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edit_propagation_engine.py

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+# -*- coding: utf-8 -*-
+"""
+Cross-Lingual Edit Propagation via Subspace Containment
+Transfer high-resource corrections to low-resource languages using containment scores
+
+Based on:
+    Zhang, Y., et al. (2024). "Deep Hierarchical Learning with Nested Subspace Networks."
+    arXiv preprint. NSN framework for hierarchical representation learning.
+"""
+import numpy as np
+from typing import Dict, List, Optional, Tuple
+from dataclasses import dataclass
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ContainmentScore:
+    """Subspace containment analysis result"""
+    source_lang: str
+    target_lang: str
+    rank: int
+    containment_score: float  # 0-1, how much target is contained in source
+    overlap_dimension: int  # Dimension of overlap
+    confidence: float
+    propagation_recommended: bool
+
+
+@dataclass
+class PropagationResult:
+    """Result of edit propagation"""
+    source_lang: str
+    target_lang: str
+    rank: int
+    edit_vector: np.ndarray
+    propagated_vector: np.ndarray
+    containment_score: float
+    success: bool
+    quality_score: float  # Predicted quality after propagation
+    propagation_path: List[str]  # Languages in propagation chain
+
+
+class EditPropagationEngine:
+    """
+    Transfer edits from high-resource to low-resource languages using
+    subspace containment analysis.
+    
+    Dashboard Extension:
+    - Heatmap of containment scores across language pairs
+    - Flow arrows showing edit propagation paths
+    """
+    
+    def __init__(self):
+        self.language_embeddings = self._initialize_language_embeddings()
+        self.containment_cache: Dict[Tuple[str, str, int], ContainmentScore] = {}
+        self.propagation_history: List[PropagationResult] = []
+        
+    def _initialize_language_embeddings(self) -> Dict[str, np.ndarray]:
+        """Initialize language subspace embeddings"""
+        # Simulated language embeddings (in practice, learned from data)
+        np.random.seed(42)
+        
+        languages = {
+            # High-resource languages (larger subspaces)
+            'english': np.random.randn(256),
+            'chinese': np.random.randn(256),
+            'spanish': np.random.randn(256),
+            'french': np.random.randn(256),
+            'german': np.random.randn(256),
+            
+            # Medium-resource languages
+            'russian': np.random.randn(256),
+            'arabic': np.random.randn(256),
+            'japanese': np.random.randn(256),
+            'korean': np.random.randn(256),
+            'portuguese': np.random.randn(256),
+            
+            # Low-resource languages (smaller subspaces)
+            'indonesian': np.random.randn(256),
+            'vietnamese': np.random.randn(256),
+            'thai': np.random.randn(256),
+            'swahili': np.random.randn(256),
+            'yoruba': np.random.randn(256)
+        }
+        
+        # Normalize embeddings
+        for lang in languages:
+            languages[lang] = languages[lang] / np.linalg.norm(languages[lang])
+        
+        return languages
+    
+    def evaluate_subspace_containment(
+        self,
+        source_lang: str,
+        target_lang: str,
+        rank: int
+    ) -> ContainmentScore:
+        """
+        Evaluate how much target language subspace is contained in source.
+        
+        Args:
+            source_lang: High-resource source language
+            target_lang: Low-resource target language
+            rank: NSN rank for analysis
+            
+        Returns:
+            ContainmentScore with containment metrics
+        """
+        cache_key = (source_lang, target_lang, rank)
+        if cache_key in self.containment_cache:
+            return self.containment_cache[cache_key]
+        
+        # Get language embeddings
+        source_emb = self.language_embeddings.get(source_lang)
+        target_emb = self.language_embeddings.get(target_lang)
+        
+        if source_emb is None or target_emb is None:
+            logger.warning(f"Unknown language: {source_lang} or {target_lang}")
+            return ContainmentScore(
+                source_lang=source_lang,
+                target_lang=target_lang,
+                rank=rank,
+                containment_score=0.0,
+                overlap_dimension=0,
+                confidence=0.0,
+                propagation_recommended=False
+            )
+        
+        # Compute containment via projection
+        # Truncate to rank dimension
+        source_subspace = source_emb[:rank]
+        target_subspace = target_emb[:rank]
+        
+        # Containment score: cosine similarity in rank-dimensional subspace
+        containment = float(np.dot(source_subspace, target_subspace))
+        containment = (containment + 1.0) / 2.0  # Normalize to [0, 1]
+        
+        # Overlap dimension: effective rank of shared subspace
+        overlap_dim = int(rank * containment)
+        
+        # Confidence based on rank and language resource levels
+        confidence = self._compute_containment_confidence(
+            source_lang, target_lang, rank, containment
+        )
+        
+        # Recommend propagation if containment > 0.75 and confidence > 0.7
+        propagation_recommended = containment > 0.75 and confidence > 0.7
+        
+        result = ContainmentScore(
+            source_lang=source_lang,
+            target_lang=target_lang,
+            rank=rank,
+            containment_score=containment,
+            overlap_dimension=overlap_dim,
+            confidence=confidence,
+            propagation_recommended=propagation_recommended
+        )
+        
+        self.containment_cache[cache_key] = result
+        return result
+    
+    def _compute_containment_confidence(
+        self,
+        source_lang: str,
+        target_lang: str,
+        rank: int,
+        containment: float
+    ) -> float:
+        """Compute confidence in containment score"""
+        # Higher confidence for:
+        # - Higher ranks (more dimensions to analyze)
+        # - Higher containment scores
+        # - Related language families
+        
+        rank_factor = min(rank / 128.0, 1.0)
+        containment_factor = containment
+        
+        # Language family bonus (simplified)
+        family_bonus = 0.0
+        if (source_lang in ['english', 'german', 'french', 'spanish'] and
+            target_lang in ['english', 'german', 'french', 'spanish']):
+            family_bonus = 0.1
+        
+        confidence = 0.5 * rank_factor + 0.4 * containment_factor + family_bonus
+        return float(np.clip(confidence, 0.0, 1.0))
+    
+    def propagate_edit(
+        self,
+        source_lang: str,
+        target_lang: str,
+        rank: int,
+        edit_vector: np.ndarray
+    ) -> PropagationResult:
+        """
+        Propagate edit from source to target language.
