Instance is live. Key facts: N=15, tokens=80, fresh game, G=2, H=g^s mod p. The title "Ровный" (smooth/even) hints at a smooth prime — let me factor p-1.

p−1 is fully smooth (2 × 13 primes of ~20 bits) — Pohlig–Hellman recovers the oracle's secret s trivially. Then we can compute every dice roll (present and future) ourselves. Let me recover s:

s recovered and verified. Now I can compute any future dice roll. Next: replicate the oracle bot exactly (model weights + engine) to simulate games before committing. Let me parse model.fly:

The model file layout is unclear — rather than guess float formats, I'll compile the handout's own Rust crate to get a bit-exact oracle policy. Let me check the Cargo setup:

No ASCII keys — need burn 0.21's exact CompactRecorder format. Let me fetch its source:

The stats (31% zero / 31% denormal / 38% normal, uniformly) smell like a periodic byte pattern. Let me check for structural periodicity:

Let me test decodings systematically — I'll port the feature encoder + forward pass and score candidate decodings against sanity benchmarks (opening ≈ 0.5 win-prob, near-win ≈ 1.0):

The denormals are likely decay-killed weights of rarely-active input features (unary slots that are almost always 0) — plain f32 may be right after all. Let me sweep layouts using a strong signal: outputs must vary across random-play positions and correlate with race progress:

tr=True (weights stored [in,out]) stands out. Let me refine — per-layer transpose combos + a cleaner benchmark (mover with k checkers off vs untouched opponent):

I need certainty on the format — installing Rust to test-load model.fly with burn 0.21's actual recorders:

All burn formats ruled out — it's a custom dump. Let me fix the sweep crash and complete the grid, plus check u16 pair relations:

Let me re-read train.rs carefully — it may reveal how weights were produced/dumped:

The zero-density spike in the l2 region suggests a real trained net with dead weights. Let me build a mirror-symmetry test (a trained evaluator must satisfy r0(board) + r0(flip(board)) ≈ 1) across all layouts:

Time is limited (instance expires soon). New approach: use the blockchain itself as ground truth — the bot's observed moves will identify the correct model decode. Let me build the full player framework:

Now the bot-replica (exact port of choose_2ply) and the observation-based decode tester:

Dice predictions confirmed live — turn 14 predicted (5,4) = actual (5,4). The sacrifice game is mid-flight; let me resume it to collect more observations, then run decode tests:

No matches — but I spotted a bug in my recorder: I recorded the bot's input board as flip(pre-my-move) instead of the board during the bot's turn. Let me fix and also sanity-check that observed ends are reachable per my engine:

My recorder captured the wrong board (pre-my-move instead of during-bot-turn). The on-chain boardPacked() read while it's the bot's turn is already the bot's perspective. Fixing the collector:

RPC is heavily rate-limited (~2.5s/read → a full state() costs 23s). I need batched JSON-RPC — one HTTP round trip per state read:

Batched reads: 0.82s. Now collect observations properly:

Rather than burn tokens, let me extract structural evidence: in a row-major weight matrix, dead hidden units create contiguous runs of near-zero weights whose length reveals the orientation (runs≈203 → [out,in], runs≈80 → [in,out]):

What if it's a byte-plane dump (all 1st bytes, then all 2nd bytes, ...)? Testing plane permutations:

Let me collect clean observations efficiently (deriving the bot's input board offline from my own move):

Engine verified (6/6 reachable). Testing decodes against clean observations:

Two decodes replicate the bot 6/6: W1=[in,out], W3=[in,out], W2 either orientation (square). Let me quantify whether the W2 ambiguity even matters, and check chain state:

Let me check chain state and build the winner engine — policy: my 1-ply greedy with known future dice vs exact bot replica, full-game simulation before committing, skip otherwise:

Simulation works — 3/4 wins with greedy play. With skip-filtering (only commit winning sims), nearly every committed game should win. Let me check win margins, then write the live winner:

Found a critical bug in my simulator: after each move I forgot to flip() the board for the next player's perspective. Fixing and re-measuring:

Realistic now: 6/8 wins, games ~24-34 plies. Time to write the live winner loop: