Adversarial Cooperation Research Validation

Version: 2.0.0 Last Updated: 2026-01-25 Review Source: TASK-REV-BLOC (re-run post-implementation) Audience: Contributors, researchers, advanced users Document Type: Research Validation Report

---

Executive Summary

GuardKit's AutoBuild feature implements the adversarial cooperation pattern (Player-Coach agents) based on Block AI's research paper "Adversarial Cooperation in Code Synthesis" (December 2025). This document validates how faithfully GuardKit implements the research principles and documents the empirical results from production usage.

Overall Fidelity Score: 88/100 (↑10 points from initial 78/100 review)

Principle	Score	Status	Change
Dialectical Loop	92/100	✅ Excellent	↑2
Independent Verification	98/100	✅ Excellent	↑3
Anchoring Prevention	88/100	✅ Strong	↑23
Context Pollution	85/100	✅ Good	↑15
Completion Criteria	92/100	✅ Excellent	↑7
Honesty Verification	98/100	✅ Excellent	↑3

All major gaps from the initial review have been successfully addressed through implementation of TASK-BRF-001 through TASK-BRF-005 and TASK-PRH-001 through TASK-PRH-003.

---

Background: The Block Research

The Problem with "Vibe Coding"

Block AI's research identifies a critical failure mode in single-agent AI coding systems: premature success declaration. Single agents tend to:

1. Claim completion before requirements are fully met
2. Self-assess optimistically rather than critically
3. Accumulate context pollution over extended sessions
4. Miss security gaps and edge cases

The Solution: Dialectical Autocoding

The research proposes a dialectical approach using adversarial cooperation between two specialized agents:

┌─────────────────────────────────────────────────────────┐
│                    DIALECTICAL LOOP                      │
│                                                          │
│    ┌──────────────┐              ┌──────────────┐      │
│    │    PLAYER    │              │    COACH     │      │
│    │              │   feedback   │              │      │
│    │ • Implement  │──────────────>│ • Review     │      │
│    │ • Create     │              │ • Test       │      │
│    │ • Execute    │<──────────────│ • Critique   │      │
│    │ • Iterate    │              │ • Approve    │      │
│    └──────────────┘              └──────────────┘      │
│                                                          │
│              WORKSPACE                                   │
│                                                          │
│    Bounds: Max Turns, Context Windows, Requirements     │
└─────────────────────────────────────────────────────────┘

Key Research Principles

The Block research identifies six core principles for effective adversarial cooperation:

1. Dialectical Loop: Player implements (thesis), Coach critiques (antithesis), iteration produces synthesis
2. Independent Verification: Coach must verify independently, not trust Player self-reports
3. Anchoring Prevention: Fresh perspective each turn; prevent accumulated assumptions
4. Context Pollution Mitigation: Isolated context windows; failed attempts don't pollute new attempts
5. Objective Completion Criteria: Coach determines completion independently
6. Honesty Verification: Coach is skeptical by design; prevent "rubber stamping"

---

GuardKit Implementation Analysis

1. Core Dialectical Loop: 92/100 ✅ EXCELLENT

Research Requirement: Player implements (thesis), Coach critiques (antithesis), iteration produces synthesis.

Implementation:

The implementation correctly follows the thesis-antithesis-synthesis pattern with enhanced completion promises tracking:

# From autobuild.py - Loop Phase
for turn in range(start_turn, self.max_turns + 1):
    # Thesis: Player implements
    turn_record = self._execute_turn(
        turn=turn,
        task_id=task_id,
        requirements=requirements,
        previous_feedback=previous_feedback,  # Synthesis from prior turn
    )

    # Antithesis: Coach validates
    coach_result = self._invoke_coach_safely(...)

    # Check for synthesis (approval) or continuation
    if turn_record.decision == "approve":
        return turn_history, "approved"  # Synthesis achieved
    elif turn_record.decision == "feedback":
        previous_feedback = turn_record.feedback  # Feed forward for next thesis

Completion Promises System:

# guardkit/orchestrator/schemas.py
@dataclass
class CompletionPromise:
    """Player's promise to satisfy an acceptance criterion."""
    criterion_id: str
    criterion_text: str
    status: CriterionStatus  # COMPLETE or INCOMPLETE
    evidence: str
    test_file: Optional[str]
    implementation_files: List[str]

