path: root/scripts/pilot_runner_v2.py

#!/usr/bin/env python3
"""
Pilot Runner v2 - Cross-Session Preference Reveal Mechanism

Upgrade from v1:
- RevealState: Tracks which preferences have been explicitly revealed by the user
- pref_true[k] vs pref_revealed_global[k] distinction
- Style constraints only enforced AFTER user reveals them
- Reveal state persists across sessions, resets on reset_user()

Key concepts:
- pref_true[k]: User's true preference (from StylePrefs)
- pref_revealed_global[k]: Whether preference k has been revealed at least once

Enforcement rule:
- A style constraint is enforced only when BOTH pref_true[k] AND pref_revealed_global[k]

Session semantics:
- reset_user(): Clears ALL state including reveal flags
- reset_session(): Keeps reveal flags (cross-session memory)
"""

import sys
import os
import json
from datetime import datetime
from dataclasses import dataclass, asdict, field
from typing import List, Dict, Any, Optional, Tuple, Set

# Add src to path
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "../src"))

from personalization.serving import PersonalizedLLM, Feedback, AssistantResponse


# =============================================================================
# Style Preferences (True Preferences)
# =============================================================================

@dataclass
class StylePrefs:
    """
    User's TRUE style preferences.
    These are the ground truth preferences that the user actually has,
    but they may not have revealed all of them to the system yet.
    """
    require_short: bool = False
    max_chars: int = 300
    require_bullets: bool = False
    lang: str = "en"  # "en" or "zh"


# =============================================================================
# Reveal State (What has been explicitly revealed)
# =============================================================================

@dataclass 
class RevealState:
    """
    Tracks which preferences have been explicitly revealed by the user.
    
    This persists across sessions for the same user but resets on reset_user().
    A preference is revealed when the user explicitly mentions it in a query.
    """
    short_revealed: bool = False      # "short", "concise", "brief", length constraints
    bullets_revealed: bool = False    # "bullet", "bullet points", "list format"
    lang_revealed: bool = False       # Language preference mentioned
    
    def reset(self):
        """Reset all reveal flags (called on reset_user)."""
        self.short_revealed = False
        self.bullets_revealed = False
        self.lang_revealed = False
    
    def to_dict(self) -> Dict[str, bool]:
        return {
            "short": self.short_revealed,
            "bullets": self.bullets_revealed,
            "lang": self.lang_revealed,
        }
    
    def __str__(self) -> str:
        flags = []
        if self.short_revealed:
            flags.append("short")
        if self.bullets_revealed:
            flags.append("bullets")
        if self.lang_revealed:
            flags.append("lang")
        return f"RevealState({', '.join(flags) if flags else 'none'})"


class RevealStateManager:
    """
    Manages reveal state for multiple users.
    Persists across sessions, resets on reset_user().
    """
    
    def __init__(self):
        self._states: Dict[str, RevealState] = {}
    
    def get_state(self, user_id: str) -> RevealState:
        """Get or create reveal state for a user."""
        if user_id not in self._states:
            self._states[user_id] = RevealState()
        return self._states[user_id]
    
    def reset_user(self, user_id: str):
        """Reset reveal state for a user (called on reset_user)."""
        if user_id in self._states:
            self._states[user_id].reset()
        else:
            self._states[user_id] = RevealState()
    
    def reset_session(self, user_id: str):
        """
        Called on reset_session - does NOT reset reveal state.
        Reveal state persists across sessions.
        """
        # Intentionally do nothing - reveal state persists
        pass


# =============================================================================
# Preference Detection from Queries
# =============================================================================

def detect_revealed_preferences(query: str) -> Dict[str, bool]:
    """
    Detect which preferences are mentioned in a query.
    
