chore: Second cleanup wave - organize root directory

- Archive: 85 agent .txt files → docs/archive/agents/legacy_txt/
- Scripts: Move 110 shell scripts → scripts/ (keep deploy.sh in root)
- Models: Move 18 .safetensors → ml/models/checkpoints/training_artifacts/
- Delete: 34 directories (~33GB freed) - target/, coverage_*, test artifacts
- Build: Clean 14 build artifacts (.rlib, .o, .pid, binaries)
- Tests: Move 14 .rs files → tests/standalone/
- SQL: Move 5 files → sql/ (keep init-db*.sql for Docker)
- Wave 153: Archive to docs/archive/historical/wave153/
- Docs: Archive 9 markdown files to wave_d/reports/ and historical/

Total impact: ~34GB freed (both waves), root directory cleaned from 583 to ~40 essential files
Directory count reduced from 65 to 31 (52% reduction)
All historical data preserved in organized archive structure

docs/patches/batch_size_180_patch.diff

@@ -0,0 +1,27 @@
+--- a/ml/examples/hyperopt_mamba2_demo.rs
++++ b/ml/examples/hyperopt_mamba2_demo.rs
+@@ -66,8 +66,9 @@ struct Args {
+     batch_size_min: usize,
+
+-    /// Maximum batch size for GPU memory constraints (default: 96 for RTX A4000 16GB)
+-    /// Examples: RTX 3050 Ti 4GB = 32, RTX A4000 16GB = 96, RTX 4090 24GB = 256
+-    #[arg(long, default_value = "96")]
++    /// Maximum batch size for GPU memory constraints (default: 180 for RTX A4000 16GB)
++    /// Validated safe limits based on VRAM analysis (AGENT_R3_A5_VRAM_ANALYSIS.md):
++    /// Examples: RTX 3050 Ti 4GB = 32, RTX A4000 16GB = 180, RTX 4090 24GB = 256
++    #[arg(long, default_value = "180")]
+     batch_size_max: usize,
+ }
+
+--- a/ml/src/hyperopt/adapters/mamba2.rs
++++ b/ml/src/hyperopt/adapters/mamba2.rs
+@@ -115,7 +115,8 @@ impl ParameterSpace for Mamba2Params {
+     fn continuous_bounds() -> Vec<(f64, f64)> {
+         vec![
+             (1e-5_f64.ln(), 1e-2_f64.ln()), // learning_rate (log scale)
+-            (4.0, 256.0),                    // batch_size (linear) - wide bounds, clamped by trainer config
++            (4.0, 180.0),                    // batch_size (linear) - validated safe for 16GB GPU
++                                             // See AGENT_R3_A5_VRAM_ANALYSIS.md for derivation
+             (0.0, 0.5),                      // dropout (linear)
+             (1e-6_f64.ln(), 1e-2_f64.ln()), // weight_decay (log scale)
+                 (0.5_f64.ln(), 5.0_f64.ln()),   // grad_clip (log scale)