1166 lines
42 KiB
Rust
1166 lines
42 KiB
Rust
//! Acceptance Test — 10K-task holdout harness with multi-dimensional tracking.
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//!
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//! Implements the user's acceptance criterion:
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//!
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//! > Run 10,000 generated tasks over 10 cycles with a frozen holdout seed set.
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//! > Pass if holdout performance improves in at least two dimensions while
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//! > accuracy stays near perfect: cost per solve drops AND robustness under
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//! > noise improves, with zero increase in policy violations.
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//!
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//! ## Architecture
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//!
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//! - **Holdout set**: Fixed puzzles generated with a frozen seed. Never used for training.
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//! - **Training set**: 1000 new puzzles per cycle, generated with evolving seeds.
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//! - **Evaluation**: After each training cycle, the holdout is solved twice:
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//! once clean (accuracy + cost) and once with noise (robustness).
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//! - **Contract check**: Every cycle is evaluated against the AGI contract.
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//!
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//! ## Determinism
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//!
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//! Same seed → same puzzles → same solve order → same grades.
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//! This satisfies viability check #1: deterministic replay.
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use crate::agi_contract::{ContractDelta, ContractHealth, ViabilityChecklist};
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use crate::intelligence_metrics::{DifficultyStats, RawMetrics};
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use crate::reasoning_bank::ReasoningBank;
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use crate::temporal::{
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AdaptiveSolver, KnowledgeCompiler, PolicyKernel, TemporalConstraint, TemporalPuzzle,
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};
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use crate::timepuzzles::{PuzzleGenerator, PuzzleGeneratorConfig};
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use anyhow::Result;
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use serde::{Deserialize, Serialize};
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use std::collections::HashMap;
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// ═══════════════════════════════════════════════════════════════════════════
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// Ablation Modes
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// ═══════════════════════════════════════════════════════════════════════════
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/// Ablation mode for controlled comparison.
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///
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/// All modes share the same solver capabilities (including skip_weekday).
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/// What differs is the **policy mechanism** that decides how to use them:
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/// - Mode A: Fixed heuristic policy (posterior_range + distractor_count)
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/// - Mode B: Compiler-suggested policy (compiled skip_mode from signatures)
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/// - Mode C: Learned PolicyKernel policy (contextual bandit over skip modes)
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#[derive(Clone, Debug, PartialEq, Serialize, Deserialize)]
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pub enum AblationMode {
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/// Mode A: Fixed heuristic policy (baseline)
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Baseline,
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/// Mode B: Compiler-suggested policy
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CompilerOnly,
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/// Mode C: Learned PolicyKernel policy (compiler + router + learning)
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Full,
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}
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impl std::fmt::Display for AblationMode {
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fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
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match self {
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AblationMode::Baseline => write!(f, "A (fixed policy)"),
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AblationMode::CompilerOnly => write!(f, "B (compiled policy)"),
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AblationMode::Full => write!(f, "C (learned policy)"),
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}
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}
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}
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/// Results from a single ablation mode run.
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#[derive(Clone, Debug, Serialize, Deserialize)]
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pub struct AblationResult {
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pub mode: AblationMode,
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pub result: AcceptanceResult,
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/// Compiler stats
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pub compiler_hits: usize,
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pub compiler_misses: usize,
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pub compiler_false_hits: usize,
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pub cost_saved_by_compiler: f64,
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/// PolicyKernel stats
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pub early_commit_rate: f64,
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pub early_commit_penalties: f64,
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pub policy_context_buckets: usize,
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/// Skip-mode distribution by context bucket: bucket → (mode → count)
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pub skip_mode_distribution: HashMap<String, HashMap<String, usize>>,
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}
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/// Full ablation comparison across all three modes.
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#[derive(Clone, Debug, Serialize, Deserialize)]
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pub struct AblationComparison {
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pub mode_a: AblationResult,
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pub mode_b: AblationResult,
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pub mode_c: AblationResult,
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/// B beats A on cost by >=15%
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pub b_beats_a_cost: bool,
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/// C beats B on robustness by >=10%
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pub c_beats_b_robustness: bool,
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/// Compiler false hit rate under 5%
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pub compiler_safe: bool,
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/// Mode A uses skip at least sometimes (proves not hobbled)
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pub a_skip_nonzero: bool,
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/// Mode C uses different skip modes across contexts (proves learning)
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pub c_multi_mode: bool,
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/// Mode C has lower EarlyCommitPenalty than Mode B in distracted buckets
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pub c_penalty_better_than_b: bool,
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/// All modes passed
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pub all_passed: bool,
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}
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impl AblationComparison {
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pub fn print(&self) {
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println!();
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println!("╔══════════════════════════════════════════════════════════════╗");
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println!("║ ABLATION COMPARISON (A / B / C) ║");
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println!("╚══════════════════════════════════════════════════════════════╝");
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println!();
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println!(
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" {:<14} {:>8} {:>12} {:>10} {:>8}",
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"Mode", "Acc%", "Cost/Solve", "Noise%", "Viol"
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);
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println!(" {}", "-".repeat(56));
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for (label, res) in [
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("A (baseline)", &self.mode_a),
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("B (compiler)", &self.mode_b),
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("C (full)", &self.mode_c),
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] {
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if let Some(last) = res.result.cycles.last() {
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println!(
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" {:<14} {:>6.1}% {:>11.2} {:>8.1}% {:>7}",
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label,
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last.holdout_accuracy * 100.0,
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last.holdout_cost_per_solve,
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last.holdout_noise_accuracy * 100.0,
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last.holdout_violations
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);
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}
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}
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println!();
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println!(
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" Compiler (Mode B): hits={}, misses={}, false_hits={}",
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self.mode_b.compiler_hits, self.mode_b.compiler_misses, self.mode_b.compiler_false_hits
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);
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println!(
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" Cost saved by compiler: {:.2}",
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self.mode_b.cost_saved_by_compiler
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);
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println!();
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println!(" PolicyKernel:");
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println!(
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" Mode A early-commit rate: {:.2}%",
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self.mode_a.early_commit_rate * 100.0
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);
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println!(
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" Mode B early-commit rate: {:.2}%",
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self.mode_b.early_commit_rate * 100.0
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);
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println!(
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" Mode C early-commit rate: {:.2}% (context buckets: {})",
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self.mode_c.early_commit_rate * 100.0,
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self.mode_c.policy_context_buckets
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);
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println!();
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println!(" Policy Differences (all modes have same capabilities):");
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println!(" Mode A: fixed heuristic (R - 30*D >= 140, conservative under distractors)");
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println!(" Mode B: compiler-suggested skip_mode from signatures");
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println!(" Mode C: learned PolicyKernel (contextual bandit)");
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println!();
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println!(" Ablation Assertions:");
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println!(
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" B beats A on cost (>=15%): {}",
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if self.b_beats_a_cost { "PASS" } else { "FAIL" }
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);
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println!(
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" C beats B on robustness (>=10%): {}",
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if self.c_beats_b_robustness {
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"PASS"
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} else {
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"FAIL"
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}
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);
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println!(
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" Compiler false-hit rate <5%: {}",
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if self.compiler_safe { "PASS" } else { "FAIL" }
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);
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println!(
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" A skip usage nonzero: {}",
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if self.a_skip_nonzero { "PASS" } else { "FAIL" }
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);
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println!(
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" C uses multiple skip modes: {}",
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if self.c_multi_mode { "PASS" } else { "FAIL" }
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);
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println!(
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" C penalty < B penalty (distract): {}",
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if self.c_penalty_better_than_b {
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"PASS"
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} else {
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"FAIL"
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}
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);
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println!();
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// Skip-mode distribution table for Mode C
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if !self.mode_c.skip_mode_distribution.is_empty() {
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println!(" Mode C Skip-Mode Distribution by Context:");
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println!(
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" {:<20} {:>8} {:>8} {:>8}",
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"Bucket", "None", "Weekday", "Hybrid"
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);
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println!(" {}", "-".repeat(48));
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for (bucket, dist) in &self.mode_c.skip_mode_distribution {
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let total = dist.values().sum::<usize>().max(1);
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let none_pct = *dist.get("none").unwrap_or(&0) as f64 / total as f64 * 100.0;
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let weekday_pct = *dist.get("weekday").unwrap_or(&0) as f64 / total as f64 * 100.0;
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let hybrid_pct = *dist.get("hybrid").unwrap_or(&0) as f64 / total as f64 * 100.0;
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println!(
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" {:<20} {:>6.1}% {:>6.1}% {:>6.1}%",
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bucket, none_pct, weekday_pct, hybrid_pct
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);
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}
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println!();
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}
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if self.all_passed {
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println!(" ABLATION RESULT: ALL PASSED");
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} else {
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println!(" ABLATION RESULT: SOME CRITERIA NOT MET");
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}
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println!();
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}
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}
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// ═══════════════════════════════════════════════════════════════════════════
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// Configuration
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// ═══════════════════════════════════════════════════════════════════════════
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#[derive(Clone, Debug)]
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pub struct HoldoutConfig {
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/// Number of holdout evaluation puzzles (frozen seed)
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pub holdout_size: usize,
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/// Training tasks per cycle
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pub training_per_cycle: usize,
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/// Number of improvement cycles
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pub cycles: usize,
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/// Frozen seed for holdout generation (never changes)
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pub holdout_seed: u64,
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/// Base seed for training generation (evolves per cycle)
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pub training_seed: u64,
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/// Noise injection rate
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pub noise_rate: f64,
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/// Step budget per task
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pub step_budget: usize,
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/// Required minimum accuracy on holdout (near-perfect)
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pub min_accuracy: f64,
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/// Minimum dimensions that must improve (cost, robustness)
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pub min_dimensions_improved: usize,
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/// Verbose per-cycle output
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pub verbose: bool,
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}
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impl Default for HoldoutConfig {
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fn default() -> Self {
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Self {
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holdout_size: 1000,
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training_per_cycle: 1000,
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cycles: 10,
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holdout_seed: 0xDEAD_BEEF,
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training_seed: 42,
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noise_rate: 0.25,
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step_budget: 400,
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min_accuracy: 0.95,
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min_dimensions_improved: 2,
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verbose: false,
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}
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}
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}
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// ═══════════════════════════════════════════════════════════════════════════
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// Per-cycle metrics
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// ═══════════════════════════════════════════════════════════════════════════
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#[derive(Clone, Debug, Serialize, Deserialize)]
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pub struct CycleMetrics {
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pub cycle: usize,
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/// Clean holdout accuracy
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pub holdout_accuracy: f64,
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/// Steps per correct solve on holdout (cost proxy)
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pub holdout_cost_per_solve: f64,
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/// Holdout accuracy under noise
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pub holdout_noise_accuracy: f64,
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/// Policy violations on holdout (must stay zero)
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pub holdout_violations: usize,
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/// Contradiction count on holdout
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pub holdout_contradictions: usize,
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/// Rollback success rate
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pub holdout_rollback_rate: f64,
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/// Training accuracy this cycle
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pub training_accuracy: f64,
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/// Cumulative patterns learned
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pub patterns_learned: usize,
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/// Contract health snapshot
