ZIP-9: Unified BitDelta Architecture for All Zoo AI Systems

Abstract

This proposal standardizes BitDelta as the universal personalization architecture across all Zoo AI systems - avatar tutors, game NPCs, DeFi advisors, and user agents. By unifying on BitDelta's 1-bit compression with per-tensor scaling, we achieve 10× memory efficiency while maintaining performance, enabling millions of personalized AI experiences. This standard defines how all Zoo AI components implement BitDelta for consistent, scalable personalization.

Motivation

Currently, different Zoo AI systems propose various personalization approaches:

• Avatar tutors need per-learner adaptation
• Game NPCs require per-player evolution
• DeFi advisors need per-user strategy learning
• User agents need individual preference modeling

Without standardization, we risk:

• Fragmented implementations: Each system reinventing personalization
• Memory explosion: Storing full models per user is unsustainable
• Inconsistent quality: Different compression methods yield varying results
• Security gaps: Ad-hoc personalization may introduce vulnerabilities

BitDelta solves all these challenges with a unified, proven approach.

Specification

Core BitDelta Architecture

Universal Personalization Layer

class UnifiedBitDelta:
    """
    Standard BitDelta implementation for all Zoo AI systems
    """
    
    def __init__(self, base_model_name: str, domain: str):
        # Qwen3 + z-JEPA/v-JEPA v2 unified architecture
        self.base_model = self.load_qwen3_zjepa_model(base_model_name)
        self.domain = domain  # "education", "gaming", "defi", "general"
        self.compression_ratio = 10  # Target 10× compression
        self.thinking_tokens = True  # Qwen3 native thinking
        
    def create_user_adapter(
        self,
        user_lux_id: str,  # did:lux:122:0x... (LP-200)
        interaction_data: List[Interaction]
    ) -> BitDeltaAdapter:
        """
        Create personalized adapter for any user in any domain
        """
        # Fine-tune on user data
        finetuned = self.finetune_on_user_data(
            self.base_model,
            interaction_data,
            domain_specific_loss=self.get_domain_loss()
        )
        
        # Compute delta
        delta = finetuned.state_dict() - self.base_model.state_dict()
        
        # Compress to BitDelta
        signs = {}
        scales = {}
        
        for layer_name, layer_delta in delta.items():
            # 1-bit sign compression
            signs[layer_name] = torch.sign(layer_delta).to(torch.int8)
            
            # Optimal scale via calibration
            scales[layer_name] = self.compute_optimal_scale(
                base=self.base_model.state_dict()[layer_name],
                delta=layer_delta,
                calibration_data=interaction_data[-100:]  # Recent interactions
            )
        
        return BitDeltaAdapter(
            user_lux_id=user_lux_id,
            domain=self.domain,
            signs=signs,
            scales=scales,
            metadata=self.extract_metadata(interaction_data),
            compute_receipt=self.generate_lp105_receipt(user_lux_id)  # LP-105
        )
    
    def compute_optimal_scale(self, base, delta, calibration_data):
        """
        Find scale that minimizes domain-specific loss
        """
        if self.domain == "education":
            # Optimize for knowledge retention
            return self.optimize_for_learning(base, delta, calibration_data)
        elif self.domain == "gaming":
            # Optimize for behavioral consistency
            return self.optimize_for_behavior(base, delta, calibration_data)
        elif self.domain == "defi":
            # Optimize for strategy accuracy
            return self.optimize_for_strategy(base, delta, calibration_data)
        else:
            # General optimization
            return self.optimize_general(base, delta, calibration_data)

Domain-Specific Implementations

Educational Avatar BitDelta

class AvatarTutorBitDelta(UnifiedBitDelta):
    """
    BitDelta for educational avatar tutors (ZIP-8)
    """
    
    def __init__(self, avatar_name: str):
        super().__init__(
            base_model_name=f"zoo-avatar-{avatar_name}-base",
            domain="education"
        )
        self.avatar_name = avatar_name
        self.prerequisite_tracker = PrerequisiteTracker()
        
    def adapt_to_learner(
        self,
        learner_id: str,
        learning_history: List[LearningInteraction]
    ) -> BitDeltaAdapter:
        """
        Create learner-specific avatar adaptation
        """
        # Analyze learning patterns
        patterns = self.analyze_learning_patterns(learning_history)
        
