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The dataset viewer is not available for this split.

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

Error code:   StreamingRowsError
Exception:    TypeError
Message:      Couldn't cast array of type
struct<n: int64, injected_flood_rate: double, rational_norm_surplus: list<item: double>, injected_norm_surplus: list<item: double>, oracle_norm_surplus: list<item: double>, gap_oracle_minus_rational: list<item: double>, gap_injected_minus_rational: list<item: double>, err_rational_bid: double, err_injected_bid: double>
to
{'n': Value('int64'), 'rational_norm_surplus': List(Value('float64')), 'injected_norm_surplus': List(Value('float64')), 'oracle_norm_surplus': List(Value('float64')), 'gap_oracle_minus_rational': List(Value('float64')), 'gap_injected_minus_rational': List(Value('float64')), 'err_rational_bid': Value('float64'), 'err_injected_bid': Value('float64')}
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 483, 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 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2312, in cast_table_to_schema
                  cast_array_to_feature(
                  ~~~~~~~~~~~~~~~~~~~~~^
                      table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                      feature,
                      ^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                                           ~~~~^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2068, in cast_array_to_feature
                  _c(array.field(name) if name in array_fields else null_array, subfeature)
                  ~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
                  return func(array, *args, **kwargs)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2158, in cast_array_to_feature
                  raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
              TypeError: Couldn't cast array of type
              struct<n: int64, injected_flood_rate: double, rational_norm_surplus: list<item: double>, injected_norm_surplus: list<item: double>, oracle_norm_surplus: list<item: double>, gap_oracle_minus_rational: list<item: double>, gap_injected_minus_rational: list<item: double>, err_rational_bid: double, err_injected_bid: double>
              to
              {'n': Value('int64'), 'rational_norm_surplus': List(Value('float64')), 'injected_norm_surplus': List(Value('float64')), 'oracle_norm_surplus': List(Value('float64')), 'gap_oracle_minus_rational': List(Value('float64')), 'gap_injected_minus_rational': List(Value('float64')), 'err_rational_bid': Value('float64'), 'err_injected_bid': Value('float64')}

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2026.RA.Auction-ValuationPosterior-KVPrefix

A learned per-layer K/V "valuation-posterior" prefix injected into an open-weight auction bidder (Qwen3-8B), proposed as a weights-level dial between the RATIONAL arm (private+public information) and the ORACLE arm (full realized information). This dataset holds the behavioral-eval bids (all arms) and trained-encoder checkpoints from the design-#2 lane of the Q/K/V auction program (rational_agents).

Headline result (research note 0072): the channel delivers cleanly but the dial does not move surplus. The value of omniscience is mechanism-dependent — oracle−rational normalized surplus is 0.000 in sealed 2nd-price and +0.070 in first-price/Dutch — but the injected public-facts prefix yields injected−rational ≈ 0 in both mechanisms (flood-rate 0.000), even though injected bids move 63% closer to the oracle bid in L1 (first-price bid-error 51.2→18.9). The rational baseline already conditions on the public facts, so a public-facts posterior injects information the agent already has; the oracle's edge is the realized rival valuations (private, capped at 0.545 nats by public facts). The omniscience gap is private-information-shaped, so a public-facts prefix cannot carry it.

What's here

data/bids.csv — one row per (held instance, stage, focal seat, mechanism), with the focal bid + realized surplus under three arms: rational (Qwen3-8B, no prefix), injected (Qwen3-8B + encoder K/V prefix), oracle (computed full-information bid). Raw model bid text in rational_raw/injected_raw. injected_flood=True marks an encoder degeneracy (bid > 2× budget), excluded from scored gaps.
runs/{val2,val3}/results.json — per-mechanism summary (normalized-surplus means ±SE, oracle−rational and injected−rational gaps, bid-reconstruction error, injected_flood_rate).
runs/val3/encoder.pt — the reported KVPrefixEncoder checkpoint (free parameterization, 3 layers [0,12,24], n_prefix=8, ~7M params).
code/auction_val_prefix.py, code/val_prefix_smoke.py — the full-run trainer/evaluator and the feasibility-gate smoke.

experiment-name mapping

experiment-name	description
`val3`	Reported run. Encoder: 3 layers, lr 0.004, weight-decay 5e-3, batch 8, 1600 steps, terminator `}` in reconstruction target. Flood-rate 0.000 both mechanisms. 400 held rows evaluated.
`val2`	Earlier pass (4 layers, no weight-decay/terminator tuning) with partial digit-flooding (sealed2 flood in bid-error ~1e4); kept to show the flood→clean progression (injected−rational stable at ≈0 across both).

(A confirmatory 1920-row eval full1 was ~53% complete when the shared GPU box was terminated; not included.)

Regenerate

Model Qwen/Qwen3-8B, one H100. From experiments/rational_agents/ with interlens installed and PYTHONPATH including that dir:

# train encoder + evaluate (the reported run)
python tom/qkv/auction_val_prefix.py --bank auction/banks/auction_single_v1 \
  --out <outdir> --steps 1600 --lr 0.004 --n-layers 3 --batch 8 --eval-cap 400

# eval-only, reusing a trained checkpoint over all held rows
python tom/qkv/auction_val_prefix.py --bank auction/banks/auction_single_v1 \
  --out <outdir> --load-encoder <outdir>/encoder.pt --n-layers 3 --n-prefix 8

# feasibility gates (injection + knob-turn)
python tom/qkv/val_prefix_smoke.py --bank auction/banks/auction_single_v1 --gates 2 4

Cluster paths / provenance

Artifacts: /nlp/scr/siddharth/ii_mats/qkv_val_prefix/{val2,val3}/ and .../logs/ (Stanford NLP).
Research note: experiments/rational_agents/research-notes/0072-valuation-posterior-kv-prefix.md.
Channel code: experiments/rational_agents/tom/qkv/kv_prefix.py (PrefixKVInjector, KVPrefixEncoder); mechanism origin proposals/2026-08-03-qkv-rational-attention.md.
No W&B run (interactive GPU eval).
Generating model: Qwen/Qwen3-8B.

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