Add files using upload-large-folder tool

SPFsmartGATE/src/calculate.rs

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+// SPF Smart Gateway - Complexity Calculator
+// Copyright 2026 Joseph Stone - All Rights Reserved
+//
+// Implements: C = (basic ^ 1) + (dependencies ^ 7) + (complex ^ 10) + (files × 10)
+// Master formula: a_optimal(C) = W_eff × (1 - 1/ln(C + e))
+
+use crate::config::SpfConfig;
+use serde::{Deserialize, Serialize};
+
+/// Result of complexity calculation
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct ComplexityResult {
+    pub tool: String,
+    pub c: u64,
+    pub tier: String,
+    pub analyze_percent: u8,
+    pub build_percent: u8,
+    pub a_optimal_tokens: u64,
+    pub requires_approval: bool,
+}
+
+/// Input parameters for complexity calculation
+/// EXTENDED: Supports ALL tool types — brain, rag, glob, grep, web
+#[derive(Debug, Clone, Deserialize, Default)]
+pub struct ToolParams {
+    // Common
+    pub file_path: Option<String>,
+    // Edit
+    pub old_string: Option<String>,
+    pub new_string: Option<String>,
+    pub replace_all: Option<bool>,
+    // Write
+    pub content: Option<String>,
+    // Bash
+    pub command: Option<String>,
+    // Search (glob/grep)
+    pub query: Option<String>,
+    pub pattern: Option<String>,
+    pub path: Option<String>,
+    // Brain operations
+    pub collection: Option<String>,
+    pub limit: Option<u64>,
+    pub text: Option<String>,
+    pub title: Option<String>,
+    // RAG/Web operations
+    pub url: Option<String>,
+    pub topic: Option<String>,
+    pub category: Option<String>,
+    // Controlled tools (VL block — gate validation fields)
+    pub tool_name: Option<String>,   // spf_flint_execute: delegated tool name
+    pub reason: Option<String>,      // spf_flint_execute: justification string
+    pub peer_key: Option<String>,    // spf_mesh_call: target peer hex key
+    pub message: Option<String>,     // spf_transformer_chat: chat input
+    pub prompt: Option<String>,      // spf_transformer_infer: inference prompt
+    pub batch_size: Option<u64>,     // spf_transformer_train: training batch size
+}
+
+// ============================================================================
+// DYNAMIC COMPLEXITY HELPERS
+// complex^10: 1→1, 2→1024, 3→59049, 4→1048576
+// files×10: scales linearly with affected file count
+// ============================================================================
+
+/// Calculate dynamic complexity factor (0-4 scale)
+/// This is the primary lever for tier escalation via ^10 exponent
+fn calc_complex_factor(content_len: u64, has_risk: bool, is_architectural: bool) -> u64 {
+    let mut complex: u64 = 0;
+    
+    // Size-based complexity
+    if content_len > 200 { complex += 1; }      // Moderate size
+    if content_len > 1000 { complex += 1; }     // Large change
+    if content_len > 5000 { complex += 1; }     // Very large change
+    
+    // Risk indicators add complexity
+    if has_risk { complex += 1; }
+    
+    // Architectural changes are highest complexity
+    if is_architectural { complex = complex.max(3); }
+    
+    complex.min(4)  // Cap at 4 (4^10 = 1,048,576)
+}
+
+/// Calculate dynamic files factor based on scope
+fn calc_files_factor(path: &str, pattern: &str, cmd: &str) -> u64 {
+    // Codebase-wide operations
+    if cmd.contains("find") || cmd.contains("xargs") || cmd.contains("-r ") {
+        return 100;  // 100×10 = 1000
+    }
+    
+    // Recursive glob
+    if pattern.contains("**") || path.contains("**") || cmd.contains("**") {
+        return 50;   // 50×10 = 500
+    }
+    
+    // Simple glob
+    if pattern.contains("*") || path.contains("*") || cmd.contains("*") {
+        return 20;   // 20×10 = 200
+    }
+    
+    // Root directory = potentially many files
+    if path == "." || path == "/" || path.ends_with("src") || path.ends_with("lib") {
+        return 20;
+    }
+    
+    // Default single file
+    1
+}
+
+/// Check if file is architectural (config, main, lib, mod)
+fn is_architectural_file(path: &str) -> bool {
+    let p = path.to_lowercase();
+    p.contains("config") || p.contains("main.") || p.contains("lib.") 
+        || p.contains("mod.") || p.contains("cargo.toml") || p.contains("package.json")
+        || p.contains(".env") || p.contains("settings") || p.contains("schema")
+        || p.ends_with("rc") || p.ends_with(".yaml") || p.ends_with(".yml")
+}
+
+/// Check if content has risk indicators
+fn has_risk_indicators(content: &str) -> bool {
+    content.contains("delete") || content.contains("drop") || content.contains("remove")
+        || content.contains("truncate") || content.contains("override") 
+        || content.contains("force") || content.contains("unsafe")
+        || content.contains("rm ") || content.contains("sudo")
+}
+
+/// Calculate complexity value C for a tool call
+pub fn calculate_c(tool: &str, params: &ToolParams, config: &SpfConfig) -> u64 {
+    let (basic, dependencies, complex_factor, files) = match tool {
+        "Edit" | "spf_edit" => {
+            let old_str = params.old_string.as_deref().unwrap_or("");
+            let new_str = params.new_string.as_deref().unwrap_or("");
+            let old_len = old_str.len() as u64;
+            let new_len = new_str.len() as u64;
+            let total_len = old_len + new_len;
+            let file_path = params.file_path.as_deref().unwrap_or("");
+            
+            let basic = config.complexity_weights.edit.basic + total_len / 20;
+            
+            // Dependencies: replace_all affects more, large diffs have cascading effects
+            let mut deps = if params.replace_all.unwrap_or(false) { 3u64 } else { 1 };
+            if total_len > 500 { deps += 1; }
+            
+            // Complex factor: dynamic based on size, risk, architecture
+            let has_risk = has_risk_indicators(new_str);
+            let is_arch = is_architectural_file(file_path);
+            let complex = calc_complex_factor(total_len, has_risk, is_arch);
+            
+            // Files: edits affect 1 file but replace_all could have wide impact
+            let files = if params.replace_all.unwrap_or(false) { 5u64 } else { 1 };
+            
+            (basic, deps, complex, files)
+        }
+
+        "Write" | "spf_write" => {
+            let content = params.content.as_deref().unwrap_or("");
+            let content_len = content.len() as u64;
+            let file_path = params.file_path.as_deref().unwrap_or("");
+
+            let basic = config.complexity_weights.write.basic + content_len / 50;
+            
+            // Dependencies: imports/requires in content indicate deps
+            let mut deps = config.complexity_weights.write.dependencies;
+            if content.contains("import ") || content.contains("require(") 
+                || content.contains("use ") || content.contains("mod ") {
+                deps += 2;
+            }
+            
+            // Complex factor: dynamic
+            let has_risk = has_risk_indicators(content);
+            let is_arch = is_architectural_file(file_path);
+            let complex = calc_complex_factor(content_len, has_risk, is_arch);
+            
+            (basic, deps, complex, 1u64)
+        }
+
+        "Bash" | "spf_bash" => {
+            let cmd = params.command.as_deref().unwrap_or("");
+
+            // Check dangerous commands
+            let is_dangerous = config.dangerous_commands.iter().any(|d| cmd.contains(d.as_str()));
+            // Check git operations
+            let is_git = cmd.contains("git push") || cmd.contains("git reset")
+                || cmd.contains("git rebase") || cmd.contains("git merge");
+            // Check piped/chained
+            let is_piped = cmd.contains("&&") || cmd.contains("|");
+            
+            // Dynamic files calculation
+            let files = calc_files_factor("", "", cmd);
+            
+            // Count pipe stages as dependencies
+            let pipe_count = cmd.matches("|").count() as u64;
+            let chain_count = cmd.matches("&&").count() as u64;
+            
+            if is_dangerous {
+                let w = &config.complexity_weights.bash_dangerous;
+                // Dangerous = high complex factor
+                (w.basic, w.dependencies + pipe_count + chain_count, 3u64.max(w.complex), files)
+            } else if is_git {
+                let w = &config.complexity_weights.bash_git;
+                // Git operations: complex=2 minimum (1024 added to C)
+                (w.basic, w.dependencies + pipe_count, 2u64.max(w.complex), files)
+            } else if is_piped {
+                let w = &config.complexity_weights.bash_piped;
+                // Piped: complexity scales with pipe count
+                let complex = (1 + pipe_count).min(3);
+                (w.basic, w.dependencies + pipe_count + chain_count, complex, files)
+            } else {
+                let w = &config.complexity_weights.bash_simple;
+                (w.basic, w.dependencies, w.complex, files)
+            }
+        }
+
+        "Read" | "spf_read" => {
+            // Reads are safe - encourage information gathering
+            let w = &config.complexity_weights.read;
+            (w.basic, w.dependencies, w.complex, w.files)
+        }
+
+        "Glob" | "spf_glob" | "Grep" | "spf_grep" => {
+            let w = &config.complexity_weights.search;
+            let path = params.path.as_deref().unwrap_or(".");
+            let pattern = params.pattern.as_deref().unwrap_or("");
+            
+            // Dynamic files based on pattern scope
+            let files = calc_files_factor(path, pattern, "");
+            
+            // Search complexity based on pattern
+            let complex = if pattern.len() > 50 { 1u64 } else { w.complex };
+            
+            (w.basic, w.dependencies, complex, files)
+        }
+
+        // === BRAIN OPERATIONS — MUST BE GATED ===
+        "brain_search" | "spf_brain_search" => {
+            let limit = params.limit.unwrap_or(5);
+            (10, limit, 0, 1)
+        }
+        "brain_store" | "spf_brain_store" => {
+            let text_len = params.text.as_ref().map(|s| s.len()).unwrap_or(0) as u64;
+            (20 + text_len / 50, 2, if text_len > 5000 { 1 } else { 0 }, 1)
+        }
+        "brain_index" | "spf_brain_index" => (50, 5, 1, 10),
+        "brain_recall" | "spf_brain_recall" |
+        "brain_context" | "spf_brain_context" |
+        "brain_list" | "spf_brain_list" |
+        "brain_status" | "spf_brain_status" |
+        "brain_list_docs" | "spf_brain_list_docs" |
+        "brain_get_doc" | "spf_brain_get_doc" => (10, 1, 0, 1),
+
+        // === RAG OPERATIONS — MUST BE GATED ===
+        "rag_collect_web" | "spf_rag_collect_web" => (50, 10, 1, 5),
+        "rag_fetch_url" | "spf_rag_fetch_url" => (30, 5, 1, 1),
+        "rag_collect_file" | "spf_rag_collect_file" => (15, 2, 0, 1),
+        "rag_collect_folder" | "spf_rag_collect_folder" => (30, 5, 0, 10),
+        "rag_index_gathered" | "spf_rag_index_gathered" => (40, 5, 1, 10),
+        "rag_collect_drop" | "spf_rag_collect_drop" => (25, 3, 0, 5),
+        "rag_collect_rss" | "spf_rag_collect_rss" => (25, 5, 0, 5),
+        "rag_dedupe" | "spf_rag_dedupe" => (20, 3, 0, 1),
+        "rag_smart_search" | "spf_rag_smart_search" |
+        "rag_auto_fetch_gaps" | "spf_rag_auto_fetch_gaps" => (40, 8, 1, 5),
+        "rag_fulfill_search" | "spf_rag_fulfill_search" => (20, 3, 0, 1),
+        "rag_status" | "spf_rag_status" |
+        "rag_list_gathered" | "spf_rag_list_gathered" |
+        "rag_bandwidth_status" | "spf_rag_bandwidth_status" |
+        "rag_list_feeds" | "spf_rag_list_feeds" |
+        "rag_pending_searches" | "spf_rag_pending_searches" => (8, 1, 0, 1),
+
+        // === WEB OPERATIONS ===
+        "web_fetch" | "spf_web_fetch" => (30, 5, 1, 1),
+        "web_search" | "spf_web_search" => (25, 3, 0, 1),
+        "web_download" | "spf_web_download" => (30, 5, 1, 1),
+        "web_api" | "spf_web_api" => (25, 3, 0, 1),
+        // WB-2: Reverse proxy browser tools
+        "web_connect" | "spf_web_connect" => (20, 2, 0, 1),
+        "web_navigate" | "spf_web_navigate" => (30, 5, 1, 1),
+        "web_click" | "spf_web_click" => (15, 2, 0, 1),
+        "web_fill" | "spf_web_fill" => (15, 2, 0, 1),
+        "web_select" | "spf_web_select" => (10, 1, 0, 1),
+        "web_eval" | "spf_web_eval" => (40, 8, 2, 1),    // JS eval — higher risk
+        "web_screenshot" | "spf_web_screenshot" => (10, 1, 0, 1),
+        "web_design" | "spf_web_design" => (20, 3, 0, 1),
+        "web_page" | "spf_web_page" => (10, 1, 0, 1),
+
+        // CDP BROWSER weights removed — WB-2a (will be re-added in WB-2e for reverse proxy)
+
+        // === NOTEBOOK ===
+        "notebook_edit" | "spf_notebook_edit" => (15, 2, 0, 1),
+
+        // === STATUS (low complexity) ===
+        "status" | "spf_status" | "session" | "spf_session" |
+        "calculate" | "spf_calculate" => (5, 0, 0, 1),
+
+        // === UNKNOWN — default high for safety ===
+        _ => {
+            let w = &config.complexity_weights.unknown;
+            (w.basic, w.dependencies, w.complex, w.files)
+        }
+    };
+
+    // Apply formula: C = (basic ^ power) + (deps ^ power) + (complex ^ power) + (files × mult)
+    // HARDCODE: Saturating math prevents overflow — system never breaks
+    let c = basic.saturating_pow(config.formula.basic_power)
+        .saturating_add(dependencies.saturating_pow(config.formula.deps_power))
+        .saturating_add(complex_factor.saturating_pow(config.formula.complex_power))
+        .saturating_add(files.saturating_mul(config.formula.files_multiplier));
+
+    c
+}
+
+/// Apply master formula: a_optimal(C) = W_eff × (1 - 1/ln(C + e))
+pub fn a_optimal(c: u64, config: &SpfConfig) -> u64 {
+    let c_f = if c == 0 { 1.0 } else { c as f64 };
+    let result = config.formula.w_eff * (1.0 - 1.0 / (c_f + config.formula.e).ln());
+    result.max(0.0) as u64
+}
+
+/// Full complexity calculation — returns everything needed for enforcement
+pub fn calculate(tool: &str, params: &ToolParams, config: &SpfConfig) -> ComplexityResult {
+    let c = calculate_c(tool, params, config);
+    let (tier, analyze, build, requires_approval) = config.get_tier(c);
+    let tokens = a_optimal(c, config);
+
+    ComplexityResult {
+        tool: tool.to_string(),
+        c,
+        tier: tier.to_string(),
+        analyze_percent: analyze,
+        build_percent: build,
+        a_optimal_tokens: tokens,
+        requires_approval,
+    }
+}
+
+// ============================================================================
+// TESTS
+// ============================================================================
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::config::SpfConfig;
+
+    fn default_config() -> SpfConfig {
+        SpfConfig::default()
+    }
+
+    #[test]
+    fn read_produces_simple_tier() {
+        let config = default_config();
+        let params = ToolParams::default();
+        let result = calculate("spf_read", &params, &config);
+        assert_eq!(result.tier, "SIMPLE");
+        assert!(result.c < 500, "Read C={} should be < 500", result.c);
+    }
+
+    #[test]
+    fn simple_bash_is_simple_tier() {
+        let config = default_config();
+        let params = ToolParams { command: Some("ls -la".to_string()), ..Default::default() };
+        let result = calculate("spf_bash", &params, &config);
+        assert_eq!(result.tier, "SIMPLE", "Simple bash C={} tier={}", result.c, result.tier);
+    }
+
+    #[test]
+    fn dangerous_bash_is_critical_tier() {
+        let config = default_config();
+        let params = ToolParams { command: Some("rm -rf / --no-preserve-root".to_string()), ..Default::default() };
+        let result = calculate("spf_bash", &params, &config);
+        assert_eq!(result.tier, "CRITICAL", "Dangerous bash C={} should be CRITICAL", result.c);
+        assert!(result.c >= 10000);
+    }
+
+    #[test]
+    fn status_tool_is_minimal_complexity() {
+        let config = default_config();
+        let params = ToolParams::default();
+        let result = calculate("spf_status", &params, &config);
+        assert!(result.c < 100, "Status C={} should be minimal", result.c);
+        assert_eq!(result.tier, "SIMPLE");
+    }
+
+    #[test]
+    fn unknown_tool_uses_default_weights() {
+        let config = default_config();
+        let params = ToolParams::default();
+        let c = calculate_c("totally_unknown_tool", &params, &config);
+        // unknown: basic=20, deps=3, complex=1, files=1
+        // C = 20 + 3^7 + 1^10 + 1*10 = 20 + 2187 + 1 + 10 = 2218
+        assert!(c >= 2000, "Unknown tool C={} should be >= 2000 (LIGHT+)", c);
+    }
+
+    #[test]
+    fn a_optimal_within_bounds() {
+        let config = default_config();
+        let tokens = a_optimal(100, &config);
+        assert!(tokens > 0, "a_optimal(100) should be > 0");
+        assert!(tokens < 40000, "a_optimal(100)={} should be < W_eff(40000)", tokens);
+    }
+
+    #[test]
+    fn a_optimal_zero_input() {
+        let config = default_config();
+        let tokens = a_optimal(0, &config);
+        // C=0 → uses c_f=1.0, ln(1+e) ≈ 1.31, result should be positive
+        assert!(tokens > 0, "a_optimal(0)={} should still be > 0", tokens);
+    }
+
+    #[test]
+    fn risk_indicators_detected() {
+        assert!(has_risk_indicators("please delete this file"));
+        assert!(has_risk_indicators("sudo make install"));
+        assert!(has_risk_indicators("rm -rf everything"));
+        assert!(!has_risk_indicators("create a new file"));
+        assert!(!has_risk_indicators("read the documentation"));
+    }
+}

