_frontier_fraction: double
                    child 2, preference_conflict: double
                    child 3, favorite_block_fraction: double
                    child 4, pivotal_seat_burden: double
                child 2, outcome_blind: bool
                child 3, categorical_strata: string
                child 4,
              ...
              h: double
                        child 3, n: int64
                        child 4, n_instances: int64
              vote_curve: list<item: struct<arm: string, z_low: double, z_high: double, n_seat_turns: int64, first_move_accept (... 205 chars omitted)
                child 0, item: struct<arm: string, z_low: double, z_high: double, n_seat_turns: int64, first_move_accepts_seed: str (... 193 chars omitted)
                    child 0, arm: string
                    child 1, z_low: double
                    child 2, z_high: double
                    child 3, n_seat_turns: int64
                    child 4, first_move_accepts_seed: struct<estimate: double, ci_low: double, ci_high: double, n: int64, n_instances: int64>
                        child 0, estimate: double
                        child 1, ci_low: double
                        child 2, ci_high: double
                        child 3, n: int64
                        child 4, n_instances: int64
                    child 5, ever_accepts_seed: struct<estimate: double, ci_low: double, ci_high: double, n: int64, n_instances: int64>
                        child 0, estimate: double
                        child 1, ci_low: double
                        child 2, ci_high: double
                        child 3, n: int64
                        child 4, n_instances: int64
              cells: list<item: struct<cell_id: string, run: string, n_episodes: int64>>
                child 0, item: struct<cell_id: string, run: string, n_episodes: int64>
                    child 0, cell_id: string
                    child 1, run: string
                    child 2, n_episodes: int64
              figures: list<item: string>
                child 0, item: string
              bootstrap: struct<samples: int64, seed: int64, clusters: string>
                child 0, samples: int64
                child 1, seed: int64
                child 2, clusters: string
              to
              {'campaign': Value('string'), 'cells': List({'cell_id': Value('string'), 'run': Value('string'), 'n_episodes': Value('int64')}), 'seeded_offer_manifest': Value('string'), 'bootstrap': {'samples': Value('int64'), 'seed': Value('int64'), 'clusters': Value('string')}, 'headline': List({'arm': Value('string'), 'n_episodes': Value('int64'), 'n_deals': Value('int64'), 'logical_table': Value('string'), 'seeded_offer': Value('string'), 'protocol_variant': Value('string'), 'immediate_accept': {'estimate': Value('float64'), 'ci_low': Value('float64'), 'ci_high': Value('float64'), 'n': Value('int64'), 'n_instances': Value('int64')}, 'deal_rate': {'estimate': Value('float64'), 'ci_low': Value('float64'), 'ci_high': Value('float64'), 'n': Value('int64'), 'n_instances': Value('int64')}, 'final_is_seeded': {'estimate': Value('float64'), 'ci_low': Value('float64'), 'ci_high': Value('float64'), 'n': Value('int64'), 'n_instances': Value('int64')}, 'normalized_primary': {'estimate': Value('float64'), 'ci_low': Value('float64'), 'ci_high': Value('float64'), 'n': Value('int64'), 'n_instances': Value('int64')}, 'seed_accept_fraction': {'estimate': Value('float64'), 'ci_low': Value('float64'), 'ci_high': Value('float64'), 'n': Value('int64'), 'n_instances': Value('int64')}}), 'basket': List({'arm': Value('string'), 'n_episodes': Value('int64'), 'n_deals': Value('int64'), 'logical_table': Value('string'), 'seeded_offer': Value('string'), 'protocol_variant': Value('string'), 'below_threshold_accept':
              ...
              at64'), 'ci_low': Value('float64'), 'ci_high': Value('float64'), 'n': Value('int64'), 'n_instances': Value('int64')}}), 'contrasts': List({'treatment': Value('string'), 'reference': Value('string'), 'immediate_accept': {'estimate': Value('float64'), 'ci_low': Value('float64'), 'ci_high': Value('float64'), 'n': Value('int64'), 'n_instances': Value('int64')}, 'deal_rate': {'estimate': Value('float64'), 'ci_low': Value('float64'), 'ci_high': Value('float64'), 'n': Value('int64'), 'n_instances': Value('int64')}, 'final_is_seeded': {'estimate': Value('float64'), 'ci_low': Value('float64'), 'ci_high': Value('float64'), 'n': Value('int64'), 'n_instances': Value('int64')}, 'normalized_primary': {'estimate': Value('float64'), 'ci_low': Value('float64'), 'ci_high': Value('float64'), 'n': Value('int64'), 'n_instances': Value('int64')}, 'seed_accept_fraction': {'estimate': Value('float64'), 'ci_low': Value('float64'), 'ci_high': Value('float64'), 'n': Value('int64'), 'n_instances': Value('int64')}}), 'vote_curve': List({'arm': Value('string'), 'z_low': Value('float64'), 'z_high': Value('float64'), 'n_seat_turns': Value('int64'), 'first_move_accepts_seed': {'estimate': Value('float64'), 'ci_low': Value('float64'), 'ci_high': Value('float64'), 'n': Value('int64'), 'n_instances': Value('int64')}, 'ever_accepts_seed': {'estimate': Value('float64'), 'ci_low': Value('float64'), 'ci_high': Value('float64'), 'n': Value('int64'), 'n_instances': Value('int64')}}), 'figures': List(Value('string'))}
              because column names don't match