    Returns a dict with keys: "short", "bullets", "lang"
    Each value is True if that preference was mentioned.
    """
    lower_q = (query or "").lower()
    
    revealed = {
        "short": False,
        "bullets": False,
        "lang": False,
    }
    
    # Short/length preference detection
    short_patterns = [
        "short", "concise", "brief", "under ", "less than",
        "keep it short", "keep responses", "keep answers",
        "maximum ", "max ", "characters", "words or less",
        "200 ", "100 ", "50 ", "300 ",  # Common char limits
    ]
    for pattern in short_patterns:
        if pattern in lower_q:
            revealed["short"] = True
            break
    
    # Bullet preference detection
    bullet_patterns = [
        "bullet", "bullet point", "bullet-point",
        "bulleted", "list format", "use bullets",
        "use bullet", "with bullets", "in bullets",
        "- format", "• ", "numbered list",
    ]
    for pattern in bullet_patterns:
        if pattern in lower_q:
            revealed["bullets"] = True
            break
    
    # Language preference detection
    lang_patterns_zh = [
        "chinese", "中文", "in chinese", "用中文",
        "speak chinese", "write chinese", "respond in chinese",
        "please use chinese", "mandarin",
    ]
    lang_patterns_en = [
        "english", "in english", "use english",
        "speak english", "write english", "respond in english",
        "please use english",
    ]
    
    for pattern in lang_patterns_zh + lang_patterns_en:
        if pattern in lower_q:
            revealed["lang"] = True
            break
    
    return revealed


def update_reveal_state(reveal_state: RevealState, query: str) -> Set[str]:
    """
    Update reveal state based on query content.
    Returns set of newly revealed preferences.
    """
    detected = detect_revealed_preferences(query)
    newly_revealed = set()
    
    if detected["short"] and not reveal_state.short_revealed:
        reveal_state.short_revealed = True
        newly_revealed.add("short")
    
    if detected["bullets"] and not reveal_state.bullets_revealed:
        reveal_state.bullets_revealed = True
        newly_revealed.add("bullets")
    
    if detected["lang"] and not reveal_state.lang_revealed:
        reveal_state.lang_revealed = True
        newly_revealed.add("lang")
    
    return newly_revealed


# =============================================================================
# Style-Aware Judge with Reveal State
# =============================================================================

@dataclass
class JudgeResult:
    """Output from the judge for one turn."""
    sat_t: float        # Satisfaction score [0, 1]
    sev_t: float        # Severity of violations [0, 1]
    prog_t: float       # Task progress [0, 1]
    violations: List[str]  # List of violated constraints
    enforced_constraints: List[str]  # Which constraints were actually enforced


def style_judge_with_reveal(
    query: str,
    answer: str,
    task_type: str,
    prefs: StylePrefs,
    reveal_state: RevealState,
) -> JudgeResult:
    """
    Style-aware judge that ONLY enforces revealed preferences.
    
    A constraint is enforced only when:
    - pref_true[k] is True (user has this preference)
    - pref_revealed_global[k] is True (user has revealed this preference)
    
    Args:
        query: User's query
        answer: Assistant's answer
        task_type: Type of task ("general", "list", "code")
        prefs: User's TRUE preferences (StylePrefs)
        reveal_state: Which preferences have been revealed
    
    Returns:
        JudgeResult with sat_t, sev_t, prog_t, violations, and enforced_constraints
    """
    violations: List[str] = []
    enforced: List[str] = []
    text = (answer or "").strip()

    # 0) Empty answer - always a violation regardless of reveal state
    if not text or len(text) < 5:
        violations.append("empty_answer")
        return JudgeResult(
            sat_t=0.0,
            sev_t=1.0,
            prog_t=0.0,
            violations=violations,
            enforced_constraints=["non_empty"],
        )

    # 1) Length preference - enforce only if BOTH true AND revealed
    if prefs.require_short and reveal_state.short_revealed:
        enforced.append("short")
        if len(text) > prefs.max_chars:
            violations.append("too_long")