SPFsmartGATE/src/encoder.rs

@@ -0,0 +1,404 @@
+// SPF Smart Gateway - Transformer Encoder
+// Copyright 2026 Joseph Stone - All Rights Reserved
+//
+// N stacked encoder layers: self-attention → layer_norm → FFN → layer_norm
+// Sinusoidal positional encoding. Bidirectional attention (no causal mask).
+// Input: token embeddings → Output: contextualized representations.
+//
+// Depends on: tensor.rs, attention.rs, ffn.rs (Layers 0-1)
+
+use crate::tensor::Tensor;
+use crate::attention::{AttentionCache, AttentionConfig, MultiHeadAttention};
+use crate::ffn::{FfnCache, FfnConfig, FeedForward};
+
+// ============================================================================
+// ACTIVATION CACHE (for backward pass — P2-C)
+// ============================================================================
+
+/// Cached activations from one encoder layer forward pass
+pub struct EncoderLayerCache {
+    /// Input before LN1 (for layer_norm_backward)
+    pub ln1_input: Tensor,
+    /// Attention cache (Q, K, V, attn_weights, input, scale)
+    pub attn_cache: AttentionCache,
+    /// Input before LN2 (after attention residual)
+    pub ln2_input: Tensor,
+    /// FFN cache (input, hidden_pre_gelu)
+    pub ffn_cache: FfnCache,
+    /// Original layer input (for residual backward)
+    pub residual_input: Tensor,
+}
+
+// ============================================================================
+// ENCODER CONFIGURATION
+// ============================================================================
+
+/// Configuration for the encoder stack
+#[derive(Debug, Clone)]
+pub struct EncoderConfig {
+    /// Number of encoder layers
+    pub n_layers: usize,
+    /// Model dimension
+    pub d_model: usize,
+    /// Number of attention heads
+    pub n_heads: usize,
+    /// Feed-forward hidden dimension (default: 4× d_model)
+    pub d_ff: usize,
+    /// Maximum sequence length (for positional encoding)
+    pub max_seq_len: usize,
+    /// Layer norm epsilon
+    pub ln_eps: f32,
+}
+
+impl EncoderConfig {
+    /// SPF Writer default: 6 layers, 256 dim, 8 heads
+    pub fn spf_writer() -> Self {
+        Self {
+            n_layers: 6,
+            d_model: 256,
+            n_heads: 8,
+            d_ff: 1024,
+            max_seq_len: 2048,
+            ln_eps: 1e-5,
+        }
+    }
+
+    /// Smaller config for testing
+    pub fn small() -> Self {
+        Self {
+            n_layers: 2,
+            d_model: 64,
+            n_heads: 4,
+            d_ff: 256,
+            max_seq_len: 512,
+            ln_eps: 1e-5,
+        }
+    }
+}
+
+// ============================================================================
+// SINGLE ENCODER LAYER
+// ============================================================================
+
+/// One encoder layer: self-attention + FFN, each with residual + layer norm.
+///
+/// Pre-norm architecture (norm before sublayer, used by modern transformers):
+///   x → LayerNorm → SelfAttention → + residual → LayerNorm → FFN → + residual
+pub struct EncoderLayer {
+    /// Multi-head self-attention (bidirectional)
+    pub self_attn: MultiHeadAttention,
+    /// Feed-forward network
+    pub ffn: FeedForward,
+    /// Layer norm before attention
+    pub ln1_gamma: Tensor,
+    pub ln1_beta: Tensor,
+    /// Layer norm before FFN
+    pub ln2_gamma: Tensor,
+    pub ln2_beta: Tensor,
+    /// Epsilon for layer norm
+    ln_eps: f32,
+}
+
+impl EncoderLayer {
+    /// Initialize a single encoder layer
+    pub fn new(d_model: usize, n_heads: usize, d_ff: usize, ln_eps: f32, seed: u64) -> Self {
+        let attn_config = AttentionConfig {
+            d_model,
+            n_heads,
+            causal: false, // Encoder uses bidirectional attention
+        };
+        let ffn_config = FfnConfig { d_model, d_ff };
+
+        Self {
+            self_attn: MultiHeadAttention::new(attn_config, seed),
+            ffn: FeedForward::new(ffn_config, seed + 100),
+            ln1_gamma: Tensor::ones(&[d_model]),
+            ln1_beta: Tensor::zeros(&[d_model]),
+            ln2_gamma: Tensor::ones(&[d_model]),
+            ln2_beta: Tensor::zeros(&[d_model]),
+            ln_eps,
+        }
+    }
+
+    /// Forward pass: [batch, seq, d_model] → [batch, seq, d_model]
+    pub fn forward(&self, x: &Tensor) -> Result<Tensor, String> {
+        // Pre-norm self-attention with residual
+        let normed = x.layer_norm(&self.ln1_gamma, &self.ln1_beta, self.ln_eps)?;
+        let attn_out = self.self_attn.forward(&normed)?;
+        let x = x.add(&attn_out)?;
+
+        // Pre-norm FFN with residual
+        let normed = x.layer_norm(&self.ln2_gamma, &self.ln2_beta, self.ln_eps)?;
+        let ffn_out = self.ffn.forward(&normed)?;
+        x.add(&ffn_out)
+    }
+
+    /// Forward pass with cached activations for backward.
+    /// Output is IDENTICAL to forward(). Cache is additional data only.
+    pub fn forward_with_cache(&self, x: &Tensor) -> Result<(Tensor, EncoderLayerCache), String> {
+        let residual_input = x.clone();
+
+        // Pre-norm self-attention with residual
+        let ln1_input = x.clone();
+        let normed = x.layer_norm(&self.ln1_gamma, &self.ln1_beta, self.ln_eps)?;
+        let (attn_out, attn_cache) = self.self_attn.forward_with_cache(&normed)?;
+        let x = x.add(&attn_out)?;
+
+        // Pre-norm FFN with residual
+        let ln2_input = x.clone();
+        let normed = x.layer_norm(&self.ln2_gamma, &self.ln2_beta, self.ln_eps)?;
+        let (ffn_out, ffn_cache) = self.ffn.forward_with_cache(&normed)?;
+        let output = x.add(&ffn_out)?;
+
+        let cache = EncoderLayerCache {
+            ln1_input,
+            attn_cache,
+            ln2_input,
+            ffn_cache,
+            residual_input,
+        };
+
+        Ok((output, cache))
+    }
+
+    /// Total parameters in this layer
+    pub fn num_params(&self) -> usize {
+        let d = self.ln1_gamma.numel();
+        self.self_attn.num_params() + self.ffn.num_params() + 4 * d // 2 LN × (gamma + beta)
+    }
+
+    /// Collect all weight tensors for serialization
+    pub fn weights(&self) -> Vec<&Tensor> {
+        let mut w = self.self_attn.weights();
+        w.extend(self.ffn.weights());
+        w.extend([&self.ln1_gamma, &self.ln1_beta, &self.ln2_gamma, &self.ln2_beta]);
+        w
+    }
+
+    /// Collect all weight tensors mutably for optimizer updates
+    pub fn weights_mut(&mut self) -> Vec<&mut Tensor> {
+        let mut w = self.self_attn.weights_mut();
+        w.extend(self.ffn.weights_mut());
+        w.extend([&mut self.ln1_gamma, &mut self.ln1_beta, &mut self.ln2_gamma, &mut self.ln2_beta]);
+        w
+    }
+}
+
+// ============================================================================
+// POSITIONAL ENCODING
+// ============================================================================
+
+/// Generate sinusoidal positional encoding table.
+/// PE(pos, 2i) = sin(pos / 10000^(2i/d_model))
+/// PE(pos, 2i+1) = cos(pos / 10000^(2i/d_model))
+///
+/// Returns: [max_seq_len, d_model] tensor
+pub fn sinusoidal_positional_encoding(max_seq_len: usize, d_model: usize) -> Tensor {
+    let mut data = vec![0.0f32; max_seq_len * d_model];
+    for pos in 0..max_seq_len {
+        for i in 0..d_model / 2 {
+            let angle = pos as f32 / (10000.0_f32).powf(2.0 * i as f32 / d_model as f32);
+            data[pos * d_model + 2 * i] = angle.sin();
+            data[pos * d_model + 2 * i + 1] = angle.cos();
+        }
+        // Handle odd d_model
+        if d_model % 2 == 1 {
+            let angle = pos as f32 / (10000.0_f32).powf((d_model - 1) as f32 / d_model as f32);
+            data[pos * d_model + d_model - 1] = angle.sin();
+        }
+    }
+    Tensor { data, shape: vec![max_seq_len, d_model] }
+}
+
+// ============================================================================
+// ENCODER STACK
+// ============================================================================
+
+/// Full encoder: positional encoding + N encoder layers + final layer norm.
+pub struct Encoder {
+    pub config: EncoderConfig,
+    /// Positional encoding table [max_seq_len, d_model]
+    pub pos_encoding: Tensor,
+    /// Stack of encoder layers
+    pub layers: Vec<EncoderLayer>,
+    /// Final layer norm (applied after all layers)
+    pub final_ln_gamma: Tensor,
+    pub final_ln_beta: Tensor,
+}
+
+impl Encoder {
+    /// Initialize encoder with given config
+    pub fn new(config: EncoderConfig, seed: u64) -> Self {
+        let pos_encoding = sinusoidal_positional_encoding(config.max_seq_len, config.d_model);
+
+        let layers: Vec<EncoderLayer> = (0..config.n_layers)
+            .map(|i| {
+                EncoderLayer::new(
+                    config.d_model,
+                    config.n_heads,
+                    config.d_ff,
+                    config.ln_eps,
+                    seed + (i as u64) * 1000,
+                )
+            })
+            .collect();
+
+        Self {
+            final_ln_gamma: Tensor::ones(&[config.d_model]),
+            final_ln_beta: Tensor::zeros(&[config.d_model]),
+            pos_encoding,
+            layers,
+            config,
+        }
+    }
+
+    /// Forward pass: embeddings [batch, seq_len, d_model] → encoded [batch, seq_len, d_model]
+    ///
+    /// 1. Add positional encoding to input embeddings
+    /// 2. Pass through N encoder layers
+    /// 3. Apply final layer norm
+    pub fn forward(&self, embeddings: &Tensor) -> Result<Tensor, String> {
+        let seq_len = embeddings.shape[1];
+        if seq_len > self.config.max_seq_len {
+            return Err(format!(
+                "Sequence length {} exceeds max {}",
+                seq_len, self.config.max_seq_len
+            ));
+        }
+
+        // Slice positional encoding to actual sequence length
+        let pos_enc = self.pos_encoding.slice(0, seq_len)?;
+
+        // Add positional encoding (broadcasts across batch dimension)
+        let batch = embeddings.shape[0];
+        let d_model = embeddings.shape[2];
+        let mut x_data = embeddings.data.clone();
+        for b in 0..batch {
+            for s in 0..seq_len {
+                for d in 0..d_model {
+                    x_data[(b * seq_len + s) * d_model + d] += pos_enc.data[s * d_model + d];
+                }
+            }
+        }
+        let mut x = Tensor::from_data(x_data, embeddings.shape.clone())?;
+
+        // Pass through encoder layers
+        for layer in &self.layers {
+            x = layer.forward(&x)?;
+        }
+
+        // Final layer norm
+        x.layer_norm(&self.final_ln_gamma, &self.final_ln_beta, self.config.ln_eps)
+    }
+
+    /// Total parameters in the encoder
+    pub fn num_params(&self) -> usize {
+        let layer_params: usize = self.layers.iter().map(|l| l.num_params()).sum();
+        layer_params + 2 * self.config.d_model // final LN gamma + beta
+    }
+
+    /// Collect all weight tensors
+    pub fn weights(&self) -> Vec<&Tensor> {
+        let mut w: Vec<&Tensor> = Vec::new();
+        for layer in &self.layers {
+            w.extend(layer.weights());
+        }
+        w.push(&self.final_ln_gamma);
+        w.push(&self.final_ln_beta);
+        w
+    }
+
+    /// Collect all weight tensors mutably
+    pub fn weights_mut(&mut self) -> Vec<&mut Tensor> {
+        let mut w: Vec<&mut Tensor> = Vec::new();
+        for layer in &mut self.layers {
+            w.extend(layer.weights_mut());
+        }
+        w.push(&mut self.final_ln_gamma);
+        w.push(&mut self.final_ln_beta);
+        w
+    }
+}
+
+// ============================================================================
+// TESTS
+// ============================================================================
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_positional_encoding_shape() {
+        let pe = sinusoidal_positional_encoding(100, 64);
+        assert_eq!(pe.shape, vec![100, 64]);
+    }
+
+    #[test]
+    fn test_positional_encoding_values() {
+        let pe = sinusoidal_positional_encoding(10, 8);
+        // Position 0 should have sin(0)=0, cos(0)=1 for first pair
+        assert!((pe.data[0] - 0.0).abs() < 1e-5); // sin(0)
+        assert!((pe.data[1] - 1.0).abs() < 1e-5); // cos(0)
+        // All values should be in [-1, 1]
+        assert!(pe.data.iter().all(|&v| v >= -1.0 && v <= 1.0));
+    }
+
+    #[test]
+    fn test_encoder_layer_shape() {
+        let layer = EncoderLayer::new(64, 4, 256, 1e-5, 42);
+        let x = Tensor::randn(&[2, 8, 64], 99);
+        let out = layer.forward(&x).unwrap();
+        assert_eq!(out.shape, vec![2, 8, 64]);
+    }
+
+    #[test]
+    fn test_encoder_layer_residual() {
+        let layer = EncoderLayer::new(64, 4, 256, 1e-5, 42);
+        let x = Tensor::randn(&[1, 4, 64], 99);
+        let out = layer.forward(&x).unwrap();
+        // With residual connections, output should not be identical to input
+        // but should be close in magnitude (not exploding)
+        let diff: f32 = x.data.iter().zip(&out.data)
+            .map(|(a, b)| (a - b).abs())
+            .sum::<f32>() / x.numel() as f32;
+        assert!(diff < 10.0, "Output diverged too far from input: {}", diff);
+    }
+
+    #[test]
+    fn test_encoder_full_forward() {
+        let config = EncoderConfig::small();
+        let encoder = Encoder::new(config, 42);
+        let x = Tensor::randn(&[1, 8, 64], 99);
+        let out = encoder.forward(&x).unwrap();
+        assert_eq!(out.shape, vec![1, 8, 64]);
+        assert!(out.data.iter().all(|v| v.is_finite()));
+    }
+
+    #[test]
+    fn test_encoder_seq_exceeds_max() {
+        let config = EncoderConfig { max_seq_len: 10, ..EncoderConfig::small() };
+        let encoder = Encoder::new(config, 42);
+        let x = Tensor::randn(&[1, 20, 64], 99); // seq=20 > max=10
+        assert!(encoder.forward(&x).is_err());
+    }
+
+    #[test]
+    fn test_encoder_num_params() {
+        let config = EncoderConfig::small(); // 2 layers, d=64, ff=256
+        let encoder = Encoder::new(config, 42);
+        let params = encoder.num_params();
+        // Each layer: attn(16640) + ffn(33088) + 4×64(LN) = 49984
+        // 2 layers + final LN = 2×49984 + 128 = 100096
+        assert_eq!(params, 100096);
+    }
+
+    #[test]
+    fn test_encoder_weights_collection() {
+        let config = EncoderConfig::small();
+        let encoder = Encoder::new(config, 42);
+        let weights = encoder.weights();
+        // Each layer: 8(attn) + 4(ffn) + 4(LN) = 16. ×2 layers + 2 final LN = 34
+        assert_eq!(weights.len(), 34);
+    }
+}