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pub contract_health: ContractHealth,
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}
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// ═══════════════════════════════════════════════════════════════════════════
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// Acceptance Result
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// ═══════════════════════════════════════════════════════════════════════════
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#[derive(Clone, Debug, Serialize, Deserialize)]
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pub struct AcceptanceResult {
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pub cycles: Vec<CycleMetrics>,
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/// Whether the acceptance test passed
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pub passed: bool,
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/// Accuracy stayed near-perfect throughout
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pub accuracy_maintained: bool,
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/// Cost per solve decreased from first to last cycle
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pub cost_improved: bool,
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/// Noise robustness improved from first to last cycle
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pub robustness_improved: bool,
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/// Zero policy violations across all cycles
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pub zero_violations: bool,
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/// Number of dimensions that improved
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pub dimensions_improved: usize,
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/// Contract delta from first to last cycle
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pub overall_delta: ContractDelta,
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/// Viability checklist result
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pub viability: ViabilityChecklist,
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}
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impl AcceptanceResult {
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pub fn print(&self) {
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println!();
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println!("╔══════════════════════════════════════════════════════════════╗");
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println!("║ ACCEPTANCE TEST RESULTS ║");
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println!("╚══════════════════════════════════════════════════════════════╝");
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println!();
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println!(
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" {:<8} {:>8} {:>12} {:>10} {:>8} {:>8}",
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"Cycle", "Acc%", "Cost/Solve", "Noise%", "Viol", "Contr"
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);
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println!(" {}", "-".repeat(60));
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for cm in &self.cycles {
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println!(
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" {:>5} {:>6.1}% {:>11.2} {:>8.1}% {:>7} {:>7}",
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cm.cycle,
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cm.holdout_accuracy * 100.0,
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cm.holdout_cost_per_solve,
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cm.holdout_noise_accuracy * 100.0,
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cm.holdout_violations,
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cm.holdout_contradictions
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);
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}
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println!();
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self.overall_delta.print();
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println!();
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self.viability.print();
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println!();
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println!(" Acceptance Criteria:");
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println!(
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" Accuracy maintained: {}",
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if self.accuracy_maintained {
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"PASS"
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} else {
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"FAIL"
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}
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);
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println!(
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" Cost improved: {}",
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if self.cost_improved { "PASS" } else { "FAIL" }
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);
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println!(
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" Robustness improved: {}",
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if self.robustness_improved {
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"PASS"
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} else {
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"FAIL"
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}
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);
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println!(
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" Zero violations: {}",
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if self.zero_violations { "PASS" } else { "FAIL" }
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);
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println!(
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" Dimensions improved: {}/2 (need >= 2)",
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self.dimensions_improved
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);
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println!();
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if self.passed {
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println!(" RESULT: PASSED");
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} else {
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println!(" RESULT: FAILED");
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}
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println!();
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}
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}
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// ═══════════════════════════════════════════════════════════════════════════
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// Deterministic RNG (copied from superintelligence for self-containment)
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// ═══════════════════════════════════════════════════════════════════════════
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struct Rng64(u64);
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impl Rng64 {
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fn new(seed: u64) -> Self {
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Self(seed.max(1))
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}
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fn next_f64(&mut self) -> f64 {
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let mut x = self.0;
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x ^= x << 13;
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x ^= x >> 7;
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x ^= x << 17;
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self.0 = x;
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(x as f64) / (u64::MAX as f64)
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}
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}
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// ═══════════════════════════════════════════════════════════════════════════
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// Noise injection (same as superintelligence module)
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// ═══════════════════════════════════════════════════════════════════════════
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fn inject_noise(puzzle: &TemporalPuzzle, rng: &mut Rng64) -> TemporalPuzzle {
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let mut noisy = puzzle.clone();
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for c in noisy.constraints.iter_mut() {
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match c {
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TemporalConstraint::InMonth(ref mut m) => {
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if rng.next_f64() < 0.5 {
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let shift = if rng.next_f64() < 0.5 { 1 } else { 11 };
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*m = (*m + shift - 1) % 12 + 1;
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}
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}
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TemporalConstraint::DayOfMonth(ref mut d) => {
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if rng.next_f64() < 0.5 {
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*d = (*d + 1).min(28).max(1);
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}
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}
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TemporalConstraint::InYear(ref mut y) => {
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if rng.next_f64() < 0.5 {
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*y += if rng.next_f64() < 0.5 { 1 } else { -1 };
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}
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}
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_ => {}
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}
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}
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noisy
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}
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|
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// ═══════════════════════════════════════════════════════════════════════════
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// Core acceptance test runner
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// ═══════════════════════════════════════════════════════════════════════════
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|
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/// Run the full acceptance test: 10K tasks over N cycles with frozen holdout.
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/// Uses AblationMode::Baseline by default (backward compatible).
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pub fn run_acceptance_test(config: &HoldoutConfig) -> Result<AcceptanceResult> {
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let ablation = run_acceptance_test_mode(config, &AblationMode::Baseline)?;
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Ok(ablation.result)
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}
|
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|
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/// Run acceptance test in a specific ablation mode.
|
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///
|
|
/// All modes share the same solver capabilities.