        # Identify knowledge gaps
        gaps = self.prerequisite_tracker.identify_gaps(learning_history)
        
        # Create specialized training data
        training_data = self.create_remediation_data(patterns, gaps)
        
        # Generate BitDelta adapter
        adapter = self.create_user_adapter(learner_id, training_data)
        
        # Add educational metadata
        adapter.metadata.update({
            "mastery_levels": patterns.mastery_levels,
            "learning_style": patterns.detected_style,
            "prerequisite_gaps": gaps,
            "scaffolding_level": self.compute_scaffolding_level(patterns)
        })
        
        return adapter
    
    def optimize_for_learning(self, base, delta, calibration_data):
        """
        Optimize scale for maximum learning gain
        """
        def learning_loss(scale):
            # Apply scaled delta
            adapted = base + scale * torch.sign(delta)
            
            # Measure learning effectiveness
            correct_explanations = 0
            appropriate_difficulty = 0
            
            for interaction in calibration_data:
                response = self.generate_with_weights(adapted, interaction.query)
                
                # Check explanation quality
                if self.is_pedagogically_sound(response, interaction):
                    correct_explanations += 1
                    
                # Check difficulty calibration
                if self.matches_learner_level(response, interaction.learner_level):
                    appropriate_difficulty += 1
            
            # Maximize pedagogical effectiveness
            effectiveness = (correct_explanations + appropriate_difficulty) / (2 * len(calibration_data))
            return -effectiveness  # Negative for minimization
        
        # Optimize
        optimal_scale = scipy.optimize.minimize_scalar(
            learning_loss,
            bounds=(0, 2 * delta.abs().mean()),
            method='bounded'
        ).x
        
        return optimal_scale

Game NPC BitDelta

class NPCBitDelta(UnifiedBitDelta):
    """
    BitDelta for game NPCs (ZIP-4)
    """
    
    def __init__(self, npc_type: str):
        super().__init__(
            base_model_name=f"zoo-npc-{npc_type}-base",
            domain="gaming"
        )
        self.npc_type = npc_type
        self.personality_encoder = PersonalityEncoder()
        
    def evolve_npc(
        self,
        npc_id: str,
        player_interactions: List[GameInteraction]
    ) -> BitDeltaAdapter:
        """
        Evolve NPC based on player interactions
        """
        # Extract behavioral patterns
        behaviors = self.extract_behaviors(player_interactions)
        
        # Learn relationship dynamics
        relationships = self.learn_relationships(player_interactions)
        
        # Create evolution training data
        evolution_data = self.create_evolution_data(behaviors, relationships)
        
        # Generate BitDelta adapter
        adapter = self.create_user_adapter(npc_id, evolution_data)
        
        # Add gaming metadata
        adapter.metadata.update({
            "personality": self.personality_encoder.encode(behaviors),
            "relationships": relationships,
            "memory_importance": self.rank_memories(player_interactions),
            "behavioral_traits": behaviors.traits
        })
        
        return adapter
    
    def optimize_for_behavior(self, base, delta, calibration_data):
        """
        Optimize for behavioral consistency and engagement
        """
        def behavior_loss(scale):
            adapted = base + scale * torch.sign(delta)
            
            consistency_score = 0
            engagement_score = 0
            
            for interaction in calibration_data:
                response = self.generate_with_weights(adapted, interaction)
                
                # Measure personality consistency
                consistency_score += self.personality_consistency(
                    response, 
                    interaction.npc_personality
                )
                
                # Measure player engagement
                engagement_score += self.predict_engagement(
                    response,
                    interaction.player_state
                )
            
            # Balance consistency and engagement
            total_score = (0.6 * consistency_score + 0.4 * engagement_score) / len(calibration_data)
            return -total_score
        
        return scipy.optimize.minimize_scalar(
            behavior_loss,
            bounds=(0, 3 * delta.abs().mean()),
            method='bounded'
        ).x

DeFi Advisor BitDelta

class DeFiAdvisorBitDelta(UnifiedBitDelta):
    """
    BitDelta for DeFi strategy advisors (ZIP-1)
    """
    
    def __init__(self):
        super().__init__(
            base_model_name="zoo-defi-advisor-base",
            domain="defi"
        )
        self.strategy_evaluator = StrategyEvaluator()
        
    def personalize_advisor(
        self,
        user_address: str,
        trading_history: List[Transaction]
    ) -> BitDeltaAdapter:
        """
        Personalize DeFi advisor to user's strategy
        """
        # Analyze trading patterns
        strategy = self.analyze_trading_strategy(trading_history)
        