@dataclass
class CriterionVerification:
    """Coach's verification of a Player promise."""
    criterion_id: str
    result: VerificationResult  # VERIFIED or REJECTED
    notes: str

Strengths:

1. Clear separation between Player implementation phase and Coach validation phase
2. Feedback from Coach is explicitly passed to next Player turn
3. Neither agent can unilaterally declare success
4. Structured promise/verification tracking for each acceptance criterion
5. Turn-by-turn iteration until synthesis (approval) or exhaustion

---

2. Independent Verification: 98/100 ✅ EXCELLENT

Research Requirement: "Discard self-reports" - Coach must independently verify, not trust Player claims.

Implementation:

The system implements comprehensive independent verification through multiple mechanisms:

A. CoachVerifier Pre-Validation

# guardkit/orchestrator/coach_verification.py
class CoachVerifier:
    def verify_player_report(self, player_report: Dict) -> HonestyVerification:
        """Verify all verifiable claims in Player report."""
        # 1. Run tests independently (trust but verify)
        test_disc = self._verify_test_results(player_report)

        # 2. Check file existence on filesystem
        file_disc = self._verify_files_exist(player_report)

        # 3. Verify test count matches actual
        count_disc = self._verify_test_count(player_report)

        # Calculate honesty score (0.0-1.0)
        honesty_score = 1.0 - (critical_failures / max(total_claims, 1))

B. Harmonized Player Report Writing (TASK-PRH-001)

# guardkit/orchestrator/agent_invoker.py
def _write_player_report_for_direct_mode(
    self, task_id: str, turn: int, result: dict, ...
) -> None:
    """Write player_turn_N.json for direct mode (harmonization)."""
    # Ensures state recovery is NOT triggered unnecessarily
    player_report = {
        "task_id": task_id,
        "turn": turn,
        "files_modified": result.get("files_modified", []),
        "files_created": result.get("files_created", []),
        "tests_written": result.get("tests_written", []),
        "tests_run": result.get("tests_run", False),
        "tests_passed": result.get("tests_passed", False),
        "implementation_mode": "direct",  # Marker for direct mode
    }

Strengths:

1. Automated honesty verification BEFORE Coach sees Player report
2. Discrepancy detection with severity levels (critical/warning)
3. Honesty score calculation (0.0-1.0)
4. Coach is explicitly instructed to run tests independently
5. Critical discrepancies automatically influence Coach decision
6. Harmonized report writing prevents false state recovery triggers

---

3. Anchoring Prevention: 88/100 ✅ STRONG

Research Requirement: Fresh perspective each turn; prevent accumulated assumptions from biasing subsequent iterations.

Previous Score: 65/100 (Major gap identified) Current Score: 88/100 (↑23 points)

Implementation (TASK-BRF-001):

The system now implements fresh perspective reset at specified turns:

# guardkit/orchestrator/autobuild.py
def __init__(
    self,
    enable_perspective_reset: bool = True,
    ...
):
    """Enable fresh perspective reset to prevent anchoring bias."""
    # Hardcoded reset turns per architectural review: [3, 5]
    self.perspective_reset_turns: List[int] = [3, 5] if enable_perspective_reset else []

def _should_reset_perspective(self, turn: int) -> bool:
    """Check if Player should receive fresh perspective on this turn.

    Fresh perspective reset prevents anchoring bias by having the Player
    receive only original requirements without prior feedback at specified
    turns. This allows the Player to reconsider the problem from first
    principles rather than being locked into early assumptions.
    """
    if turn in self.perspective_reset_turns:
        logger.info(f"Perspective reset triggered at turn {turn} (scheduled reset)")
        return True
    return False

# In loop phase:
for turn in range(start_turn, self.max_turns + 1):
    # Check if perspective should be reset to prevent anchoring bias
    if self._should_reset_perspective(turn):
        previous_feedback = None  # Reset feedback - fresh perspective

Strengths:

1. Fresh perspective reset at turns 3 and 5
2. Player receives only original requirements (no feedback) on reset turns
3. Comprehensive logging when reset occurs
4. Each agent invocation is a fresh SDK call with new context

Acceptable Trade-offs:

• Implementation plan from pre-loop is still passed through (minor anchoring vector)
• Hardcoded reset turns rather than dynamic detection (YAGNI principle)

---

4. Context Pollution Mitigation: 85/100 ✅ GOOD

Research Requirement: Isolated context windows; failed attempts don't pollute new attempts.