    # 2) Bullet preference - enforce only if BOTH true AND revealed
    #    Also only for list-type tasks
    if prefs.require_bullets and reveal_state.bullets_revealed:
        if task_type in ("general", "list"):
            enforced.append("bullets")
            has_bullets = ("- " in text) or ("• " in text) or ("* " in text) or ("\n- " in text)
            if not has_bullets:
                violations.append("no_bullets")

    # 3) Language preference - enforce only if BOTH true AND revealed
    if reveal_state.lang_revealed:
        enforced.append("lang")
        if prefs.lang == "zh":
            ascii_count = sum(c.isascii() for c in text)
            ascii_ratio = ascii_count / max(1, len(text))
            if ascii_ratio > 0.7:
                violations.append("wrong_lang")
        elif prefs.lang == "en":
            ascii_count = sum(c.isascii() for c in text)
            ascii_ratio = ascii_count / max(1, len(text))
            if ascii_ratio < 0.5:
                violations.append("wrong_lang")

    # 4) Code task: always enforce code markers (not a user preference)
    prog_t = 1.0
    if task_type == "code":
        enforced.append("code_block")
        has_code = ("```" in text) or ("def " in text) or ("function " in text)
        if not has_code:
            violations.append("no_code_block")
            prog_t = 0.0

    # 5) Compute sat_t and sev_t from violations
    if not violations:
        sat_t = 1.0
        sev_t = 0.0
    else:
        sat_t = max(0.0, 1.0 - 0.3 * float(len(violations)))
        hard_violations = {"empty_answer", "too_long", "wrong_lang"}
        sev_t = 1.0 if any(v in hard_violations for v in violations) else 0.0

    return JudgeResult(
        sat_t=sat_t,
        sev_t=sev_t,
        prog_t=prog_t,
        violations=violations,
        enforced_constraints=enforced,
    )


# =============================================================================
# Feedback Computation (reward + gating)
# =============================================================================

def compute_feedback_for_turn(
    turn_id: int,
    query: str,
    query_type: str,
    task_type: str,
    judge_result: JudgeResult,
) -> Tuple[float, float]:
    """
    Convert JudgeResult into (reward, gating).
    Same as v1 - reward = sat_t, gating = 1 for preference turns.
    """
    reward = judge_result.sat_t

    lower_q = (query or "").lower()
    
    is_pref_turn = (
        query_type == "preference"
        or "i prefer" in lower_q
        or "my preference" in lower_q
        or "please use" in lower_q
        or "please keep" in lower_q
        or "you didn't follow" in lower_q
        or "you forgot" in lower_q
        or "remember that i" in lower_q
        or "i told you" in lower_q
        or "i asked for" in lower_q
    )

    gating = 1.0 if is_pref_turn else 0.0
    return reward, gating


# =============================================================================
# Multi-Session Queries for Pilot v2
# =============================================================================

def get_session_1_queries() -> List[Dict[str, Any]]:
    """
    Session 1: User reveals preferences and does some tasks.
    """
    return [
        {
            "query": "I prefer short, concise answers. Please keep responses under 200 characters.",
            "type": "preference",
            "task_type": "general",
        },
        {
            "query": "What are three tips for better sleep?",
            "type": "task",
            "task_type": "list",
        },
        {
            "query": "I also prefer bullet points when listing things.",
            "type": "preference",
            "task_type": "general",
        },
        {
            "query": "What are the main benefits of exercise?",
            "type": "task",
            "task_type": "list",
        },
        {
            "query": "Name five programming languages.",
            "type": "task",
            "task_type": "list",
        },
    ]


def get_session_2_queries() -> List[Dict[str, Any]]:
    """
    Session 2: User does NOT restate preferences.
    Tests cross-session preference retention.
    """
    return [
        {
            "query": "What are three healthy breakfast ideas?",
            "type": "task",
            "task_type": "list",
        },
        {
            "query": "List four seasons of the year.",
            "type": "task",
            "task_type": "list",
        },
        {
            "query": "What is the capital of France?",
            "type": "task",
            "task_type": "general",
        },
        {
            "query": "Name three types of renewable energy.",
            "type": "task",
            "task_type": "list",
        },
    ]


def get_session_3_queries() -> List[Dict[str, Any]]:
    """
    Session 3: Mix of tasks and one complaint/reminder.
    """
    return [
        {
            "query": "What are five common fruits?",
            "type": "task",
            "task_type": "list",
        },
        {
            "query": "Remember that I asked for short bullet points. List three ocean animals.",
            "type": "preference",
            "task_type": "list",
        },
        {
            "query": "What is 2 + 2?",
            "type": "task",
            "task_type": "general",
        },
    ]