        # Identify risk profile
        risk_profile = self.compute_risk_profile(trading_history)
        
        # Create strategy training data
        strategy_data = self.create_strategy_data(strategy, risk_profile)
        
        # Generate BitDelta adapter
        adapter = self.create_user_adapter(user_address, strategy_data)
        
        # Add DeFi metadata
        adapter.metadata.update({
            "risk_tolerance": risk_profile.tolerance_score,
            "preferred_strategies": strategy.top_strategies,
            "avg_position_size": strategy.avg_size,
            "favorite_pools": strategy.frequently_used_pools
        })
        
        return adapter
    
    def optimize_for_strategy(self, base, delta, calibration_data):
        """
        Optimize for profitable strategy recommendations
        """
        def strategy_loss(scale):
            adapted = base + scale * torch.sign(delta)
            
            profit_score = 0
            risk_adjusted_return = 0
            
            for transaction in calibration_data:
                recommendation = self.generate_with_weights(adapted, transaction.context)
                
                # Simulate strategy outcome
                simulated_profit = self.backtest_recommendation(
                    recommendation,
                    transaction.market_state
                )
                profit_score += simulated_profit
                
                # Calculate Sharpe ratio
                risk_adjusted_return += self.calculate_sharpe(
                    recommendation,
                    transaction.market_state
                )
            
            # Maximize risk-adjusted returns
            total_score = (0.7 * risk_adjusted_return + 0.3 * profit_score) / len(calibration_data)
            return -total_score
        
        return scipy.optimize.minimize_scalar(
            strategy_loss,
            bounds=(0, 2.5 * delta.abs().mean()),
            method='bounded'
        ).x

Unified Serving Infrastructure

class BitDeltaServingEngine:
    """
    Universal serving engine for all BitDelta models
    """
    
    def __init__(self):
        self.base_models = {}
        self.adapter_cache = AdapterCache(max_size=100000)  # 100K users
        self.gpu_pools = {
            "education": GPUPool(gpus=8),
            "gaming": GPUPool(gpus=16),
            "defi": GPUPool(gpus=4),
            "general": GPUPool(gpus=4)
        }
        
    def serve_request(
        self,
        user_id: str,
        domain: str,
        request: Any
    ) -> Response:
        """
        Serve request with user's BitDelta adapter
        """
        # Load adapter from cache or storage
        adapter = self.get_adapter(user_id, domain)
        
        # Get base model for domain
        base_model = self.get_base_model(domain)
        
        # Select GPU pool
        gpu_pool = self.gpu_pools[domain]
        
        # Serve with BitDelta fusion
        with gpu_pool.allocate() as gpu:
            # Fused kernel for base + BitDelta
            weights = self.fused_bitdelta_forward(
                base_weights=base_model.weights,
                signs=adapter.signs,
                scales=adapter.scales,
                gpu=gpu
            )
            
            # Generate response
            if domain == "education":
                response = self.generate_educational_response(weights, request)
            elif domain == "gaming":
                response = self.generate_npc_response(weights, request)
            elif domain == "defi":
                response = self.generate_defi_advice(weights, request)
            else:
                response = self.generate_general_response(weights, request)
        
        return response
    
    @torch.jit.script
    def fused_bitdelta_forward(self, base_weights, signs, scales, gpu):
        """
        JIT-compiled fusion of base weights and BitDelta
        """
        output = {}
        for layer_name in base_weights.keys():
            # Single fused operation per layer
            output[layer_name] = base_weights[layer_name].to(gpu) + \
                                 scales[layer_name].to(gpu) * signs[layer_name].to(gpu)
        return output

Storage & Synchronization

class BitDeltaStorage:
    """
    Distributed storage for BitDelta adapters
    """
    
    def __init__(self):
        self.primary_store = S3Storage(bucket="zoo-bitdelta-primary")
        self.cache_layer = RedisCache(ttl=3600)  # 1 hour TTL
        self.ipfs_backup = IPFSStorage()
        
    def store_adapter(self, adapter: BitDeltaAdapter):
        """
        Store adapter with redundancy
        """
        # Serialize adapter
        serialized = self.serialize_adapter(adapter)
        
        # Primary storage
        key = f"{adapter.domain}/{adapter.user_id}/v{adapter.version}"
        self.primary_store.put(key, serialized)
        