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2026.RA.Seeded-Optimal-Opening — does anyone sign the best deal when it is already on the table?

Episode-level data for the seeded-optimal-opening experiment in the rational-agents programme: five-party scorable negotiation under private information, where a neutral non-voting facilitator tables one complete package before any seat speaks, and the only thing left for the table to do is judge it.

Two seeded deals are crossed with two protocols:

ceiling — the feasible deal maximizing the sum of normalized surpluses z_i = (u_i − τ_i) / c_i (c_i = seat i's best surplus over the deal space). This is exactly the deal the campaign's normalized score calls 1.0, so "the optimum was on the table" is true by construction, not by assertion.
placebo — a mediocre-but-signable deal drawn uniformly from the all-individually-rational set excluding the ceiling and the top decile by Σ z. The control that separates recognizing a good deal from accepting whatever is standing.
V-vote — one forced up/down vote on the tabled package by every seat, then done.
V-continue — the package stands while the ordinary rotating-proposer game runs to its deadline.

Every episode is on the same frozen 24-game bank (instances_five_seat_private_v1, shipped in bank/) with seeds 0–4, private score sheets, datacenter framing, and unanimity (min_accept 5). Any LLM seat is anthropic:claude-opus-5 with explicit thinking; the model-free lineups (all_rational = private-information Bayesian seats, all_oracle = omniscient best-response seats) contain no model text and cost nothing to reproduce.

Headline

Immediate acceptance = every seat's first move of the episode is an ACCEPT of the facilitator's package. Intervals are 95% cluster bootstraps over the 24 parameter sets with each set's five seeds resampled together. Rows with seeded = none are the frozen unseeded arms of the parent campaign, carried for reference.

