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TASK. Write, from scratch, a policy network that plays 9 x 9 Go as strongly as possible. All of its weights start at zero; you set them. The goal is to win as many games as possible against the opponents described under HOW IT WILL BE MEASURED. THE GAME: Go on a 9 x 9 board (the pgx implementation, "go_9x9"). Rows are numbered 0 (top) to 8 (bottom), columns 0 (left) to 8 (right). Black moves first; players alternate. A move either places a stone of the mover's colour on an empty point or passes. A group of stones with no adjacent empty point is captured and removed. Suicide is illegal, and so is retaking a single stone immediately (simple ko). A move that repeats an earlier whole-board position ends the game at once, and the player who made it loses. The game ends after two consecutive passes, or after 162 moves in total. Then it is scored by area: each player's stones on the board plus the empty points surrounded only by that player's stones; White receives a komi of 7.5 points. The higher score wins (no draws). THE NETWORK. Input: 243 numbers, each 0 or 1: three planes over the 81 points, from the point of view of the player to move; input number (row * 9 + column) * 3 + plane, where plane 0 is 1 where the player to move has a stone, plane 1 is 1 where the opponent has a stone, and plane 2 is 1 everywhere when White is to move (0 when Black is). Output: 82 logits: output row * 9 + column = play at that point, output 81 = pass. h_1 = relu(x @ L1.W + L1.b) (512 units) h_k = relu(h_(k-1) @ Lk.W + Lk.b) for k = 2..6 (512 units each) logits = h_6 @ out.W + out.b (82) Tensors: L1.W (243 x 512), L1.b (512), L2.W .. L6.W (512 x 512), L2.b .. L6.b (512), out.W (512 x 82), out.b (82); 1,480,274 numbers. Row i of a weight matrix belongs to input (or unit) i, column j to unit (or output) j. Only the values can be set; the architecture is fixed. Illegal moves (occupied points, suicide, simple ko) are removed before every move, and the move is sampled from the softmax of the remaining logits (temperature 1). There is no search: the network's output is the whole policy. HOW IT WILL BE MEASURED. The original hidden opponent checkpoints are unavailable. After you finish, your network plays 2,000 games against each of two held-out released same-size PPO policy networks (seeds 0 and 2), and a uniform random legal player. Your network is Black in half of the games; the moves of both players are sampled from their policies; there is no search. Score: wins / games; the main measure is the average over the two trained opponents. No historical win rate against random is claimed for these substitutes. You never see these opponents. WHAT YOU HAVE. 1. A Python sandbox (no internet, no GPU, no Go library) with a working folder /work that is kept between runs. Available: numpy, scipy, scikit-learn, PyTorch (CPU), pandas, sympy, networkx. Before your code runs, these are defined: SHAPES and NAMES (the tensors), load(name) (a float32 numpy array: the values you saved, zeros at the start), save(name, array) (stores a tensor of the same shape), saved() (the names saved so far), inp(row, col, plane) (the input index), mv(row, col) (the output index) and PASS = 81. A run may take up to 900 seconds and 96 GB; its printed output (cut at 20,000 characters) is sent back to you. To run code, reply with exactly one block that starts with ```python and nothing else. You may run code up to 120 times. 2. No pass tools (budget 0): your network cannot be played or run before the evaluation. Work from your design alone. At the end, the tensors you saved with save() are your network; tensors never saved stay zero. RULES. Not allowed: (a) gradient descent or any other training: no backward passes, autograd or optimizers, and no fitting of weights to data or labels (no regression or least squares on examples); (b) search: no game-tree search (MCTS, minimax, rollouts, looking ahead over sequences of moves), neither to choose moves nor to produce target moves or values for setting weights; (c) running the network outside the pass tools: do not compute your network's output or activations on any input in the sandbox; (d) internet access (there is none). Allowed: writing the weights by code from your own design (formulas, loops, symmetries, linear algebra on the weights themselves), Go-rule code in the sandbox (for example to work out the liberties of groups on boards you choose) as long as it does not run the network or search, and any analysis of the pass results. HOW TO FINISH. When your network is saved, reply WITHOUT a code block: describe its design and why you expect it to win.