Previous Score: 70/100 (Major gap identified) Current Score: 85/100 (↑15 points)

Implementation (TASK-BRF-002):

The system now includes worktree checkpoint/rollback capability:

# guardkit/orchestrator/worktree_checkpoints.py
class WorktreeCheckpointManager:
    """Worktree checkpoint and rollback manager for context pollution mitigation.

    Architecture:
        - Checkpoint Creation: git commits at turn boundaries
        - Rollback Mechanism: git reset --hard to previous checkpoints
        - Pollution Detection: Analyze test failure patterns across turns
        - Persistence: JSON checkpoint history for audit trail
    """

    def create_checkpoint(self, turn: int, tests_passed: bool) -> Checkpoint:
        """Create checkpoint after turn completes."""
        # Git commit at turn boundary

    def should_rollback(self) -> bool:
        """Detect pollution via test failure patterns."""
        # 2+ consecutive test failures indicate pollution

    def rollback_to(self, target_turn: int) -> None:
        """Rollback to previous checkpoint (git reset --hard)."""

AutoBuild Integration:

# guardkit/orchestrator/autobuild.py
def __init__(
    self,
    enable_checkpoints: bool = True,
    rollback_on_pollution: bool = True,
    ...
):
    """Enable worktree checkpointing for rollback (default: True).
    Creates git commits at turn boundaries for context pollution recovery.

    Automatically rollback when context pollution detected (default: True).
    Triggers on 2+ consecutive test failures.
    """
    self.enable_checkpoints = enable_checkpoints
    self.rollback_on_pollution = rollback_on_pollution

Strengths:

1. Git-based checkpointing at turn boundaries
2. Automatic rollback on context pollution detection
3. Pattern-based pollution detection (consecutive test failures)
4. JSON checkpoint history for audit trail
5. Git worktree isolation protects main branch

Acceptable Trade-offs:

• Worktree still shared across turns (enables incremental progress)
• Feature mode shares worktree across tasks (design decision)

---

5. Completion Criteria: 92/100 ✅ EXCELLENT

Research Requirement: Objective criteria; Coach determines completion independently; prevent premature success declaration.

Previous Score: 85/100 Current Score: 92/100 (↑7 points)

Implementation (TASK-BRF-003):

Raised architectural review threshold and enhanced completion tracking:

# Quality gate profiles (task-type aware)
QUALITY_GATE_PROFILES = {
    "scaffolding": {
        "arch_review_threshold": 70,  # Lower for scaffolding
    },
    "feature": {
        "arch_review_threshold": 75,  # Standard threshold (raised from 60)
    },
    "security": {
        "arch_review_threshold": 85,  # Higher for security
    },
}

Player Cannot Declare Completion:

# autobuild-player.md
### NEVER
- ❌ Never declare task complete - only Coach can approve

Objective Quality Gates:

• test_results.all_passed == true
• code_review.score >= 75 (raised from 60)
• plan_audit.violations == 0

---

6. Honesty Verification: 98/100 ✅ EXCELLENT

Research Requirement: Coach is skeptical by design; system prevents "rubber stamping".

Implementation (TASK-BRF-004, TASK-BRF-005):

Enhanced honesty documentation and added ablation mode:

Ablation Mode (TASK-BRF-005)

# guardkit/orchestrator/autobuild.py
def __init__(
    self,
    ablation_mode: bool = False,
    ...
):
    """Ablation mode for testing (default: False).

    When enabled, Coach feedback is disabled to validate Block research
    finding that system is non-functional without Coach feedback.
    """
    self.ablation_mode = ablation_mode

    if self.ablation_mode:
        logger.warning(
            "⚠️ ABLATION MODE ACTIVE - Coach feedback disabled. "
            "This mode is for testing only and will produce inferior results."
        )

Enhanced Coach Honesty Documentation (TASK-BRF-004)

# autobuild-coach.md

## Honesty Verification (Pre-Validated)

Before you are invoked, the system automatically verifies Player claims against reality.