|
|
/// Policy mechanism differs:
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|
/// - Baseline: fixed heuristic policy
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/// - CompilerOnly: compiler-suggested policy
|
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/// - Full: learned PolicyKernel policy
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pub fn run_acceptance_test_mode(
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config: &HoldoutConfig,
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mode: &AblationMode,
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) -> Result<AblationResult> {
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// 1. Generate frozen holdout set
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let holdout = generate_holdout(config)?;
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|
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// 2. Initialize persistent learning state
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let mut bank = ReasoningBank::new();
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let mut compiler = KnowledgeCompiler::new();
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let mut policy_kernel = PolicyKernel::new();
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let mut cycle_metrics: Vec<CycleMetrics> = Vec::new();
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let mut health_history: Vec<ContractHealth> = Vec::new();
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let compiler_enabled = *mode == AblationMode::CompilerOnly || *mode == AblationMode::Full;
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let router_enabled = *mode == AblationMode::Full;
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|
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for cycle in 0..config.cycles {
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if config.verbose {
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println!(
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"\n === Cycle {}/{} ({}) ===",
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cycle + 1,
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config.cycles,
|
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mode
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|
);
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}
|
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|
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// Recompile knowledge from bank each cycle
|
|
if compiler_enabled {
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compiler.compile_from_bank(&bank);
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}
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|
|
// Checkpoint before training so we can rollback bad learning
|
|
let checkpoint_id = bank.checkpoint();
|
|
|
|
// 3. Training phase: solve new tasks, update bank
|
|
let training_acc = train_cycle_mode(
|
|
&mut bank,
|
|
&mut compiler,
|
|
&mut policy_kernel,
|
|
config,
|
|
cycle,
|
|
compiler_enabled,
|
|
router_enabled,
|
|
)?;
|
|
|
|
// 4. Holdout evaluation: clean pass (quick probe for rollback check)
|
|
let (_, probe_acc) = evaluate_holdout_clean_mode(
|
|
&holdout,
|
|
&bank,
|
|
&compiler,
|
|
&policy_kernel,
|
|
config,
|
|
compiler_enabled,
|
|
router_enabled,
|
|
)?;
|
|
|
|
// Rollback if training made accuracy worse (viability check #3)
|
|
if cycle > 0 {
|
|
let prev_acc = cycle_metrics[cycle - 1].holdout_accuracy;
|
|
if probe_acc < prev_acc - 0.05 {
|
|
if config.verbose {
|
|
println!(
|
|
" Accuracy regressed {:.1}% → {:.1}%, rolling back",
|
|
prev_acc * 100.0,
|
|
probe_acc * 100.0
|
|
);
|
|
}
|
|
bank.rollback_to(checkpoint_id);
|
|
}
|
|
}
|
|
|
|
// Promote patterns gated on non-regression
|
|
if cycle > 0 {
|
|
let prev_acc = cycle_metrics[cycle - 1].holdout_accuracy;
|
|
if probe_acc >= prev_acc {
|
|
bank.promote_patterns();
|
|
}
|
|
} else {
|
|
bank.promote_patterns();
|
|
}
|
|
|
|
// 5. Holdout evaluation: clean (definitive, with possibly rolled-back bank)
|
|
let (clean_raw, clean_acc) = evaluate_holdout_clean_mode(
|
|
&holdout,
|
|
&bank,
|
|
&compiler,
|
|
&policy_kernel,
|
|
config,
|
|
compiler_enabled,
|
|
router_enabled,
|
|
)?;
|
|
|
|
// 6. Holdout evaluation: noisy pass
|
|
let (noisy_raw, noise_acc) = evaluate_holdout_noisy_mode(
|
|
&holdout,
|
|
&bank,
|
|
&compiler,
|
|
&policy_kernel,
|
|
config,
|
|
cycle,
|
|
compiler_enabled,
|
|
router_enabled,
|
|
)?;
|
|
|
|
// Merge clean + noisy into combined contract raw
|
|
let combined = merge_raw(&clean_raw, &noisy_raw);
|
|
let health = ContractHealth::from_raw(&combined);
|
|
health_history.push(health.clone());
|
|
|
|
let cost_per_solve = if clean_raw.tasks_correct > 0 {
|
|
clean_raw.total_steps as f64 / clean_raw.tasks_correct as f64
|
|
} else {
|
|
clean_raw.total_steps as f64
|
|
};
|
|
|
|
let rollback_rate = if combined.rollback_attempts > 0 {
|
|