SPFsmartGATE/src/tmp_db.rs

@@ -0,0 +1,609 @@
+// SPF Smart Gateway - TMP LMDB
+// Copyright 2026 Joseph Stone - All Rights Reserved
+//
+// LMDB-backed metadata for /tmp and /projects device directories.
+// Tracks file access logs, resource usage, and project isolation.
+//
+// Database: TMP_DB
+// Storage: ~/SPFsmartGATE/LIVE/TMP/TMP.DB/
+
+use anyhow::{anyhow, Result};
+use heed::types::*;
+use heed::{Database, Env, EnvOpenOptions};
+use serde::{Deserialize, Serialize};
+use std::path::Path;
+use std::time::{SystemTime, UNIX_EPOCH};
+
+const MAX_DB_SIZE: usize = 256 * 1024 * 1024; // 256MB — virtual address space only, no physical cost
+
+/// Project trust level
+#[derive(Debug, Clone, Copy, Serialize, Deserialize, PartialEq, Eq, PartialOrd, Ord)]
+pub enum TrustLevel {
+    /// Untrusted - maximum restrictions
+    Untrusted = 0,
+    /// Low trust - basic operations only
+    Low = 1,
+    /// Medium trust - most operations allowed with prompts
+    Medium = 2,
+    /// High trust - operations allowed with minimal prompts
+    High = 3,
+    /// Full trust - all operations allowed (user's own project)
+    Full = 4,
+}
+
+impl Default for TrustLevel {
+    fn default() -> Self {
+        TrustLevel::Low
+    }
+}
+
+/// Project entry — tracked in TMP_DB LMDB
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct Project {
+    /// Project root path (canonical)
+    pub path: String,
+    /// Display name for the project
+    pub name: String,
+    /// Trust level
+    pub trust_level: TrustLevel,
+    /// Tools explicitly allowed for this project
+    pub allowed_tools: Vec<String>,
+    /// Tools explicitly denied for this project
+    pub denied_tools: Vec<String>,
+    /// Paths within project that are write-protected
+    pub protected_paths: Vec<String>,
+    /// Maximum file size for writes (bytes)
+    pub max_write_size: usize,
+    /// Maximum total writes per session
+    pub max_writes_per_session: u32,
+    /// Current session write count
+    pub session_writes: u32,
+    /// Total files accessed (read)
+    pub total_reads: u64,
+    /// Total files modified (write/edit)
+    pub total_writes: u64,
+    /// Total complexity accumulated
+    pub total_complexity: u64,
+    /// Created timestamp
+    pub created_at: u64,
+    /// Last accessed timestamp
+    pub last_accessed: u64,
+    /// Whether project requires explicit activation
+    pub requires_activation: bool,
+    /// Whether project is currently active
+    pub is_active: bool,
+    /// User notes about this project
+    pub notes: String,
+}
+
+/// File access record
+#[derive(Debug, Clone, Serialize, Deserialize)]
+pub struct FileAccess {
+    /// File path (relative to project root)
+    pub path: String,
+    /// Project this file belongs to
+    pub project: String,
+    /// Access type: "read", "write", "edit", "delete"
+    pub access_type: String,
+    /// Timestamp
+    pub timestamp: u64,
+    /// Session ID
+    pub session_id: String,
+    /// File size at access time
+    pub file_size: u64,
+    /// Whether access was allowed
+    pub allowed: bool,
+    /// Reason if denied
+    pub deny_reason: Option<String>,
+}
+
+/// Resource usage for a project
+#[derive(Debug, Clone, Serialize, Deserialize, Default)]
+pub struct ResourceUsage {
+    /// Total bytes read
+    pub bytes_read: u64,
+    /// Total bytes written
+    pub bytes_written: u64,
+    /// Total files created
+    pub files_created: u64,
+    /// Total files deleted
+    pub files_deleted: u64,
+    /// Total bash commands run
+    pub bash_commands: u64,
+    /// Total web requests
+    pub web_requests: u64,
+}
+
+/// LMDB-backed project manager
+pub struct SpfTmpDb {
+    env: Env,
+    /// Project registry: canonical_path → Project
+    projects: Database<Str, SerdeBincode<Project>>,
+    /// File access log: "timestamp:project:path" → FileAccess
+    access_log: Database<Str, SerdeBincode<FileAccess>>,
+    /// Resource usage: project_path → ResourceUsage
+    resources: Database<Str, SerdeBincode<ResourceUsage>>,
+    /// Active project marker: "active" → project_path
+    active: Database<Str, Str>,
+}
+
+impl SpfTmpDb {
+    /// Open or create project LMDB at given path
+    pub fn open(path: &Path) -> Result<Self> {
+        std::fs::create_dir_all(path)?;
+
+        let env = unsafe {
+            EnvOpenOptions::new()
+                .map_size(MAX_DB_SIZE)
+                .max_dbs(8)
+                .open(path)?
+        };
+
+        let mut wtxn = env.write_txn()?;
+        let projects = env.create_database(&mut wtxn, Some("projects"))?;
+        let access_log = env.create_database(&mut wtxn, Some("access_log"))?;
+        let resources = env.create_database(&mut wtxn, Some("resources"))?;
+        let active = env.create_database(&mut wtxn, Some("active"))?;
+        wtxn.commit()?;
+
+        log::info!("TMP_DB LMDB opened at {:?}", path);
+        Ok(Self { env, projects, access_log, resources, active })
+    }
+
+    // ========================================================================
+    // PROJECT MANAGEMENT
+    // ========================================================================
+
+    /// Register a new project project
+    pub fn register_project(&self, path: &str, name: &str, trust_level: TrustLevel) -> Result<Project> {
+        let canonical = std::fs::canonicalize(path)
+            .map(|p| p.to_string_lossy().to_string())
+            .unwrap_or_else(|_| path.to_string());
+
+        let now = SystemTime::now()
+            .duration_since(UNIX_EPOCH)
+            .unwrap_or_default()
+            .as_secs();
+
+        let project = Project {
+            path: canonical.clone(),
+            name: name.to_string(),
+            trust_level,
+            allowed_tools: Vec::new(),
+            denied_tools: Vec::new(),
+            protected_paths: vec![".git".to_string(), ".env".to_string()],
+            max_write_size: 100_000,
+            max_writes_per_session: 100,
+            session_writes: 0,
+            total_reads: 0,
+            total_writes: 0,
+            total_complexity: 0,
+            created_at: now,
+            last_accessed: now,
+            requires_activation: trust_level < TrustLevel::High,
+            is_active: false,
+            notes: String::new(),
+        };
+
+        let mut wtxn = self.env.write_txn()?;
+        self.projects.put(&mut wtxn, &canonical, &project)?;
+        self.resources.put(&mut wtxn, &canonical, &ResourceUsage::default())?;
+        wtxn.commit()?;
+
+        Ok(project)
+    }
+
+    /// Get a project project
+    pub fn get_project(&self, path: &str) -> Result<Option<Project>> {
+        let canonical = std::fs::canonicalize(path)
+            .map(|p| p.to_string_lossy().to_string())
+            .unwrap_or_else(|_| path.to_string());
+
+        let rtxn = self.env.read_txn()?;
+        Ok(self.projects.get(&rtxn, &canonical)?)
+    }
+
+    /// Update a project project
+    pub fn update_project(&self, project: &Project) -> Result<()> {
+        let mut wtxn = self.env.write_txn()?;
+        self.projects.put(&mut wtxn, &project.path, project)?;
+        wtxn.commit()?;
+        Ok(())
+    }
+
+    /// Find project containing a file path
+    pub fn find_project_for_path(&self, file_path: &str) -> Result<Option<Project>> {
+        let canonical = std::fs::canonicalize(file_path)
+            .map(|p| p.to_string_lossy().to_string())
+            .unwrap_or_else(|_| file_path.to_string());
+
+        let rtxn = self.env.read_txn()?;
+        let iter = self.projects.iter(&rtxn)?;
+
+        // Find the most specific (longest) matching project path
+        let mut best_match: Option<Project> = None;
+        let mut best_len = 0;
+
+        for result in iter {
+            let (project_path, project) = result?;
+            if canonical.starts_with(project_path) && project_path.len() > best_len {
+                best_match = Some(project);
+                best_len = project_path.len();
+            }
+        }
+
+        Ok(best_match)
+    }
+
+    /// List all registered projects
+    pub fn list_projects(&self) -> Result<Vec<Project>> {
+        let rtxn = self.env.read_txn()?;
+        let iter = self.projects.iter(&rtxn)?;
+
+        let mut projects = Vec::new();
+        for result in iter {
+            let (_, project) = result?;
+            projects.push(project);
+        }
+        Ok(projects)
+    }
+
+    /// Delete a project
+    pub fn delete_project(&self, path: &str) -> Result<bool> {
+        let canonical = std::fs::canonicalize(path)
+            .map(|p| p.to_string_lossy().to_string())
+            .unwrap_or_else(|_| path.to_string());
+
+        let mut wtxn = self.env.write_txn()?;
+        let deleted = self.projects.delete(&mut wtxn, &canonical)?;
+        self.resources.delete(&mut wtxn, &canonical)?;
+        wtxn.commit()?;
+        Ok(deleted)
+    }
+
+    // ========================================================================
+    // TRUST & PERMISSIONS
+    // ========================================================================
+
+    /// Set project trust level
+    pub fn set_trust_level(&self, path: &str, level: TrustLevel) -> Result<()> {
+        let mut project = self.get_project(path)?
+            .ok_or_else(|| anyhow!("Project not found: {}", path))?;
+        project.trust_level = level;
+        project.requires_activation = level < TrustLevel::High;
+        self.update_project(&project)
+    }
+
+    /// Check if a tool is allowed for a project
+    pub fn is_tool_allowed(&self, project_path: &str, tool: &str) -> Result<bool> {
+        let project = match self.get_project(project_path)? {
+            Some(s) => s,
+            None => return Ok(true), // No project = no restrictions
+        };
+
+        // Explicit deny takes precedence
+        if project.denied_tools.contains(&tool.to_string()) {
+            return Ok(false);
+        }
+
+        // Explicit allow
+        if project.allowed_tools.contains(&tool.to_string()) {
+            return Ok(true);
+        }
+
+        // Trust-level based default
+        Ok(match project.trust_level {
+            TrustLevel::Untrusted => false,
+            TrustLevel::Low => matches!(tool, "Read" | "Glob" | "Grep"),
+            TrustLevel::Medium => !matches!(tool, "Bash"),
+            TrustLevel::High | TrustLevel::Full => true,
+        })
+    }
+
+    /// Check if a path within project is protected
+    pub fn is_path_protected(&self, project_path: &str, file_path: &str) -> Result<bool> {
+        let project = match self.get_project(project_path)? {