# =============================================================================
# Logging (Extended for v2)
# =============================================================================

@dataclass
class TurnLog:
    """Log entry for one turn (extended for v2)."""
    session_id: int
    turn_id: int
    query: str
    query_type: str
    task_type: str
    answer: str
    answer_length: int
    sat_t: float
    sev_t: float
    prog_t: float
    violations: List[str]
    enforced_constraints: List[str]
    reward: float
    gating: float
    reveal_state_before: Dict[str, bool]
    reveal_state_after: Dict[str, bool]
    newly_revealed: List[str]
    z_long_norm_before: float
    z_long_norm_after: float
    z_short_norm_before: float
    z_short_norm_after: float
    prompt_tokens: int
    completion_tokens: int
    total_tokens: int
    num_memories_retrieved: int
    num_prefs_extracted: int


def log_to_jsonl(logs: List[TurnLog], filepath: str):
    """Save logs to JSONL file."""
    os.makedirs(os.path.dirname(filepath), exist_ok=True)
    with open(filepath, "w") as f:
        for log in logs:
            f.write(json.dumps(asdict(log)) + "\n")


# =============================================================================
# Pilot Runner v2 (Multi-Session with Reveal State)
# =============================================================================

def run_session(
    llm: PersonalizedLLM,
    user_id: str,
    session_id: int,
    prefs: StylePrefs,
    reveal_state: RevealState,
    queries: List[Dict[str, Any]],
) -> List[TurnLog]:
    """
    Run a single session with reveal-aware judging.
    """
    logs: List[TurnLog] = []
    
    print(f"\n{'='*60}")
    print(f"SESSION {session_id}: user_id={user_id}, turns={len(queries)}")
    print(f"Reveal state (start): {reveal_state}")
    print(f"{'='*60}")
    
    # Reset session (clears history, z_short; keeps z_long and reveal state)
    llm.reset_session(user_id)
    
    state_before = llm.get_user_state_summary(user_id)
    print(f"[Session] z_long={state_before['z_long_norm']:.6f}, z_short={state_before['z_short_norm']:.6f}")
    
    for turn_id, q_info in enumerate(queries):
        query = q_info["query"]
        query_type = q_info.get("type", "task")
        task_type = q_info.get("task_type", "general")
        
        print(f"\n{'─'*60}")
        print(f"Session {session_id} / Turn {turn_id} [{query_type}]")
        print(f"{'─'*60}")
        print(f"[Query] {query}")
        
        # Capture reveal state BEFORE this turn
        reveal_before = reveal_state.to_dict()
        
        # Update reveal state based on query content
        newly_revealed = update_reveal_state(reveal_state, query)
        if newly_revealed:
            print(f"[Reveal] Newly revealed: {newly_revealed}")
        print(f"[Reveal] State: {reveal_state}")
        
        # Capture reveal state AFTER update
        reveal_after = reveal_state.to_dict()
        
        # Get user state before
        state_before = llm.get_user_state_summary(user_id)
        z_long_before = state_before["z_long_norm"]
        z_short_before = state_before["z_short_norm"]
        