+        
+        Args:
+            source_lang: Source language
+            target_lang: Target language
+            rank: NSN rank
+            edit_vector: Edit vector in source language
+            
+        Returns:
+            PropagationResult with propagated edit
+        """
+        # Evaluate containment
+        containment = self.evaluate_subspace_containment(
+            source_lang, target_lang, rank
+        )
+        
+        if not containment.propagation_recommended:
+            logger.warning(
+                f"Propagation not recommended: {source_lang} → {target_lang} "
+                f"(containment: {containment.containment_score:.3f})"
+            )
+            
+            result = PropagationResult(
+                source_lang=source_lang,
+                target_lang=target_lang,
+                rank=rank,
+                edit_vector=edit_vector,
+                propagated_vector=np.zeros_like(edit_vector),
+                containment_score=containment.containment_score,
+                success=False,
+                quality_score=0.0,
+                propagation_path=[source_lang, target_lang]
+            )
+            
+            self.propagation_history.append(result)
+            return result
+        
+        # Propagate edit via subspace projection
+        propagated_vector = self._transfer_edit(
+            edit_vector, source_lang, target_lang, rank
+        )
+        
+        # Compute quality score
+        quality_score = self._compute_propagation_quality(
+            edit_vector, propagated_vector, containment.containment_score
+        )
+        
+        result = PropagationResult(
+            source_lang=source_lang,
+            target_lang=target_lang,
+            rank=rank,
+            edit_vector=edit_vector,
+            propagated_vector=propagated_vector,
+            containment_score=containment.containment_score,
+            success=True,
+            quality_score=quality_score,
+            propagation_path=[source_lang, target_lang]
+        )
+        
+        self.propagation_history.append(result)
+        logger.info(
+            f"Propagated edit: {source_lang} → {target_lang} "
+            f"(quality: {quality_score:.3f})"
+        )
+        
+        return result
+    
+    def _transfer_edit(
+        self,
+        edit_vector: np.ndarray,
+        source_lang: str,
+        target_lang: str,
+        rank: int
+    ) -> np.ndarray:
+        """Transfer edit vector from source to target language"""
+        # Get language embeddings
+        source_emb = self.language_embeddings[source_lang]
+        target_emb = self.language_embeddings[target_lang]
+        
+        # Project edit onto shared subspace
+        # Simplified: weighted combination based on containment
+        source_subspace = source_emb[:rank]
+        target_subspace = target_emb[:rank]
+        
+        # Compute transfer matrix (simplified)
+        transfer_weight = np.dot(source_subspace, target_subspace)
+        
+        # Apply transfer
+        propagated = edit_vector * transfer_weight
+        
+        return propagated
+    
+    def _compute_propagation_quality(
+        self,
+        original: np.ndarray,
+        propagated: np.ndarray,
+        containment: float
+    ) -> float:
+        """Compute quality of propagated edit"""
+        # Quality based on:
+        # - Containment score
+        # - Vector similarity
+        # - Magnitude preservation
+        
+        if np.linalg.norm(propagated) < 1e-6:
+            return 0.0
+        
+        # Cosine similarity
+        similarity = np.dot(original, propagated) / (
+            np.linalg.norm(original) * np.linalg.norm(propagated)
+        )
+        similarity = (similarity + 1.0) / 2.0  # Normalize to [0, 1]
+        
+        # Magnitude preservation
+        mag_ratio = np.linalg.norm(propagated) / np.linalg.norm(original)
+        mag_score = 1.0 - abs(1.0 - mag_ratio)
+        
+        # Combined quality
+        quality = 0.5 * containment + 0.3 * similarity + 0.2 * mag_score
+        
+        return float(np.clip(quality, 0.0, 1.0))
+    
+    def compute_containment_heatmap(
+        self,
+        languages: List[str],
+        rank: int
+    ) -> np.ndarray:
+        """
+        Compute containment heatmap for dashboard visualization.
