Five figures: the latency frontier, the per-type matrix, knapsack, the advantage chart, and cost

README.md

@@ -58,7 +58,7 @@ All nine systems on all 1,710 questions, 90 per decision type.
 | `deepseek-v4.1-flash` | 0.511 | ~4 | ~800 ms |
 | `deepseek-v4-pro` | 0.470 | ~4 | ~900 ms |
 | `this-that-model-1.0` | 0.406 | 0 | 30.9 ms |
-| `laya-typed-decisions` | 0.310 | 0 | **25 ms** |
+| `laya-typed-decisions` | 0.310 | 0 | **28.5 ms** |
 | chance | 0.258 | — | — |
 
 ## By decision type
@@ -87,6 +87,34 @@ All nine systems on all 1,710 questions, 90 per decision type.
 | `but_for_cause` | 0.200 | 1.000 | 1.000 | 0.944 | 1.000 | 0.967 | 0.711 | 0.733 | 0.411 | 0.467 |
 | `value_of_information` | 0.050 | 1.000 | 1.000 | 1.000 | 0.844 | 1.000 | 0.944 | 0.922 | 0.689 | 0.033 |
 
+## One decision type defeats almost everything
+
+![knapsack subset](knapsack.png)
+
+`knapsack_subset` asks for the group of items that fits a budget and maximises value. Greedy
+selection fails on it about half the time, so it cannot be answered by taking the best-looking
+option. Eight of the nine systems land between 0.067 and 0.267 against a chance rate of 0.084. The
+ninth is 1.88 B parameters and returns no tokens.
+
+## Where a small model wins and where it does not
+
+![advantage over the frontier](advantage.png)
+
+Each bar is `this-that-model-1.1` minus the better of `claude-opus-5` and `gpt-5.6` on that decision
+type. Four types go the small model's way, two are a tie at 1.000, and three more are inside a tenth.
+The nine that go the other way are worth as much attention: judging that a state does not contain
+enough to decide (−0.389) and iterating a cascade to its fixed point (−0.300) are where the gap is
+widest.
+
+## What one decision costs
+
+![accuracy against cost](accuracy-vs-cost-11.png)
+
+The horizontal axis is not like for like and the chart says so: a hosted system's figure is its
+published price, which must cover serving and margin; ours is electricity at 80 W and $0.30/kWh for
+the 30.9 ms the card is busy. Read the distance as one order of magnitude rather than five. What it
+does show is that one of these is a rounding error on a machine you already own.
+
 ## What the table says that the ranking does not
 
 **One decision type defeats almost everything.** On `knapsack_subset` — choose the group that fits