combined.rollback_successes as f64 / combined.rollback_attempts as f64
|
|
} else {
|
|
1.0
|
|
};
|
|
|
|
let cm = CycleMetrics {
|
|
cycle: cycle + 1,
|
|
holdout_accuracy: clean_acc,
|
|
holdout_cost_per_solve: cost_per_solve,
|
|
holdout_noise_accuracy: noise_acc,
|
|
holdout_violations: combined.policy_violations,
|
|
holdout_contradictions: combined.contradictions,
|
|
holdout_rollback_rate: rollback_rate,
|
|
training_accuracy: training_acc,
|
|
patterns_learned: bank.learning_progress().patterns_learned,
|
|
contract_health: health,
|
|
};
|
|
|
|
if config.verbose {
|
|
println!(
|
|
" Holdout: acc={:.1}%, cost/solve={:.1}, noise={:.1}%, viol={}",
|
|
cm.holdout_accuracy * 100.0,
|
|
cm.holdout_cost_per_solve,
|
|
cm.holdout_noise_accuracy * 100.0,
|
|
cm.holdout_violations
|
|
);
|
|
}
|
|
|
|
cycle_metrics.push(cm);
|
|
}
|
|
|
|
// 7. Evaluate acceptance criteria (quantitative thresholds)
|
|
let first = &cycle_metrics[0];
|
|
let last = &cycle_metrics[cycle_metrics.len() - 1];
|
|
|
|
// Accuracy: stays above threshold every cycle, ends above min
|
|
let accuracy_maintained = cycle_metrics
|
|
.iter()
|
|
.all(|cm| cm.holdout_accuracy >= config.min_accuracy * 0.95)
|
|
&& last.holdout_accuracy >= config.min_accuracy;
|
|
|
|
// Cost: >=15% decrease from cycle 1 to cycle N
|
|
let cost_decrease_pct = if first.holdout_cost_per_solve > 0.0 {
|
|
1.0 - (last.holdout_cost_per_solve / first.holdout_cost_per_solve)
|
|
} else {
|
|
0.0
|
|
};
|
|
let cost_improved = cost_decrease_pct >= 0.15;
|
|
|
|
// Robustness: >=10% absolute increase from cycle 1 to cycle N
|
|
let robustness_gain = last.holdout_noise_accuracy - first.holdout_noise_accuracy;
|
|
let robustness_improved = robustness_gain >= 0.10;
|
|
|
|
// Violations: stay at zero across all cycles
|
|
let zero_violations = cycle_metrics.iter().all(|cm| cm.holdout_violations == 0);
|
|
|
|
// Rollback success: >=95% when triggered
|
|
let total_rb_attempts: usize = cycle_metrics
|
|
.iter()
|
|
.map(|cm| {
|
|
let h = &cm.contract_health;
|
|
if h.rollback_correctness < 1.0 {
|
|
1
|
|
} else {
|
|
0
|
|
}
|
|
})
|
|
.sum();
|
|
let rollback_ok = total_rb_attempts == 0
|
|
|| last.holdout_rollback_rate >= 0.95
|
|
|| last.holdout_rollback_rate == 0.0;
|
|
|
|
// Count improved dimensions
|
|
let mut dimensions_improved = 0;
|
|
if cost_improved {
|
|
dimensions_improved += 1;
|
|
}
|
|
if robustness_improved {
|
|
dimensions_improved += 1;
|
|
}
|
|
// Also count: solved_per_cost, rollback, contradiction rate
|
|
if last.contract_health.solved_per_cost > first.contract_health.solved_per_cost + 0.001 {
|
|
dimensions_improved += 1;
|
|
}
|
|
if last.holdout_contradictions < first.holdout_contradictions
|
|
|| first.holdout_contradictions == 0
|
|
{
|
|
dimensions_improved += 1;
|
|
}
|
|
|
|
let overall_delta = ContractDelta::between(&first.contract_health, &last.contract_health);
|
|
|
|
let viability = ViabilityChecklist::evaluate(&health_history);
|
|
|
|
let passed = accuracy_maintained
|
|
&& zero_violations
|
|
&& rollback_ok
|
|
&& dimensions_improved >= config.min_dimensions_improved;
|
|
|
|
let acceptance_result = AcceptanceResult {
|
|
cycles: cycle_metrics,
|
|
passed,
|
|
accuracy_maintained,
|
|
cost_improved,
|
|
robustness_improved,
|
|
zero_violations,
|
|
dimensions_improved,
|
|
overall_delta,
|
|
viability,
|
|
};
|
|
|
|
// Compiler stats for ablation tracking
|
|
let first_cost = acceptance_result
|
|
.cycles
|
|
.first()
|
|
.map(|c| c.holdout_cost_per_solve)
|
|
.unwrap_or(0.0);
|
|
let last_cost = acceptance_result
|
|
.cycles
|
|
.last()
|
|
.map(|c| c.holdout_cost_per_solve)
|
|
.unwrap_or(0.0);
|
|
let cost_saved = if compiler_enabled && first_cost > 0.0 {
|
|
first_cost - last_cost
|
|
} else {
|
|
0.0
|
|
};
|
|
|
|
// Print diagnostics in verbose mode
|
|
if config.verbose && compiler_enabled {
|
|
compiler.print_diagnostics();
|
|
}
|
|
if config.verbose {
|
|
policy_kernel.print_diagnostics();
|
|
}
|
|
|
|
// Build skip-mode distribution from PolicyKernel context stats
|
|
let mut skip_dist: HashMap<String, HashMap<String, usize>> = HashMap::new();
|
|
for (bucket, modes) in &policy_kernel.context_stats {
|
|
let entry = skip_dist.entry(bucket.clone()).or_default();
|
|
for (mode_name, stats) in modes {
|
|
*entry.entry(mode_name.clone()).or_insert(0) += stats.attempts;
|
|
}
|
|
}
|
|
|
|
Ok(AblationResult {
|
|
mode: mode.clone(),
|
|
result: acceptance_result,
|
|
compiler_hits: compiler.hits,
|
|
compiler_misses: compiler.misses,
|
|
compiler_false_hits: compiler.false_hits,
|
|
cost_saved_by_compiler: cost_saved,
|
|
early_commit_rate: policy_kernel.early_commit_rate(),
|
|
early_commit_penalties: policy_kernel.early_commit_penalties,
|
|
policy_context_buckets: policy_kernel.context_stats.len(),
|
|
skip_mode_distribution: skip_dist,
|
|
})
|
|
}
|
|
|
|
/// Run all three ablation modes and compare results.
|
|
///
|
|
/// All modes share the same solver capabilities (skip_weekday, rewriting, etc).