+        
+        Args:
+            languages: List of languages to analyze
+            rank: NSN rank
+            
+        Returns:
+            Heatmap matrix (languages x languages)
+        """
+        n = len(languages)
+        heatmap = np.zeros((n, n))
+        
+        for i, source in enumerate(languages):
+            for j, target in enumerate(languages):
+                if i == j:
+                    heatmap[i, j] = 1.0
+                else:
+                    containment = self.evaluate_subspace_containment(
+                        source, target, rank
+                    )
+                    heatmap[i, j] = containment.containment_score
+        
+        return heatmap
+    
+    def find_propagation_paths(
+        self,
+        source_lang: str,
+        target_langs: List[str],
+        rank: int,
+        min_containment: float = 0.75
+    ) -> Dict[str, List[str]]:
+        """
+        Find optimal propagation paths from source to multiple targets.
+        
+        Returns:
+            Dict mapping target language to propagation path
+        """
+        paths = {}
+        
+        for target in target_langs:
+            # Direct path
+            direct_containment = self.evaluate_subspace_containment(
+                source_lang, target, rank
+            )
+            
+            if direct_containment.containment_score >= min_containment:
+                paths[target] = [source_lang, target]
+            else:
+                # Try indirect path through intermediate language
+                best_path = None
+                best_score = 0.0
+                
+                for intermediate in self.language_embeddings.keys():
+                    if intermediate in [source_lang, target]:
+                        continue
+                    
+                    c1 = self.evaluate_subspace_containment(
+                        source_lang, intermediate, rank
+                    )
+                    c2 = self.evaluate_subspace_containment(
+                        intermediate, target, rank
+                    )
+                    
+                    combined_score = c1.containment_score * c2.containment_score
+                    
+                    if combined_score > best_score and combined_score >= min_containment:
+                        best_score = combined_score
+                        best_path = [source_lang, intermediate, target]
+                
+                if best_path:
+                    paths[target] = best_path
+                else:
+                    paths[target] = []  # No viable path
+        
+        return paths

ensemble_inference_manager.py

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+# -*- coding: utf-8 -*-
+"""
+Ensemble Inference Across Backends
+Run edits across multiple backends and compute agreement scores
+
+"""
+import numpy as np
+from typing import Dict, List, Optional, Tuple
+from dataclasses import dataclass
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class BackendResult:
+    """Result from a single backend"""
+    backend_id: str
+    edit_vector: np.ndarray
+    output: np.ndarray
+    confidence: float
+    latency: float  # seconds
+    success: bool
+    error_message: Optional[str] = None
+
+
+@dataclass
+class EnsembleResult:
+    """Result from ensemble inference"""
+    edit_vector: np.ndarray
+    backend_results: List[BackendResult]
+    consensus_output: np.ndarray
+    agreement_score: float
+    reliability_boost: float
+    agreement_matrix: np.ndarray
+    best_backend: str
+    ensemble_confidence: float
+
+
+class EnsembleInferenceManager:
+    """
+    Run edits across multiple quantum backends and compute agreement scores.
+    
+    Dashboard Extension:
+    - Agreement matrix across backends
+    - Reliability boost from ensemble consensus
+    """
+    
+    def __init__(self):
+        self.backend_configs = self._initialize_backend_configs()
+        self.inference_history: List[EnsembleResult] = []
+        
+    def _initialize_backend_configs(self) -> Dict[str, Dict]:
+        """Initialize backend configurations"""
+        return {
+            'ibm_manila': {
+                'qubits': 5,
+                'error_rate': 0.08,
+                'gate_fidelity': 0.92,
+                'coherence_time': 30.0,
+                'base_latency': 0.05
+            },
+            'ibm_washington': {
+                'qubits': 127,
+                'error_rate': 0.02,
+                'gate_fidelity': 0.98,
+                'coherence_time': 120.0,
+                'base_latency': 0.15
+            },
+            'russian_simulator': {
+                'qubits': 256,
+                'error_rate': 0.001,
+                'gate_fidelity': 0.999,
+                'coherence_time': 1000.0,
+                'base_latency': 0.30
+            },
+            'ibm_kyoto': {
+                'qubits': 127,
+                'error_rate': 0.025,
+                'gate_fidelity': 0.975,
+                'coherence_time': 100.0,
+                'base_latency': 0.12
+            },
+            'google_sycamore': {
+                'qubits': 53,
+                'error_rate': 0.015,
+                'gate_fidelity': 0.985,
+                'coherence_time': 80.0,
+                'base_latency': 0.08
+            }
+        }
+    
+    def run_ensemble_inference(
+        self,
+        edit_vector: np.ndarray,
+        backend_list: List[str]
+    ) -> EnsembleResult:
+        """
+        Run inference across multiple backends and compute ensemble result.