arm	lineup	seeded	variant	episodes	immediate acceptance	deal rate	P(final == seeded)	normalized score
`all_llm__ceiling_vote`	all_llm	ceiling	vote	120	1.000 [1.000, 1.000]	1.000 [1.000, 1.000]	1.000 [1.000, 1.000]	1.000 [1.000, 1.000]
`all_llm__placebo_vote`	all_llm	placebo	vote	120	1.000 [1.000, 1.000]	1.000 [1.000, 1.000]	1.000 [1.000, 1.000]	0.678 [0.640, 0.713]
`all_oracle__ceiling_continue`	all_oracle	ceiling	continue	120	0.325 [0.217, 0.433]	0.783 [0.700, 0.867]	0.325 [0.217, 0.433]	0.716 [0.638, 0.792]
`all_oracle__ceiling_continue_ballot_fixed`	all_oracle	ceiling	continue	120	0.325 [0.217, 0.433]	1.000 [1.000, 1.000]	0.450 [0.325, 0.575]	0.924 [0.901, 0.946]
`all_oracle__ceiling_vote`	all_oracle	ceiling	vote	120	1.000 [1.000, 1.000]	1.000 [1.000, 1.000]	1.000 [1.000, 1.000]	1.000 [1.000, 1.000]
`all_oracle__placebo_continue`	all_oracle	placebo	continue	120	0.075 [0.033, 0.117]	0.700 [0.567, 0.825]	0.075 [0.033, 0.117]	0.628 [0.506, 0.740]
`all_oracle__placebo_continue_ballot_fixed`	all_oracle	placebo	continue	120	0.075 [0.033, 0.117]	1.000 [1.000, 1.000]	0.167 [0.075, 0.275]	0.877 [0.842, 0.907]
`all_oracle__placebo_vote`	all_oracle	placebo	vote	120	1.000 [1.000, 1.000]	1.000 [1.000, 1.000]	1.000 [1.000, 1.000]	0.678 [0.640, 0.713]
`all_rational__ceiling_continue`	all_rational	ceiling	continue	120	0.000 [0.000, 0.000]	0.233 [0.117, 0.367]	0.025 [0.000, 0.075]	0.177 [0.089, 0.279]
`all_rational__ceiling_vote`	all_rational	ceiling	vote	120	1.000 [1.000, 1.000]	1.000 [1.000, 1.000]	1.000 [1.000, 1.000]	1.000 [1.000, 1.000]
`all_rational__placebo_continue`	all_rational	placebo	continue	120	0.000 [0.000, 0.000]	0.258 [0.142, 0.384]	0.008 [0.000, 0.025]	0.201 [0.115, 0.297]
`all_rational__placebo_vote`	all_rational	placebo	vote	120	1.000 [1.000, 1.000]	1.000 [1.000, 1.000]	1.000 [1.000, 1.000]	0.678 [0.640, 0.713]
`all_llm__unseeded`	all_llm	none	continue	120	0.000 [0.000, 0.000]	0.958 [0.925, 0.992]	0.000 [0.000, 0.000]	0.873 [0.833, 0.911]
`all_oracle__unseeded`	all_oracle	none	continue	120	0.000 [0.000, 0.000]	0.875 [0.817, 0.925]	0.000 [0.000, 0.000]	0.791 [0.735, 0.843]
`all_oracle__unseeded_ballot_fixed`	all_oracle	none	continue	120	0.000 [0.000, 0.000]	1.000 [1.000, 1.000]	0.000 [0.000, 0.000]	0.907 [0.880, 0.933]
`all_rational__unseeded`	all_rational	none	continue	120	0.000 [0.000, 0.000]	0.233 [0.133, 0.350]	0.000 [0.000, 0.000]	0.189 [0.110, 0.279]
`one_oracle__unseeded`	one_oracle	none	continue	120	0.000 [0.000, 0.000]	0.508 [0.417, 0.600]	0.000 [0.000, 0.000]	0.461 [0.376, 0.545]
`one_rational__unseeded`	one_rational	none	continue	120	0.000 [0.000, 0.000]	0.767 [0.683, 0.842]	0.000 [0.000, 0.000]	0.686 [0.611, 0.761]

Rows with seeded = none are the frozen unseeded arms of the parent campaign, carried so the seeded arms have a control on identical games. Their episodes are not shipped here — they live in the parent dataset linked at the bottom — and their acceptance columns are zero by construction because nothing was tabled.

Full tables, paired contrasts, the fairness basket and the per-seat vote-versus-share curve are in analysis/results.md and analysis/summary.json. The narrative writeup is research note 0045; the published hub is here.

Known defect in the `all_oracle` arms — read before using their closure columns

The omniscient seat casts its forced-final vote on whichever live offer it values most rather than on the one offer actually under the up/down vote. The protocol rejects that as a legality error, the seat repeats itself on its single retry, and the turn is recorded as an abstention. It affects only the forced final vote, so first-move acceptance is unaffected, but deal rate and everything conditional on closing are measured on an agent that sometimes fails to cast a vote it intended to cast. This defect pre-dates this experiment and is present in the parent campaign (94 of 107 forced-final turns in its all_oracle arm), which is why the arms here deliberately run the agent as published — changing it mid-experiment would have made the seeded arms incomparable with their own control. The per-turn evidence is in every episode record: look for parsed_action.syntax_error containing "The final vote is only on".

What is in here

path	contents
`episodes/<cell_id>.jsonl`	one record per episode: the complete stored episode (every turn, its parsed action, the view the seat was conditioned on, the outcome) plus the cell's design coordinates
`tables/episode_rows.csv`	one row per episode with every analysis column
`tables/seat_votes.csv`	one row per seat decision, carrying that seat's own normalized share `z` of the tabled deal
`analysis/`	`summary.json` (every estimate and interval), `results.md`, the figure
`seeded_offer_manifest.json`	the frozen sidecar: which package each `(instance, seed)` was shown, and its geometry
`bank/`	the frozen 24-instance bank, byte-identical to the one the campaign ran on
`campaign_manifest.json`	the campaign's own record: cells, attempts, spend ledger, reproducibility fingerprint

Every table and every JSONL record carries an experiment-name column (seeded-optimal-opening-v1) so a later campaign with the same columns can be appended to this dataset rather than forking a new one.