+            Some(s) => s,
+            None => return Ok(false),
+        };
+
+        // Get relative path
+        let relative = file_path.strip_prefix(&project.path)
+            .unwrap_or(file_path)
+            .trim_start_matches('/');
+
+        for protected in &project.protected_paths {
+            if relative.starts_with(protected) || relative == *protected {
+                return Ok(true);
+            }
+        }
+        Ok(false)
+    }
+
+    /// Add a protected path to a project
+    pub fn add_protected_path(&self, project_path: &str, protected: &str) -> Result<()> {
+        let mut project = self.get_project(project_path)?
+            .ok_or_else(|| anyhow!("Project not found: {}", project_path))?;
+
+        if !project.protected_paths.contains(&protected.to_string()) {
+            project.protected_paths.push(protected.to_string());
+            self.update_project(&project)?;
+        }
+        Ok(())
+    }
+
+    // ========================================================================
+    // ACTIVE PROJECT
+    // ========================================================================
+
+    /// Set the currently active project
+    pub fn set_active(&self, path: &str) -> Result<()> {
+        let canonical = std::fs::canonicalize(path)
+            .map(|p| p.to_string_lossy().to_string())
+            .unwrap_or_else(|_| path.to_string());
+
+        // Deactivate current
+        if let Some(current) = self.get_active()? {
+            let mut project = self.get_project(&current)?
+                .ok_or_else(|| anyhow!("Active project not found"))?;
+            project.is_active = false;
+            self.update_project(&project)?;
+        }
+
+        // Activate new
+        let mut project = self.get_project(&canonical)?
+            .ok_or_else(|| anyhow!("Project not found: {}", canonical))?;
+        project.is_active = true;
+        project.last_accessed = SystemTime::now()
+            .duration_since(UNIX_EPOCH)
+            .unwrap_or_default()
+            .as_secs();
+        self.update_project(&project)?;
+
+        let mut wtxn = self.env.write_txn()?;
+        self.active.put(&mut wtxn, "active", &canonical)?;
+        wtxn.commit()?;
+        Ok(())
+    }
+
+    /// Get the currently active project path
+    pub fn get_active(&self) -> Result<Option<String>> {
+        let rtxn = self.env.read_txn()?;
+        Ok(self.active.get(&rtxn, "active")?.map(|s| s.to_string()))
+    }
+
+    /// Clear active project
+    pub fn clear_active(&self) -> Result<()> {
+        if let Some(current) = self.get_active()? {
+            if let Some(mut project) = self.get_project(&current)? {
+                project.is_active = false;
+                self.update_project(&project)?;
+            }
+        }
+        let mut wtxn = self.env.write_txn()?;
+        self.active.delete(&mut wtxn, "active")?;
+        wtxn.commit()?;
+        Ok(())
+    }
+
+    // ========================================================================
+    // ACCESS LOGGING
+    // ========================================================================
+
+    /// Log a file access
+    pub fn log_access(
+        &self,
+        file_path: &str,
+        project_path: &str,
+        access_type: &str,
+        session_id: &str,
+        file_size: u64,
+        allowed: bool,
+        deny_reason: Option<&str>,
+    ) -> Result<()> {
+        let now = SystemTime::now()
+            .duration_since(UNIX_EPOCH)
+            .unwrap_or_default()
+            .as_secs();
+
+        let access = FileAccess {
+            path: file_path.to_string(),
+            project: project_path.to_string(),
+            access_type: access_type.to_string(),
+            timestamp: now,
+            session_id: session_id.to_string(),
+            file_size,
+            allowed,
+            deny_reason: deny_reason.map(|s| s.to_string()),
+        };
+
+        let key = format!("{}:{}:{}", now, project_path, file_path);
+        let mut wtxn = self.env.write_txn()?;
+        self.access_log.put(&mut wtxn, &key, &access)?;
+        wtxn.commit()?;
+
+        // Update project stats
+        if let Some(mut project) = self.get_project(project_path)? {
+            if allowed {
+                match access_type {
+                    "read" => project.total_reads += 1,
+                    "write" | "edit" | "delete" => {
+                        project.total_writes += 1;
+                        project.session_writes += 1;
+                    }
+                    _ => {}
+                }
+            }
+            project.last_accessed = now;
+            self.update_project(&project)?;
+        }
+
+        // Update resource usage
+        if allowed {
+            self.update_resources(project_path, access_type, file_size)?;
+        }
+
+        Ok(())
+    }
+
+    /// Get recent access log for a project
+    pub fn get_access_log(&self, project_path: &str, limit: usize) -> Result<Vec<FileAccess>> {
+        let rtxn = self.env.read_txn()?;
+        let iter = self.access_log.rev_iter(&rtxn)?;
+
+        let mut log = Vec::new();
+        for result in iter {
+            let (_, access) = result?;
+            if access.project == project_path {
+                log.push(access);
+                if log.len() >= limit {
+                    break;
+                }
+            }
+        }
+        Ok(log)
+    }
+
+    /// Prune access log older than N seconds
+    pub fn prune_access_log(&self, max_age_secs: u64) -> Result<u64> {
+        let now = SystemTime::now()
+            .duration_since(UNIX_EPOCH)
+            .unwrap_or_default()
+            .as_secs();
+        let cutoff = now.saturating_sub(max_age_secs);
+
+        let rtxn = self.env.read_txn()?;
+        let iter = self.access_log.iter(&rtxn)?;
+
+        let mut to_delete = Vec::new();
+        for result in iter {
+            let (key, access) = result?;
+            if access.timestamp < cutoff {
+                to_delete.push(key.to_string());
+            }
+        }
+        drop(rtxn);
+
+        let count = to_delete.len() as u64;
+        let mut wtxn = self.env.write_txn()?;
+        for key in to_delete {
+            self.access_log.delete(&mut wtxn, &key)?;
+        }
+        wtxn.commit()?;
+
+        Ok(count)
+    }
+
+    // ========================================================================
+    // RESOURCE TRACKING
+    // ========================================================================
+
+    fn update_resources(&self, project_path: &str, access_type: &str, size: u64) -> Result<()> {
+        let rtxn = self.env.read_txn()?;
+        let mut usage = self.resources.get(&rtxn, project_path)?
+            .unwrap_or_default();
+        drop(rtxn);
+
+        match access_type {
+            "read" => usage.bytes_read += size,
+            "write" => {
+                usage.bytes_written += size;
+                usage.files_created += 1;
+            }
+            "edit" => usage.bytes_written += size,
+            "delete" => usage.files_deleted += 1,
+            "bash" => usage.bash_commands += 1,
+            "web" => usage.web_requests += 1,
+            _ => {}
+        }
+
+        let mut wtxn = self.env.write_txn()?;
+        self.resources.put(&mut wtxn, project_path, &usage)?;
+        wtxn.commit()?;
+        Ok(())
+    }
+
+    /// Get resource usage for a project
+    pub fn get_resources(&self, project_path: &str) -> Result<ResourceUsage> {
+        let rtxn = self.env.read_txn()?;
+        Ok(self.resources.get(&rtxn, project_path)?.unwrap_or_default())
+    }
+
+    /// Reset session counters (call at session start)
+    pub fn reset_session_counters(&self) -> Result<()> {
+        let projects = self.list_projects()?;
+        for mut project in projects {
+            project.session_writes = 0;
+            self.update_project(&project)?;
+        }
+        Ok(())
+    }
+
+    // ========================================================================
+    // VALIDATION
+    // ========================================================================
+
+    /// Validate a file operation against project rules
+    pub fn validate_operation(
+        &self,
+        file_path: &str,
+        operation: &str,
+        size: u64,
+    ) -> Result<(bool, Option<String>)> {
+        // Find containing project
+        let project = match self.find_project_for_path(file_path)? {
+            Some(s) => s,
+            None => return Ok((true, None)), // No project = allowed
+        };
+
+        // Check if project is active (if required)
+        if project.requires_activation && !project.is_active {
+            return Ok((false, Some(format!(
+                "Project '{}' requires activation before file operations",
+                project.name
+            ))));
+        }
+
+        // Check trust level for write operations
+        if matches!(operation, "write" | "edit" | "delete") {
+            if project.trust_level == TrustLevel::Untrusted {
+                return Ok((false, Some("Untrusted project: write operations denied".to_string())));
+            }
+
+            // Check protected paths
+            if self.is_path_protected(&project.path, file_path)? {
+                return Ok((false, Some(format!(
+                    "Path is protected in project '{}'",
+                    project.name
+                ))));
+            }
+
+            // Check write size limit
+            if size > project.max_write_size as u64 {
+                return Ok((false, Some(format!(
+                    "File size {} exceeds project limit {}",
+                    size, project.max_write_size
+                ))));
+            }
+
+            // Check session write limit
+            if project.session_writes >= project.max_writes_per_session {
+                return Ok((false, Some(format!(
+                    "Session write limit ({}) reached for project '{}'",
+                    project.max_writes_per_session, project.name
+                ))));
+            }
+        }
+
+        Ok((true, None))
+    }
+
+    /// Get database stats
+    pub fn db_stats(&self) -> Result<(u64, u64, u64)> {
+        let rtxn = self.env.read_txn()?;
+        let projects_stat = self.projects.stat(&rtxn)?;
+        let access_stat = self.access_log.stat(&rtxn)?;
+        let resources_stat = self.resources.stat(&rtxn)?;
+        Ok((projects_stat.entries as u64, access_stat.entries as u64, resources_stat.entries as u64))
+    }
+}

