What changed in v3: the language model's contribution is reported as position sizing, not directional
corroboration. v2 led with corroboration and treated sizing as a second axis. Testing both channels on data
published after the evaluation window closed reverses that ordering.
Live scoreboard · the two channels scored forward since 2026-08-06
v2 as traded
…
always-INC × σ^1.5
…
v3 · 5-voter ensemble
…
confidence effect
…
weights only
direction effect
…
direction only
v2 as tradedalways-INC × σ^1.5v3 · mean-vote × confidence
Loading the live ledger…
Leaderboard · every direction rule, same universe and weights
…
Each row swaps only the direction. Universe, daily budget and
the σ^1.5 × confidence weights are identical, so the column is a clean read on direction alone.
bandit* is the Thompson bandit rebuilt around a Normal posterior over the spread rather than a
Beta over a win rate: same arms, but it learns the expected payoff instead of which side wins more often.
That one change moves it from −$2,519 to −$204.
The voter set was chosen on redundancy, not on this column. Over 91 days v3's daily
standard deviation is $243, so a 91-day total carries a standard deviation of $2,317, and every
drop-or-add variant tried spanned about $1,500, which is 0.65σ. What separates the priors is
how much they agree: bandit* matches mean14 93% of the time, mean10 92% and mean7 89%, because a
recency-weighted posterior mean is a trailing mean. Voting four near-duplicates is one voter
with extra confidence. So mean10 was dropped as a 92% duplicate, and persistence added as the only
prior that genuinely disagrees with the rest, at 56–66% agreement, despite being weak alone.
Day by day
date
v2
prior
v3
cum v2
cum prior
cum v3
…
Every settled day since the live deployment began. Daily P&L on the left,
running total on the right.
Where the result moves
The vote itself is untouched. What changes is what the voters are trying to be right
about. v2's three voters all predict a sign and are graded on how often they are right, and on this record
the most accurate rule is also the one that loses the most money, because the book is paid in dollars, not
in hit rate. v3 keeps the LLM and the bandit but has each statistical voter estimate the expected spread
instead, and lets the LLM's confidence set position weight, which is the one thing it measurably does
well. bandit* is the same two arms rebuilt around a Normal posterior over the spread instead of a Beta
over a win rate.
Channel 1 · corroboration
in sample
out of sample
direction margin
+$871
+$18
longs retained
1,351
14
Not a mis-specified veto. The 415 rejected proposals realize a positive-spread rate
of 0.443 and would have lost $421 if held. The screen works; there is no directional edge left to keep.
Channel 2 · confidence allocation
in sample
out of sample
effect on always-INC
+$5,769
+$964
per MWh
+$0.092
+$0.467
Concentrated where it matters: −$427 over Jun 3 – Jul 31, then +$1,391
over Aug 1 – Sep 6, the stretch in which the premium inverted and the unweighted book lost $7,390.
The benchmark is one episode
always-INC, 63,148 hub-hours
P&L
share
full evaluation window
$108,553
100%
January 2026 alone
$79,229
73.0%
top five days
$107,131
98.7%
excluding January
$29,323
27.0%
Every margin in the paper, ours included, is measured against a level that a handful of
days determines. v3 states this in the abstract rather than leaving it implicit.
After the window closed · 2026-04-23 to 2026-08-25
strategy
evaluation window
after
always-INC
+76,147
−2,150
always-INC × σ^1.5
+56,455
−3,159
supervised LR
+74,567
−1,990
Thompson TS
+68,390
−1,879
BTZ-DPDS
+62,603
+3,255
The forward premium inverts, volatility weighting makes the loss larger rather than
smaller, and both statistical corroborators turn negative. Only DPDS, which sizes its own capital, survives.
Out-of-sample LLM figures use the production deployment's logged directions and confidence at HB_NORTH,
2,064 hub-hours over 2026-06-03 to 2026-09-06. That deployment uses a larger model and a different prompt
than the archived runs, and covers one hub and one run, so it is a proxy for the evaluated agent rather
than that agent.