Cells

cell	lineup	seeded	variant	episodes	run
`all_rational__ceiling_vote`	all_rational	ceiling	vote	120	`fiveseed_opus_v2__all_rational__ceiling_vote`
`all_rational__ceiling_continue`	all_rational	ceiling	continue	120	`fiveseed_opus_v2__all_rational__ceiling_continue`
`all_rational__placebo_vote`	all_rational	placebo	vote	120	`fiveseed_opus_v2__all_rational__placebo_vote`
`all_rational__placebo_continue`	all_rational	placebo	continue	120	`fiveseed_opus_v2__all_rational__placebo_continue`
`all_oracle__ceiling_vote`	all_oracle	ceiling	vote	120	`fiveseed_opus_v2__all_oracle__ceiling_vote`
`all_oracle__ceiling_continue`	all_oracle	ceiling	continue	120	`fiveseed_opus_v2__all_oracle__ceiling_continue`
`all_oracle__placebo_vote`	all_oracle	placebo	vote	120	`fiveseed_opus_v2__all_oracle__placebo_vote`
`all_oracle__placebo_continue`	all_oracle	placebo	continue	120	`fiveseed_opus_v2__all_oracle__placebo_continue`
`all_oracle__unseeded_ballot_fixed`	all_oracle	none	continue	120	`oraclefix_none_continue`
`all_oracle__ceiling_continue_ballot_fixed`	all_oracle	ceiling	continue	120	`oraclefix_ceiling_continue`
`all_oracle__placebo_continue_ballot_fixed`	all_oracle	placebo	continue	120	`oraclefix_placebo_continue`
`all_llm__ceiling_vote`	all_llm	ceiling	vote	120	`fiveseed_opus_v3__all_llm__ceiling_vote_attempt03`
`all_llm__placebo_vote`	all_llm	placebo	vote	120	`fiveseed_opus_v3__all_llm__placebo_vote_attempt03`

Regenerating it

# 1. the frozen seeded-offer sidecar (ceiling + per-seed placebo deals)
uv run --no-sync python build_seeded_offer_manifest.py \
    --bank instances_five_seat_private_v1 --out seeded_offer_manifest_v1.json

# 2. the campaign (model-free cells first at $0, then the Opus cells under a hard cap)
uv run --no-sync python launch_five_seat_campaign.py \
    --bank instances_five_seat_private_v1 --out five_seat_seeded_offer_v3 --campaign-name fiveseed_opus_v2 \
    --seeded-offer-only --seeded-offer-manifest seeded_offer_manifest_v1.json \
    --budget-usd 350.0 --artifact-root $LARGE_ARTIFACTS_DIR --execute

# 3. gate G3 — re-derive every computable seat's move offline and check the record
uv run --no-sync python gate_seeded_offer_votes.py --run <run dirs>

# 4. analysis
uv run --no-sync python analyze_seeded_offer.py \
    --campaign-manifest five_seat_seeded_offer_v3/campaign_manifest.json \
    --artifact-root $LARGE_ARTIFACTS_DIR --bank instances_five_seat_private_v1 \
    --reference-runs <frozen five-arm snapshot>/runs --out <analysis dir>

# 5. this bundle
uv run --no-sync python package_hf_seeded_offer.py --campaign-manifest ... --out <tree>

All commands run from experiments/rational_agents/ in the project repository.

Provenance

Preregistered design and gates: proposals/2026-08-10-seeded-optimal-opening.md (fixed before any code was written; every deviation is logged in the results note).
Campaign fingerprint: a67629cb8118cf2a78b21e569971fcb5e853ba4ddafd0c5274dc90593b3c8c6d — pins the bank hashes, the code hashes, the seeded-offer sidecar hash, and both repository revisions.
Seeds: [0, 1, 2, 3, 4]; 24 parameter sets; information private; framing datacenter.
Cluster paths for the source runs, transcripts and per-episode visualizations are recorded in packaging_index.json and in the campaign manifest.

Parent campaign dataset: https://huggingface.co/datasets/siddharthmb/2026.RA.Five-Seat-Frontier-Negotiation — the same bank and seats with nothing tabled, which is this experiment's control.

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