+        
+        Args:
+            edit_vector: Edit vector to apply
+            backend_list: List of backend IDs (e.g., ['ibm_manila', 'ibm_washington'])
+            
+        Returns:
+            EnsembleResult with consensus and agreement metrics
+        """
+        # Run inference on each backend
+        backend_results = []
+        
+        for backend_id in backend_list:
+            result = self._run_single_backend(backend_id, edit_vector)
+            backend_results.append(result)
+        
+        # Compute agreement matrix
+        agreement_matrix = self._compute_agreement_matrix(backend_results)
+        
+        # Compute consensus output
+        consensus_output = self._compute_consensus(backend_results)
+        
+        # Compute overall agreement score
+        agreement_score = self._compute_overall_agreement(agreement_matrix)
+        
+        # Compute reliability boost
+        reliability_boost = self._compute_reliability_boost(
+            backend_results, agreement_score
+        )
+        
+        # Find best backend
+        best_backend = self._select_best_backend(backend_results)
+        
+        # Compute ensemble confidence
+        ensemble_confidence = self._compute_ensemble_confidence(
+            backend_results, agreement_score
+        )
+        
+        result = EnsembleResult(
+            edit_vector=edit_vector,
+            backend_results=backend_results,
+            consensus_output=consensus_output,
+            agreement_score=agreement_score,
+            reliability_boost=reliability_boost,
+            agreement_matrix=agreement_matrix,
+            best_backend=best_backend,
+            ensemble_confidence=ensemble_confidence
+        )
+        
+        self.inference_history.append(result)
+        
+        logger.info(
+            f"Ensemble inference complete: {len(backend_list)} backends, "
+            f"agreement: {agreement_score:.3f}, boost: {reliability_boost:.3f}"
+        )
+        
+        return result
+    
+    def _run_single_backend(
+        self, backend_id: str, edit_vector: np.ndarray
+    ) -> BackendResult:
+        """Run inference on a single backend"""
+        config = self.backend_configs.get(backend_id)
+        
+        if config is None:
+            logger.warning(f"Unknown backend: {backend_id}")
+            return BackendResult(
+                backend_id=backend_id,
+                edit_vector=edit_vector,
+                output=np.zeros_like(edit_vector),
+                confidence=0.0,
+                latency=0.0,
+                success=False,
+                error_message=f"Unknown backend: {backend_id}"
+            )
+        
+        # Simulate inference with backend-specific noise
+        noise_level = config['error_rate']
+        noise = np.random.randn(*edit_vector.shape) * noise_level
+        
+        output = edit_vector + noise
+        
+        # Confidence based on gate fidelity
+        confidence = config['gate_fidelity']
+        
+        # Latency based on backend and vector size
+        latency = config['base_latency'] * (1 + len(edit_vector) / 1000.0)
+        
+        return BackendResult(
+            backend_id=backend_id,
+            edit_vector=edit_vector,
+            output=output,
+            confidence=confidence,
+            latency=latency,
+            success=True
+        )
+    
+    def _compute_agreement_matrix(
+        self, results: List[BackendResult]
+    ) -> np.ndarray:
+        """Compute pairwise agreement matrix between backends"""
+        n = len(results)
+        agreement_matrix = np.zeros((n, n))
+        
+        for i in range(n):
+            for j in range(n):
+                if i == j:
+                    agreement_matrix[i, j] = 1.0
+                else:
+                    # Cosine similarity between outputs
+                    output_i = results[i].output
+                    output_j = results[j].output
+                    
+                    if np.linalg.norm(output_i) < 1e-6 or np.linalg.norm(output_j) < 1e-6:
+                        agreement_matrix[i, j] = 0.0
+                    else:
+                        similarity = np.dot(output_i, output_j) / (
+                            np.linalg.norm(output_i) * np.linalg.norm(output_j)
+                        )
+                        # Normalize to [0, 1]
+                        agreement_matrix[i, j] = (similarity + 1.0) / 2.0
+        
+        return agreement_matrix
+    
+    def _compute_consensus(
+        self, results: List[BackendResult]
+    ) -> np.ndarray:
+        """Compute consensus output from all backends"""
+        successful_results = [r for r in results if r.success]
+        
+        if not successful_results:
+            return np.zeros_like(results[0].edit_vector)
+        
+        # Weighted average by confidence
+        total_confidence = sum(r.confidence for r in successful_results)
+        
+        if total_confidence < 1e-6:
+            # Unweighted average
+            outputs = [r.output for r in successful_results]
+            return np.mean(outputs, axis=0)
+        
+        # Confidence-weighted average
+        consensus = np.zeros_like(successful_results[0].output)
+        
+        for result in successful_results:
+            weight = result.confidence / total_confidence
+            consensus += weight * result.output
+        
+        return consensus
+    
+    def _compute_overall_agreement(self, agreement_matrix: np.ndarray) -> float:
+        """Compute overall agreement score from matrix"""
+        # Average of off-diagonal elements
+        n = agreement_matrix.shape[0]
+        
+        if n <= 1:
+            return 1.0
+        
+        # Sum off-diagonal elements
+        total = 0.0
+        count = 0
+        
+        for i in range(n):
+            for j in range(n):
+                if i != j:
+                    total += agreement_matrix[i, j]
+                    count += 1
+        
+        return total / count if count > 0 else 0.0
+    
+    def _compute_reliability_boost(
+        self, results: List[BackendResult], agreement_score: float
+    ) -> float:
+        """
+        Compute reliability boost from ensemble consensus.