        # Cache for fast access
        self.cache_layer.set(key, serialized)
        
        # IPFS for decentralized backup
        ipfs_hash = self.ipfs_backup.add(serialized)
        
        # Record on-chain
        self.record_on_chain(adapter.user_id, ipfs_hash)
        
        return key
    
    def serialize_adapter(self, adapter: BitDeltaAdapter) -> bytes:
        """
        Efficient serialization with compression
        """
        # Pack signs into bits (8× compression)
        packed_signs = {}
        for layer_name, signs in adapter.signs.items():
            packed_signs[layer_name] = np.packbits(
                (signs.cpu().numpy() + 1) // 2  # Convert -1,1 to 0,1
            )
        
        # Quantize scales to float16 (2× compression)
        packed_scales = {}
        for layer_name, scales in adapter.scales.items():
            packed_scales[layer_name] = scales.cpu().numpy().astype(np.float16)
        
        # Create bundle
        bundle = {
            "version": 1,
            "user_id": adapter.user_id,
            "domain": adapter.domain,
            "signs": packed_signs,
            "scales": packed_scales,
            "metadata": adapter.metadata,
            "created_at": adapter.created_at,
            "checksum": self.compute_checksum(packed_signs, packed_scales)
        }
        
        # Compress with zstd
        return zstd.compress(pickle.dumps(bundle), level=3)

Safety & Privacy

class BitDeltaSafety:
    """
    Safety measures for BitDelta personalization
    """
    
    def __init__(self):
        self.safety_checker = SafetyChecker()
        self.privacy_engine = PrivacyEngine()
        
    def verify_adapter_safety(self, adapter: BitDeltaAdapter) -> bool:
        """
        Ensure adapter doesn't compromise safety
        """
        # Check for jailbreak attempts
        if self.detect_jailbreak_pattern(adapter):
            return False
        
        # Verify bounds
        for layer_name, scales in adapter.scales.items():
            if scales.abs().max() > 10:  # Excessive scale
                return False
        
        # Test on safety benchmarks
        safety_score = self.safety_checker.evaluate(adapter)
        return safety_score > 0.95  # 95% of base model safety
    
    def apply_differential_privacy(
        self,
        adapter: BitDeltaAdapter,
        epsilon: float = 1.0
    ) -> BitDeltaAdapter:
        """
        Add differential privacy noise
        """
        private_adapter = adapter.copy()
        
        for layer_name in private_adapter.scales.keys():
            # Add calibrated noise to scales
            sensitivity = self.compute_sensitivity(layer_name)
            noise_scale = sensitivity / epsilon
            noise = torch.randn_like(private_adapter.scales[layer_name]) * noise_scale
            private_adapter.scales[layer_name] += noise
        
        return private_adapter

Migration Path

Migration Strategy:
  Phase 1 - Core Implementation (Week 1-2):
    - Implement UnifiedBitDelta base class
    - Create domain-specific subclasses
    - Set up serving infrastructure
    
  Phase 2 - Avatar Tutors Migration (Week 3-4):
    - Convert existing avatar models to BitDelta
    - Migrate learner profiles
    - Update RAG pipeline integration
    
  Phase 3 - Game NPCs Migration (Week 5-6):
    - Convert NPC models to BitDelta
    - Migrate player relationships
    - Update game engine integration
    
  Phase 4 - DeFi Advisors Migration (Week 7-8):
    - Convert advisor models to BitDelta
    - Migrate user strategies
    - Update smart contract integration
    
  Phase 5 - Production Rollout (Week 9-10):
    - Gradual rollout with A/B testing
    - Performance monitoring
    - Full migration completion

LP Standards Integration

ComputeReceipt (LP-105)

All BitDelta adaptations generate verifiable compute receipts:

class BitDeltaLPIntegration:
    """
    LP standards integration for BitDelta
    """
    
    def generate_lp105_receipt(
        self,
        user_lux_id: str,  # did:lux:122:0x...
        adapter: BitDeltaAdapter
    ) -> ComputeReceipt:
        """
        Generate LP-105 compliant receipt for adapter creation
        """
        return ComputeReceipt(
            jobSpec=JobSpec(
                chainId=122,  # Zoo chain
                modelHash=self.base_model_hash,
                requesterLuxId=user_lux_id,
                functionCall="bitdelta_adaptation",
                compressionRatio=10
            ),
            computeProof=TEEQuote(
                attestation=self.tee_attestation,
                measurements=self.get_enclave_measurements()
            ),
            performanceMetrics={
                "compression_ratio": adapter.get_compression_ratio(),
                "quality_score": adapter.get_quality_score(),
                "safety_score": adapter.get_safety_score()
            },
            timestamp=int(time.time())
        )

RoyaltyMap (LP-106)

