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@@ -13,6 +13,7 @@ band; what does it actually buy?). The second is the active one.
 | What worked / what failed / design rules / ops pitfalls | `LESSONS.md` — read before running anything on this hardware |
 | Pre-registrations + scored outcomes (17 items, incl. refutations) | `results-loop/PROTOCOL_UNIFIED.md` — the methods backbone; every claim in PAPER.md §3 traces to an item here |
 | Significance tests behind any claimed number | `results-loop/STATS.md` |
+| The v2 prototype plan (self-paced workspace: learned gating) | `PLAN_SELFPACED.md` |
 | Lab-notebook narrative of the looping investigation | `WORKSPACE_LOOPING.md` (superseded where it disagrees with PAPER.md) |
 | Base-reproduction results (lens replication itself) | `RESULTS.md`, `README.md` |
 | Per-model lens maps: workspace bands, KV-share boundaries, pinned revisions | `results/REGIMES.json` (canonical registry) + `results/jbar*.pt` (raw J̄) + `results/exp4*.log` (regime scans) |
diff --git a/PLAN_SELFPACED.md b/PLAN_SELFPACED.md
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+# Prototype plan: the self-paced workspace (v2)
+
+*Drafted 2026-07-16, pre-registration-style. Goal: test whether the model
+can learn to allocate workspace-loop compute ON ITS OWN — per prompt and
+per generation step — rather than at a swept hyperparameter k.*
+
+## The concept
+
+At every step the system chooses: emit, or spend a band iteration updating
+the workspace first. Make that choice a learned gate g(workspace state).
+Compute becomes a decision, not a constant. Gate-bought iterations emit no
+tokens, so they are exposure-safe by construction (deterministic given
+state — the pause-position property).
+
+## What already exists (de-risked ingredients)
+
+| ingredient | evidence | where |
+|---|---|---|
+| static per-prompt gate (E0) | gated 52.0 overall, easy 97.5 (vs 88.5 uniform), hard 28.6; bottleneck = probe recall (18/28 tp, 95 predicted hard) | `gate_probe.py`, `eval_gated.json` |
+| state-dependent control heads train | adaptive-α rescued 12B (3.8K params) | AdaptiveMergeAdapter |
+| evolving state during generation | carry beats registers on GSM (hard 0→9.4) | `carry_common.py`, `eval_carry.json` |
+| loop-capacity knob | rung-2 band-LoRA = best hard numbers (42.9/46.4) | `lora_band.py` |
+| gates over DEPTH (complementary axis) | learnable compute envelope g[t,l] | `path_gates.py` (Nils, in progress) |
+| free gate supervision | STaR difficulty labels; per-position labels derivable | prep_star/prep_mbpp |
+
+## Experiments
+
+### E1 — learned per-prompt halting (prompt side, MBPP)
+Replace fixed k with a trained soft halting gate. Architecture: after each
+iteration i, gate head h(e, ŝ_i) → p_halt,i (zero-init to fixed-k
+behavior); training uses the soft mixture of iteration outputs weighted by
+halting distribution (ACT-style), CE + λ·E[iterations] compute penalty;
+deploy = argmax halt. Trains end-to-end, NO RL. Arms: λ ∈ {1e-3, 1e-2},
+vs E0 probe-gate and uniform-k anchors.
+**Pre-registered predictions:** (a) accuracy ≥ uniform k=4 overall at ≤60%
+of its mean iterations; (b) easy ≥ 95% (gate protects the substrate);
+(c) allocation correlates with STaR label (point-biserial r > 0.3);
+(d) hard ≥ E0's 28.6% (learned gate beats frozen probe recall).
+**Failure mode to watch:** gate collapse (all-0/all-1) — mitigate with
+penalty warmup + entropy bonus; collapse at all λ falsifies E1.
+
+### E2 — generation-side gating (GSM, gated carry)
+Substrate: design-C carry + short verified-CoT supervision (dense targets;
+harvest with "solve in ≤3 short steps", answer-verified). Gate per token
+step decides whether the carry state updates through the band or passes
+through: x_t = g·merge(e_t, s_{t−1}) + (1−g)·e_t, penalty λ·E[g].
+Anchors: carry-always, carry-never (same supervision).
+**Predictions:** (a) gate fires non-uniformly, concentrated near numeric/
+operator tokens (measurable); (b) accuracy ≥ carry-always (gating as
+protection); (c) easy-bucket damage < carry-always's (83→45 was the
+unprotected number). Hard-bucket *gain* over carry-always is hoped for,
+not predicted.
+
+### E3 — power knob (only if E1 or E2 shows clean gating)
+Warm-start rung-2 band-LoRA under the gate; joint fine-tune. Question: do
+gate-bought iterations do MORE per iteration with a trainable band?
+Metric: the internalization count (how many scratchpad steps can be
+removed post-hoc, E2 curriculum) as a function of LoRA rank.
+
+### Lens verification (throughout — our home advantage)
+J-lens reads of gated vs ungated positions: do bought iterations sharpen
+task-relevant concepts at the positions where the gate fired? This is the
+mechanistic check that the gate allocates *meaningfully*, not just
+correlationally.
+
+## Explicitly out of scope for the prototype
+Outcome-RL training of the gate (GRPO with compute price) — stage 2, only
+if E1–E3 show selective gating. 12B/scale transfer. Cross-task gates.
+
+## Budget & order
+E1: 3 arms × ~75 min (Spark). E2: harvest ~30 min + 3 arms × ~90 min.
+E3: +2 arms. Total ≈ 1.5 Spark-days. Runs after the lens campaign; queue
+via gpuq as usual, every arm pre-registered in PROTOCOL_UNIFIED.md before
+launch (items 18+).
+
+## Kill criteria (decided in advance)
+- E1 gate collapse at all λ AND E2 uniform firing → the state does not
+  carry usable "needs compute" signal at this scale; program stops, E0's
+  static-gate deployment note stands as the practical answer.
+- E1 works but hard < E0 → learned gate worse than probe; ship probe-gate,
+  keep E2 only if its (a)/(b) hold.