Each round: read the board, form or update a
hypothesis about how the game works and what
clears the level, then act on it. When a level's
reference keys resist decoding, reverse-infer the
rule from a level you ALREADY cleared [...]
Never phrase a goal as a fact you have not tested.

HYPOTHESIS: the failed submit means the target
is not simply the lower-left copy - re-read the
macro-tile state and clue panels exactly.
clicks: ACTION6(40,54), ACTION6(56,54)
engine: levels 0 -> 1  (L0 CLEARED)

fold(record, {"key": "tfclr:5", "saved": credit})
# the ONLY function that may touch alpha/beta.
# idempotent by key: one observation, one charge.
# r33's real trail:
#  tfclr:5      SURVIVOR   a+8  -> 0.90 (winner)
#  tfanom:7,8   ANOMALY    b++  <- eroded on L1 (bug: no winner shield)
#  tfclr:12     SURVIVOR   a+
#  tfanom:24,25 ANOMALY    b++  -> final 0.34

packet = {credence, target_f1, residual_cells,
          evidence_refs: ("transition:0", ...)}
# LLM judge writes advisory + future-rank f only.
# KNOWN GAPS (external audit, confirmed):
#  - cites are not checked against evidence_refs
#  - settle events (false_clear/ANOMALY) missing
#  -> both queued in the next package (h12)

def predict_next(state, action):
    # click toggles a connected same-colour tile
    # between the board's two play colours
    x, y = int(action[1]), int(action[2])
    old = int(state[y, x])
    play = sorted(v for v in np.unique(state)
                  if v not in {0,2,4,5,11,12})
    lo, hi = play[0], play[-1]
    new = lo if old == hi else hi if old == lo else None
    if new is None: return {}
    return {c: new for c in flood_fill(state, x, y)}

def target(state):
    # goal guess: the bottom status strip,
    # filled end-to-end with its "done" value
    bg = most_common_value(state)
    goal = state.copy()
    row, done = goal[-1], goal[-1][-1]
    xs = where(row != bg)
    goal[-1, xs.min():xs.max()+1] = done
    return goal