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```python
import numpy as np
# Analytically constructed weights only: no training, search, or forward passes.
P = 81
WIDTH = 512
points = [(r, c) for r in range(9) for c in range(9)]
O = np.arange(P)
E = O + 81
A = O + 162
B = O + 243
C = O + 324
D = O + 405
WHITE, TOTAL, EARLY, LATE, DEFICIT = 486, 487, 488, 489, 490
globals_used = [WHITE, TOTAL, EARLY, LATE, DEFICIT]
ix_own = np.array([inp(r, c, 0) for r, c in points])
ix_opp = np.array([inp(r, c, 1) for r, c in points])
ix_white = inp(0, 0, 2)
def neighbors(offsets):
result = []
for r, c in points:
result.append(np.array([
(r + dr) * 9 + c + dc
for dr, dc in offsets
if 0 <= r + dr < 9 and 0 <= c + dc < 9
], dtype=np.int64))
return result
adj = neighbors([(-1, 0), (1, 0), (0, -1), (0, 1)])
diag = neighbors([(-1, -1), (-1, 1), (1, -1), (1, 1)])
two = neighbors([(-2, 0), (2, 0), (0, -2), (0, 2)])
knight = neighbors([
(-2, -1), (-2, 1), (2, -1), (2, 1),
(-1, -2), (-1, 2), (1, -2), (1, 2)
])
params = {}
for layer in range(1, 7):
params[f"L{layer}.W"] = np.zeros(
(243 if layer == 1 else WIDTH, WIDTH), dtype=np.float32
)
params[f"L{layer}.b"] = np.zeros(WIDTH, dtype=np.float32)
params["out.W"] = np.zeros((WIDTH, 82), dtype=np.float32)
params["out.b"] = np.zeros(82, dtype=np.float32)
def add(W, dest, sources, coefficient=1.0):
sources = np.asarray(sources, dtype=np.int64).reshape(-1)
np.add.at(W[:, int(dest)], sources, np.asarray(coefficient, np.float32))
def relay(W, indices):
indices = np.asarray(indices, dtype=np.int64)
W[indices, indices] += 1.0
coords = np.asarray(points, dtype=np.float64)
dist2 = np.sum((coords[:, None, :] - coords[None, :, :]) ** 2, axis=2)
# Static spatial kernels, not evaluations on board inputs.
influence = np.exp(-np.sqrt(dist2) / 1.55)
reference_mass = influence[40].sum()
influence *= (reference_mass / influence.sum(axis=1))[:, None] ** 0.60
territory_kernel = np.exp(-dist2 / (2.0 * 1.60 ** 2))
territory_kernel /= territory_kernel.sum(axis=1, keepdims=True)
# ----------------------------------------------------------------------
# L1: occupancy, exact per-stone immediate liberties, diagonal information,
# opening/endgame clocks, and coarse regional ownership features.
# ----------------------------------------------------------------------
W, b = params["L1.W"], params["L1.b"]
for p in range(P):
n, d = adj[p], diag[p]
degree = len(n)
add(W, O[p], ix_own[p])
add(W, E[p], ix_opp[p])
# relu(local_empty_neighbors - 4 * (1 - own/opp_at_p)).
for destination, center in [(A[p], ix_own[p]), (B[p], ix_opp[p])]:
b[destination] = degree - 4
add(W, destination, center, 4.0)
add(W, destination, ix_own[n], -1.0)
add(W, destination, ix_opp[n], -1.0)
# Exactly one iff every existing diagonal is occupied.
b[C[p]] = 1 - len(d)
add(W, C[p], ix_own[d])
add(W, C[p], ix_opp[d])
add(W, WHITE, ix_white)
add(W, TOTAL, ix_own)
add(W, TOTAL, ix_opp)
b[EARLY] = 1.0
add(W, EARLY, ix_own, -1.0 / 32.0)
add(W, EARLY, ix_opp, -1.0 / 32.0)
b[LATE] = -45.0
add(W, LATE, ix_own)
add(W, LATE, ix_opp)
regions = []
for r in (1, 4, 7):
for c in (1, 4, 7):
center = np.array([r, c], dtype=np.float64)
weights = 0.30 * np.exp(
-np.sum((coords - center) ** 2, axis=1) / (2.0 * 1.80 ** 2)
)
t = len(regions)
lo, hi = 490 + 2 * t, 491 + 2 * t
regions.append((lo, hi))
for destination, bias in [(lo, 1.0), (hi, -1.0)]:
b[destination] = bias
add(W, destination, ix_own, weights)
add(W, destination, ix_opp, -weights)
