llm-kernel 0.3.0

//! `llm-kernel-eval` — Quality evaluation CLI for llm-kernel modules.

use std::fmt;
use std::fs::File;
use std::io::{BufRead, BufReader};
use std::path::{Path, PathBuf};
use std::process::exit;

use clap::{Parser, Subcommand};
use serde::de::DeserializeOwned;
use serde::{Deserialize, Serialize};

// ── CLI ──────────────────────────────────────────────────────────────────────

#[derive(Parser)]
#[command(
    name = "llm-kernel-eval",
    about = "Quality evaluation for llm-kernel modules"
)]
struct Cli {
    #[command(subcommand)]
    command: Commands,

    /// Output format
    #[arg(long, default_value = "markdown", global = true)]
    format: String,

    /// Datasets directory
    #[arg(long, default_value = "eval/datasets", global = true)]
    datasets_dir: PathBuf,

    /// Baseline JSON to compare against (regression detection mode)
    #[arg(long, global = true)]
    baseline: Option<PathBuf>,
}

#[derive(Subcommand)]
enum Commands {
    /// Token estimation accuracy
    Tokens,
    /// Safety masking completeness
    Safety,
    /// Embedding cosine similarity correctness
    Embedding,
    /// RRF search fusion quality
    Search,
    /// Graph query quality
    #[cfg(feature = "graph")]
    Graph,
    /// Run all available evaluations
    All,
}

// ── Common types ─────────────────────────────────────────────────────────────

#[derive(Debug, Clone, Serialize, Deserialize)]
struct EvalReport {
    module: String,
    metrics: serde_json::Value,
    passed: bool,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
struct EvalSummary {
    results: Vec<EvalReport>,
}

impl fmt::Display for EvalSummary {
    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
        if self.results.is_empty() {
            writeln!(f, "No evaluations run.")?;
            return Ok(());
        }

        let total = self.results.len();
        let passed = self.results.iter().filter(|r| r.passed).count();

        writeln!(f, "┌─────────────────────────────────────────────┐")?;
        writeln!(f, "│  llm-kernel-eval — Quality Report           │")?;
        writeln!(f, "├─────────────────────────────────────────────┤")?;

        for report in &self.results {
            let status = if report.passed { "PASS" } else { "FAIL" };
            writeln!(f, "│  {} │ {}", pad(&report.module, 15), status)?;
        }

        writeln!(f, "├─────────────────────────────────────────────┤")?;
        writeln!(
            f,
            "│  Total: {} / {} passed                     ",
            passed, total
        )?;
        writeln!(f, "└─────────────────────────────────────────────┘")?;

        for report in &self.results {
            writeln!(f)?;
            writeln!(
                f,
                "## {} [{}]",
                report.module,
                if report.passed { "PASS" } else { "FAIL" }
            )?;
            if let serde_json::Value::Object(map) = &report.metrics {
                for (key, val) in map {
                    match val {
                        serde_json::Value::Number(n) => {
                            if let Some(fv) = n.as_f64() {
                                writeln!(f, "  {key}: {fv:.4}")?;
                            } else {
                                writeln!(f, "  {key}: {n}")?;
                            }
                        }
                        _ => writeln!(f, "  {key}: {val}")?,
                    }
                }
            }
        }

        Ok(())
    }
}

fn pad(s: &str, width: usize) -> String {
    let mut out = s.to_string();
    while out.chars().count() < width {
        out.push(' ');
    }
    out.truncate(width);
    out
}

fn load_jsonl<T: DeserializeOwned>(path: &Path) -> anyhow::Result<Vec<T>> {
    let file = File::open(path)?;
    let reader = BufReader::new(file);
    let mut items = Vec::new();
    for line in reader.lines() {
        let line = line?;
        let trimmed = line.trim();
        if trimmed.is_empty() {
            continue;
        }
        items.push(serde_json::from_str(trimmed)?);
    }
    Ok(items)
}

// ── Tokens eval ──────────────────────────────────────────────────────────────

mod eval_tokens {
    use super::*;