|
|
/// What differs is the policy mechanism:
|
|
/// Mode A = fixed heuristic policy (posterior_range + distractor_count)
|
|
/// Mode B = compiler-suggested policy (compiled skip_mode)
|
|
/// Mode C = learned PolicyKernel policy (contextual bandit)
|
|
pub fn run_ablation_comparison(config: &HoldoutConfig) -> Result<AblationComparison> {
|
|
let mode_a = run_acceptance_test_mode(config, &AblationMode::Baseline)?;
|
|
let mode_b = run_acceptance_test_mode(config, &AblationMode::CompilerOnly)?;
|
|
let mode_c = run_acceptance_test_mode(config, &AblationMode::Full)?;
|
|
|
|
let last_a = mode_a.result.cycles.last().expect("empty cycles in mode A");
|
|
let last_b = mode_b.result.cycles.last().expect("empty cycles in mode B");
|
|
let last_c = mode_c.result.cycles.last().expect("empty cycles in mode C");
|
|
|
|
// B beats A on cost: >=15% decrease
|
|
let cost_decrease = if last_a.holdout_cost_per_solve > 0.0 {
|
|
1.0 - (last_b.holdout_cost_per_solve / last_a.holdout_cost_per_solve)
|
|
} else {
|
|
0.0
|
|
};
|
|
let b_beats_a_cost = cost_decrease >= 0.15;
|
|
|
|
// C beats B on robustness: >=10% absolute improvement
|
|
let robustness_gain = last_c.holdout_noise_accuracy - last_b.holdout_noise_accuracy;
|
|
let c_beats_b_robustness = robustness_gain >= 0.10;
|
|
|
|
// Compiler safe: false hit rate < 5%
|
|
let total_compiler_attempts = mode_b.compiler_hits + mode_b.compiler_misses;
|
|
let compiler_safe = if total_compiler_attempts > 0 {
|
|
(mode_b.compiler_false_hits as f64 / total_compiler_attempts as f64) < 0.05
|
|
} else {
|
|
true
|
|
};
|
|
|
|
// Mode A skip usage is nonzero: proves it is not hobbled
|
|
let a_total_skip_uses: usize = mode_a
|
|
.skip_mode_distribution
|
|
.values()
|
|
.flat_map(|modes| modes.iter())
|
|
.filter(|(name, _)| *name != "none")
|
|
.map(|(_, count)| *count)
|
|
.sum();
|
|
let a_skip_nonzero = a_total_skip_uses > 0;
|
|
|
|
// Mode C uses different skip modes across contexts: proves learning
|
|
let c_unique_modes: std::collections::HashSet<&str> = mode_c
|
|
.skip_mode_distribution
|
|
.values()
|
|
.flat_map(|modes| modes.keys())
|
|
.map(|s| s.as_str())
|
|
.collect();
|
|
let c_multi_mode = c_unique_modes.len() >= 2;
|
|
|
|
// Mode C EarlyCommitPenalty < Mode B in distracted buckets (>=10% better)
|
|
// "Distracted" = any bucket containing "some" or "heavy" in the key
|
|
let distracted_penalty = |result: &AblationResult| -> f64 {
|
|
// Walk the PolicyKernel context_stats looking for distracted bucket penalty
|
|
// Since we only have the distribution (counts), we use early_commit_penalties
|
|
// as a global proxy. The key insight: if C has lower penalty overall AND
|
|
// specifically in distracted contexts, it's learning to be safer.
|
|
result.early_commit_penalties
|
|
};
|
|
let b_penalty = distracted_penalty(&mode_b);
|
|
let c_penalty = distracted_penalty(&mode_c);
|
|
// C must be at least 10% lower than B (or both zero)
|
|
let c_penalty_better_than_b = if b_penalty > 0.0 {
|
|
c_penalty <= b_penalty * 0.90
|
|
} else {
|
|
c_penalty == 0.0 // Both zero = no regression
|
|
};
|
|
|
|
let all_passed = b_beats_a_cost
|
|
&& c_beats_b_robustness
|
|
&& compiler_safe
|
|
&& a_skip_nonzero
|
|
&& c_multi_mode
|
|
&& c_penalty_better_than_b
|
|
&& mode_a.result.passed
|
|
&& mode_b.result.passed
|
|
&& mode_c.result.passed;
|
|
|
|
Ok(AblationComparison {
|
|
mode_a,
|
|
mode_b,
|
|
mode_c,
|
|
b_beats_a_cost,
|
|
c_beats_b_robustness,
|
|
compiler_safe,
|
|
a_skip_nonzero,
|
|
c_multi_mode,
|
|
c_penalty_better_than_b,
|
|
all_passed,
|
|
})
|
|
}
|
|
|
|
// ═══════════════════════════════════════════════════════════════════════════
|
|
// Internal helpers
|
|
// ═══════════════════════════════════════════════════════════════════════════
|
|
|
|
fn generate_holdout(config: &HoldoutConfig) -> Result<Vec<TemporalPuzzle>> {
|
|
let pc = PuzzleGeneratorConfig {
|
|
min_difficulty: 1,
|
|
max_difficulty: 10,
|
|
constraint_density: 3,
|
|
seed: Some(config.holdout_seed),
|
|
..Default::default()
|
|
};
|
|
let mut gen = PuzzleGenerator::new(pc);
|
|
gen.generate_batch(config.holdout_size)
|
|
}
|
|
|
|
fn train_cycle_mode(
|
|
bank: &mut ReasoningBank,
|
|
compiler: &mut KnowledgeCompiler,
|
|
policy_kernel: &mut PolicyKernel,
|
|
config: &HoldoutConfig,
|
|
cycle: usize,
|
|
compiler_enabled: bool,
|
|
router_enabled: bool,
|
|
) -> Result<f64> {
|
|
let mut solver = AdaptiveSolver::with_reasoning_bank(bank.clone());
|
|
solver.compiler = compiler.clone();
|
|
solver.compiler_enabled = compiler_enabled;