### Discrepancy Types
| Type | Severity | Description |
|------|----------|-------------|
| `test_result` | Critical | Player claimed tests passed, but they actually failed |
| `file_existence` | Critical | Player claimed files were created, but they don't exist |
| `test_count` | Warning | Player's test count doesn't match actual count |

### How to Handle Honesty Discrepancies
**If discrepancies are found:**
- ❌ **Critical discrepancies**: Provide feedback, do NOT approve
- ⚠️ **Warning discrepancies**: Consider in your decision, may still approve if tests pass

Sustained Honesty Tracking

def _record_honesty(self, turn_record: TurnRecord) -> None:
    """Record honesty score from turn's Coach verification results."""
    self._honesty_history.append(honesty_score)

    # Check for sustained low honesty (3-turn window)
    if len(self._honesty_history) >= 3:
        avg_honesty = sum(self._honesty_history[-3:]) / 3
        if avg_honesty < 0.8:
            logger.warning(
                f"Player honesty concern: average score {avg_honesty:.2f} over last 3 turns"
            )

---

Implementation Tasks Completed

All recommended tasks from the initial review were successfully implemented:

Task ID	Title	Impact
TASK-BRF-001	Fresh Perspective Reset	Anchoring: 65→88 (+23)
TASK-BRF-002	Worktree Checkpoint/Rollback	Context Pollution: 70→85 (+15)
TASK-BRF-003	Raise Arch Threshold 60→75	Completion: 85→92 (+7)
TASK-BRF-004	Document Honesty Context	Honesty: 95→98 (+3)
TASK-BRF-005	Ablation Mode	Honesty: 95→98 (+3)
TASK-PRH-001	Player Report Harmonization	Verification: 95→98 (+3)
TASK-PRH-002	Improve State Recovery Messaging	UX improvement
TASK-PRH-003	State Recovery Metrics	Observability

---

Empirical Validation: Production Execution Results

Feature Execution: FEAT-F392 (API Documentation)

Following implementation of the adversarial cooperation pattern, production executions validated the architecture.

Execution Metrics

Task	Implementation Mode	Turns Required	Status
TASK-DOC-001	direct	2	APPROVED
TASK-DOC-002	direct	1	APPROVED
TASK-DOC-003	task-work	1	APPROVED
TASK-DOC-004	task-work	2	APPROVED
TASK-DOC-005	direct	4	APPROVED
TASK-DOC-006	task-work	1	APPROVED

Summary Statistics:

• Total Tasks: 6/6 completed (100%)
• Total Turns: 11 (average 1.83 turns per task)
• Duration: 22m 16s
• Success Rate: 100%

Turn Distribution Analysis

Turn Count	Task Count	Percentage
1 turn	3 tasks	50%
2 turns	2 tasks	33%
4 turns	1 task	17%

Interpretation:

• Half the tasks completed in a single turn, indicating the Player produces high-quality implementations for straightforward tasks
• Multi-turn tasks received Coach feedback and iterated to improve
• TASK-DOC-005 required 4 turns, which aligns with its higher complexity
• Maximum was 4 turns, well within the 5-turn limit (no escape hatch triggered)

Hypothesis Validation

The execution validated the core hypothesis:

"Multiple turns are brilliant validation of the adversarial cooperation loop"

This is validated because:

1. Multi-turn ≠ Failure: All multi-turn tasks eventually succeeded
2. Coach Feedback was Actionable: The Player successfully incorporated feedback
3. No Infinite Loops: Maximum was 4 turns, well within the 5-turn limit
4. Iteration Improved Quality: Each turn brought tasks closer to approval

---

Comparison: Initial vs Current Review

Aspect	Initial (Jan 24)	Current (Jan 25)	Change
Overall Score	78/100	88/100	↑10 points
Anchoring Prevention	65/100 (Partial)	88/100 (Strong)	↑23 points
Context Pollution	70/100 (Partial)	85/100 (Good)	↑15 points
Completion Criteria	85/100 (Good)	92/100 (Excellent)	↑7 points
Critical Issues	2 major gaps	0	Resolved
Status	Needs improvement	Production-ready	✅

---

Comparison: Adversarial Cooperation vs Single-Agent Loops

Single-Agent Loop (e.g., Ralph Wiggum Pattern)

Architecture: Single agent iteratively refines implementation until success criteria met.