        # Apply feedback for previous turn (from turn 1 onwards in this session)
        if turn_id > 0 and len(logs) > 0:
            # Find the last log from THIS session
            session_logs = [l for l in logs if l.session_id == session_id]
            if session_logs:
                prev_log = session_logs[-1]
                feedback = Feedback(
                    user_id=user_id,
                    turn_id=prev_log.turn_id,
                    reward=prev_log.reward,
                    gating=prev_log.gating,
                    meta={
                        "sat_t": prev_log.sat_t,
                        "violations": prev_log.violations,
                        "source": "pilot_v2",
                        "session_id": session_id,
                    }
                )
                print(f"[Feedback] turn={prev_log.turn_id}, reward={feedback.reward:.2f}, gating={feedback.gating:.1f}")
                llm.apply_feedback(feedback)
        
        # Chat
        resp: AssistantResponse = llm.chat(user_id, query)
        
        answer_display = resp.answer[:150] + "..." if len(resp.answer) > 150 else resp.answer
        print(f"[Answer] ({len(resp.answer)} chars) {answer_display}")
        print(f"[Usage] prompt={resp.usage.prompt_tokens}, completion={resp.usage.completion_tokens}")
        
        # Judge with reveal-aware logic
        judge_result = style_judge_with_reveal(query, resp.answer, task_type, prefs, reveal_state)
        print(f"[Judge] sat={judge_result.sat_t:.2f}, enforced={judge_result.enforced_constraints}")
        if judge_result.violations:
            print(f"[Judge] violations={judge_result.violations}")
        
        # Compute feedback
        reward, gating = compute_feedback_for_turn(
            turn_id=turn_id,
            query=query,
            query_type=query_type,
            task_type=task_type,
            judge_result=judge_result,
        )
        print(f"[Feedback] reward={reward:.2f}, gating={gating:.1f}")
        
        # Get state after
        state_after = llm.get_user_state_summary(user_id)
        z_long_after = state_after["z_long_norm"]
        z_short_after = state_after["z_short_norm"]
        
        z_long_delta = z_long_after - z_long_before
        z_short_delta = z_short_after - z_short_before
        print(f"[State] z_long: {z_long_before:.6f} → {z_long_after:.6f} (Δ={z_long_delta:+.6f})")
        print(f"[State] z_short: {z_short_before:.6f} → {z_short_after:.6f} (Δ={z_short_delta:+.6f})")
        
        # Debug info
        num_memories = len(resp.debug.selected_memory_ids) if resp.debug else 0
        num_prefs = len(resp.debug.extracted_preferences) if resp.debug else 0
        print(f"[Debug] memories={num_memories}, prefs_extracted={num_prefs}")
        
        # Log
        log = TurnLog(
            session_id=session_id,
            turn_id=turn_id,
            query=query,
            query_type=query_type,
            task_type=task_type,
            answer=resp.answer,
            answer_length=len(resp.answer),
            sat_t=judge_result.sat_t,
            sev_t=judge_result.sev_t,
            prog_t=judge_result.prog_t,
            violations=judge_result.violations,
            enforced_constraints=judge_result.enforced_constraints,
            reward=reward,
            gating=gating,
            reveal_state_before=reveal_before,
            reveal_state_after=reveal_after,
            newly_revealed=list(newly_revealed),
            z_long_norm_before=z_long_before,
            z_long_norm_after=z_long_after,
            z_short_norm_before=z_short_before,
            z_short_norm_after=z_short_after,
            prompt_tokens=resp.usage.prompt_tokens,
            completion_tokens=resp.usage.completion_tokens,
            total_tokens=resp.usage.total_tokens,
            num_memories_retrieved=num_memories,
            num_prefs_extracted=num_prefs,
        )
        logs.append(log)
    
    # Apply final feedback for this session
    session_logs = [l for l in logs if l.session_id == session_id]
    if session_logs:
        last_log = session_logs[-1]
        feedback = Feedback(
            user_id=user_id,
            turn_id=last_log.turn_id,
            reward=last_log.reward,
            gating=last_log.gating,
            meta={"source": "pilot_v2", "session_id": session_id, "final": True}
        )
        print(f"\n[Final Feedback] turn={last_log.turn_id}, reward={feedback.reward:.2f}, gating={feedback.gating:.1f}")
        llm.apply_feedback(feedback)
    
    print(f"\n[Session {session_id} End] Reveal state: {reveal_state}")
    
    return logs


def run_pilot_v2(
    llm: PersonalizedLLM,
    user_id: str = "pilot_user_v2",
    prefs: Optional[StylePrefs] = None,
) -> List[TurnLog]:
    """
    Run multi-session pilot with reveal state tracking.
    