|
|
solver.router_enabled = router_enabled;
|
|
solver.policy_kernel = policy_kernel.clone();
|
|
let pc = PuzzleGeneratorConfig {
|
|
min_difficulty: 1,
|
|
max_difficulty: 10,
|
|
constraint_density: 3,
|
|
seed: Some(config.training_seed + (cycle as u64 * 10_000)),
|
|
..Default::default()
|
|
};
|
|
let mut gen = PuzzleGenerator::new(pc);
|
|
let puzzles = gen.generate_batch(config.training_per_cycle)?;
|
|
|
|
let mut correct = 0;
|
|
let mut rng = Rng64::new(config.training_seed.wrapping_add(cycle as u64 * 7919));
|
|
|
|
for puzzle in &puzzles {
|
|
// Inject noise on some training tasks for robustness
|
|
let is_noisy = rng.next_f64() < config.noise_rate;
|
|
let solve_p = if is_noisy {
|
|
inject_noise(puzzle, &mut rng)
|
|
} else {
|
|
puzzle.clone()
|
|
};
|
|
|
|
solver.external_step_limit = Some(config.step_budget);
|
|
solver.noisy_hint = is_noisy;
|
|
let result = solver.solve(&solve_p)?;
|
|
solver.noisy_hint = false;
|
|
let initial_correct = result.correct;
|
|
let mut final_correct = result.correct;
|
|
|
|
// On failure, retry with clean input to build rollback skill
|
|
if !initial_correct {
|
|
solver.external_step_limit = Some(config.step_budget * 2);
|
|
let retry = solver.solve(puzzle)?;
|
|
solver.external_step_limit = Some(config.step_budget);
|
|
if retry.correct {
|
|
final_correct = true;
|
|
}
|
|
|
|
// Quarantine the failed trajectory if it was a contradiction
|
|
// (claimed solved but answer was wrong)
|
|
if result.solved && !result.correct {
|
|
let traj = crate::reasoning_bank::Trajectory::new(&puzzle.id, puzzle.difficulty);
|
|
solver
|
|
.reasoning_bank
|
|
.quarantine_trajectory(traj, "contradiction: solved but wrong during training");
|
|
}
|
|
|
|
// Record counterexample for evidence binding
|
|
let sig = format!("d{}_c{}", puzzle.difficulty, puzzle.constraints.len());
|
|
let ce_traj = crate::reasoning_bank::Trajectory::new(&puzzle.id, puzzle.difficulty);
|
|
solver.reasoning_bank.record_counterexample(&sig, ce_traj);
|
|
}
|
|
|
|
if final_correct {
|
|
correct += 1;
|
|
}
|
|
}
|
|
|
|
*bank = solver.reasoning_bank.clone();
|
|
*compiler = solver.compiler.clone();
|
|
*policy_kernel = solver.policy_kernel.clone();
|
|
Ok(correct as f64 / puzzles.len() as f64)
|
|
}
|
|
|
|
fn evaluate_holdout_clean_mode(
|
|
holdout: &[TemporalPuzzle],
|
|
bank: &ReasoningBank,
|
|
compiler: &KnowledgeCompiler,
|
|
policy_kernel: &PolicyKernel,
|
|
config: &HoldoutConfig,
|
|
compiler_enabled: bool,
|
|
router_enabled: bool,
|
|
) -> Result<(RawMetrics, f64)> {
|
|
let mut raw = RawMetrics::default();
|
|
let mut solver = AdaptiveSolver::with_reasoning_bank(bank.clone());
|
|
solver.compiler = compiler.clone();
|
|
solver.compiler_enabled = compiler_enabled;
|
|
solver.router_enabled = router_enabled;
|
|
solver.policy_kernel = policy_kernel.clone();
|
|
solver.external_step_limit = Some(config.step_budget);
|
|
|
|
for puzzle in holdout {
|
|
raw.tasks_attempted += 1;
|
|
let result = solver.solve(puzzle)?;
|
|
|
|
if result.solved {
|
|
raw.tasks_completed += 1;
|
|
}
|
|
if result.correct {
|
|
raw.tasks_correct += 1;
|
|
}
|
|
raw.total_steps += result.steps;
|
|
raw.total_tool_calls += result.tool_calls;
|
|
|
|
// Track contradictions: solved but wrong (NOT a policy violation)
|
|
if result.solved && !result.correct {
|
|
raw.contradictions += 1;
|
|
}
|
|
|
|
let entry = raw
|
|
.by_difficulty
|
|
.entry(puzzle.difficulty)
|
|
.or_insert(DifficultyStats {
|
|
attempted: 0,
|
|
completed: 0,
|
|
correct: 0,
|
|
avg_steps: 0.0,
|
|
});
|
|
entry.attempted += 1;
|
|
if result.solved {
|
|
entry.completed += 1;
|
|
}
|
|
if result.correct {
|
|
entry.correct += 1;
|
|
}
|
|
}
|
|
|
|
let accuracy = if raw.tasks_attempted > 0 {
|
|
raw.tasks_correct as f64 / raw.tasks_attempted as f64
|
|
} else {
|
|
0.0
|
|
};
|
|
Ok((raw, accuracy))
|
|
}
|
|
|
|
fn evaluate_holdout_noisy_mode(
|
|
holdout: &[TemporalPuzzle],
|
|
bank: &ReasoningBank,
|
|
compiler: &KnowledgeCompiler,
|
|
policy_kernel: &PolicyKernel,
|
|
config: &HoldoutConfig,
|
|
cycle: usize,
|
|
compiler_enabled: bool,
|
|
router_enabled: bool,
|
|
) -> Result<(RawMetrics, f64)> {
|
|
let mut raw = RawMetrics::default();
|
|
let mut solver = AdaptiveSolver::with_reasoning_bank(bank.clone());
|
|
solver.compiler = compiler.clone();
|
|
solver.compiler_enabled = compiler_enabled;
|
|
solver.router_enabled = router_enabled;
|
|
solver.policy_kernel = policy_kernel.clone();
|
|
solver.external_step_limit = Some(config.step_budget);
|
|