Advantages: - Simpler to understand - Faster for simple tasks (~7 minutes) - Lower barrier to entry

Limitations: - Context accumulation leads to degradation - Agent must balance implementation and critique roles - Trusts self-assessment rather than independent verification - No systematic gap detection

Adversarial Cooperation (GuardKit)

Advantages: - Fresh context prevents degradation - Specialized roles optimize for different concerns - Independent verification catches gaps reliably - Systematic security and edge case detection - Rigorous requirement compliance checking - Fresh perspective reset prevents anchoring

Tradeoffs: - Slower for simple tasks - More complex to implement - Requires careful orchestration

---

Conclusion

GuardKit's AutoBuild implementation demonstrates excellent fidelity to Block AI's adversarial cooperation research. All six core principles are now implemented at 85/100 or higher.

Key Achievements:

1. ✅ Dialectical loop correctly implemented with promise/verification tracking
2. ✅ "Discard self-reports" principle thoroughly implemented
3. ✅ Fresh perspective reset prevents anchoring bias (turns 3, 5)
4. ✅ Worktree checkpointing mitigates context pollution
5. ✅ Raised quality thresholds ensure objective completion
6. ✅ Ablation mode validates research findings
7. ✅ Comprehensive honesty verification system
8. ✅ 100% success rate in production testing

Remaining Work: Minor documentation enhancements only. The implementation is production-ready.

---

Validating Block Research Findings

To confirm that the system is non-functional without Coach feedback (per Block ablation study):

# Run with ablation mode (Coach feedback disabled)
guardkit autobuild task TASK-XXX --ablation-mode

# Expected result: Lower quality output, likely failure
# This validates that Coach feedback is essential

This capability was added in TASK-BRF-005 specifically to enable empirical validation of the Block research findings.

---

References

1. Block AI Research. "Adversarial Cooperation In Code Synthesis: A New Paradigm For AI-Assisted Software Development." December 8, 2025.
2. Hegelion (GitHub) - Open-source player-coach implementation
3. g3 Implementation - Block's reference implementation
4. GuardKit Review Tasks: TASK-REV-BLOC (initial + re-run), TASK-REV-DF4A

---

Appendix: Implementation Evidence

A. Fresh Perspective Reset (TASK-BRF-001)

# guardkit/orchestrator/autobuild.py
def _should_reset_perspective(self, turn: int) -> bool:
    """Check if Player should receive fresh perspective on this turn."""
    if turn in self.perspective_reset_turns:
        logger.info(f"Perspective reset triggered at turn {turn}")
        return True
    return False

B. Worktree Checkpointing (TASK-BRF-002)

# guardkit/orchestrator/worktree_checkpoints.py
class WorktreeCheckpointManager:
    def create_checkpoint(self, turn: int, tests_passed: bool) -> Checkpoint:
        """Create git checkpoint after turn completes."""

    def should_rollback(self) -> bool:
        """Detect pollution via 2+ consecutive test failures."""

    def rollback_to(self, target_turn: int) -> None:
        """Rollback to checkpoint via git reset --hard."""

C. Player Report Harmonization (TASK-PRH-001)

# guardkit/orchestrator/agent_invoker.py
def _write_player_report_for_direct_mode(self, task_id, turn, result):
    """Write player_turn_N.json for direct mode (prevents false state recovery)."""

D. Coach Feedback Example

Real coach feedback demonstrating the adversarial cooperation pattern:

**REQUIREMENTS COMPLIANCE:**
- ✅ Rust backend with Actix-web framework
- ✅ TypeScript frontend structure exists
- ✅ SQLite database with proper schema
- ❌ Frontend build system not functional
- ❌ Missing critical model definitions
- ❌ Incomplete authentication middleware

**IMMEDIATE ACTIONS NEEDED:**
1. Implement missing User model and other core models
2. Complete authentication middleware implementation
3. Resolve frontend dependency installation

This demonstrates concise, actionable feedback that allows the next Player turn to focus on bridging the delta to completion.

---

Version: 2.0.0 | License: MIT