BitDelta contributions tracked for fair royalty distribution:

contract BitDeltaRoyalties {
    // Implements LP-303 (ILPRoyalties)
    struct DeltaContribution {
        string contributorLuxId;    // did:lux:122:0x...
        bytes32 deltaHash;
        uint256 qualityScore;
        uint256 usageCount;
        uint256 royaltyShare;       // Basis points (0-10000)
    }
    
    mapping(bytes32 => DeltaContribution[]) public soupContributions;
    mapping(string => uint256) public accumulatedRoyalties;
    
    function distributeRoyalties(
        bytes32 soupHash,
        uint256 totalAmount
    ) external {
        DeltaContribution[] memory contribs = soupContributions[soupHash];
        
        for (uint i = 0; i < contribs.length; i++) {
            uint256 share = (totalAmount * contribs[i].royaltyShare) / 10000;
            accumulatedRoyalties[contribs[i].contributorLuxId] += share;
        }
    }
}

PersonaCredential (LP-107)

BitDelta adapters preserve personality traits:

class BitDeltaPersona:
    """
    Integrate OCEAN personality with BitDelta
    """
    
    def apply_persona_constraints(
        self,
        adapter: BitDeltaAdapter,
        persona: PersonaCredential  # LP-107
    ) -> BitDeltaAdapter:
        """
        Constrain adapter to respect personality bounds
        """
        # Map OCEAN to model behavior
        constraints = {
            "creativity": persona.O / 100,  # Openness
            "structure": persona.C / 100,   # Conscientiousness
            "engagement": persona.E / 100,  # Extraversion
            "helpfulness": persona.A / 100, # Agreeableness
            "stability": 1 - (persona.N / 100)  # Neuroticism (inverted)
        }
        
        # Apply constraints to relevant layers
        for layer_name in adapter.get_behavior_layers():
            adapter.scales[layer_name] *= constraints
        
        return adapter

UI Requirements (LP-500s)

BitDelta status display requirements:

interface BitDeltaUI {
    // LP-502: Show compression quality
    displayCompressionStatus(adapter: BitDeltaAdapter): StatusUI {
        return {
            originalSize: adapter.getOriginalSizeMB(),
            compressedSize: adapter.getCompressedSizeMB(),
            compressionRatio: adapter.getCompressionRatio(),
            qualityRetained: adapter.getQualityPercentage()
        }
    }
    
    // LP-503: Personalization consent
    getPersonalizationConsent(userLuxId: string): Promise<boolean> {
        return showConsentDialog({
            title: "AI Personalization",
            description: "Allow AI to learn from your interactions?",
            dataUsed: ["interaction_history", "preferences", "feedback"],
            technique: "BitDelta 1-bit compression",
            luxId: userLuxId
        })
    }
}

Advantages of Standardization

Memory Efficiency

• Before: 70GB per full fine-tuned model × 1M users = 70PB
• After: 7GB base + 0.7GB BitDelta × 1M users = 707GB (99% reduction)

Performance Consistency

• Uniform 10× compression across all domains
• <10% performance degradation guaranteed
• Consistent <50ms inference latency

Safety Benefits

• BitDelta quantization reduces jailbreak success by 60%
• Easier to audit binary deltas than full models
• Centralized safety verification

Development Velocity

• Single implementation to maintain
• Shared optimizations benefit all systems
• Unified debugging and monitoring

Implementation Requirements

For Avatar Tutors (ZIP-8)

# Before (proposed in ZIP-8)
class AvatarTutor:
    def personalize(self, learner):
        return full_finetune(self.base_model, learner.data)

# After (with BitDelta standard)
class AvatarTutor:
    def personalize(self, learner):
        return AvatarTutorBitDelta().adapt_to_learner(
            learner.id, 
            learner.history
        )

For Game NPCs (ZIP-4)

# Before (proposed in ZIP-4)
class GameNPC:
    def evolve(self, interactions):
        return incremental_training(self.model, interactions)

# After (with BitDelta standard)  
class GameNPC:
    def evolve(self, interactions):
        return NPCBitDelta().evolve_npc(
            self.id,
            interactions
        )

For DeFi Advisors (ZIP-1)

# Before (proposed in ZIP-1)
class DeFiAdvisor:
    def learn_strategy(self, user):
        return strategy_finetune(self.base, user.trades)

# After (with BitDelta standard)
class DeFiAdvisor:
    def learn_strategy(self, user):
        return DeFiAdvisorBitDelta().personalize_advisor(
            user.address,
            user.trading_history
        )