+        
+        Boost is higher when:
+        - More backends agree
+        - Individual backends have high confidence
+        - Agreement score is high
+        """
+        if not results:
+            return 0.0
+        
+        # Average individual confidence
+        avg_confidence = np.mean([r.confidence for r in results if r.success])
+        
+        # Ensemble size factor
+        ensemble_factor = min(len(results) / 5.0, 1.0)
+        
+        # Boost formula
+        boost = (
+            0.4 * agreement_score +
+            0.3 * avg_confidence +
+            0.3 * ensemble_factor
+        )
+        
+        return float(np.clip(boost, 0.0, 1.0))
+    
+    def _select_best_backend(self, results: List[BackendResult]) -> str:
+        """Select best backend based on confidence and success"""
+        successful_results = [r for r in results if r.success]
+        
+        if not successful_results:
+            return results[0].backend_id if results else "none"
+        
+        # Score by confidence and inverse latency
+        scores = {}
+        
+        for result in successful_results:
+            scores[result.backend_id] = (
+                0.7 * result.confidence +
+                0.3 * (1.0 / (1.0 + result.latency))
+            )
+        
+        return max(scores, key=scores.get)
+    
+    def _compute_ensemble_confidence(
+        self, results: List[BackendResult], agreement_score: float
+    ) -> float:
+        """Compute overall ensemble confidence"""
+        if not results:
+            return 0.0
+        
+        # Combine individual confidences with agreement
+        avg_confidence = np.mean([r.confidence for r in results if r.success])
+        
+        # Ensemble confidence is boosted by agreement
+        ensemble_confidence = 0.6 * avg_confidence + 0.4 * agreement_score
+        
+        return float(np.clip(ensemble_confidence, 0.0, 1.0))
+    
+    def compare_backends(
+        self, edit_vectors: List[np.ndarray]
+    ) -> Dict[str, Dict[str, float]]:
+        """
+        Compare all backends across multiple edit vectors.
+        
+        Returns:
+            Dict mapping backend_id to performance metrics
+        """
+        backend_stats = {
+            backend_id: {
+                'avg_confidence': [],
+                'avg_latency': [],
+                'success_rate': []
+            }
+            for backend_id in self.backend_configs.keys()
+        }
+        
+        for edit_vector in edit_vectors:
+            for backend_id in self.backend_configs.keys():
+                result = self._run_single_backend(backend_id, edit_vector)
+                
+                backend_stats[backend_id]['avg_confidence'].append(result.confidence)
+                backend_stats[backend_id]['avg_latency'].append(result.latency)
+                backend_stats[backend_id]['success_rate'].append(1.0 if result.success else 0.0)
+        
+        # Compute averages
+        comparison = {}
+        
+        for backend_id, stats in backend_stats.items():
+            comparison[backend_id] = {
+                'avg_confidence': float(np.mean(stats['avg_confidence'])),
+                'avg_latency': float(np.mean(stats['avg_latency'])),
+                'success_rate': float(np.mean(stats['success_rate']))
+            }
+        
+        return comparison
+    
+    def get_agreement_heatmap(
+        self, backend_list: List[str], edit_vector: np.ndarray
+    ) -> Tuple[np.ndarray, List[str]]:
+        """
+        Get agreement heatmap for visualization.
+        
+        Returns:
+            Tuple of (agreement_matrix, backend_labels)
+        """
+        result = self.run_ensemble_inference(edit_vector, backend_list)
+        return result.agreement_matrix, backend_list
+    
+    def compute_reliability_metrics(self) -> Dict[str, float]:
+        """Compute overall reliability metrics from history"""
+        if not self.inference_history:
+            return {
+                'avg_agreement': 0.0,
+                'avg_reliability_boost': 0.0,
+                'avg_ensemble_confidence': 0.0
+            }
+        
+        return {
+            'avg_agreement': float(np.mean([
+                r.agreement_score for r in self.inference_history
+            ])),
+            'avg_reliability_boost': float(np.mean([
+                r.reliability_boost for r in self.inference_history
+            ])),
+            'avg_ensemble_confidence': float(np.mean([
+                r.ensemble_confidence for r in self.inference_history
+            ]))
+        }

rank_feedback_generator.py

@@ -0,0 +1,484 @@
+# -*- coding: utf-8 -*-
+"""
+Contributor-Aware Rank Feedback Loop
+Recommend optimal ranks based on contributor history and efficiency
+
+Based on:
+    Zhang, Y., et al. (2024). "Deep Hierarchical Learning with Nested Subspace Networks."
+    arXiv preprint. NSN framework for hierarchical representation learning.
+"""
+import numpy as np
+from typing import Dict, List, Optional, Tuple
+from dataclasses import dataclass
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class SubmissionRecord:
+    """Record of a contributor submission"""
+    contributor_id: str
+    language: str
+    rank: int
+    accuracy: float
+    flops: float
+    uncertainty: float
+    timestamp: str
+    efficiency: float  # accuracy / flops
+
+
+@dataclass
+class RankRecommendation:
+    """Rank recommendation for contributor"""
+    contributor_id: str
+    recommended_rank: int
+    confidence: float
+    rationale: str
+    unexplored_pairs: List[Tuple[int, str]]  # (rank, language) pairs
+    efficiency_prediction: float
+    personalized_badge: str
+
+
+class RankFeedbackGenerator:
+    """
+    Recommend optimal ranks based on contributor history and efficiency.
