Run 1 — knowledge delivery, wave 1

Goal

Wave 1 compares two knowledge-delivery conditions, called rungs, on Sonnet 5 and gpt-5.2. In the control rung, the agent can read the game knowledge from a folder. The pushed rung keeps that folder and also delivers knowledge through the system prompt, search, inline facts, and notes after failed tool calls.

Each model runs once on each rung, creating four model–rung pairs, or cells. Each cell has one arm. This wave is the first test in the broader Knowledge Delivery family, the multi-wave experiment described on the design page; the two selected models define the tier tested here.

One question: does pushing the game's knowledge to a capable model change how it plays? Five stack-validation runs failed to level a single Kami. The design argues that the failure came not because the models were small but because the knowledge sat in a folder nobody read. This wave tests that interpretation by comparing the control and pushed outcomes for each model.

The two rungs

• Control — today's scaffold: the design document as a read-only folder, the agent's remaining ETH shown each session, one sentence that every action costs gas.
• Pushed — control plus: a fixed rules-only paragraph on what the core loop is; a keyword search tool over the design document; item and room facts inline in what the agent perceives; and, on every failed tool call, a short note on why it failed and which action applies now.

Both rungs see the same 101 tools with byte-identical schemas. Nothing tells the agent what to do.

What we're watching

Quests completed is the primary outcome. The other observations help explain how each arm behaved, although a landed level-up also appears in the pre-registered verdict rule.

• Quests completed (decides).
• Any level-up, and how many sessions it took.
• Kamis owned over time, and ETH spent buying them.
• Experience left unspent, MUSU banked, gas per quest, survival.
• On the pushed arms: does the agent search, and act on what it finds; after an error note, is the next call the right one.

Pre-registered verdict: the pushed rung works if both models land at least one level-up and match or beat their own control on quests. Both moving → wave 2 bisects. Neither → "delivery is not the bottleneck at this tier." One → suggestive; wave 2 on that model.

The arrows specify the next step for each possible result: both models moving, neither model moving, or one model moving. In that order, the family proceeds to a Wave 2 bisection, stops with the stated conclusion for this model tier, or continues Wave 2 on the one model as suggestive evidence.

Launch note

The wave registered with gpt-5.4 for the two OpenAI cells: one control arm and one pushed arm. When those arms reached their first model call, the provider rejected the combination of reasoning and tool use on the endpoint used by the pinned adapter.

That incompatibility voided both gpt-5.4 arms at zero model turns. The Sonnet 5 arms were unaffected.

The two invalid OpenAI arms were replaced the same evening with gpt-5.2 arms using reasoning medium. The replacement model is price- and capability-matched and reasons with the full tool surface. The replacement arms reused the same untouched wallets and kept the same pins.

The resulting wave again has four arms: control and pushed for Sonnet 5, and control and pushed for gpt-5.2.

Cohort identifiers (wallets, accounts) are embargoed until close-out.