    #[derive(serde::Deserialize)]
    struct Entry {
        text: String,
        #[allow(dead_code)]
        model: String,
        actual_tokens: usize,
        #[allow(dead_code)]
        category: String,
    }

    pub fn run(datasets_dir: &Path) -> EvalReport {
        let path = datasets_dir.join("tokens.jsonl");
        let entries: Vec<Entry> = match load_jsonl(&path) {
            Ok(e) => e,
            Err(e) => {
                return EvalReport {
                    module: "tokens".into(),
                    metrics: serde_json::json!({"error": format!("Failed to load {}: {e}", path.display())}),
                    passed: false,
                };
            }
        };

        if entries.is_empty() {
            return EvalReport {
                module: "tokens".into(),
                metrics: serde_json::json!({"error": "empty dataset"}),
                passed: false,
            };
        }

        let mut total_abs_err = 0.0_f64;
        let mut max_err = 0.0_f64;
        let mut within_3 = 0usize;
        let mut within_10pct = 0usize;

        for entry in &entries {
            let estimated = llm_kernel::tokens::estimate_tokens(&entry.text);
            let err = (estimated as f64 - entry.actual_tokens as f64).abs();
            total_abs_err += err;
            max_err = max_err.max(err);

            if err <= 3.0 {
                within_3 += 1;
            }
            if entry.actual_tokens > 0 && err / entry.actual_tokens as f64 <= 0.10 {
                within_10pct += 1;
            }
        }

        let n = entries.len();
        let mae = total_abs_err / n as f64;

        EvalReport {
            module: "tokens".into(),
            metrics: serde_json::json!({
                "entries": n,
                "mae": mae,
                "max_error": max_err,
                "pct_within_3": within_3 as f64 / n as f64 * 100.0,
                "pct_within_10pct": within_10pct as f64 / n as f64 * 100.0,
            }),
            passed: mae < 10.0,
        }
    }
}

// ── Safety eval ──────────────────────────────────────────────────────────────

mod eval_safety {
    use super::*;

    #[derive(serde::Deserialize)]
    struct Entry {
        input: String,
        expected_masked: String,
        #[allow(dead_code)]
        pattern_type: String,
    }

    pub fn run(datasets_dir: &Path) -> EvalReport {
        let path = datasets_dir.join("safety.jsonl");
        let entries: Vec<Entry> = match load_jsonl(&path) {
            Ok(e) => e,
            Err(e) => {
                return EvalReport {
                    module: "safety".into(),
                    metrics: serde_json::json!({"error": format!("Failed to load {}: {e}", path.display())}),
                    passed: false,
                };
            }
        };

        if entries.is_empty() {
            return EvalReport {
                module: "safety".into(),
                metrics: serde_json::json!({"error": "empty dataset"}),
                passed: false,
            };
        }

        let mut exact_match = 0usize;
        let mut total_precision = 0.0_f64;
        let mut total_recall = 0.0_f64;
        let mut missed = 0usize;

        for entry in &entries {
            let actual = llm_kernel::safety::sanitize::mask_secrets(&entry.input);

            if actual == entry.expected_masked {
                exact_match += 1;
            }

            let (tp, fp, fn_) = char_metrics(&actual, &entry.expected_masked);
            let precision = if tp + fp > 0 {
                tp as f64 / (tp + fp) as f64
            } else {
                1.0
            };
            let recall = if tp + fn_ > 0 {
                tp as f64 / (tp + fn_) as f64
            } else {
                1.0
            };
            total_precision += precision;
            total_recall += recall;

            if fn_ > 0 {
                missed += 1;
            }
        }

        let n = entries.len();
        let avg_p = total_precision / n as f64;
        let avg_r = total_recall / n as f64;
        let avg_f1 = if avg_p + avg_r > 0.0 {
            2.0 * avg_p * avg_r / (avg_p + avg_r)
        } else {
            0.0
        };

        EvalReport {
            module: "safety".into(),
            metrics: serde_json::json!({
                "entries": n,
                "exact_match_rate": exact_match as f64 / n as f64,
                "avg_precision": avg_p,
                "avg_recall": avg_r,
                "avg_f1": avg_f1,
                "missed_secrets": missed,
            }),
            passed: avg_f1 >= 0.90,
        }
    }

    fn char_metrics(actual: &str, expected: &str) -> (usize, usize, usize) {
        let a: Vec<char> = actual.chars().collect();
        let e: Vec<char> = expected.chars().collect();
        let len = a.len().max(e.len());

        let mut tp = 0usize;
        let mut fp = 0usize;
        let mut fn_ = 0usize;

        for i in 0..len {
            match (a.get(i), e.get(i)) {
                (Some(&c1), Some(&c2)) if c1 == c2 => tp += 1,
                (Some(_), _) => fp += 1,
                (None, Some(_)) => fn_ += 1,
                (None, None) => {}
            }
        }

