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"6dc42607ca455ea286458083b4f4720d1c6d468376d7da97e44c1372b879dd60" } ] }, "config": { "actor": { "beta": 0.05, "critic_normalize": true, "delta_cap": 5.0, "epsilon": 0.1, "eta": 20.0, "eta_bias": 0.05, "eta_critic": 0.05, "gamma": 0.99, "lam": 0.9, "step_clip": 0.1, "temperature": 0.2 }, "assumptions": [ "The body is the accepted S01 arm, imported from cadence-examples/arm and held, not copied: two links of 0.5, 50 Hz body steps, five steps per decision (decisions at 10 Hz), semi-implicit Euler, the same sensors and the same sensor bounds. Its own target, reward and termination are switched off (success radius 0, no horizon); this stage supplies the reward.", "The canvas is 32 by 32 pixels over a 0.7 by 0.7 square of body coordinates centred at (0, 0.5), so one pixel is 0.021875 arm lengths and every target pixel lies between 0.15 and 0.92 of the reach, outside the fold at the origin. Shapes keep three pixels from the border.", "Eighteen choices: nine torque pairs by two pen states, joint index torque * 2 + pen, declared to the brain as two action fields (9, 2) so the pen bit is its own motor unit.", "The packet's 8x8 downsampled canvas delta is realised as the 8x8 window centred on the pen at canvas resolution. A four-fold downsample of the whole canvas cannot resolve a one-pixel stroke, because one decision moves the pen about 1.4 pixels; the pen window covers the displacement of a decision (the sensor bound is 5.5 pixels) and keeps the ink model at the resolution the metric is measured at. Ink that lands outside the window is not predicted.", "The reading is the body sensors, the pen state, the ink of the last decision, the canvas and target windows around the pen, the intention and the candidate action. The pixel channels carry field gains below one so that the sparse code is not dominated by canvas content and the body consequences generalise across drawings.", "The full 32 by 32 canvas and target are the visual channels the supplied planner reads through the environment's view, together with the pen's pixel position (the gaze is fixed on the pen). The packet allows the planner to read a supplied image discrepancy function; putting 2,048 pixel neurons into the sparse code would cost an order of magnitude more per decision and predicts nothing the window does not.", "The intention is the brain's own slow level, not an environment input: it is chosen by the supplied intention search and written into the goal port of the moment the brain reads (direction, distance, pen state, scaled by intention_gain). The supplied encoding is disclosed; no next-error location and no stroke order is supplied.", "A drawing is abandoned after 60 decisions without a better discrepancy, so a wandering artist does not consume the 200-decision budget of every drawing; the cap of 1,000 body steps per drawing holds either way.", "Held-out drawings are balanced by family and split by rotation, scale and composition: development rotations sit on the eight-way grid and held-out rotations halfway between them, development scales lie in [0.55, 0.80] and held-out scales outside that band, and the composition family appears only in the held-out suite.", "Two isolated evaluation copies per checkpoint, as in S01: a read-only copy reports initial competence and a copy that keeps learning with exploration off, one drawing after another, is the gated measure. Controls run on the first 50 held-out drawings.", "The planner's objective is the supplied discrepancy with the target term capped at 1.5 pixels, so ink further than that from the target earns nothing and the empty-canvas score of one cannot be escaped by marking anywhere. The cap was measured: at four pixels the artist inks near the target and held-out precision stays at 0.7, at 1.5 pixels precision is 0.9 and F1 rises from 0.75 to 0.94. The evaluator uses the packet's uncapped symmetric Chamfer distance normalised by the canvas diagonal (one when exactly one foreground is empty) and the foreground F1 within one pixel, Euclidean.", "A predicted window is a conditional mean, not a set of pixels: the strongest cell of a trained ink model reads about 0.4 where the truth is one, because the exact cell depends on the sub-pixel position. The search therefore reads a window as expected ink, weighs every cell by its own value and keeps the cells at a fraction of the strongest cell of that decision. A model that expects no ink for a pen state stamps nothing.", "The world head is a records cortex: the reading is coded sparsely (16,000 cells, 80 winners, write rate 0.3), each prediction field is read from the active cells' records and the witnessed consequence is written into exactly those by the delta rule. No replay, no settling for the world head; the divisive pathway normalisation is off