diff --git a/docs/superpowers/plans/2026-03-30-gems-and-pearls-generalization.md b/docs/superpowers/plans/2026-03-30-gems-and-pearls-generalization.md
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--- a/docs/superpowers/plans/2026-03-30-gems-and-pearls-generalization.md
+++ b/docs/superpowers/plans/2026-03-30-gems-and-pearls-generalization.md
@@ -726,3 +726,117 @@ should use f32 copies to eliminate bf16 quantization artifacts during data colle
 - [ ] Update CUDA kernels to read f32 weights for forward pass during experience collection
 - [ ] Verify Q-value precision improvement in smoke tests
 - [ ] Compile + test + commit
+
+---
+
+## THE GEM OF GEMS
+
+### Task 31: Temporal Causal Bottleneck — 2D Information Compression [PLAN]
+
+**Phase:** 0 (HIGHEST PRIORITY) | **Difficulty:** Medium | **Impact:** TRANSFORMATIVE
+
+**Files:**
+- Modify: `crates/ml/src/cuda_pipeline/batched_forward.rs` (add bottleneck GEMM layer)
+- Modify: `crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs` (bottleneck weights, backward pass)
+- Modify: `crates/ml/src/cuda_pipeline/gpu_experience_collector.rs` (bottleneck in experience forward)
+- Modify: `crates/ml-dqn/src/branching.rs` (add bottleneck layer to network definition)
+- Modify: `crates/ml/src/trainers/dqn/config.rs` (bottleneck_dim config)
+
+**The Core Idea:**
+
+Force ALL information from the 14-feature market state through a 2-dimensional
+bottleneck before it reaches the Q-network. Two neurons. The entire market must be
+compressed into 2 numbers — call them **opportunity** and **risk**.
+
+**Why this is the gem of all gems:**
+
+1. **Information-theoretic impossibility of memorization.** With only 2 floats, the
+   network physically CANNOT encode "which specific price sequence am I looking at."
+   A 2D bottleneck has ~100 bits of capacity vs ~450 bits in the full 14-feature vector.
+   Memorizing 100K episodes requires far more than 100 bits.
+
+2. **Forced abstraction.** The bottleneck encoder must learn to compress 14 features
+   into the 2 most DECISION-RELEVANT dimensions. These are by definition
+   regime-invariant — if they weren't, the Q-network couldn't use them to trade
+   profitably across regimes.
+
+3. **Interpretability for free.** Plot the 2D bottleneck activations and literally
+   see what the model thinks "opportunity" and "risk" look like. Immediate diagnostic
+   for whether it learned signal or garbage.
+
+4. **Architecture integration:** Add a single [market_dim → 2] linear + tanh layer
+   before h_s1. One extra GEMM in the cuBLAS forward pass. The 2D output concatenates
+   with 3 portfolio features → [5 → shared_h1] trunk. Tiny compute cost.
+
+5. **The killer diagnostic:** During hyperopt, compare 2D bottleneck distributions
+   between IS and OOS data. If they OVERLAP, the model learned regime-invariant
+   features. If they DON'T overlap, the model is overfitting — visible BEFORE
+   checking OOS Sharpe.
+
+**Why 2 and not 4 or 8?** 2 is the minimum to express long/short/flat (need ≥2 bits
+for 3+ actions). Any higher leaks memorization capacity. Start maximally constrained.
+
+This attacks the ROOT CAUSE of the generalization gap — all 28 other techniques fight
+symptoms. The bottleneck makes memorization structurally impossible at the architecture
+level.
+
+**Implementation steps:**
+
+- [ ] **Step 1: Add bottleneck weights to network definition**
+
+  In `branching.rs`, add:
+  ```rust
+  pub bottleneck_w: Tensor,  // [market_dim, bottleneck_dim] (e.g. [42, 2])
+  pub bottleneck_b: Tensor,  // [bottleneck_dim]
+  ```
+
+- [ ] **Step 2: Add bottleneck GEMM to cuBLAS forward pass**
+
+  In `batched_forward.rs`, before the first shared layer:
+  ```
+  // Bottleneck: [B, market_dim] → [B, bottleneck_dim] via GemmEx + tanh
+  gemmex_bf16(W_bn, states, h_bn, bottleneck_dim, batch, market_dim)
+  add_bias_tanh(h_bn, b_bn, bottleneck_dim, batch)
+
+  // Concatenate: [B, bottleneck_dim + portfolio_dim] → input to h_s1
+  concat_kernel(h_bn, portfolio_features, h_concat, bottleneck_dim, portfolio_dim, batch)
+  ```
+
+  The existing h_s1 GEMM changes from [state_dim → shared_h1] to [bottleneck_dim+3 → shared_h1].
+
+- [ ] **Step 3: Add bottleneck to training backward pass**
+
+  Chain rule through the bottleneck: d_loss/d_W_bn = d_loss/d_h_bn @ states.T.
+  The existing backward pass already computes d_loss/d_states. Replace the first
+  backward GEMM to route through the bottleneck.
+
+- [ ] **Step 4: Add to experience collector forward pass**
+
+  The experience collector has its own cuBLAS forward. Add the same bottleneck
+  GEMM before the existing h_s1 computation.
+
+- [ ] **Step 5: Add config field `bottleneck_dim`**
+
+  ```rust
+  pub bottleneck_dim: usize,  // 2 = maximum compression, 0 = disabled (bypass)
+  ```
+  Default: 2. When 0, skip the bottleneck (backward compatible).
+
+- [ ] **Step 6: Add bottleneck activation logging**
+
+  At epoch boundary, read the 2D bottleneck activations for a sample of training
+  data. Log min/max/mean of each dimension. This provides free interpretability.
+
+- [ ] **Step 7: Add IS/OOS distribution overlap metric**
+
+  During walk-forward evaluation, compute the Wasserstein distance between IS and
+  OOS bottleneck activation distributions. Low distance = good generalization.
+  Log as `bottleneck_w_distance` metric.
+
+- [ ] **Step 8: Compile + smoke test + commit**
+
+  Verify that:
+  - Bottleneck forward/backward produces non-zero gradients
+  - Q-values are in normal range (not collapsed to constant)
+  - 2D activations show non-trivial structure (not all same value)
+  - Smoke tests pass with bottleneck_dim=2