        (tp, fp, fn_)
    }
}

// ── Embedding eval ───────────────────────────────────────────────────────────

mod eval_embedding {
    use super::*;

    pub fn run() -> EvalReport {
        let mut identity_pass = 0usize;
        let mut identity_total = 0usize;
        let mut ortho_pass = 0usize;
        let mut ortho_total = 0usize;
        let mut symmetry_pass = 0usize;
        let mut symmetry_total = 0usize;
        let mut range_pass = 0usize;
        let mut range_total = 0usize;

        // Identity: cosine_similarity(v, v) == 1.0
        let test_vectors: Vec<Vec<f32>> = vec![
            vec![1.0, 0.0, 0.0, 0.0],
            vec![0.0, 1.0, 0.0, 0.0],
            vec![1.0, 1.0, 1.0, 1.0],
            vec![-1.0, 2.0, -3.0, 4.0],
            vec![0.1, 0.2, 0.3, 0.4],
        ];

        for v in &test_vectors {
            let sim = llm_kernel::embedding::cosine_similarity(v, v);
            identity_total += 1;
            if (sim - 1.0).abs() < 1e-10 {
                identity_pass += 1;
            }
        }

        // Orthogonality: cosine_similarity(a, b) == 0.0
        let ortho_pairs: [(Vec<f32>, Vec<f32>); 3] = [
            (vec![1.0, 0.0], vec![0.0, 1.0]),
            (vec![1.0, 0.0, 0.0], vec![0.0, 1.0, 0.0]),
            (vec![1.0, 0.0, 0.0], vec![0.0, 0.0, 1.0]),
        ];

        for (a, b) in &ortho_pairs {
            let sim = llm_kernel::embedding::cosine_similarity(a, b);
            ortho_total += 1;
            if sim.abs() < 1e-10 {
                ortho_pass += 1;
            }
        }

        // Symmetry: cosine_similarity(a, b) == cosine_similarity(b, a)
        let asym_pairs: [(Vec<f32>, Vec<f32>); 4] = [
            (vec![1.0, 2.0, 3.0], vec![4.0, 5.0, 6.0]),
            (vec![-1.0, 0.5, 2.0], vec![3.0, -1.0, 0.0]),
            (vec![0.1; 128], vec![0.2; 128]),
            (vec![1.0, 0.0, 0.0, 0.0, 0.0], vec![0.0, 1.0, 0.0, 0.0, 0.0]),
        ];

        for (a, b) in &asym_pairs {
            let sim_ab = llm_kernel::embedding::cosine_similarity(a, b);
            let sim_ba = llm_kernel::embedding::cosine_similarity(b, a);
            symmetry_total += 1;
            if (sim_ab - sim_ba).abs() < 1e-10 {
                symmetry_pass += 1;
            }
        }

        // Range: all similarities in [-1, 1]
        let range_pairs: [(Vec<f32>, Vec<f32>); 5] = [
            (vec![1.0, 0.0], vec![0.0, 1.0]),
            (vec![1.0, 1.0], vec![1.0, 1.0]),
            (vec![1.0, 0.0], vec![-1.0, 0.0]),
            (vec![3.0, 4.0], vec![6.0, 8.0]),
            (vec![1.0, 2.0, 3.0], vec![-1.0, -2.0, -3.0]),
        ];

        for (a, b) in &range_pairs {
            let sim = llm_kernel::embedding::cosine_similarity(a, b);
            range_total += 1;
            if (-1.0 - 1e-10..=1.0 + 1e-10).contains(&sim) {
                range_pass += 1;
            }
        }

        let all_total = identity_total + ortho_total + symmetry_total + range_total;
        let all_pass = identity_pass + ortho_pass + symmetry_pass + range_pass;

        EvalReport {
            module: "embedding".into(),
            metrics: serde_json::json!({
                "identity_accuracy": identity_pass as f64 / identity_total as f64,
                "orthogonality_accuracy": ortho_pass as f64 / ortho_total as f64,
                "symmetry_accuracy": symmetry_pass as f64 / symmetry_total as f64,
                "range_accuracy": range_pass as f64 / range_total as f64,
                "total_checks": all_total,
                "total_passed": all_pass,
            }),
            passed: all_pass == all_total,
        }
    }
}