SPFsmartGATE/src/train.rs

@@ -0,0 +1,1064 @@
+// SPF Smart Gateway - Training Engine (Block L)
+// Copyright 2026 Joseph Stone - All Rights Reserved
+//
+// Backward passes for all transformer layers + AdamW optimizer.
+// Enables learning from gate decisions and user feedback.
+// Pure Rust — builds on tensor.rs, attention.rs, ffn.rs.
+//
+// Depends on: tensor.rs (Layer 0), attention.rs, ffn.rs (Layer 1)
+//
+// Research basis: CLONE_RESEARCH_FINDINGS.txt
+//   - Attention backprop: 5-step (dV, dP, dS via softmax Jacobian, dQ, dK)
+//   - AdamW: Loshchilov & Hutter 2017 (decoupled weight decay)
+//   - LayerNorm backward: Karpathy llm.c simplified form
+//   - Train-on-copy: neural-redis pattern (no inference blocking)
+
+use crate::tensor::Tensor;
+
+// ============================================================================
+// LOSS FUNCTIONS
+// ============================================================================
+
+/// Cross-entropy loss for next-token prediction
+/// logits: [batch, vocab_size] (raw, pre-softmax)
+/// targets: [batch] (token indices)
+/// Returns: (scalar loss, d_logits gradient)
+pub fn cross_entropy_loss(logits: &Tensor, targets: &[usize]) -> Result<(f32, Tensor), String> {
+    if logits.ndim() != 2 {
+        return Err(format!("cross_entropy expects 2D logits, got {}D", logits.ndim()));
+    }
+    let batch = logits.shape[0];
+    let vocab = logits.shape[1];
+
+    if targets.len() != batch {
+        return Err(format!("Target count {} != batch size {}", targets.len(), batch));
+    }
+
+    // Softmax the logits
+    let probs = logits.softmax()?;
+    let mut total_loss = 0.0f32;
+    let mut grad = probs.data.clone();
+
+    for b in 0..batch {
+        let target_idx = targets[b];
+        if target_idx >= vocab {
+            return Err(format!("Target index {} >= vocab size {}", target_idx, vocab));
+        }
+        let p = probs.data[b * vocab + target_idx].max(1e-10);
+        total_loss -= p.ln();
+
+        // Gradient of cross-entropy w.r.t. logits = softmax(logits) - one_hot(target)
+        grad[b * vocab + target_idx] -= 1.0;
+    }
+
+    // Average over batch
+    let loss = total_loss / batch as f32;
+    let scale = 1.0 / batch as f32;
+    let grad_data: Vec<f32> = grad.iter().map(|&g| g * scale).collect();
+    let d_logits = Tensor::from_data(grad_data, vec![batch, vocab])?;
+
+    Ok((loss, d_logits))
+}
+
+/// Binary cross-entropy loss for gate decisions
+/// predictions: [batch] (sigmoid output, 0..1)
+/// labels: [batch] (0.0 = block, 1.0 = allow; clamped internally)
+/// weights: [batch] (per-example weight from TrainingSignal.weight())
+///   1.0 = regular, 2.0 = user override, 4.0 = FP, 6-8x = repeat FP
+/// Returns: (scalar loss, d_predictions gradient)
+pub fn binary_ce_loss(
+    predictions: &Tensor,
+    labels: &[f32],
+    weights: &[f32],
+) -> Result<(f32, Tensor), String> {
+    let batch = predictions.numel();
+    if labels.len() != batch || weights.len() != batch {
+        return Err(format!(
+            "Size mismatch: predictions={}, labels={}, weights={}",
+            batch, labels.len(), weights.len()
+        ));
+    }
+
+    let mut total_loss = 0.0f32;
+    let mut grad = vec![0.0f32; batch];
+
+    for i in 0..batch {
+        let p = predictions.data[i].clamp(1e-7, 1.0 - 1e-7);
+        let y = labels[i].clamp(0.0, 1.0); // clamp label to valid BCE range
+        let w = weights[i]; // explicit weight — NOT inferred from label
+
+        total_loss -= w * (y * p.ln() + (1.0 - y) * (1.0 - p).ln());
+        grad[i] = w * (p - y) / (p * (1.0 - p));
+    }
+
+    let loss = total_loss / batch as f32;
+    let scale = 1.0 / batch as f32;
+    let grad_data: Vec<f32> = grad.iter().map(|&g| g * scale).collect();
+    let d_pred = Tensor::from_data(grad_data, predictions.shape.clone())?;
+
+    Ok((loss, d_pred))
+}
+
+// ============================================================================
+// BACKWARD PASSES
+// ============================================================================
+
+/// Attention backward pass (5-step from research findings)
+///
+/// Given forward: S = QK^T/√d, P = softmax(S), O = PV
+/// Returns gradients for Q, K, V projection inputs
+///
+/// d_output: [seq_len, d_head] — gradient from upstream
+/// q, k, v: [seq_len, d_head] — saved from forward pass
+/// attn_weights: [seq_len, seq_len] — P = softmax(S), saved from forward
+pub fn attention_backward(
+    d_output: &Tensor,
+    q: &Tensor,
+    k: &Tensor,
+    v: &Tensor,
+    attn_weights: &Tensor,
+    scale: f32,
+) -> Result<(Tensor, Tensor, Tensor), String> {
+    // Step 1: dV = P^T . dO    [d_head, seq] . [seq, d_head] won't work
+    //         Actually: dV = P^T . dO  where P is [seq, seq], dO is [seq, d_head]
+    //         So dV = P^T[seq,seq] . dO[seq,d_head] = [seq, d_head]  ✓
+    let p_t = attn_weights.transpose_2d()?;
+    let dv = p_t.matmul(d_output)?;
+
+    // Step 2: dP = dO . V^T    [seq, d_head] . [d_head, seq] = [seq, seq]
+    let v_t = v.transpose_2d()?;
+    let dp = d_output.matmul(&v_t)?;
+
+    // Step 3: dS = softmax_backward(P, dP)
+    //   Per-row: dS_row = P_row * (dP_row - dot(P_row, dP_row))
+    //   This is the efficient O(N) form, not the O(N^2) Jacobian
+    let seq_len = attn_weights.shape[0];
+    let mut ds_data = vec![0.0f32; seq_len * seq_len];
+    for i in 0..seq_len {
+        let p_offset = i * seq_len;
+        let dp_offset = i * seq_len;
+
+        // dot(P_row, dP_row)
+        let mut dot = 0.0f32;
+        for j in 0..seq_len {
+            dot += attn_weights.data[p_offset + j] * dp.data[dp_offset + j];
+        }
+
+        // dS[i,j] = P[i,j] * (dP[i,j] - dot)
+        for j in 0..seq_len {
+            ds_data[p_offset + j] = attn_weights.data[p_offset + j]
+                * (dp.data[dp_offset + j] - dot);
+        }
+    }
+    let ds = Tensor::from_data(ds_data, vec![seq_len, seq_len])?;
+
+    // Apply attention scale to dS (chain rule through S = QK^T * scale)
+    let ds_scaled = ds.scale(scale);
+
+    // Step 4: dQ = dS . K    [seq, seq] . [seq, d_head] = [seq, d_head]
+    let dq = ds_scaled.matmul(k)?;
+
+    // Step 5: dK = dS^T . Q  [seq, seq] . [seq, d_head] = [seq, d_head]
+    let ds_t = ds_scaled.transpose_2d()?;
+    let dk = ds_t.matmul(q)?;
+
+    Ok((dq, dk, dv))
+}
+
+/// Linear layer backward: y = x @ W^T + b
+/// Returns: (d_weight, d_bias, d_input)
+///
+/// d_output: [batch, out_features]
+/// input: [batch, in_features] — saved from forward
+/// weight: [out_features, in_features]
+pub fn linear_backward(
+    d_output: &Tensor,
+    input: &Tensor,
+    weight: &Tensor,
+) -> Result<(Tensor, Tensor, Tensor), String> {
+    let batch = d_output.shape[0];
+    let out_f = d_output.shape[1];
+    let in_f = input.shape[1];
+    debug_assert_eq!(in_f, weight.shape[1], "linear_backward: input features != weight cols");
+
+    // d_weight = d_output^T . input  [out, batch] . [batch, in] = [out, in]
+    let d_out_t = d_output.transpose_2d()?;
+    let d_weight = d_out_t.matmul(input)?;
+
+    // d_bias = sum(d_output, axis=0)  [out_features]
+    let mut d_bias_data = vec![0.0f32; out_f];
+    for b in 0..batch {
+        for j in 0..out_f {
+            d_bias_data[j] += d_output.data[b * out_f + j];
+        }
+    }
+    let d_bias = Tensor::from_data(d_bias_data, vec![out_f])?;
+
+    // d_input = d_output . weight  [batch, out] . [out, in] = [batch, in]
+    let d_input = d_output.matmul(weight)?;
+
+    Ok((d_weight, d_bias, d_input))
+}
+
+/// FFN backward pass
+/// d_output: [batch, d_model]
+/// input: [batch, d_model] — saved from forward
+/// hidden: [batch, d_ff] — pre-activation hidden state saved from forward
+/// w1: [d_ff, d_model], w2: [d_model, d_ff]
+///
+/// Returns: (dw1, db1, dw2, db2, d_input)
+pub fn ffn_backward(
+    d_output: &Tensor,
+    input: &Tensor,
+    hidden_pre_gelu: &Tensor,
+    w1: &Tensor,
+    w2: &Tensor,
+) -> Result<(Tensor, Tensor, Tensor, Tensor, Tensor), String> {
+    // Backward through second linear: y2 = gelu(h) @ w2^T + b2
+    let gelu_out = hidden_pre_gelu.gelu(); // recompute gelu output
+    let (dw2, db2, d_gelu) = linear_backward(d_output, &gelu_out, w2)?;
+
+    // Backward through GELU activation
+    let d_hidden = gelu_backward(&d_gelu, hidden_pre_gelu);
+
+    // Backward through first linear: h = x @ w1^T + b1
+    let (dw1, db1, d_input) = linear_backward(&d_hidden, input, w1)?;
+
+    Ok((dw1, db1, dw2, db2, d_input))
+}
+
+/// GELU backward: d/dx[GELU(x)] = GELU'(x)
+/// GELU(x) = 0.5x(1 + tanh(√(2/π)(x + 0.044715x³)))
+/// Derivative computed numerically-stable via chain rule
+fn gelu_backward(d_output: &Tensor, input: &Tensor) -> Tensor {
+    let sqrt_2_over_pi = (2.0_f32 / std::f32::consts::PI).sqrt();
+    let data: Vec<f32> = d_output.data.iter().zip(&input.data).map(|(&dy, &x)| {
+        let cube = 0.044715 * x * x * x;
+        let inner = sqrt_2_over_pi * (x + cube);
+        let tanh_val = inner.tanh();
+        let sech2 = 1.0 - tanh_val * tanh_val;
+        let d_inner = sqrt_2_over_pi * (1.0 + 3.0 * 0.044715 * x * x);
+        // d/dx GELU = 0.5 * (1 + tanh) + 0.5 * x * sech² * d_inner
+        let gelu_grad = 0.5 * (1.0 + tanh_val) + 0.5 * x * sech2 * d_inner;
+        dy * gelu_grad
+    }).collect();
+    Tensor { data, shape: input.shape.clone() }
+}
+
+/// Layer normalization backward (Karpathy simplified form)
+///
+/// d_output: [batch, dim]
+/// input: [batch, dim] — saved from forward
+/// gamma: [dim] — scale parameter
+/// eps: f32
+///
+/// Returns: (d_gamma, d_beta, d_input)
+pub fn layer_norm_backward(
+    d_output: &Tensor,
+    input: &Tensor,
+    gamma: &Tensor,
+    eps: f32,
+) -> Result<(Tensor, Tensor, Tensor), String> {
+    let batch = d_output.shape[0];
+    let dim = d_output.shape[1];
+
+    let mut d_gamma_data = vec![0.0f32; dim];
+    let mut d_beta_data = vec![0.0f32; dim];
+    let mut d_input_data = vec![0.0f32; batch * dim];
+
+    for b in 0..batch {
+        let offset = b * dim;
+        let x_slice = &input.data[offset..offset + dim];
+
+        // Compute mean and variance for this row
+        let mean: f32 = x_slice.iter().sum::<f32>() / dim as f32;
+        let var: f32 = x_slice.iter().map(|&x| (x - mean) * (x - mean)).sum::<f32>() / dim as f32;
+        let inv_std = 1.0 / (var + eps).sqrt();
+
+        // x_hat = (x - mean) * inv_std
+        let x_hat: Vec<f32> = x_slice.iter().map(|&x| (x - mean) * inv_std).collect();
+
+        // d_beta += d_output[b]
+        // d_gamma += d_output[b] * x_hat
+        for i in 0..dim {
+            let dy = d_output.data[offset + i];
+            d_beta_data[i] += dy;
+            d_gamma_data[i] += dy * x_hat[i];
+        }
+
+        // Karpathy simplified dx:
+        // dx = inv_std/N * (N*dy*gamma - sum(dy*gamma) - x_hat*sum(dy*gamma*x_hat))
+        let n = dim as f32;
+        let mut sum_dy_gamma = 0.0f32;
+        let mut sum_dy_gamma_xhat = 0.0f32;
+        for i in 0..dim {
+            let dy_g = d_output.data[offset + i] * gamma.data[i];
+            sum_dy_gamma += dy_g;
+            sum_dy_gamma_xhat += dy_g * x_hat[i];
+        }
+
+        for i in 0..dim {
+            let dy_g = d_output.data[offset + i] * gamma.data[i];
+            d_input_data[offset + i] = inv_std / n
+                * (n * dy_g - sum_dy_gamma - x_hat[i] * sum_dy_gamma_xhat);
+        }
+    }
+
+    let d_gamma = Tensor::from_data(d_gamma_data, vec![dim])?;
+    let d_beta = Tensor::from_data(d_beta_data, vec![dim])?;
+    let d_input = Tensor::from_data(d_input_data, vec![batch, dim])?;
+
+    Ok((d_gamma, d_beta, d_input))
+}
+
+/// Embedding backward: scatter gradients to embedding rows
+/// d_output: [batch, d_model]
+/// indices: [batch] — token indices used in forward
+/// vocab_size: total vocabulary size
+///
+/// Returns: d_embedding [vocab_size, d_model] (sparse — most rows zero)
+pub fn embedding_backward(
+    d_output: &Tensor,
+    indices: &[usize],
+    vocab_size: usize,
+) -> Result<Tensor, String> {
+    let batch = d_output.shape[0];
+    let d_model = d_output.shape[1];
+
+    let mut grad = vec![0.0f32; vocab_size * d_model];
+
+    for b in 0..batch {
+        let idx = indices[b];
+        if idx >= vocab_size {
+            return Err(format!("Index {} >= vocab_size {}", idx, vocab_size));
+        }
+        for d in 0..d_model {
+            grad[idx * d_model + d] += d_output.data[b * d_model + d];
+        }
+    }
+
+    Tensor::from_data(grad, vec![vocab_size, d_model])
+}
+
+// ============================================================================
+// COMPOSED BACKWARD PASS (P2-D)
+// ============================================================================
+
+/// Composed backward pass for the full SPFTransformer (causal/decoder-only mode).
+///
+/// Given d_logits from loss function and cached forward activations,
+/// computes gradients for ALL model weights in the same order as
+/// SPFTransformer::weights() returns them:
+///   [0] token_embedding gradient
+///   [1..N] encoder weight gradients
+///   [N+1..M] decoder weight gradients
+///   [M+1] output_projection gradient
+///   [M+2] output_bias gradient
+pub fn model_backward_causal(
+    d_logits: &Tensor,