# ----------------------------------------------------------------------
# L2: conservative certificates that a connected string has >=2 liberties.
# These certificates cannot mark a genuine one-liberty string as safe.
# ----------------------------------------------------------------------
W, b = params["L2.W"], params["L2.b"]
relay(W, O)
relay(W, E)
relay(W, [WHITE, TOTAL, EARLY, LATE])
for p in range(P):
n = adj[p]
for occupancy, liberties, seed_a, seed_b in [
(O, A, A, C),
(E, B, B, D),
]:
# Two local liberties, or a local liberty plus a liberty on an
# adjacent friendly stone. Adjacent points have disjoint neighbors.
b[seed_a[p]] = -16.0
add(W, seed_a[p], liberties[p], 16.0)
add(W, seed_a[p], liberties[n])
# Three neighboring liberty incidences imply >=2 distinct liberties.
# Two suffice when no empty diagonal can be counted twice.
b[seed_b[p]] = -18.0
add(W, seed_b[p], occupancy[p], 16.0)
add(W, seed_b[p], liberties[n])
add(W, seed_b[p], C[p])
# Coarse area/stone deficit, with komi, used only to discourage losing passes.
regional_fraction = 0.65
b[DEFICIT] = regional_fraction * 81.0 + 7.5
for lo, hi in regions:
add(W, DEFICIT, lo, -9.0 * regional_fraction)
add(W, DEFICIT, hi, 9.0 * regional_fraction)
add(W, DEFICIT, O, -(1.0 - regional_fraction))
add(W, DEFICIT, E, +(1.0 - regional_fraction))
add(W, DEFICIT, WHITE, -15.0)
# ----------------------------------------------------------------------
# L3: propagate safety once; construct signed spatial influence.
# Unsafe flags remain binary.
# ----------------------------------------------------------------------
W, b = params["L3.W"], params["L3.b"]
relay(W, O)
relay(W, E)
relay(W, globals_used)
for p in range(P):
neighborhood = np.concatenate(([p], adj[p]))
add(W, A[p], O[p])
add(W, A[p], A[neighborhood], -1.0)
add(W, A[p], C[neighborhood], -1.0)
add(W, B[p], E[p])
add(W, B[p], B[neighborhood], -1.0)
add(W, B[p], D[neighborhood], -1.0)
add(W, C[p], O, influence[p])
add(W, C[p], E, -influence[p])
add(W, D[p], O, -influence[p])
add(W, D[p], E, influence[p])
# ----------------------------------------------------------------------
# L4: propagate safety again; estimate empty, unsettled territory and
# distinguish true eyes from eyes spoiled by enemy diagonals.
# ----------------------------------------------------------------------
W, b = params["L4.W"], params["L4.b"]
relay(W, O)
relay(W, E)
relay(W, globals_used)
TERRITORY_THRESHOLD = 1.35
for p, (r, c) in enumerate(points):
n = adj[p]
for occupancy, unsafe in [(O, A), (E, B)]:
add(W, unsafe[p], unsafe[p])
add(W, unsafe[p], unsafe[n])
add(W, unsafe[p], occupancy[n], -1.0)
b[C[p]] = TERRITORY_THRESHOLD
add(W, C[p], C[p], -1.0)
add(W, C[p], D[p], -1.0)
add(W, C[p], O[p], -TERRITORY_THRESHOLD)
add(W, C[p], E[p], -TERRITORY_THRESHOLD)
is_edge = r in (0, 8) or c in (0, 8)
b[D[p]] = 0.0 if is_edge else -1.0
add(W, D[p], E[diag[p]])
# ----------------------------------------------------------------------
# L5: final safety propagation. Enemy urgency is locally size-weighted.
# ----------------------------------------------------------------------
W, b = params["L5.W"], params["L5.b"]
relay(W, O)
relay(W, E)
relay(W, C)
relay(W, D)
relay(W, globals_used)
for p in range(P):
n = adj[p]
add(W, A[p], A[p])
add(W, A[p], A[n])
add(W, A[p], O[n], -1.0)
# Positive only for an enemy stone with no propagated safety certificate.
# Larger nearby strings receive greater capture urgency.
b[B[p]] = -3.0
add(W, B[p], B[p], 4.0)
add(W, B[p], B[n], 4.0)
add(W, B[p], E[n], -3.70)
add(W, B[p], E[diag[p]], 0.10)