// ── Search eval ──────────────────────────────────────────────────────────────

mod eval_search {
    use super::*;

    #[derive(serde::Deserialize, Clone)]
    struct SearchItem {
        id: String,
        score: f32,
        #[allow(dead_code)]
        text: String,
    }

    #[derive(serde::Deserialize)]
    struct Entry {
        #[allow(dead_code)]
        scenario: String,
        result_sets: Vec<Vec<SearchItem>>,
        k: u32,
        expected_top5_ids: Vec<String>,
    }

    impl From<SearchItem> for llm_kernel::search::SearchResult {
        fn from(item: SearchItem) -> Self {
            Self {
                id: item.id,
                score: item.score,
                text: item.text,
            }
        }
    }

    pub fn run(datasets_dir: &Path) -> EvalReport {
        let path = datasets_dir.join("search.jsonl");
        let entries: Vec<Entry> = match load_jsonl(&path) {
            Ok(e) => e,
            Err(e) => {
                return EvalReport {
                    module: "search".into(),
                    metrics: serde_json::json!({"error": format!("Failed to load {}: {e}", path.display())}),
                    passed: false,
                };
            }
        };

        if entries.is_empty() {
            return EvalReport {
                module: "search".into(),
                metrics: serde_json::json!({"error": "empty dataset"}),
                passed: false,
            };
        }

        let mut total_p5 = 0.0_f64;
        let mut total_r5 = 0.0_f64;
        let mut total_mrr = 0.0_f64;

        for entry in &entries {
            let sets: Vec<Vec<llm_kernel::search::SearchResult>> = entry
                .result_sets
                .iter()
                .map(|rs| {
                    rs.iter()
                        .cloned()
                        .map(llm_kernel::search::SearchResult::from)
                        .collect()
                })
                .collect();

            let fused = llm_kernel::search::rrf_fuse(&sets, entry.k);

            let top5_ids: Vec<&str> = fused.iter().take(5).map(|r| r.id.as_str()).collect();

            let relevant_in_top5 = top5_ids
                .iter()
                .filter(|id| entry.expected_top5_ids.iter().any(|e| e == **id))
                .count();
            total_p5 += relevant_in_top5 as f64 / top5_ids.len().max(1) as f64;

            let recalled = entry
                .expected_top5_ids
                .iter()
                .filter(|e| top5_ids.iter().any(|t| t == e))
                .count();
            total_r5 += recalled as f64 / entry.expected_top5_ids.len().max(1) as f64;

            let mut mrr = 0.0_f64;
            for (i, result) in fused.iter().enumerate() {
                if entry.expected_top5_ids.contains(&result.id) {
                    mrr = 1.0 / (i as f64 + 1.0);
                    break;
                }
            }
            total_mrr += mrr;
        }

        let n = entries.len();
        let avg_p5 = total_p5 / n as f64;
        let avg_r5 = total_r5 / n as f64;
        let avg_mrr = total_mrr / n as f64;

        EvalReport {
            module: "search".into(),
            metrics: serde_json::json!({
                "entries": n,
                "avg_precision_at_5": avg_p5,
                "avg_recall_at_5": avg_r5,
                "avg_mrr": avg_mrr,
            }),
            passed: avg_p5 >= 0.5 && avg_r5 >= 0.5,
        }
    }
}

// ── Graph eval ───────────────────────────────────────────────────────────────

#[cfg(feature = "graph")]
mod eval_graph {
    use super::*;
    use llm_kernel::graph::schema::init_graph_schema;
    use llm_kernel::graph::search::search_nodes;
    use llm_kernel::graph::store::{append_edge, upsert_node};
    use llm_kernel::graph::types::GraphNode;
    use rusqlite::Connection;

    #[derive(serde::Deserialize)]
    struct GraphEntry {
        #[allow(dead_code)]
        scenario: String,
        nodes: Vec<NodeDef>,
        edges: Vec<EdgeDef>,
        query_type: String,
        query_term: Option<String>,
        expected_ids: Vec<String>,
    }

    #[derive(serde::Deserialize)]
    struct NodeDef {
        id: String,
        node_type: String,
        title: String,
        body: String,
        tags: Vec<String>,
        projects: Vec<String>,
        importance: f64,
        created: String,
    }