    Session 1: User reveals preferences
    Session 2: User does NOT restate preferences (tests cross-session retention)
    Session 3: Mix of tasks and reminders
    """
    if prefs is None:
        prefs = StylePrefs(
            require_short=True,
            max_chars=200,
            require_bullets=True,
            lang="en",
        )
    
    # Initialize reveal state manager
    reveal_manager = RevealStateManager()
    
    print(f"\n{'#'*60}")
    print(f"PILOT v2: CROSS-SESSION PREFERENCE REVEAL TEST")
    print(f"User: {user_id}")
    print(f"True prefs: short={prefs.require_short}, bullets={prefs.require_bullets}, lang={prefs.lang}")
    print(f"{'#'*60}")
    
    # Reset user completely (clears all state including reveal)
    print(f"\n[Pilot] Resetting user: {user_id}")
    llm.reset_user(user_id)
    reveal_manager.reset_user(user_id)
    
    all_logs: List[TurnLog] = []
    reveal_state = reveal_manager.get_state(user_id)
    
    # Session 1: Reveal preferences
    session_1_queries = get_session_1_queries()
    logs_s1 = run_session(llm, user_id, 1, prefs, reveal_state, session_1_queries)
    all_logs.extend(logs_s1)
    
    # Session 2: NO preference restatement (test cross-session retention)
    # Note: reveal_state persists, but reset_session clears history
    reveal_manager.reset_session(user_id)  # Does nothing to reveal state
    session_2_queries = get_session_2_queries()
    logs_s2 = run_session(llm, user_id, 2, prefs, reveal_state, session_2_queries)
    all_logs.extend(logs_s2)
    
    # Session 3: Reminder and more tasks
    reveal_manager.reset_session(user_id)
    session_3_queries = get_session_3_queries()
    logs_s3 = run_session(llm, user_id, 3, prefs, reveal_state, session_3_queries)
    all_logs.extend(logs_s3)
    
    return all_logs


def print_summary_v2(logs: List[TurnLog], prefs: StylePrefs):
    """Print summary for pilot v2."""
    print(f"\n{'='*60}")
    print("PILOT v2 SUMMARY - Cross-Session Reveal")
    print(f"{'='*60}")
    
    if not logs:
        print("No logs to summarize.")
        return
    
    # Per-session stats
    sessions = sorted(set(l.session_id for l in logs))
    
    print(f"\n--- Per-Session Statistics ---")
    for sid in sessions:
        session_logs = [l for l in logs if l.session_id == sid]
        avg_sat = sum(l.sat_t for l in session_logs) / len(session_logs)
        violations = [v for l in session_logs for v in l.violations]
        
        # What was revealed at session end
        if session_logs:
            final_reveal = session_logs[-1].reveal_state_after
        else:
            final_reveal = {}
        
        print(f"\nSession {sid}: {len(session_logs)} turns")
        print(f"  Avg sat_t: {avg_sat:.3f}")
        print(f"  Violations: {len(violations)} ({violations if violations else 'none'})")
        print(f"  Reveal state at end: {final_reveal}")
    
    # Overall stats
    total = len(logs)
    avg_sat = sum(l.sat_t for l in logs) / total
    total_tokens = sum(l.total_tokens for l in logs)
    
    print(f"\n--- Overall Statistics ---")
    print(f"Total turns: {total}")
    print(f"Overall avg sat_t: {avg_sat:.3f}")
    print(f"Total tokens: {total_tokens}")
    