# ----------------------------------------------------------------------
# L6: combine territory, shape, captures, viable escapes, self-atari
# avoidance, and eye preservation.
# ----------------------------------------------------------------------
W, b = params["L6.W"], params["L6.b"]
relay(W, globals_used)
OFFSET = 64.0
TERRITORY_GAIN = 4.30
CAPTURE_GAIN = 8.50
SAVE_GAIN = 8.00
FRIEND_LIBERTY_CREDIT = 0.80
anchors = np.array([(2, 2), (2, 6), (6, 2), (6, 6), (4, 4)])
for p, (r, c) in enumerate(points):
n, d = adj[p], diag[p]
degree = len(n)
edge_distance = min(r, c, 8 - r, 8 - c)
# Six final feature maps.
base, escape_plus, escape_minus = O[p], E[p], A[p]
risk_one, risk_two, eye = B[p], C[p], D[p]
static_position = (-0.65, -0.15, 0.10, 0.10, 0.10)[edge_distance]
anchor_distance = np.min(
np.sum((anchors - np.array([r, c])) ** 2, axis=1)
)
opening_position = (
(-2.50, -1.20, 0.35, 0.25, 0.25)[edge_distance]
- 0.12 * anchor_distance
)
b[base] = (
OFFSET + 2.20 + static_position
- TERRITORY_GAIN * TERRITORY_THRESHOLD
)
add(W, base, EARLY, opening_position)
add(W, base, C, TERRITORY_GAIN * territory_kernel[p])
# Local shape and support, with a preference against unsupported contact.
add(W, base, O[n], 0.65)
add(W, base, E[n], -0.35)
add(W, base, O[d], 0.30)
add(W, base, E[d], 0.05)
add(W, base, O[two[p]], 0.14)
add(W, base, E[two[p]], 0.14)
add(W, base, O[knight[p]], 0.08)
add(W, base, E[knight[p]], 0.07)
add(W, base, O, 0.06 * influence[p])
add(W, base, E, -0.06 * influence[p])
add(W, base, B[n], CAPTURE_GAIN)
# Let a = adjacent unsafe friends, s = adjacent safe friends, and
# e = immediate empty neighbors.
# z = e + 2*s - 2 + capture_relief.
# The difference of these ReLUs rewards actual adjacent endangered
# friends only when the escape has room or joins a safe string.
for destination in (escape_plus, escape_minus):
b[destination] = degree - 2.0
add(W, destination, O[n], 1.0)
add(W, destination, E[n], -1.0)
add(W, destination, A[n], -2.0)
add(W, destination, B[n], 4.0)
add(W, escape_plus, A[n], 0.25)
# Conservative post-move freedom, discounting possible duplicate
# liberties obtained through multiple friendly contacts.
for destination, required in [(risk_one, 2.0), (risk_two, 3.0)]:
b[destination] = required - degree
add(W, destination, O[n], 1.0 - FRIEND_LIBERTY_CREDIT)
add(W, destination, E[n], 1.0)
add(W, destination, A[n], FRIEND_LIBERTY_CREDIT)
add(W, destination, B[n], -4.0)
b[eye] = 1.0 - degree
add(W, eye, O[n])
add(W, eye, D[p], -1.0)
# ----------------------------------------------------------------------
# Output: additive policy logits. Pass stays unattractive while useful
# moves remain, especially when estimated to be behind.
# ----------------------------------------------------------------------
W, b = params["out.W"], params["out.b"]
for p, (r, c) in enumerate(points):
action = mv(r, c)
W[O[p], action] = 1.0
W[E[p], action] = 4.0 * SAVE_GAIN
W[A[p], action] = -4.0 * SAVE_GAIN
W[B[p], action] = -4.50
W[C[p], action] = -1.30
W[D[p], action] = -11.0
b[action] = -OFFSET
b[PASS] = -5.50
W[LATE, PASS] = 0.12
W[DEFICIT, PASS] = -0.10
# Validate only the weights and tensor shapes; never evaluate the network.
assert sum(array.size for array in params.values()) == 1_480_274
for name, array in params.items():
assert array.dtype == np.float32
assert np.isfinite(array).all()
assert tuple(load(name).shape) == tuple(array.shape), name
for name, array in params.items():
save(name, array)
print("Saved all 14 tensors: 1,480,274 float32 values.")
print("Design: conservative string-liberty propagation, captures, viable saves,")
print("self-atari avoidance, eye preservation, territorial influence, and komi-aware passing.")
print("No training, network evaluation, or game search was performed.")