    #[derive(serde::Deserialize)]
    struct EdgeDef {
        source: String,
        target: String,
        #[allow(dead_code)]
        relation: String,
        weight: f64,
    }

    fn mem_db() -> Connection {
        let conn = Connection::open_in_memory().unwrap();
        init_graph_schema(&conn).unwrap();
        conn
    }

    fn seed_graph(conn: &Connection, entry: &GraphEntry) {
        for n in &entry.nodes {
            let node = GraphNode {
                id: n.id.clone(),
                node_type: n.node_type.clone(),
                title: n.title.clone(),
                body: n.body.clone(),
                tags: n.tags.clone(),
                projects: n.projects.clone(),
                agents: vec![],
                created: n.created.clone(),
                updated: n.created.clone(),
                importance: n.importance,
                access_count: 0,
                accessed_at: String::new(),
            };
            upsert_node(conn, &node).unwrap();
        }
        for e in &entry.edges {
            let edge = llm_kernel::graph::types::GraphEdge {
                id: format!("e-{}-{}", e.source, e.target),
                source: e.source.clone(),
                target: e.target.clone(),
                relation: "related".into(),
                weight: e.weight,
                ts: "2025-01-01T00:00:00Z".into(),
            };
            append_edge(conn, &edge).unwrap();
        }
    }

    pub fn run(datasets_dir: &Path) -> EvalReport {
        let path = datasets_dir.join("graph.jsonl");
        let entries: Vec<GraphEntry> = match load_jsonl(&path) {
            Ok(e) => e,
            Err(e) => {
                return EvalReport {
                    module: "graph".into(),
                    metrics: serde_json::json!({"error": format!("Failed to load {}: {e}", path.display())}),
                    passed: false,
                };
            }
        };

        if entries.is_empty() {
            return EvalReport {
                module: "graph".into(),
                metrics: serde_json::json!({"error": "empty dataset"}),
                passed: false,
            };
        }

        let mut total_precision = 0.0_f64;
        let mut total_recall = 0.0_f64;
        let mut total_f1 = 0.0_f64;
        let mut scenarios_run = 0usize;

        for entry in &entries {
            let conn = mem_db();
            seed_graph(&conn, entry);

            let found_ids: Vec<String> = match entry.query_type.as_str() {
                "fts_search" => {
                    let term = entry.query_term.as_deref().unwrap_or("");
                    search_nodes(&conn, term, 20)
                        .unwrap_or_default()
                        .into_iter()
                        .map(|n| n.id)
                        .collect()
                }
                _ => continue,
            };

            let relevant: std::collections::HashSet<&str> =
                entry.expected_ids.iter().map(|s| s.as_str()).collect();
            let found: std::collections::HashSet<&str> =
                found_ids.iter().map(|s| s.as_str()).collect();

            let tp = relevant.intersection(&found).count() as f64;
            let precision = if found.is_empty() {
                1.0
            } else {
                tp / found.len() as f64
            };
            let recall = if relevant.is_empty() {
                1.0
            } else {
                tp / relevant.len() as f64
            };
            let f1 = if precision + recall > 0.0 {
                2.0 * precision * recall / (precision + recall)
            } else {
                0.0
            };

            total_precision += precision;
            total_recall += recall;
            total_f1 += f1;
            scenarios_run += 1;
        }

        if scenarios_run == 0 {
            return EvalReport {
                module: "graph".into(),
                metrics: serde_json::json!({"error": "no supported scenarios"}),
                passed: false,
            };
        }

        let n = scenarios_run as f64;

        EvalReport {
            module: "graph".into(),
            metrics: serde_json::json!({
                "scenarios_run": scenarios_run,
                "avg_precision": total_precision / n,
                "avg_recall": total_recall / n,
                "avg_f1": total_f1 / n,
            }),
            passed: total_f1 / n >= 0.80,
        }
    }
}

// ── Main ─────────────────────────────────────────────────────────────────────

/// Metrics where higher is better. Absent keys are treated as neutral.
const HIGHER_IS_BETTER: &[&str] = &[
    "pct_within_3",
    "pct_within_10pct",
    "exact_match_rate",
    "avg_precision",
    "avg_recall",
    "avg_f1",
    "avg_precision_at_5",
    "avg_recall_at_5",
    "avg_mrr",
    "avg_recall_at_k",
    "avg_filtered_precision",
    "recall_2bit",
    "recall_4bit",
    "identity_accuracy",
    "orthogonality_accuracy",
    "symmetry_accuracy",
    "range_accuracy",
];