+    
+    Leaderboard Extension:
+    - Personalized rank badges
+    - Suggestion panel for unexplored rank-language pairs
+    """
+    
+    def __init__(self):
+        self.submission_history: Dict[str, List[SubmissionRecord]] = {}
+        self.rank_options = [8, 16, 32, 64, 128, 256]
+        self.language_options = [
+            'english', 'chinese', 'spanish', 'french', 'german',
+            'russian', 'arabic', 'japanese', 'korean', 'portuguese',
+            'indonesian', 'vietnamese', 'thai', 'swahili', 'yoruba'
+        ]
+        
+    def record_submission(
+        self,
+        contributor_id: str,
+        language: str,
+        rank: int,
+        accuracy: float,
+        flops: float,
+        uncertainty: float,
+        timestamp: str = None
+    ):
+        """Record a contributor submission"""
+        if timestamp is None:
+            from datetime import datetime
+            timestamp = datetime.now().isoformat()
+        
+        efficiency = accuracy / flops if flops > 0 else 0.0
+        
+        record = SubmissionRecord(
+            contributor_id=contributor_id,
+            language=language,
+            rank=rank,
+            accuracy=accuracy,
+            flops=flops,
+            uncertainty=uncertainty,
+            timestamp=timestamp,
+            efficiency=efficiency
+        )
+        
+        if contributor_id not in self.submission_history:
+            self.submission_history[contributor_id] = []
+        
+        self.submission_history[contributor_id].append(record)
+        logger.info(
+            f"Recorded submission: {contributor_id} - {language} @ rank {rank} "
+            f"(accuracy: {accuracy:.3f}, efficiency: {efficiency:.2e})"
+        )
+    
+    def recommend_rank(
+        self,
+        contributor_id: str,
+        target_language: Optional[str] = None
+    ) -> RankRecommendation:
+        """
+        Recommend optimal rank based on contributor history.
+        
+        Args:
+            contributor_id: Contributor identifier
+            target_language: Optional target language for recommendation
+            
+        Returns:
+            RankRecommendation with personalized suggestions
+        """
+        submissions = self.submission_history.get(contributor_id, [])
+        
+        if not submissions:
+            # New contributor: recommend starting rank
+            return RankRecommendation(
+                contributor_id=contributor_id,
+                recommended_rank=32,
+                confidence=0.5,
+                rationale="Starting recommendation for new contributor",
+                unexplored_pairs=self._get_unexplored_pairs(contributor_id),
+                efficiency_prediction=0.0,
+                personalized_badge="🌟 Newcomer"
+            )
+        
+        # Analyze submission history
+        if target_language:
+            # Language-specific recommendation
+            lang_submissions = [s for s in submissions if s.language == target_language]
+            if lang_submissions:
+                return self._recommend_from_history(
+                    contributor_id, lang_submissions, target_language
+                )
+        
+        # General recommendation based on all submissions
+        return self._recommend_from_history(contributor_id, submissions)
+    
+    def _recommend_from_history(
+        self,
+        contributor_id: str,
+        submissions: List[SubmissionRecord],
+        target_language: Optional[str] = None
+    ) -> RankRecommendation:
+        """Generate recommendation from submission history"""
+        # Find best efficiency rank
+        best_submission = max(submissions, key=lambda s: s.efficiency)
+        
+        # Analyze rank performance
+        rank_performance = self._analyze_rank_performance(submissions)
+        
+        # Find optimal rank
+        recommended_rank = self._select_optimal_rank(rank_performance)
+        
+        # Compute confidence
+        confidence = self._compute_recommendation_confidence(
+            submissions, recommended_rank
+        )
+        
+        # Generate rationale
+        rationale = self._generate_rationale(
+            submissions, recommended_rank, best_submission
+        )
+        
+        # Find unexplored pairs
+        unexplored = self._get_unexplored_pairs(contributor_id)
+        
+        # Predict efficiency
+        efficiency_prediction = self._predict_efficiency(
+            submissions, recommended_rank
+        )
+        
+        # Assign badge
+        badge = self._assign_badge(submissions)
+        
+        return RankRecommendation(
+            contributor_id=contributor_id,
+            recommended_rank=recommended_rank,
+            confidence=confidence,
+            rationale=rationale,
+            unexplored_pairs=unexplored[:5],  # Top 5 suggestions
+            efficiency_prediction=efficiency_prediction,
+            personalized_badge=badge
+        )
+    
+    def _analyze_rank_performance(
+        self, submissions: List[SubmissionRecord]
+    ) -> Dict[int, Dict[str, float]]:
+        """Analyze performance at each rank"""
+        rank_stats = {}
+        
+        for rank in self.rank_options:
+            rank_subs = [s for s in submissions if s.rank == rank]
+            
+            if rank_subs:
+                rank_stats[rank] = {
+                    'avg_accuracy': np.mean([s.accuracy for s in rank_subs]),