```{
"request_sha256": "7c148014040a9703375ffa114482cdf3192990985febcb2c7c8c8e56c160e935",
"request": {
"kind": "python",
"case_id": "b80ce267539e414a927671e47cf8b5f0",
"code": "import numpy as np\n\n# Analytically constructed weights only: no training, search, or forward passes.\nP = 81\nWIDTH = 512\npoints = [(r, c) for r in range(9) for c in range(9)]\n\nO = np.arange(P)\nE = O + 81\nA = O + 162\nB = O + 243\nC = O + 324\nD = O + 405\n\nWHITE, TOTAL, EARLY, LATE, DEFICIT = 486, 487, 488, 489, 490\nglobals_used = [WHITE, TOTAL, EARLY, LATE, DEFICIT]\n\nix_own = np.array([inp(r, c, 0) for r, c in points])\nix_opp = np.array([inp(r, c, 1) for r, c in points])\nix_white = inp(0, 0, 2)\n\ndef neighbors(offsets):\n result = []\n for r, c in points:\n result.append(np.array([\n (r + dr) * 9 + c + dc\n for dr, dc in offsets\n if 0 <= r + dr < 9 and 0 <= c + dc < 9\n ], dtype=np.int64))\n return result\n\nadj = neighbors([(-1, 0), (1, 0), (0, -1), (0, 1)])\ndiag = neighbors([(-1, -1), (-1, 1), (1, -1), (1, 1)])\ntwo = neighbors([(-2, 0), (2, 0), (0, -2), (0, 2)])\nknight = neighbors([\n (-2, -1), (-2, 1), (2, -1), (2, 1),\n (-1, -2), (-1, 2), (1, -2), (1, 2)\n])\n\nparams = {}\nfor layer in range(1, 7):\n params[f\"L{layer}.W\"] = np.zeros(\n (243 if layer == 1 else WIDTH, WIDTH), dtype=np.float32\n )\n params[f\"L{layer}.b\"] = np.zeros(WIDTH, dtype=np.float32)\nparams[\"out.W\"] = np.zeros((WIDTH, 82), dtype=np.float32)\nparams[\"out.b\"] = np.zeros(82, dtype=np.float32)\n\ndef add(W, dest, sources, coefficient=1.0):\n sources = np.asarray(sources, dtype=np.int64).reshape(-1)\n np.add.at(W[:, int(dest)], sources, np.asarray(coefficient, np.float32))\n\ndef relay(W, indices):\n indices = np.asarray(indices, dtype=np.int64)\n W[indices, indices] += 1.0\n\ncoords = np.asarray(points, dtype=np.float64)\ndist2 = np.sum((coords[:, None, :] - coords[None, :, :]) ** 2, axis=2)\n\n# Static spatial kernels, not evaluations on board inputs.\ninfluence = np.exp(-np.sqrt(dist2) / 1.55)\nreference_mass = influence[40].sum()\ninfluence *= (reference_mass / influence.sum(axis=1))[:, None] ** 0.60\n\nterritory_kernel = np.exp(-dist2 / (2.0 * 1.60 ** 2))\nterritory_kernel /= territory_kernel.sum(axis=1, keepdims=True)\n\n# ----------------------------------------------------------------------\n# L1: occupancy, exact per-stone immediate liberties, diagonal information,\n# opening/endgame clocks, and coarse regional ownership features.\n# ----------------------------------------------------------------------\nW, b = params[\"L1.W\"], params[\"L1.b\"]\n\nfor p in range(P):\n n, d = adj[p], diag[p]\n degree = len(n)\n\n add(W, O[p], ix_own[p])\n add(W, E[p], ix_opp[p])\n\n # relu(local_empty_neighbors - 4 * (1 - own/opp_at_p)).\n for destination, center in [(A[p], ix_own[p]), (B[p], ix_opp[p])]:\n b[destination] = degree - 4\n add(W, destination, center, 4.0)\n add(W, destination, ix_own[n], -1.0)\n add(W, destination, ix_opp[n], -1.0)\n\n # Exactly one iff every existing diagonal is occupied.\n b[C[p]] = 1 - len(d)\n add(W, C[p], ix_own[d])\n add(W, C[p], ix_opp[d])\n\nadd(W, WHITE, ix_white)\nadd(W, TOTAL, ix_own)\nadd(W, TOTAL, ix_opp)\n\nb[EARLY] = 1.0\nadd(W, EARLY, ix_own, -1.0 / 32.0)\nadd(W, EARLY, ix_opp, -1.0 / 32.0)\n\nb[LATE] = -45.0\nadd(W, LATE, ix_own)\nadd(W, LATE, ix_opp)\n\nregions = []\nfor r in (1, 4, 7):\n for c in (1, 4, 7):\n center = np.array([r, c], dtype=np.float64)\n weights = 0.30 * np.exp(\n -np.sum((coords - center) ** 2, axis=1) / (2.0 * 1.80 ** 2)\n )\n t = len(regions)\n lo, hi = 490 + 2 * t, 491 + 2 * t\n regions.append((lo, hi))\n for destination, bias in [(lo, 1.0), (hi, -1.0)]:\n b[destination] = bias\n add(W, destination, ix_own, weights)\n add(W, destination, ix_opp, -weights)\n\n# ----------------------------------------------------------------------\n# L2: conservative certificates that a connected string has >=2 liberties.