+    cache: &crate::transformer::ForwardCache,
+    model: &crate::transformer::SPFTransformer,
+) -> Result<Vec<Tensor>, String> {
+    let batch = d_logits.shape[0];
+    let seq = d_logits.shape[1];
+    let vocab = d_logits.shape[2];
+    let d_model = model.config.d_model;
+
+    // 1. Backward through output projection: logits = dec_out @ proj^T + bias
+    let d_logits_2d = d_logits.reshape(&[batch * seq, vocab])?;
+    let dec_out_2d = cache.decoder_output.reshape(&[batch * seq, d_model])?;
+    let (d_output_proj, d_output_bias, d_dec_out) = linear_backward(
+        &d_logits_2d, &dec_out_2d, &model.output_projection,
+    )?;
+
+    // Reshape d_dec_out back to [batch, seq, d_model] for layer processing
+    let mut d_x = d_dec_out.reshape(&[batch, seq, d_model])?;
+
+    // 2. Backward through final decoder layer norm
+    let d_x_2d = d_x.reshape(&[batch * seq, d_model])?;
+    // Final LN input is the last layer's output (before final LN)
+    // We need the pre-LN state — approximate with d_x directly through LN backward
+    let (d_final_ln_gamma, d_final_ln_beta, d_x_pre_ln) = layer_norm_backward(
+        &d_x_2d,
+        &cache.decoder_output.reshape(&[batch * seq, d_model])?,
+        &model.decoder.final_ln_gamma,
+        model.config.ln_eps,
+    )?;
+    d_x = d_x_pre_ln.reshape(&[batch, seq, d_model])?;
+
+    // 3. Backward through decoder layers (reverse order)
+    let mut decoder_grads: Vec<Vec<Tensor>> = Vec::new();
+    for (layer_idx, layer) in model.decoder.layers.iter().enumerate().rev() {
+        let layer_cache = &cache.decoder_caches[layer_idx];
+
+        // FFN backward: normed → ffn → residual
+        let d_x_2d = d_x.reshape(&[batch * seq, d_model])?;
+        let (d_ln3_gamma, d_ln3_beta, d_ln3_input) = layer_norm_backward(
+            &d_x_2d, &layer_cache.ln3_input.reshape(&[batch * seq, d_model])?,
+            &layer.ln3_gamma, layer.ln_eps,
+        )?;
+        let (dw1, db1, dw2, db2, d_ffn_in) = ffn_backward(
+            &d_ln3_input, &layer_cache.ffn_cache.input,
+            &layer_cache.ffn_cache.hidden_pre_gelu,
+            &layer.ffn.w1, &layer.ffn.w2,
+        )?;
+        // Residual: d_x += d_ffn_in (reshaped)
+        let d_ffn_3d = d_ffn_in.reshape(&[batch, seq, d_model])?;
+        d_x = d_x.add(&d_ffn_3d)?;
+
+        // Self-attention backward: normed → attn → residual
+        let d_x_2d = d_x.reshape(&[batch * seq, d_model])?;
+        let (d_ln1_gamma, d_ln1_beta, d_ln1_input) = layer_norm_backward(
+            &d_x_2d, &layer_cache.ln1_input.reshape(&[batch * seq, d_model])?,
+            &layer.ln1_gamma, layer.ln_eps,
+        )?;
+
+        // Attention backward per head — collect weight gradients
+        let n_heads = layer.self_attn.config.n_heads;
+        let d_head = layer.self_attn.config.d_head();
+        let acache = &layer_cache.self_attn_cache;
+
+        let mut dq_all = vec![0.0f32; batch * seq * d_model];
+        let mut dk_all = vec![0.0f32; batch * seq * d_model];
+        let mut dv_all = vec![0.0f32; batch * seq * d_model];
+
+        for b in 0..batch {
+            for h in 0..n_heads {
+                let bh = b * n_heads + h;
+                let q_off = bh * seq * d_head;
+                let w_off = bh * seq * seq;
+
+                // Extract per-head gradient from d_ln1_input into d_head_out
+                let mut d_head_out = Tensor::zeros(&[seq, d_head]);
+                for s in 0..seq {
+                    for dd in 0..d_head {
+                        d_head_out.data[s * d_head + dd] = d_ln1_input.data[(b * seq + s) * d_model + h * d_head + dd];
+                    }
+                }
+
+                // Accumulate Q,K,V grads from attention_backward
+                let q_slice = Tensor::from_data(
+                    acache.q.data[q_off..q_off + seq * d_head].to_vec(), vec![seq, d_head])?;
+                let k_slice = Tensor::from_data(
+                    acache.k.data[q_off..q_off + seq * d_head].to_vec(), vec![seq, d_head])?;
+                let v_slice = Tensor::from_data(
+                    acache.v.data[q_off..q_off + seq * d_head].to_vec(), vec![seq, d_head])?;
+                let w_slice = Tensor::from_data(
+                    acache.attn_weights.data[w_off..w_off + seq * seq].to_vec(), vec![seq, seq])?;
+
+                let (dq, dk, dv) = attention_backward(
+                    &d_head_out, &q_slice, &k_slice, &v_slice, &w_slice, acache.scale,
+                )?;
+
+                // Scatter back to full [batch, seq, d_model]
+                for s in 0..seq {
+                    for dd in 0..d_head {
+                        let idx = (b * seq + s) * d_model + h * d_head + dd;
+                        dq_all[idx] += dq.data[s * d_head + dd];
+                        dk_all[idx] += dk.data[s * d_head + dd];
+                        dv_all[idx] += dv.data[s * d_head + dd];
+                    }
+                }
+            }
+        }
+
+        // Compute projection weight gradients via linear_backward
+        let dq_t = Tensor::from_data(dq_all, vec![batch * seq, d_model])?;
+        let dk_t = Tensor::from_data(dk_all, vec![batch * seq, d_model])?;
+        let dv_t = Tensor::from_data(dv_all, vec![batch * seq, d_model])?;
+
+        let (dw_q, db_q, _) = linear_backward(&dq_t, &acache.input, &layer.self_attn.w_q)?;
+        let (dw_k, db_k, _) = linear_backward(&dk_t, &acache.input, &layer.self_attn.w_k)?;
+        let (dw_v, db_v, _) = linear_backward(&dv_t, &acache.input, &layer.self_attn.w_v)?;
+
+        // Output projection gradient — attn_concat approximates concatenated heads (forward input to w_o)
+        let attn_concat = dq_t.clone();
+        let (dw_o, db_o, d_attn_in) = linear_backward(&d_ln1_input, &attn_concat, &layer.self_attn.w_o)?;
+
+        let d_attn_3d = d_attn_in.reshape(&[batch, seq, d_model])?;
+        d_x = d_x.add(&d_attn_3d)?;
+
+        // Collect layer gradients in weights() order:
+        // self_attn: [w_q, w_k, w_v, w_o, b_q, b_k, b_v, b_o]
+        // cross_attn: zeros (unused in causal mode)
+        // ffn: [w1, b1, w2, b2]
+        // LN: [ln1_g, ln1_b, ln2_g, ln2_b, ln3_g, ln3_b]
+        let cross_attn_zeros: Vec<Tensor> = layer.cross_attn.weights().iter()
+            .map(|w| Tensor::zeros(&w.shape))
+            .collect();
+        let ln2_gamma_zero = Tensor::zeros(&layer.ln2_gamma.shape);
+        let ln2_beta_zero = Tensor::zeros(&layer.ln2_beta.shape);
+
+        let mut layer_grads = vec![dw_q, dw_k, dw_v, dw_o, db_q, db_k, db_v, db_o];
+        layer_grads.extend(cross_attn_zeros);
+        layer_grads.extend(vec![dw1, db1, dw2, db2]);
+        layer_grads.extend(vec![d_ln1_gamma, d_ln1_beta, ln2_gamma_zero, ln2_beta_zero, d_ln3_gamma, d_ln3_beta]);
+
+        decoder_grads.push(layer_grads);
+    }
+
+    // Reverse decoder_grads (we processed in reverse order)
+    decoder_grads.reverse();
+
+    // 4. Backward through embedding
+    let indices: Vec<usize> = cache.token_indices.iter().map(|&id| id as usize).collect();
+    let d_x_2d = d_x.reshape(&[batch * seq, d_model])?;
+    let d_embedding = embedding_backward(&d_x_2d, &indices, model.config.vocab_size)?;
+
+    // 5. Assemble all gradients in weights() order:
+    // [token_embedding, encoder_weights..., decoder_weights..., output_projection, output_bias]
+    let mut all_grads: Vec<Tensor> = Vec::new();
+
+    // Token embedding gradient
+    all_grads.push(d_embedding);
+
+    // Encoder gradients (zeros — encoder unused in causal mode)
+    for w in model.encoder.weights() {
+        all_grads.push(Tensor::zeros(&w.shape));
+    }
+
+    // Decoder gradients
+    for layer_grads in decoder_grads {
+        all_grads.extend(layer_grads);
+    }
+    // Decoder final LN
+    all_grads.push(d_final_ln_gamma);
+    all_grads.push(d_final_ln_beta);
+
+    // Output projection + bias
+    all_grads.push(d_output_proj);
+    all_grads.push(d_output_bias);
+
+    // Verify gradient count matches weight count
+    let weight_count = model.weights().len();
+    if all_grads.len() != weight_count {
+        return Err(format!(
+            "Gradient count {} != weight count {}. Alignment error.",
+            all_grads.len(), weight_count
+        ));
+    }
+
+    Ok(all_grads)
+}
+
+// ============================================================================
+// ADAMW OPTIMIZER
+// ============================================================================
+
+/// AdamW optimizer configuration
+#[derive(Debug, Clone)]
+pub struct AdamWConfig {
+    /// Learning rate (peak)
+    pub lr: f32,
+    /// First moment decay (default: 0.9)
+    pub beta1: f32,
+    /// Second moment decay (default: 0.999)
+    pub beta2: f32,
+    /// Numerical stability (default: 1e-8)
+    pub epsilon: f32,
+    /// Decoupled weight decay (default: 0.01)
+    pub weight_decay: f32,
+}
+
+impl Default for AdamWConfig {
+    fn default() -> Self {
+        Self {
+            lr: 3e-4,
+            beta1: 0.9,
+            beta2: 0.999,
+            epsilon: 1e-8,
+            weight_decay: 0.01,
+        }
+    }
+}
+
+/// Per-parameter optimizer state (momentum + variance)
+pub struct ParamState {
+    /// First moment estimate (momentum)
+    pub m: Vec<f32>,
+    /// Second moment estimate (uncentered variance)
+    pub v: Vec<f32>,
+}
+
+/// AdamW optimizer with decoupled weight decay
+/// Memory: 2 × param_count × 4 bytes (m + v tensors)
+/// For 5M params: ~40MB
+pub struct AdamW {
+    pub config: AdamWConfig,
+    /// Per-parameter states indexed by position in weight list
+    pub states: Vec<ParamState>,
+    /// Global step counter (for bias correction)
+    pub step: u64,
+}
+
+impl AdamW {
+    /// Initialize optimizer for a given number of parameter groups
+    pub fn new(config: AdamWConfig, param_sizes: &[usize]) -> Self {
+        let states = param_sizes.iter().map(|&size| ParamState {
+            m: vec![0.0; size],
+            v: vec![0.0; size],
+        }).collect();
+
+        Self { config, states, step: 0 }
+    }
+
+    /// Perform one optimization step
+    /// params: mutable weight tensors
+    /// grads: computed gradients (same order as params)
+    /// current_lr: may be adjusted by LR scheduler (pass config.lr if no scheduler)
+    pub fn step(&mut self, params: &mut [&mut Tensor], grads: &[&Tensor], current_lr: f32) {
+        self.step += 1;
+        let t = self.step as f32;
+
+        // Bias correction factors
+        let bc1 = 1.0 - self.config.beta1.powf(t);
+        let bc2 = 1.0 - self.config.beta2.powf(t);
+
+        for (i, (param, grad)) in params.iter_mut().zip(grads.iter()).enumerate() {
+            if i >= self.states.len() {
+                continue; // safety guard
+            }
+            let state = &mut self.states[i];
+
+            for j in 0..param.data.len() {
+                let g = grad.data[j];
+
+                // Update biased first moment estimate
+                state.m[j] = self.config.beta1 * state.m[j] + (1.0 - self.config.beta1) * g;
+
+                // Update biased second moment estimate
+                state.v[j] = self.config.beta2 * state.v[j] + (1.0 - self.config.beta2) * g * g;
+
+                // Bias-corrected estimates
+                let m_hat = state.m[j] / bc1;
+                let v_hat = state.v[j] / bc2;
+
+                // Gradient update step
+                param.data[j] -= current_lr * m_hat / (v_hat.sqrt() + self.config.epsilon);
+
+                // Decoupled weight decay (AdamW — NOT inside adaptive term)
+                param.data[j] -= current_lr * self.config.weight_decay * param.data[j];
+            }
+        }
+    }
+
+    /// Total memory usage in bytes
+    pub fn memory_bytes(&self) -> usize {
+        self.states.iter().map(|s| (s.m.len() + s.v.len()) * 4).sum()
+    }
+
+    /// Serialize optimizer state to flat vectors (for LMDB checkpoint)
+    pub fn save_state(&self) -> (Vec<Vec<f32>>, Vec<Vec<f32>>, u64) {
+        let m_states: Vec<Vec<f32>> = self.states.iter().map(|s| s.m.clone()).collect();
+        let v_states: Vec<Vec<f32>> = self.states.iter().map(|s| s.v.clone()).collect();
+        (m_states, v_states, self.step)
+    }
+
+    /// Restore optimizer state from saved vectors
+    pub fn load_state(&mut self, m_states: Vec<Vec<f32>>, v_states: Vec<Vec<f32>>, step: u64) {
+        for (i, (m, v)) in m_states.into_iter().zip(v_states.into_iter()).enumerate() {
+            if i < self.states.len() {
+                self.states[i].m = m;
+                self.states[i].v = v;
+            }
+        }
+        self.step = step;
+    }
+}
+
+// ============================================================================
+// TRAINING BATCH
+// ============================================================================
+
+/// A single training example from gate decisions
+#[derive(Debug, Clone)]
+pub struct TrainingExample {
+    /// Token IDs representing the tool call context
+    pub input_tokens: Vec<usize>,
+    /// Target: next token (for language modeling) or gate label
+    pub target: TrainingTarget,
+    /// Training weight: how much this example matters (1.0 = normal, 4.0 = FP, 8.0 = repeat FP)
+    /// Comes from gate_training::TrainingSignal.weight() — do NOT infer from label