+                    'avg_efficiency': np.mean([s.efficiency for s in rank_subs]),
+                    'avg_uncertainty': np.mean([s.uncertainty for s in rank_subs]),
+                    'count': len(rank_subs)
+                }
+            else:
+                rank_stats[rank] = {
+                    'avg_accuracy': 0.0,
+                    'avg_efficiency': 0.0,
+                    'avg_uncertainty': 1.0,
+                    'count': 0
+                }
+        
+        return rank_stats
+    
+    def _select_optimal_rank(
+        self, rank_performance: Dict[int, Dict[str, float]]
+    ) -> int:
+        """Select optimal rank based on performance"""
+        # Score each rank by efficiency and accuracy
+        scores = {}
+        
+        for rank, stats in rank_performance.items():
+            if stats['count'] == 0:
+                scores[rank] = 0.0
+            else:
+                # Weighted score: 60% efficiency, 40% accuracy
+                scores[rank] = (
+                    0.6 * stats['avg_efficiency'] * 1e8 +  # Scale efficiency
+                    0.4 * stats['avg_accuracy']
+                )
+        
+        # Return rank with highest score
+        if not scores or max(scores.values()) == 0:
+            return 32  # Default
+        
+        return max(scores, key=scores.get)
+    
+    def _compute_recommendation_confidence(
+        self, submissions: List[SubmissionRecord], recommended_rank: int
+    ) -> float:
+        """Compute confidence in recommendation"""
+        # Confidence based on:
+        # - Number of submissions at recommended rank
+        # - Consistency of performance
+        # - Total submission count
+        
+        rank_subs = [s for s in submissions if s.rank == recommended_rank]
+        
+        if not rank_subs:
+            return 0.3  # Low confidence for untested rank
+        
+        # Sample size factor
+        sample_factor = min(len(rank_subs) / 10.0, 1.0)
+        
+        # Consistency factor (low variance in efficiency)
+        efficiencies = [s.efficiency for s in rank_subs]
+        if len(efficiencies) > 1:
+            consistency = 1.0 - min(np.std(efficiencies) / np.mean(efficiencies), 1.0)
+        else:
+            consistency = 0.5
+        
+        # Experience factor
+        experience = min(len(submissions) / 20.0, 1.0)
+        
+        confidence = 0.4 * sample_factor + 0.3 * consistency + 0.3 * experience
+        
+        return float(np.clip(confidence, 0.0, 1.0))
+    
+    def _generate_rationale(
+        self,
+        submissions: List[SubmissionRecord],
+        recommended_rank: int,
+        best_submission: SubmissionRecord
+    ) -> str:
+        """Generate human-readable rationale"""
+        rank_subs = [s for s in submissions if s.rank == recommended_rank]
+        
+        if not rank_subs:
+            return (
+                f"Rank {recommended_rank} recommended based on interpolation "
+                f"from your best performance at rank {best_submission.rank} "
+                f"(efficiency: {best_submission.efficiency:.2e})"
+            )
+        
+        avg_accuracy = np.mean([s.accuracy for s in rank_subs])
+        avg_efficiency = np.mean([s.efficiency for s in rank_subs])
+        
+        return (
+            f"Rank {recommended_rank} shows best efficiency ({avg_efficiency:.2e}) "
+            f"with {len(rank_subs)} submissions averaging {avg_accuracy:.3f} accuracy. "
+            f"This balances compute cost and performance for your editing style."
+        )
+    
+    def _get_unexplored_pairs(
+        self, contributor_id: str
+    ) -> List[Tuple[int, str]]:
+        """Get unexplored rank-language pairs"""
+        submissions = self.submission_history.get(contributor_id, [])
+        
+        explored = set((s.rank, s.language) for s in submissions)
+        
+        all_pairs = [
+            (rank, lang)
+            for rank in self.rank_options
+            for lang in self.language_options
+        ]
+        
+        unexplored = [pair for pair in all_pairs if pair not in explored]
+        
+        # Prioritize by potential value
+        # Prefer: medium ranks, diverse languages
+        def priority_score(pair):
+            rank, lang = pair
+            rank_score = 1.0 - abs(rank - 64) / 128.0  # Prefer rank 64
+            
+            # Prefer low-resource languages (more impact)
+            low_resource = ['indonesian', 'vietnamese', 'thai', 'swahili', 'yoruba']
+            lang_score = 1.5 if lang in low_resource else 1.0
+            
+            return rank_score * lang_score
+        
+        unexplored.sort(key=priority_score, reverse=True)
+        
+        return unexplored
+    
+    def _predict_efficiency(
+        self, submissions: List[SubmissionRecord], rank: int
+    ) -> float:
+        """Predict efficiency at given rank"""
+        # Simple linear interpolation from existing data
+        rank_subs = [s for s in submissions if s.rank == rank]
+        
+        if rank_subs:
+            return np.mean([s.efficiency for s in rank_subs])
+        
+        # Interpolate from nearby ranks
+        nearby_ranks = sorted([s.rank for s in submissions])
+        
+        if not nearby_ranks:
+            return 0.0
+        
+        # Find closest ranks
+        lower = [r for r in nearby_ranks if r < rank]
+        upper = [r for r in nearby_ranks if r > rank]
+        
+        if lower and upper:
+            lower_rank = max(lower)
+            upper_rank = min(upper)
+            
+            lower_eff = np.mean([
+                s.efficiency for s in submissions if s.rank == lower_rank
+            ])
+            upper_eff = np.mean([
+                s.efficiency for s in submissions if s.rank == upper_rank
+            ])
+            
+            # Linear interpolation
+            weight = (rank - lower_rank) / (upper_rank - lower_rank)
+            return lower_eff * (1 - weight) + upper_eff * weight
+        
+        # Use closest available rank
+        closest_rank = min(nearby_ranks, key=lambda r: abs(r - rank))
+        return np.mean([s.efficiency for s in submissions if s.rank == closest_rank])
+    
+    def _assign_badge(self, submissions: List[SubmissionRecord]) -> str:
+        """Assign personalized badge based on performance"""
+        if not submissions:
+            return "🌟 Newcomer"
+        
+        # Analyze submission characteristics
+        total_subs = len(submissions)
+        unique_langs = len(set(s.language for s in submissions))
+        unique_ranks = len(set(s.rank for s in submissions))
+        avg_accuracy = np.mean([s.accuracy for s in submissions])
+        avg_efficiency = np.mean([s.efficiency for s in submissions])
+        
+        # Badge criteria
+        if total_subs >= 50 and unique_langs >= 10:
+            return "🏆 Master Contributor"
+        elif avg_efficiency > 1e-7:
+            return "⚡ Efficiency Expert"
+        elif avg_accuracy > 0.95:
+            return "🎯 Accuracy Champion"
+        elif unique_ranks >= 5:
+            return "🔬 Rank Explorer"
+        elif unique_langs >= 8:
+            return "🌍 Multilingual Specialist"
+        elif total_subs >= 20:
+            return "💪 Active Contributor"
+        elif total_subs >= 10:
+            return "📈 Rising Star"
+        else:
+            return "🚀 Getting Started"
+    
+    def generate_feedback_panel(
+        self, contributor_id: str
+    ) -> Dict[str, any]:
+        """
+        Generate comprehensive feedback panel for dashboard.
+        
+        Returns:
+            Dict with recommendations, stats, and suggestions
+        """
+        submissions = self.submission_history.get(contributor_id, [])
+        recommendation = self.recommend_rank(contributor_id)
+        
+        if not submissions:
+            return {
+                'recommendation': recommendation,
+                'stats': {},
+                'suggestions': [
+                    "Start with rank 32 for balanced performance",
+                    "Try high-resource languages (English, Chinese) first",
+                    "Focus on accuracy before optimizing efficiency"
+                ]
+            }
+        
+        # Compute statistics
+        stats = {
+            'total_submissions': len(submissions),
+            'unique_languages': len(set(s.language for s in submissions)),
+            'unique_ranks': len(set(s.rank for s in submissions)),
+            'avg_accuracy': float(np.mean([s.accuracy for s in submissions])),
+            'avg_efficiency': float(np.mean([s.efficiency for s in submissions])),
+            'best_accuracy': float(max(s.accuracy for s in submissions)),
+            'best_efficiency': float(max(s.efficiency for s in submissions))
+        }
+        
+        # Generate suggestions
+        suggestions = self._generate_suggestions(submissions, recommendation)
+        
+        return {
+            'recommendation': recommendation,
+            'stats': stats,
+            'suggestions': suggestions
+        }
+
+    
+    def _generate_suggestions(
+        self,
+        submissions: List[SubmissionRecord],
+        recommendation: RankRecommendation
+    ) -> List[str]:
+        """Generate actionable suggestions"""
+        suggestions = []
+        
+        # Analyze gaps
+        tested_ranks = set(s.rank for s in submissions)
+        tested_langs = set(s.language for s in submissions)
+        
+        # Rank diversity
+        if len(tested_ranks) < 3:
+            suggestions.append(
+                f"Try exploring more ranks - you've only tested {len(tested_ranks)} so far"
+            )
+        
+        # Language diversity
+        low_resource = ['indonesian', 'vietnamese', 'thai', 'swahili', 'yoruba']
+        tested_low_resource = [l for l in tested_langs if l in low_resource]
+        
+        if len(tested_low_resource) < 2:
+            suggestions.append(
+                "Consider testing low-resource languages for higher impact"
+            )
+        
+        # Efficiency optimization
+        avg_efficiency = np.mean([s.efficiency for s in submissions])
+        if avg_efficiency < 5e-8:
+            suggestions.append(
+                "Focus on efficiency - try lower ranks to reduce FLOPs"
+            )
+        
+        # Accuracy improvement
+        avg_accuracy = np.mean([s.accuracy for s in submissions])
+        if avg_accuracy < 0.85:
+            suggestions.append(
+                "Accuracy could be improved - try higher ranks or refine your edits"
+            )
+        
+        # Unexplored pairs
+        if recommendation.unexplored_pairs:
+            top_pair = recommendation.unexplored_pairs[0]
+            suggestions.append(
+                f"High-value opportunity: Try rank {top_pair[0]} with {top_pair[1]}"
+            )
+        
+        return suggestions[:5]  # Top 5 suggestions