\n# These certificates cannot mark a genuine one-liberty string as safe.\n# ----------------------------------------------------------------------\nW, b = params[\"L2.W\"], params[\"L2.b\"]\nrelay(W, O)\nrelay(W, E)\nrelay(W, [WHITE, TOTAL, EARLY, LATE])\n\nfor p in range(P):\n n = adj[p]\n for occupancy, liberties, seed_a, seed_b in [\n (O, A, A, C),\n (E, B, B, D),\n ]:\n # Two local liberties, or a local liberty plus a liberty on an\n # adjacent friendly stone. Adjacent points have disjoint neighbors.\n b[seed_a[p]] = -16.0\n add(W, seed_a[p], liberties[p], 16.0)\n add(W, seed_a[p], liberties[n])\n\n # Three neighboring liberty incidences imply >=2 distinct liberties.\n # Two suffice when no empty diagonal can be counted twice.\n b[seed_b[p]] = -18.0\n add(W, seed_b[p], occupancy[p], 16.0)\n add(W, seed_b[p], liberties[n])\n add(W, seed_b[p], C[p])\n\n# Coarse area/stone deficit, with komi, used only to discourage losing passes.\nregional_fraction = 0.65\nb[DEFICIT] = regional_fraction * 81.0 + 7.5\nfor lo, hi in regions:\n add(W, DEFICIT, lo, -9.0 * regional_fraction)\n add(W, DEFICIT, hi, 9.0 * regional_fraction)\nadd(W, DEFICIT, O, -(1.0 - regional_fraction))\nadd(W, DEFICIT, E, +(1.0 - regional_fraction))\nadd(W, DEFICIT, WHITE, -15.0)\n\n# ----------------------------------------------------------------------\n# L3: propagate safety once; construct signed spatial influence.\n# Unsafe flags remain binary.\n# ----------------------------------------------------------------------\nW, b = params[\"L3.W\"], params[\"L3.b\"]\nrelay(W, O)\nrelay(W, E)\nrelay(W, globals_used)\n\nfor p in range(P):\n neighborhood = np.concatenate(([p], adj[p]))\n\n add(W, A[p], O[p])\n add(W, A[p], A[neighborhood], -1.0)\n add(W, A[p], C[neighborhood], -1.0)\n\n add(W, B[p], E[p])\n add(W, B[p], B[neighborhood], -1.0)\n add(W, B[p], D[neighborhood], -1.0)\n\n add(W, C[p], O, influence[p])\n add(W, C[p], E, -influence[p])\n add(W, D[p], O, -influence[p])\n add(W, D[p], E, influence[p])\n\n# ----------------------------------------------------------------------\n# L4: propagate safety again; estimate empty, unsettled territory and\n# distinguish true eyes from eyes spoiled by enemy diagonals.\n# ----------------------------------------------------------------------\nW, b = params[\"L4.W\"], params[\"L4.b\"]\nrelay(W, O)\nrelay(W, E)\nrelay(W, globals_used)\nTERRITORY_THRESHOLD = 1.35\n\nfor p, (r, c) in enumerate(points):\n n = adj[p]\n for occupancy, unsafe in [(O, A), (E, B)]:\n add(W, unsafe[p], unsafe[p])\n add(W, unsafe[p], unsafe[n])\n add(W, unsafe[p], occupancy[n], -1.0)\n\n b[C[p]] = TERRITORY_THRESHOLD\n add(W, C[p], C[p], -1.0)\n add(W, C[p], D[p], -1.0)\n add(W, C[p], O[p], -TERRITORY_THRESHOLD)\n add(W, C[p], E[p], -TERRITORY_THRESHOLD)\n\n is_edge = r in (0, 8) or c in (0, 8)\n b[D[p]] = 0.0 if is_edge else -1.0\n add(W, D[p], E[diag[p]])\n\n# ----------------------------------------------------------------------\n# L5: final safety propagation. Enemy urgency is locally size-weighted.\n# ----------------------------------------------------------------------\nW, b = params[\"L5.W\"], params[\"L5.b\"]\nrelay(W, O)\nrelay(W, E)\nrelay(W, C)\nrelay(W, D)\nrelay(W, globals_used)\n\nfor p in range(P):\n n = adj[p]\n\n add(W, A[p], A[p])\n add(W, A[p], A[n])\n add(W, A[p], O[n], -1.0)\n\n # Positive only for an enemy stone with no propagated safety certificate.\n # Larger nearby strings receive greater capture urgency.\n b[B[p]] = -3.0\n add(W, B[p], B[p], 4.0)\n add(W, B[p], B[n], 4.0)\n add(W, B[p], E[n], -3.70)\n add(W, B[p], E[diag[p]], 0.10)\n\n# ----------------------------------------------------------------------\n# L6: combine territory, shape, captures, viable escapes, self-atari\n# avoidance, and eye preservation.