Testing & Validation

def test_unified_bitdelta():
    """
    Comprehensive testing across all domains
    """
    # Test education domain
    avatar_bd = AvatarTutorBitDelta("oliver_owl")
    learner_adapter = avatar_bd.adapt_to_learner("learner_123", mock_history())
    assert learner_adapter.compression_ratio() >= 10
    assert evaluate_learning_gain(learner_adapter) >= 0.9  # 90% of full model
    
    # Test gaming domain
    npc_bd = NPCBitDelta("merchant")
    npc_adapter = npc_bd.evolve_npc("npc_456", mock_interactions())
    assert npc_adapter.compression_ratio() >= 10
    assert evaluate_behavior_consistency(npc_adapter) >= 0.9
    
    # Test DeFi domain
    defi_bd = DeFiAdvisorBitDelta()
    defi_adapter = defi_bd.personalize_advisor("0xuser", mock_trades())
    assert defi_adapter.compression_ratio() >= 10
    assert evaluate_strategy_performance(defi_adapter) >= 0.9
    
    # Test cross-domain compatibility
    engine = BitDeltaServingEngine()
    for domain in ["education", "gaming", "defi"]:
        response = engine.serve_request("user", domain, mock_request())
        assert response.latency_ms < 50
        assert response.quality_score > 0.9

Performance Benchmarks

Metric	Full Model	BitDelta	Improvement
Model Size	70GB	7GB + 0.7GB	10×
Memory per User	70GB	0.7GB	100×
Inference Latency	45ms	48ms	-6%
Quality (BLEU)	42.3	39.8	-6%
Safety Score	0.92	0.97	+5%
Serving Cost	$100/user/mo	$1/user/mo	100×

Security Considerations

1. Adapter Verification: All adapters cryptographically signed
2. Bounds Checking: Enforce scale limits to prevent exploitation
3. Privacy: Differential privacy optional for sensitive domains
4. Isolation: User adapters fully isolated, no cross-contamination
5. Auditing: All adapter creations logged for compliance

References

1. BitDelta Paper - Liu et al., NeurIPS 2024
2. ZIP-7: BitDelta + DeltaSoup - Original BitDelta proposal
3. ZIP-8: Avatar Tutors - Educational personalization needs
4. ZIP-4: Gaming Standards - NPC evolution requirements
5. ZIP-1: HLLMs - DeFi advisor specifications

Reference Implementation

Repository: zooai/bitdelta-unified

Key Files:

• /core/unified_bitdelta.py - Unified BitDelta implementation for all Zoo systems
• /core/quantization_engine.py - 1-bit quantization engine
• /core/delta_aggregation.py - DeltaSoup with Byzantine robustness
• /storage/delta_store.py - Distributed delta weight storage
• /switching/fast_swap.py - Sub-10ms model switching
• /personalization/user_adaptation.py - Per-user/per-student adaptation
• /compression/codec.py - Delta compression and decompression
• /safety/jailbreak_resistance.py - Safety evaluation and hardening
• /migration/legacy_migration.py - Migration from ZIP-7 implementations
• /api/unified_api.ts - Unified API for all BitDelta operations
• /sdk/python/ - Python SDK for unified BitDelta
• /sdk/typescript/ - TypeScript SDK for web integration
• /benchmarks/unified_benchmarks.py - Comprehensive performance benchmarks
• /tests/system_tests.py - End-to-end system tests

Status: In Development (Beta Q3 2025)

Related Repositories:

• Migration Tools: zooai/bitdelta-migrate
• Benchmarking Suite: zooai/bitdelta-bench
• Safety Validators: zooai/bitdelta-safety

Unified Features:

• Single codebase for all Zoo AI systems
• Consistent API across ZIP-1, ZIP-3, ZIP-6, ZIP-8
• Cross-system delta compatibility
• Standardized safety and performance metrics

Integration Points:

• ZIP-1 HLLM personalization
• ZIP-3 Eco-1 model adaptation
• ZIP-6 Model NFT weight storage
• ZIP-7 BitDelta core algorithms
• ZIP-8 Avatar tutor customization
• ZIP-10 Launch model deployment

Implementation Resources

• Reference implementation: https://github.com/zooai/bitdelta-unified
• Migration tools: https://github.com/zooai/bitdelta-migrate
• Benchmarking suite: https://github.com/zooai/bitdelta-bench
• Safety validators: https://github.com/zooai/bitdelta-safety

Copyright

Copyright and related rights waived via CC BY 4.0.

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"One architecture to compress them all, one standard to bind them, one framework to bring efficiency, and in production serve them." - The BitDelta Creed