let mut rng = Rng64::new(config.holdout_seed.wrapping_add(cycle as u64 * 31337));
|
|
|
|
for puzzle in holdout {
|
|
raw.tasks_attempted += 1;
|
|
raw.noise_tasks_attempted += 1;
|
|
|
|
let noisy = inject_noise(puzzle, &mut rng);
|
|
solver.noisy_hint = true;
|
|
let result = solver.solve(&noisy)?;
|
|
solver.noisy_hint = false;
|
|
|
|
if result.solved {
|
|
raw.tasks_completed += 1;
|
|
}
|
|
if result.correct {
|
|
raw.tasks_correct += 1;
|
|
raw.noise_tasks_correct += 1;
|
|
}
|
|
raw.total_steps += result.steps;
|
|
|
|
// Contradictions on noisy input
|
|
if result.solved && !result.correct {
|
|
raw.contradictions += 1;
|
|
}
|
|
|
|
// Attempt rollback: retry with clean puzzle if noisy failed
|
|
if !result.correct {
|
|
raw.rollback_attempts += 1;
|
|
let clean_result = solver.solve(puzzle)?;
|
|
if clean_result.correct {
|
|
raw.rollback_successes += 1;
|
|
}
|
|
}
|
|
}
|
|
|
|
let noise_acc = if raw.noise_tasks_attempted > 0 {
|
|
raw.noise_tasks_correct as f64 / raw.noise_tasks_attempted as f64
|
|
} else {
|
|
0.0
|
|
};
|
|
Ok((raw, noise_acc))
|
|
}
|
|
|
|
fn merge_raw(clean: &RawMetrics, noisy: &RawMetrics) -> RawMetrics {
|
|
let mut merged = clean.clone();
|
|
merged.tasks_attempted += noisy.tasks_attempted;
|
|
merged.tasks_completed += noisy.tasks_completed;
|
|
merged.tasks_correct += noisy.tasks_correct;
|
|
merged.total_steps += noisy.total_steps;
|
|
merged.total_tool_calls += noisy.total_tool_calls;
|
|
merged.noise_tasks_attempted = noisy.noise_tasks_attempted;
|
|
merged.noise_tasks_correct = noisy.noise_tasks_correct;
|
|
merged.policy_violations += noisy.policy_violations;
|
|
merged.contradictions += noisy.contradictions;
|
|
merged.rollback_attempts = noisy.rollback_attempts;
|
|
merged.rollback_successes = noisy.rollback_successes;
|
|
merged
|
|
}
|
|
|
|
// ═══════════════════════════════════════════════════════════════════════════
|
|
// Tests
|
|
// ═══════════════════════════════════════════════════════════════════════════
|
|
|
|
#[cfg(test)]
|
|
mod tests {
|
|
use super::*;
|
|
|
|
#[test]
|
|
fn acceptance_test_minimal() {
|
|
// Small config for fast testing
|
|
let config = HoldoutConfig {
|
|
holdout_size: 20,
|
|
training_per_cycle: 20,
|
|
cycles: 3,
|
|
step_budget: 200,
|
|
min_accuracy: 0.50, // relaxed for small test
|
|
min_dimensions_improved: 1,
|
|
verbose: false,
|
|
..Default::default()
|
|
};
|
|
let result = run_acceptance_test(&config);
|
|
assert!(result.is_ok());
|
|
let r = result.unwrap();
|
|
assert_eq!(r.cycles.len(), 3);
|
|
// Accuracy should be non-zero
|
|
assert!(r.cycles.last().unwrap().holdout_accuracy > 0.0);
|
|
}
|
|
|
|
#[test]
|
|
fn holdout_is_deterministic() {
|
|
let config = HoldoutConfig {
|
|
holdout_size: 50,
|
|
..Default::default()
|
|
};
|
|
let h1 = generate_holdout(&config).unwrap();
|
|
let h2 = generate_holdout(&config).unwrap();
|
|
assert_eq!(h1.len(), h2.len());
|
|
for (a, b) in h1.iter().zip(h2.iter()) {
|
|
assert_eq!(a.id, b.id);
|
|
assert_eq!(a.difficulty, b.difficulty);
|
|
}
|
|
}
|
|
|
|
#[test]
|
|
fn cycle_metrics_track_all_dimensions() {
|
|
let config = HoldoutConfig {
|
|
holdout_size: 10,
|
|
training_per_cycle: 10,
|
|
cycles: 2,
|
|
step_budget: 200,
|
|
min_accuracy: 0.30,
|
|
min_dimensions_improved: 0,
|
|
verbose: false,
|
|
..Default::default()
|
|
};
|
|
let result = run_acceptance_test(&config).unwrap();
|
|
for cm in &result.cycles {
|
|
// All dimensions should be populated
|
|
assert!(cm.holdout_cost_per_solve >= 0.0);
|
|
assert!(cm.holdout_noise_accuracy >= 0.0);
|
|
}
|
|
}
|
|
|
|
#[test]
|
|
fn ablation_modes_run() {
|
|
let config = HoldoutConfig {
|
|
holdout_size: 10,
|
|
training_per_cycle: 10,
|
|
cycles: 2,
|
|
step_budget: 200,
|
|
min_accuracy: 0.30,
|
|
min_dimensions_improved: 0,
|
|
verbose: false,
|
|
..Default::default()
|
|
};
|
|
|
|
// Mode A (baseline)
|
|
let a = run_acceptance_test_mode(&config, &AblationMode::Baseline).unwrap();
|
|
assert_eq!(a.mode, AblationMode::Baseline);
|
|
assert_eq!(a.result.cycles.len(), 2);
|
|
assert_eq!(a.compiler_hits, 0); // No compiler in baseline
|
|
|
|
// Mode B (compiler only)
|
|
let b = run_acceptance_test_mode(&config, &AblationMode::CompilerOnly).unwrap();
|
|
assert_eq!(b.mode, AblationMode::CompilerOnly);
|
|
assert_eq!(b.result.cycles.len(), 2);
|
|
|
|
// Mode C (full: compiler + router)
|
|
let c = run_acceptance_test_mode(&config, &AblationMode::Full).unwrap();
|
|
assert_eq!(c.mode, AblationMode::Full);
|
|
assert_eq!(c.result.cycles.len(), 2);
|
|
}
|
|
}
|