\n# ----------------------------------------------------------------------\nW, b = params[\"L6.W\"], params[\"L6.b\"]\nrelay(W, globals_used)\n\nOFFSET = 64.0\nTERRITORY_GAIN = 4.30\nCAPTURE_GAIN = 8.50\nSAVE_GAIN = 8.00\nFRIEND_LIBERTY_CREDIT = 0.80\n\nanchors = np.array([(2, 2), (2, 6), (6, 2), (6, 6), (4, 4)])\n\nfor p, (r, c) in enumerate(points):\n n, d = adj[p], diag[p]\n degree = len(n)\n edge_distance = min(r, c, 8 - r, 8 - c)\n\n # Six final feature maps.\n base, escape_plus, escape_minus = O[p], E[p], A[p]\n risk_one, risk_two, eye = B[p], C[p], D[p]\n\n static_position = (-0.65, -0.15, 0.10, 0.10, 0.10)[edge_distance]\n anchor_distance = np.min(\n np.sum((anchors - np.array([r, c])) ** 2, axis=1)\n )\n opening_position = (\n (-2.50, -1.20, 0.35, 0.25, 0.25)[edge_distance]\n - 0.12 * anchor_distance\n )\n\n b[base] = (\n OFFSET + 2.20 + static_position\n - TERRITORY_GAIN * TERRITORY_THRESHOLD\n )\n add(W, base, EARLY, opening_position)\n add(W, base, C, TERRITORY_GAIN * territory_kernel[p])\n\n # Local shape and support, with a preference against unsupported contact.\n add(W, base, O[n], 0.65)\n add(W, base, E[n], -0.35)\n add(W, base, O[d], 0.30)\n add(W, base, E[d], 0.05)\n add(W, base, O[two[p]], 0.14)\n add(W, base, E[two[p]], 0.14)\n add(W, base, O[knight[p]], 0.08)\n add(W, base, E[knight[p]], 0.07)\n add(W, base, O, 0.06 * influence[p])\n add(W, base, E, -0.06 * influence[p])\n add(W, base, B[n], CAPTURE_GAIN)\n\n # Let a = adjacent unsafe friends, s = adjacent safe friends, and\n # e = immediate empty neighbors.\n # z = e + 2*s - 2 + capture_relief.\n # The difference of these ReLUs rewards actual adjacent endangered\n # friends only when the escape has room or joins a safe string.\n for destination in (escape_plus, escape_minus):\n b[destination] = degree - 2.0\n add(W, destination, O[n], 1.0)\n add(W, destination, E[n], -1.0)\n add(W, destination, A[n], -2.0)\n add(W, destination, B[n], 4.0)\n add(W, escape_plus, A[n], 0.25)\n\n # Conservative post-move freedom, discounting possible duplicate\n # liberties obtained through multiple friendly contacts.\n for destination, required in [(risk_one, 2.0), (risk_two, 3.0)]:\n b[destination] = required - degree\n add(W, destination, O[n], 1.0 - FRIEND_LIBERTY_CREDIT)\n add(W, destination, E[n], 1.0)\n add(W, destination, A[n], FRIEND_LIBERTY_CREDIT)\n add(W, destination, B[n], -4.0)\n\n b[eye] = 1.0 - degree\n add(W, eye, O[n])\n add(W, eye, D[p], -1.0)\n\n# ----------------------------------------------------------------------\n# Output: additive policy logits. Pass stays unattractive while useful\n# moves remain, especially when estimated to be behind.\n# ----------------------------------------------------------------------\nW, b = params[\"out.W\"], params[\"out.b\"]\n\nfor p, (r, c) in enumerate(points):\n action = mv(r, c)\n W[O[p], action] = 1.0\n W[E[p], action] = 4.0 * SAVE_GAIN\n W[A[p], action] = -4.0 * SAVE_GAIN\n W[B[p], action] = -4.50\n W[C[p], action] = -1.30\n W[D[p], action] = -11.0\n b[action] = -OFFSET\n\nb[PASS] = -5.50\nW[LATE, PASS] = 0.12\nW[DEFICIT, PASS] = -0.10\n\n# Validate only the weights and tensor shapes; never evaluate the network.\nassert sum(array.size for array in params.values()) == 1_480_274\nfor name, array in params.items():\n assert array.dtype == np.float32\n assert np.isfinite(array).all()\n assert tuple(load(name).shape) == tuple(array.shape), name\n\nfor name, array in params.items():\n save(name, array)\n\nprint(\"Saved all 14 tensors: 1,480,274 float32 values.\")\nprint(\"Design: conservative string-liberty propagation, captures, viable saves,\")\nprint(\"self-atari avoidance, eye preservation, territorial influence, and komi-aware passing.\")\nprint(\"No training, network evaluation, or game search was performed.\")"