    # Violations by type
    print(f"\n--- Violations Breakdown ---")
    from collections import Counter
    all_violations = [v for l in logs for v in l.violations]
    if all_violations:
        for v, count in Counter(all_violations).most_common():
            print(f"  {v}: {count}")
    else:
        print("  No violations")
    
    # Enforcement tracking
    print(f"\n--- Constraint Enforcement ---")
    for constraint in ["short", "bullets", "lang"]:
        enforced_count = sum(1 for l in logs if constraint in l.enforced_constraints)
        print(f"  {constraint}: enforced in {enforced_count}/{total} turns")
    
    # Cross-session reveal verification
    print(f"\n--- Cross-Session Reveal Verification ---")
    
    # Session 1: Should have some reveals
    s1_logs = [l for l in logs if l.session_id == 1]
    s1_reveals = set()
    for l in s1_logs:
        s1_reveals.update(l.newly_revealed)
    print(f"Session 1 revealed: {s1_reveals if s1_reveals else 'none'}")
    
    # Session 2: Should NOT have new reveals (no preference queries)
    s2_logs = [l for l in logs if l.session_id == 2]
    s2_reveals = set()
    for l in s2_logs:
        s2_reveals.update(l.newly_revealed)
    print(f"Session 2 revealed: {s2_reveals if s2_reveals else 'none (expected)'}")
    
    # But Session 2 should still ENFORCE the constraints revealed in Session 1
    if s2_logs:
        s2_enforced = set()
        for l in s2_logs:
            s2_enforced.update(l.enforced_constraints)
        print(f"Session 2 enforced: {s2_enforced}")
        
        if s1_reveals and s1_reveals.issubset(s2_enforced):
            print("✓ Cross-session retention VERIFIED: Session 1 reveals enforced in Session 2")
        else:
            print("✗ Cross-session retention issue: some reveals not enforced")
    
    # Turn-by-turn table
    print(f"\n--- Turn-by-Turn Summary ---")
    print(f"{'S':>2} {'T':>2} {'Type':>10} {'Len':>5} {'sat':>5} {'enforced':<20} {'violations'}")
    print("-" * 70)
    for l in logs:
        enforced_str = ",".join(l.enforced_constraints) if l.enforced_constraints else "-"
        viol_str = ",".join(l.violations) if l.violations else "-"
        print(f"{l.session_id:>2} {l.turn_id:>2} {l.query_type:>10} {l.answer_length:>5} {l.sat_t:>5.2f} {enforced_str:<20} {viol_str}")


def main():
    print("=" * 60)
    print("PILOT RUNNER v2 - Cross-Session Preference Reveal")
    print("=" * 60)
    print(f"Started at: {datetime.now().isoformat()}")
    
    # Define user's TRUE preferences
    prefs = StylePrefs(
        require_short=True,
        max_chars=200,
        require_bullets=True,
        lang="en",
    )
    print(f"\n[Config] True preferences: {prefs}")
    print("[Config] Note: Constraints only enforced AFTER user reveals them")
    
    # Initialize LLM
    print("\n[Init] Loading PersonalizedLLM...")
    llm = PersonalizedLLM(
        user_store_path="data/users/user_store_pilot_v2.npz",
        only_own_memories=True,
        enable_preference_extraction=True,
        enable_rl_updates=True,
    )
    
    # Run pilot
    user_id = "pilot_user_v2"
    logs = run_pilot_v2(llm, user_id=user_id, prefs=prefs)
    
    # Summary
    print_summary_v2(logs, prefs)
    
    # Save logs
    log_path = f"data/logs/pilot_v2_{datetime.now().strftime('%Y%m%d_%H%M%S')}.jsonl"
    log_to_jsonl(logs, log_path)
    print(f"\n[Logs] Saved to: {log_path}")
    
    # Final state
    final_state = llm.get_user_state_summary(user_id)
    print(f"\n[Final State] {final_state}")
    
    print(f"\nCompleted at: {datetime.now().isoformat()}")
    print("=" * 60)


if __name__ == "__main__":
    main()