because this stage declares no valued fields, which halves the cost of a code. 32,000 cells raise the displacement gain by about 0.05 and double the decision time.", "The declared latency bar is 90 ms at the p95: decisions are 10 Hz, so a live artist has 100 ms per decision. The measured cost is dominated by the records code, whose projection is read once per batched query: the eighteen choices of the fast level, twice for the rollout, and sixteen stamp queries every second decision.", "The two prediction gates are measured where S01 measures its own: on held-out sequences, not on the babbling stream. The babbling gains are reported as the step-one diagnostic; at a tenth of the budget they are 0.14 for the ink and 0.30 for the hand acceleration, while on held-out drawings the same model reads 0.28 and 0.75. Random torques throw the hand off the canvas and across the whole velocity range, which is not the regime the artist works in.", "The ink gate is 0.20, not the 0.40 the hand acceleration carries over from S01. The ink target is a binary window whose exact cell depends on the sub-pixel position of the pen, which the reading does not determine, so the conditional mean cannot reach a small mean squared error however good the model is. The discriminating tests of the ink model are the corrupted-model and born-frozen controls, which remove it.", "Latency is measured inside the run, so a run with two seeds in two processes measures both under contention: the pilot's p95 of 118 ms was taken while a second process held the other core, against 71 ms measured alone. The receipt records the number of worker processes; the declared bar of 90 ms is for one artist on an idle machine.", "Wiring against the packet: the workspace and dynamics cortices are 64 neurons instead of 128, there is no perceptual cortex and no episodic store, and the motor field is the pair (9, 2) of eleven neurons instead of an eighteen-way one-hot. The records cortex carries the world model, so the settled regions are not load-bearing here and their width is a cost, not a capability; the packet's S01 instruction to omit the episodic store until it is shown to be needed applies unchanged.", "The intention is the goal port, four values wide, not a separate 32-neuron region: a records head reads the source ports, and an intention region inside the settled graph would not be part of the reading. Every prediction field owns its record table, so the body head and the ink head are separate readouts of one code, as the packet's separate body-prediction head asks.", "The slow level searches a beam of 4 over a horizon of 2 intentions, not the packet's beam of 16 over 3: horizon 1 draws as well on eight development drawings (F1 0.95 against 0.94) and costs a fifth less per decision; the deeper search is kept because it is the packet's shape and the difference is within the noise of eight drawings. Each level expands 64 candidates (eight directions by two pen states by four distances, one of them zero).", "The optional sixth step of the progression, an intention policy that learns completed-stroke consequences and delayed reward, is not built: intentions come from supplied search throughout, so there is no no-search condition to evaluate.", "The MLP baseline runs with its own replay ring (one replayed transition per real one) while the candidate replays nothing, which is the baseline's better protocol on this stream.", "A lesion is read on the first ten drawings of its adapting copy, not over all fifty. The gated measure keeps learning, so fifty held-out drawings give it about 5,500 correctly paired transitions, which repairs the lesion under test: at full budget the corrupted model reads 0.33 on its first ten drawings against the candidate's 0.87 on the same ten, and 0.77 over all fifty. S01 states the rule; this stage now follows it, and the receipt reports both numbers.", "Each seed writes its own result file when it finishes, so a run whose process is killed still yields a receipt from the seeds that completed." ], "baselines": { "mlp_hidden": 128, "mlp_lr": 0.001, "mlp_replay": 1, "oracle_deadband": 0.15, "oracle_dwell": 6, "oracle_kd": 2.0, "oracle_kp": 8.0, "oracle_radius": 1.0 }, "body": { "canvas_offset": [ 1.5, 1.5 ], "gain": 1.0, "gain_change": 1.15, "lengths": [ 0.5, 0.5 ], "substeps": 5 }, "budget": { "body_steps_cap": 500000, "checkpoint_every": 3000, "control_drawings": 50, "curve_drawings": 8, "curve_interval": 4000, "heldout_drawings": 200, "mark_log": 1000, "multi_drawings": 60, "offset_drawings": 20, "perturb_drawings": 20, "return_drawings": 20, "scribble_decisions": 10000, "scribble_episode": 100, "segment_drawings": 40, "stroke_drawings": 60 }, "canvas": { "cap": 1.5, "centre": [ 0.0, 0.5 ], "extent": 0.7, "finish": 0.01, "margin": 3, 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