},
"result": {
"ok": true,
"out": "Saved all 14 tensors: 1,480,274 float32 values.\nDesign: conservative string-liberty propagation, captures, viable saves,\nself-atari avoidance, eye preservation, territorial influence, and komi-aware passing.\nNo training, network evaluation, or game search was performed.\n",
"stdout": "Saved all 14 tensors: 1,480,274 float32 values.\nDesign: conservative string-liberty propagation, captures, viable saves,\nself-atari avoidance, eye preservation, territorial influence, and komi-aware passing.\nNo training, network evaluation, or game search was performed.\n",
"stderr": "",
"rc": 0,
"timed_out": false,
"truncated": false,
"seconds": 0.2990972278639674,
"sandbox_command": [
"docker",
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"lm-exp6-ee215cf75690478ba708e9be1340e63e",
"--network",
"none",
"--cap-drop",
"ALL",
"--security-opt",
"no-new-privileges",
"--read-only",
"--user",
"1000:1000",
"--cpus",
"8",
"--memory",
"103079215104",
"--pids-limit",
"256",
"--tmpfs",
"/tmp:rw,nosuid,mode=1777,size=536870912",
"--mount",
"type=bind,src=/usr,dst=/usr,readonly",
"--mount",
"type=bind,src=<gpu-project>/.venv,dst=/env,readonly",
"--mount",
"type=bind,src=<gpu-project>/reconstructed/exp6/work/b80ce267539e414a927671e47cf8b5f0,dst=/work",
"--workdir",
"/work",
"--entrypoint",
"/usr/bin/env",
"sha256:5b8c0c14690ed170da4e663fe0bae0d58efe59661e791296ffab28ed2113b650",
"-i",
"HOME=/tmp",
"PATH=/usr/bin",
"OMP_NUM_THREADS=8",
"OPENBLAS_NUM_THREADS=8",
"MKL_NUM_THREADS=8",
"CUDA_VISIBLE_DEVICES=",
"/env/bin/python",
"-I",
"-"
],
"prelude_sha256": "6c02f33e84d0631bbb88b06f0f5d138176c4888240a0db7828733f8d5883835b"
}
}OUTPUT of Python run 1 of at most 120: ``` Saved all 14 tensors: 1,480,274 float32 values. Design: conservative string-liberty propagation, captures, viable saves, self-atari avoidance, eye preservation, territorial influence, and komi-aware passing. No training, network evaluation, or game search was performed. ```
Recorded prompt · Raw CLI events · Settings and audit
OUTPUT of Python run 1 of at most 120: ``` Saved all 14 tensors: 1,480,274 float32 values. Design: conservative string-liberty propagation, captures, viable saves, self-atari avoidance, eye preservation, territorial influence, and komi-aware passing. No training, network evaluation, or game search was performed. ```
Failed to read global AGENTS.md instructions from `<local-home>/.codex/AGENTS.md`: Operation not permitted (os error 1)
Code Mode is unavailable because code-mode host is disabled. Code mode will fail closed; enable `features.code_mode_host` and install `codex-code-mode-host`.
All 14 tensors are saved. The policy combines: - Local string-liberty propagation for captures and endangered-group saves. - Self-atari avoidance and eye preservation. - Territorial influence, connection preferences, and opening placement. - Komi-aware passing. It is designed to exploit tactical mistakes while keeping groups alive and securing territory. No training, search, or network testing was performed; its strength against the held-out opponents remains unmeasured.
All 14 tensors are saved. The policy combines: - Local string-liberty propagation for captures and endangered-group saves. - Self-atari avoidance and eye preservation. - Territorial influence, connection preferences, and opening placement. - Komi-aware passing. It is designed to exploit tactical mistakes while keeping groups alive and securing territory. No training, search, or network testing was performed; its strength against the held-out opponents remains unmeasured.