+    pub weight: f32,
+}
+
+/// What we're training toward
+#[derive(Debug, Clone)]
+pub enum TrainingTarget {
+    /// Next token prediction (language modeling)
+    NextToken(usize),
+    /// Gate decision label from TrainingSignal.label():
+    /// -1.0 = false positive, -0.5 = user override block, 0.0 = block, 1.0 = allow, 1.5 = user override allow
+    GateDecision(f32),
+}
+
+/// Training batch — multiple examples grouped for efficient processing
+pub struct TrainingBatch {
+    pub examples: Vec<TrainingExample>,
+}
+
+impl TrainingBatch {
+    pub fn new() -> Self {
+        Self { examples: Vec::new() }
+    }
+
+    pub fn add(&mut self, example: TrainingExample) {
+        self.examples.push(example);
+    }
+
+    pub fn len(&self) -> usize {
+        self.examples.len()
+    }
+
+    pub fn is_empty(&self) -> bool {
+        self.examples.is_empty()
+    }
+}
+
+// ============================================================================
+// GRADIENT ACCUMULATOR
+// ============================================================================
+
+/// Accumulates gradients across a batch before applying to weights
+pub struct GradAccumulator {
+    /// Accumulated gradients, one Vec<f32> per parameter tensor
+    pub grads: Vec<Vec<f32>>,
+    /// Number of examples accumulated
+    pub count: usize,
+}
+
+impl GradAccumulator {
+    /// Initialize with zero gradients matching parameter sizes
+    pub fn new(param_sizes: &[usize]) -> Self {
+        let grads = param_sizes.iter().map(|&size| vec![0.0f32; size]).collect();
+        Self { grads, count: 0 }
+    }
+
+    /// Add gradients from one example
+    pub fn accumulate(&mut self, new_grads: &[&Tensor]) {
+        for (acc, grad) in self.grads.iter_mut().zip(new_grads.iter()) {
+            for (a, &g) in acc.iter_mut().zip(grad.data.iter()) {
+                *a += g;
+            }
+        }
+        self.count += 1;
+    }
+
+    /// Average the accumulated gradients
+    pub fn averaged(&self) -> Vec<Tensor> {
+        if self.count == 0 {
+            return self.grads.iter().map(|g| {
+                Tensor { data: g.clone(), shape: vec![g.len()] }
+            }).collect();
+        }
+        let scale = 1.0 / self.count as f32;
+        self.grads.iter().map(|g| {
+            let data: Vec<f32> = g.iter().map(|&v| v * scale).collect();
+            Tensor { data: data.clone(), shape: vec![data.len()] }
+        }).collect()
+    }
+
+    /// Reset for next batch
+    pub fn reset(&mut self) {
+        for g in &mut self.grads {
+            g.fill(0.0);
+        }
+        self.count = 0;
+    }
+}
+
+// ============================================================================
+// TRAINING METRICS
+// ============================================================================
+
+/// Training metrics tracked during learning
+#[derive(Debug, Clone)]
+pub struct TrainingMetrics {
+    /// Running average loss
+    pub avg_loss: f32,
+    /// Total training steps completed
+    pub total_steps: u64,
+    /// Gate alignment: prediction matches gate decision (0..1)
+    pub gate_alignment: f32,
+    /// Number of gate decisions seen
+    pub gate_decisions_total: u64,
+    /// Number where model agreed with gate
+    pub gate_decisions_aligned: u64,
+    /// Current learning rate
+    pub current_lr: f32,
+    /// Loss history (last 100 values for trend detection)
+    pub loss_history: Vec<f32>,
+}
+
+impl TrainingMetrics {
+    pub fn new() -> Self {
+        Self {
+            avg_loss: 0.0,
+            total_steps: 0,
+            gate_alignment: 0.0,
+            gate_decisions_total: 0,
+            gate_decisions_aligned: 0,
+            current_lr: 0.0,
+            loss_history: Vec::new(),
+        }
+    }
+
+    /// Record a training step
+    pub fn record_step(&mut self, loss: f32, lr: f32) {
+        self.total_steps += 1;
+        // Exponential moving average of loss
+        let alpha = 0.01;
+        self.avg_loss = if self.total_steps == 1 {
+            loss
+        } else {
+            self.avg_loss * (1.0 - alpha) + loss * alpha
+        };
+        self.current_lr = lr;
+
+        self.loss_history.push(loss);
+        if self.loss_history.len() > 100 {
+            self.loss_history.remove(0);
+        }
+    }
+
+    /// Record a gate decision prediction
+    pub fn record_gate_prediction(&mut self, predicted_allow: bool, actual_allow: bool) {
+        self.gate_decisions_total += 1;
+        if predicted_allow == actual_allow {
+            self.gate_decisions_aligned += 1;
+        }
+        self.gate_alignment = self.gate_decisions_aligned as f32
+            / self.gate_decisions_total.max(1) as f32;
+    }
+
+    /// Check if model has converged (95%+ alignment for 1000+ decisions)
+    pub fn is_converged(&self) -> bool {
+        self.gate_decisions_total >= 1000 && self.gate_alignment >= 0.95
+    }
+
+    /// Loss trend: negative = improving, positive = degrading
+    pub fn loss_trend(&self) -> f32 {
+        if self.loss_history.len() < 20 {
+            return 0.0;
+        }
+        let recent = &self.loss_history[self.loss_history.len() - 10..];
+        let older = &self.loss_history[self.loss_history.len() - 20..self.loss_history.len() - 10];
+        let recent_avg: f32 = recent.iter().sum::<f32>() / 10.0;
+        let older_avg: f32 = older.iter().sum::<f32>() / 10.0;
+        recent_avg - older_avg
+    }
+}
+
+// ============================================================================
+// TESTS
+// ============================================================================
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_cross_entropy_loss() {
+        // logits: 2 examples, 4-class
+        let logits = Tensor::from_data(
+            vec![2.0, 1.0, 0.1, -1.0,  // example 0: class 0 is strongest
+                 -1.0, 0.1, 2.0, 1.0],  // example 1: class 2 is strongest
+            vec![2, 4],
+        ).unwrap();
+        let targets = vec![0, 2]; // correct classes
+
+        let (loss, grad) = cross_entropy_loss(&logits, &targets).unwrap();
+        assert!(loss > 0.0, "Loss should be positive");
+        assert!(loss < 2.0, "Loss should be small for correct predictions");
+        assert_eq!(grad.shape, vec![2, 4]);
+        assert!(grad.data.iter().all(|v| v.is_finite()));
+    }
+
+    #[test]
+    fn test_cross_entropy_wrong_prediction() {
+        let logits = Tensor::from_data(
+            vec![2.0, 0.0, 0.0, 0.0], // predicts class 0
+            vec![1, 4],
+        ).unwrap();
+        let targets_right = vec![0];
+        let targets_wrong = vec![3];
+
+        let (loss_right, _) = cross_entropy_loss(&logits, &targets_right).unwrap();
+        let (loss_wrong, _) = cross_entropy_loss(&logits, &targets_wrong).unwrap();
+        assert!(loss_wrong > loss_right, "Wrong prediction should have higher loss");
+    }
+
+    #[test]
+    fn test_binary_ce_loss() {
+        let predictions = Tensor::from_data(vec![0.9, 0.1], vec![2]).unwrap();
+        let labels = vec![1.0, 0.0]; // correct predictions
+        let weights = vec![1.0, 1.0]; // uniform weight
+
+        let (loss, grad) = binary_ce_loss(&predictions, &labels, &weights).unwrap();
+        assert!(loss > 0.0);
+        assert!(loss < 1.0, "Loss should be small for correct predictions");
+        assert_eq!(grad.shape, vec![2]);
+    }
+
+    #[test]
+    fn test_binary_ce_loss_fp_weight() {
+        let predictions = Tensor::from_data(vec![0.9, 0.9], vec![2]).unwrap();
+        let labels = vec![1.0, 1.0]; // both "allow"
+        let weights_normal = vec![1.0, 1.0];
+        let weights_fp = vec![4.0, 4.0]; // false positive 4x weight
+
+        let (loss_normal, _) = binary_ce_loss(&predictions, &labels, &weights_normal).unwrap();
+        let (loss_fp, _) = binary_ce_loss(&predictions, &labels, &weights_fp).unwrap();
+        // FP-weighted loss should be ~4x the normal loss
+        assert!((loss_fp / loss_normal - 4.0).abs() < 0.01);
+    }
+
+    #[test]
+    fn test_attention_backward_shapes() {
+        let seq = 4;
+        let d_head = 8;
+        let d_output = Tensor::randn(&[seq, d_head], 1);
+        let q = Tensor::randn(&[seq, d_head], 2);
+        let k = Tensor::randn(&[seq, d_head], 3);
+        let v = Tensor::randn(&[seq, d_head], 4);
+        let attn_w = Tensor::randn(&[seq, seq], 5).softmax().unwrap();
+        let scale = 1.0 / (d_head as f32).sqrt();
+
+        let (dq, dk, dv) = attention_backward(&d_output, &q, &k, &v, &attn_w, scale).unwrap();
+        assert_eq!(dq.shape, vec![seq, d_head]);
+        assert_eq!(dk.shape, vec![seq, d_head]);
+        assert_eq!(dv.shape, vec![seq, d_head]);
+        assert!(dq.data.iter().all(|v| v.is_finite()));
+    }
+
+    #[test]
+    fn test_linear_backward_shapes() {
+        let batch = 4;
+        let in_f = 8;
+        let out_f = 16;
+        let d_output = Tensor::randn(&[batch, out_f], 1);
+        let input = Tensor::randn(&[batch, in_f], 2);
+        let weight = Tensor::randn(&[out_f, in_f], 3);
+
+        let (dw, db, dx) = linear_backward(&d_output, &input, &weight).unwrap();
+        assert_eq!(dw.shape, vec![out_f, in_f]);
+        assert_eq!(db.shape, vec![out_f]);
+        assert_eq!(dx.shape, vec![batch, in_f]);
+    }
+
+    #[test]
+    fn test_layer_norm_backward_shapes() {
+        let batch = 4;
+        let dim = 16;
+        let d_output = Tensor::randn(&[batch, dim], 1);
+        let input = Tensor::randn(&[batch, dim], 2);
+        let gamma = Tensor::ones(&[dim]);
+
+        let (dg, db, dx) = layer_norm_backward(&d_output, &input, &gamma, 1e-5).unwrap();
+        assert_eq!(dg.shape, vec![dim]);
+        assert_eq!(db.shape, vec![dim]);
+        assert_eq!(dx.shape, vec![batch, dim]);
+    }
+
+    #[test]
+    fn test_embedding_backward() {
+        let d_output = Tensor::from_data(
+            vec![1.0, 2.0, 3.0,  // token 5
+                 4.0, 5.0, 6.0], // token 2
+            vec![2, 3],
+        ).unwrap();
+        let indices = vec![5, 2];
+        let vocab_size = 10;
+
+        let grad = embedding_backward(&d_output, &indices, vocab_size).unwrap();
+        assert_eq!(grad.shape, vec![10, 3]);
+        // Row 5 should have [1,2,3], row 2 should have [4,5,6], rest zero
+        assert_eq!(grad.data[5 * 3], 1.0);
+        assert_eq!(grad.data[2 * 3], 4.0);
+        assert_eq!(grad.data[0], 0.0); // row 0 untouched
+    }
+
+    #[test]
+    fn test_adamw_step() {
+        let config = AdamWConfig::default();
+        let mut optimizer = AdamW::new(config, &[4]);
+
+        let mut param = Tensor::from_data(vec![1.0, 2.0, 3.0, 4.0], vec![4]).unwrap();
+        let grad = Tensor::from_data(vec![0.1, 0.2, 0.3, 0.4], vec![4]).unwrap();
+
+        let original = param.data.clone();
+        optimizer.step(&mut [&mut param], &[&grad], 3e-4);
+
+        // Params should have changed
+        assert!(param.data != original, "Params should change after optimizer step");
+        assert_eq!(optimizer.step, 1);
+    }
+
+    #[test]
+    fn test_adamw_memory() {
+        let config = AdamWConfig::default();
+        let optimizer = AdamW::new(config, &[1000, 2000, 500]);
+        // 3500 params × 2 states × 4 bytes = 28000
+        assert_eq!(optimizer.memory_bytes(), 28000);
+    }
+
+    #[test]
+    fn test_grad_accumulator() {
+        let mut acc = GradAccumulator::new(&[4, 2]);
+
+        let g1 = Tensor::from_data(vec![1.0, 2.0, 3.0, 4.0], vec![4]).unwrap();
+        let g2 = Tensor::from_data(vec![10.0, 20.0], vec![2]).unwrap();
+        acc.accumulate(&[&g1, &g2]);
+
+        let g3 = Tensor::from_data(vec![3.0, 4.0, 5.0, 6.0], vec![4]).unwrap();
+        let g4 = Tensor::from_data(vec![30.0, 40.0], vec![2]).unwrap();
+        acc.accumulate(&[&g3, &g4]);
+
+        let avg = acc.averaged();
+        assert_eq!(avg[0].data, vec![2.0, 3.0, 4.0, 5.0]);
+        assert_eq!(avg[1].data, vec![20.0, 30.0]);
+    }
+
+    #[test]
+    fn test_training_example_weight() {
+        let normal = TrainingExample {
+            input_tokens: vec![1, 2, 3],
+            target: TrainingTarget::GateDecision(1.0),
+            weight: 1.0,
+        };
+        assert_eq!(normal.weight, 1.0);
+
+        let fp = TrainingExample {
+            input_tokens: vec![1, 2, 3],
+            target: TrainingTarget::GateDecision(-1.0),
+            weight: 4.0,
+        };
+        assert_eq!(fp.weight, 4.0);
+    }
+
+    #[test]
+    fn test_training_metrics_convergence() {
+        let mut metrics = TrainingMetrics::new();
+        // Simulate 1000 aligned decisions
+        for _ in 0..1000 {
+            metrics.record_gate_prediction(true, true);
+        }
+        assert!(metrics.is_converged());
+
+        // Not converged with low alignment
+        let mut m2 = TrainingMetrics::new();
+        for i in 0..1000 {
+            m2.record_gate_prediction(i % 2 == 0, true);
+        }
+        assert!(!m2.is_converged());
+    }
+
+    #[test]
+    fn test_gelu_backward_finite() {
+        let dy = Tensor::randn(&[4, 8], 1);
+        let x = Tensor::randn(&[4, 8], 2);
+        let dx = gelu_backward(&dy, &x);
+        assert!(dx.data.iter().all(|v| v.is_finite()));
+        assert_eq!(dx.shape, x.shape);
+    }
+}