/// Metrics where lower is better.
const LOWER_IS_BETTER: &[&str] = &[
    "mae",
    "max_error",
    "missed_secrets",
    "degradation_2bit_vs_4bit",
];

fn compare_reports(current: &[EvalReport], baseline: &[EvalReport]) -> (Vec<String>, bool) {
    let mut diffs = Vec::new();
    let mut has_regression = false;

    let base_metrics: std::collections::HashMap<&str, &serde_json::Map<String, serde_json::Value>> =
        baseline
            .iter()
            .filter(|r| r.metrics.get("error").is_none())
            .filter_map(|r| r.metrics.as_object().map(|m| (r.module.as_str(), m)))
            .collect();

    for report in current {
        if report.metrics.get("error").is_some() {
            continue;
        }
        let Some(base_obj) = base_metrics.get(report.module.as_str()) else {
            continue;
        };
        let Some(cur_obj) = report.metrics.as_object() else {
            continue;
        };

        for (key, cur_val) in cur_obj {
            let Some(cur_f) = cur_val.as_f64() else {
                continue;
            };
            let Some(base_val) = base_obj.get(key) else {
                continue;
            };
            let Some(base_f) = base_val.as_f64() else {
                continue;
            };

            let delta = cur_f - base_f;
            if delta.abs() < 1e-10 {
                continue;
            }

            let (arrow, is_regression) = if HIGHER_IS_BETTER.contains(&key.as_str()) {
                if delta < 0.0 {
                    ("↓", true)
                } else {
                    ("↑", false)
                }
            } else if LOWER_IS_BETTER.contains(&key.as_str()) {
                if delta > 0.0 {
                    ("↑ (worse)", true)
                } else {
                    ("↓ (better)", false)
                }
            } else {
                ("~", false)
            };

            if is_regression {
                has_regression = true;
            }

            diffs.push(format!(
                "  {}.{}: {:.4} → {:.4} {arrow} ({:+.4}){}",
                report.module,
                key,
                base_f,
                cur_f,
                delta,
                if is_regression { " ⚠ REGRESSION" } else { "" },
            ));
        }
    }

    (diffs, has_regression)
}

fn main() {
    let cli = Cli::parse();
    let mut reports = Vec::new();

    let should_run = |cmd: &Commands| -> bool {
        match &cli.command {
            Commands::All => true,
            c => std::mem::discriminant(c) == std::mem::discriminant(cmd),
        }
    };

    if should_run(&Commands::Tokens) {
        reports.push(eval_tokens::run(&cli.datasets_dir));
    }
    if should_run(&Commands::Safety) {
        reports.push(eval_safety::run(&cli.datasets_dir));
    }
    if should_run(&Commands::Embedding) {
        reports.push(eval_embedding::run());
    }
    if should_run(&Commands::Search) {
        reports.push(eval_search::run(&cli.datasets_dir));
    }
    #[cfg(feature = "graph")]
    if should_run(&Commands::Graph) {
        reports.push(eval_graph::run(&cli.datasets_dir));
    }

    let summary = EvalSummary { results: reports };

    // Load baseline if provided.
    let baseline: Option<EvalSummary> = cli.baseline.as_ref().and_then(|path| {
        let data = std::fs::read_to_string(path).ok()?;
        serde_json::from_str(&data).ok()
    });

    match cli.format.as_str() {
        "json" => println!("{}", serde_json::to_string_pretty(&summary).unwrap()),
        _ => print!("{summary}"),
    }

    // Diff against baseline.
    if let Some(ref base) = baseline {
        let (diffs, has_regression) = compare_reports(&summary.results, &base.results);
        if diffs.is_empty() {
            eprintln!("\n✅ No metric changes detected vs baseline.");
        } else {
            eprintln!("\n## Baseline Diff");
            for line in &diffs {
                eprintln!("{line}");
            }
            if has_regression {
                eprintln!("\n❌ Regression detected — at least one metric worsened.");
                exit(1);
            } else {
                eprintln!("\n✅ No regression — all changes are improvements or neutral.");
            }
        }
    }
}