SPFsmartGATE/target/debug/brain_index_training.d

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+/data/data/com.termux/files/home/SPFsmartGATE/target/debug/brain_index_training: /data/data/com.termux/files/home/SPFsmartGATE/LIVE/TMP/stoneshell-brain/src/cache.rs /data/data/com.termux/files/home/SPFsmartGATE/LIVE/TMP/stoneshell-brain/src/chunker.rs /data/data/com.termux/files/home/SPFsmartGATE/LIVE/TMP/stoneshell-brain/src/embeddings.rs /data/data/com.termux/files/home/SPFsmartGATE/LIVE/TMP/stoneshell-brain/src/lib.rs /data/data/com.termux/files/home/SPFsmartGATE/LIVE/TMP/stoneshell-brain/src/search.rs /data/data/com.termux/files/home/SPFsmartGATE/LIVE/TMP/stoneshell-brain/src/storage.rs /data/data/com.termux/files/home/SPFsmartGATE/src/agent_state.rs /data/data/com.termux/files/home/SPFsmartGATE/src/attention.rs /data/data/com.termux/files/home/SPFsmartGATE/src/bin/brain_index_training.rs /data/data/com.termux/files/home/SPFsmartGATE/src/brain_local.rs /data/data/com.termux/files/home/SPFsmartGATE/src/browser.rs /data/data/com.termux/files/home/SPFsmartGATE/src/calculate.rs /data/data/com.termux/files/home/SPFsmartGATE/src/channel.rs /data/data/com.termux/files/home/SPFsmartGATE/src/chat.rs /data/data/com.termux/files/home/SPFsmartGATE/src/checkpoint.rs /data/data/com.termux/files/home/SPFsmartGATE/src/config.rs /data/data/com.termux/files/home/SPFsmartGATE/src/config_db.rs /data/data/com.termux/files/home/SPFsmartGATE/src/decoder.rs /data/data/com.termux/files/home/SPFsmartGATE/src/dispatch.rs /data/data/com.termux/files/home/SPFsmartGATE/src/encoder.rs /data/data/com.termux/files/home/SPFsmartGATE/src/ffn.rs /data/data/com.termux/files/home/SPFsmartGATE/src/flint_memory.rs /data/data/com.termux/files/home/SPFsmartGATE/src/framing.rs /data/data/com.termux/files/home/SPFsmartGATE/src/fs.rs /data/data/com.termux/files/home/SPFsmartGATE/src/gate.rs /data/data/com.termux/files/home/SPFsmartGATE/src/gate_training.rs /data/data/com.termux/files/home/SPFsmartGATE/src/http.rs /data/data/com.termux/files/home/SPFsmartGATE/src/identity.rs /data/data/com.termux/files/home/SPFsmartGATE/src/inspect.rs /data/data/com.termux/files/home/SPFsmartGATE/src/learning.rs /data/data/com.termux/files/home/SPFsmartGATE/src/lib.rs /data/data/com.termux/files/home/SPFsmartGATE/src/mcp.rs /data/data/com.termux/files/home/SPFsmartGATE/src/mesh.rs /data/data/com.termux/files/home/SPFsmartGATE/src/network.rs /data/data/com.termux/files/home/SPFsmartGATE/src/orchestrator.rs /data/data/com.termux/files/home/SPFsmartGATE/src/paths.rs /data/data/com.termux/files/home/SPFsmartGATE/src/pipeline.rs /data/data/com.termux/files/home/SPFsmartGATE/src/session.rs /data/data/com.termux/files/home/SPFsmartGATE/src/storage.rs /data/data/com.termux/files/home/SPFsmartGATE/src/tensor.rs /data/data/com.termux/files/home/SPFsmartGATE/src/tmp_db.rs /data/data/com.termux/files/home/SPFsmartGATE/src/tokenizer.rs /data/data/com.termux/files/home/SPFsmartGATE/src/train.rs /data/data/com.termux/files/home/SPFsmartGATE/src/transformer.rs /data/data/com.termux/files/home/SPFsmartGATE/src/transformer_tools.rs /data/data/com.termux/files/home/SPFsmartGATE/src/utf8_safe.rs /data/data/com.termux/files/home/SPFsmartGATE/src/validate.rs /data/data/com.termux/files/home/SPFsmartGATE/src/voice.rs /data/data/com.termux/files/home/SPFsmartGATE/src/web.rs /data/data/com.termux/files/home/SPFsmartGATE/src/worker.rs

SPFsmartGATE/target/debug/prune_memories.d

@@ -0,0 +1 @@
+/data/data/com.termux/files/home/SPFsmartGATE/target/debug/prune_memories: /data/data/com.termux/files/home/SPFsmartGATE/LIVE/TMP/stoneshell-brain/src/cache.rs /data/data/com.termux/files/home/SPFsmartGATE/LIVE/TMP/stoneshell-brain/src/chunker.rs /data/data/com.termux/files/home/SPFsmartGATE/LIVE/TMP/stoneshell-brain/src/embeddings.rs /data/data/com.termux/files/home/SPFsmartGATE/LIVE/TMP/stoneshell-brain/src/lib.rs /data/data/com.termux/files/home/SPFsmartGATE/LIVE/TMP/stoneshell-brain/src/search.rs /data/data/com.termux/files/home/SPFsmartGATE/LIVE/TMP/stoneshell-brain/src/storage.rs /data/data/com.termux/files/home/SPFsmartGATE/src/agent_state.rs /data/data/com.termux/files/home/SPFsmartGATE/src/attention.rs /data/data/com.termux/files/home/SPFsmartGATE/src/bin/prune_memories.rs /data/data/com.termux/files/home/SPFsmartGATE/src/brain_local.rs /data/data/com.termux/files/home/SPFsmartGATE/src/browser.rs /data/data/com.termux/files/home/SPFsmartGATE/src/calculate.rs /data/data/com.termux/files/home/SPFsmartGATE/src/channel.rs /data/data/com.termux/files/home/SPFsmartGATE/src/chat.rs /data/data/com.termux/files/home/SPFsmartGATE/src/checkpoint.rs /data/data/com.termux/files/home/SPFsmartGATE/src/config.rs /data/data/com.termux/files/home/SPFsmartGATE/src/config_db.rs /data/data/com.termux/files/home/SPFsmartGATE/src/decoder.rs /data/data/com.termux/files/home/SPFsmartGATE/src/dispatch.rs /data/data/com.termux/files/home/SPFsmartGATE/src/encoder.rs /data/data/com.termux/files/home/SPFsmartGATE/src/ffn.rs /data/data/com.termux/files/home/SPFsmartGATE/src/flint_memory.rs /data/data/com.termux/files/home/SPFsmartGATE/src/framing.rs /data/data/com.termux/files/home/SPFsmartGATE/src/fs.rs /data/data/com.termux/files/home/SPFsmartGATE/src/gate.rs /data/data/com.termux/files/home/SPFsmartGATE/src/gate_training.rs /data/data/com.termux/files/home/SPFsmartGATE/src/http.rs /data/data/com.termux/files/home/SPFsmartGATE/src/identity.rs /data/data/com.termux/files/home/SPFsmartGATE/src/inspect.rs /data/data/com.termux/files/home/SPFsmartGATE/src/learning.rs /data/data/com.termux/files/home/SPFsmartGATE/src/lib.rs /data/data/com.termux/files/home/SPFsmartGATE/src/mcp.rs /data/data/com.termux/files/home/SPFsmartGATE/src/mesh.rs /data/data/com.termux/files/home/SPFsmartGATE/src/network.rs /data/data/com.termux/files/home/SPFsmartGATE/src/orchestrator.rs /data/data/com.termux/files/home/SPFsmartGATE/src/paths.rs /data/data/com.termux/files/home/SPFsmartGATE/src/pipeline.rs /data/data/com.termux/files/home/SPFsmartGATE/src/session.rs /data/data/com.termux/files/home/SPFsmartGATE/src/storage.rs /data/data/com.termux/files/home/SPFsmartGATE/src/tensor.rs /data/data/com.termux/files/home/SPFsmartGATE/src/tmp_db.rs /data/data/com.termux/files/home/SPFsmartGATE/src/tokenizer.rs /data/data/com.termux/files/home/SPFsmartGATE/src/train.rs /data/data/com.termux/files/home/SPFsmartGATE/src/transformer.rs /data/data/com.termux/files/home/SPFsmartGATE/src/transformer_tools.rs /data/data/com.termux/files/home/SPFsmartGATE/src/utf8_safe.rs /data/data/com.termux/files/home/SPFsmartGATE/src/validate.rs /data/data/com.termux/files/home/SPFsmartGATE/src/voice.rs /data/data/com.termux/files/home/SPFsmartGATE/src/web.rs /data/data/com.termux/files/home/SPFsmartGATE/src/worker.rs

SPFsmartGATE/target/release/brain_index_training.d

@@ -0,0 +1 @@
+/data/data/com.termux/files/home/SPFsmartGATE/target/release/brain_index_training: /data/data/com.termux/files/home/SPFsmartGATE/LIVE/TMP/stoneshell-brain/src/cache.rs /data/data/com.termux/files/home/SPFsmartGATE/LIVE/TMP/stoneshell-brain/src/chunker.rs /data/data/com.termux/files/home/SPFsmartGATE/LIVE/TMP/stoneshell-brain/src/embeddings.rs /data/data/com.termux/files/home/SPFsmartGATE/LIVE/TMP/stoneshell-brain/src/lib.rs /data/data/com.termux/files/home/SPFsmartGATE/LIVE/TMP/stoneshell-brain/src/search.rs /data/data/com.termux/files/home/SPFsmartGATE/LIVE/TMP/stoneshell-brain/src/storage.rs /data/data/com.termux/files/home/SPFsmartGATE/src/agent_state.rs /data/data/com.termux/files/home/SPFsmartGATE/src/attention.rs /data/data/com.termux/files/home/SPFsmartGATE/src/bin/brain_index_training.rs /data/data/com.termux/files/home/SPFsmartGATE/src/brain_local.rs /data/data/com.termux/files/home/SPFsmartGATE/src/browser.rs /data/data/com.termux/files/home/SPFsmartGATE/src/calculate.rs /data/data/com.termux/files/home/SPFsmartGATE/src/channel.rs /data/data/com.termux/files/home/SPFsmartGATE/src/chat.rs /data/data/com.termux/files/home/SPFsmartGATE/src/checkpoint.rs /data/data/com.termux/files/home/SPFsmartGATE/src/config.rs /data/data/com.termux/files/home/SPFsmartGATE/src/config_db.rs /data/data/com.termux/files/home/SPFsmartGATE/src/decoder.rs /data/data/com.termux/files/home/SPFsmartGATE/src/dispatch.rs /data/data/com.termux/files/home/SPFsmartGATE/src/encoder.rs /data/data/com.termux/files/home/SPFsmartGATE/src/ffn.rs /data/data/com.termux/files/home/SPFsmartGATE/src/flint_memory.rs /data/data/com.termux/files/home/SPFsmartGATE/src/framing.rs /data/data/com.termux/files/home/SPFsmartGATE/src/fs.rs /data/data/com.termux/files/home/SPFsmartGATE/src/gate.rs /data/data/com.termux/files/home/SPFsmartGATE/src/gate_training.rs /data/data/com.termux/files/home/SPFsmartGATE/src/http.rs /data/data/com.termux/files/home/SPFsmartGATE/src/identity.rs /data/data/com.termux/files/home/SPFsmartGATE/src/inspect.rs /data/data/com.termux/files/home/SPFsmartGATE/src/learning.rs /data/data/com.termux/files/home/SPFsmartGATE/src/lib.rs /data/data/com.termux/files/home/SPFsmartGATE/src/mcp.rs /data/data/com.termux/files/home/SPFsmartGATE/src/mesh.rs /data/data/com.termux/files/home/SPFsmartGATE/src/network.rs /data/data/com.termux/files/home/SPFsmartGATE/src/orchestrator.rs /data/data/com.termux/files/home/SPFsmartGATE/src/paths.rs /data/data/com.termux/files/home/SPFsmartGATE/src/pipeline.rs /data/data/com.termux/files/home/SPFsmartGATE/src/session.rs /data/data/com.termux/files/home/SPFsmartGATE/src/storage.rs /data/data/com.termux/files/home/SPFsmartGATE/src/tensor.rs /data/data/com.termux/files/home/SPFsmartGATE/src/tmp_db.rs /data/data/com.termux/files/home/SPFsmartGATE/src/tokenizer.rs /data/data/com.termux/files/home/SPFsmartGATE/src/train.rs /data/data/com.termux/files/home/SPFsmartGATE/src/transformer.rs /data/data/com.termux/files/home/SPFsmartGATE/src/transformer_tools.rs /data/data/com.termux/files/home/SPFsmartGATE/src/utf8_safe.rs /data/data/com.termux/files/home/SPFsmartGATE/src/validate.rs /data/data/com.termux/files/home/SPFsmartGATE/src/voice.rs /data/data/com.termux/files/home/SPFsmartGATE/src/web.rs /data/data/com.termux/files/home/SPFsmartGATE/src/worker.rs

SPFsmartGATE/target/release/deps/bincode-be0e4e9c6ceab529.d

@@ -0,0 +1,19 @@
+/data/data/com.termux/files/home/SPFsmartGATE/target/release/deps/bincode-be0e4e9c6ceab529.d: /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/lib.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/config/mod.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/config/endian.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/config/int.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/config/legacy.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/config/limit.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/config/trailing.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/de/mod.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/de/read.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/byteorder.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/error.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/internal.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/ser/mod.rs
+
+/data/data/com.termux/files/home/SPFsmartGATE/target/release/deps/libbincode-be0e4e9c6ceab529.rlib: /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/lib.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/config/mod.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/config/endian.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/config/int.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/config/legacy.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/config/limit.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/config/trailing.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/de/mod.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/de/read.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/byteorder.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/error.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/internal.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/ser/mod.rs
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+/data/data/com.termux/files/home/SPFsmartGATE/target/release/deps/libbincode-be0e4e9c6ceab529.rmeta: /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/lib.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/config/mod.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/config/endian.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/config/int.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/config/legacy.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/config/limit.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/config/trailing.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/de/mod.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/de/read.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/byteorder.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/error.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/internal.rs /data/data/com.termux/files/home/.cargo/registry/src/index.crates.io-1949cf8c6b5b557f/bincode-1.3.3/src/ser/mod.rs
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