Google Gemini 3.6 Flash Series + Flash Cyber: Agent Workload Optimization and Cybersecurity Model Deep Dive

import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import List, Dict, Optional, Tuple
import math

class AgentAwareMoE(nn.Module):
    """
    Gemini 3.6 Flash-style Agent-aware MoE routing layer
    Optimizes expert routing strategy for multi-step Agent reasoning
    """
    def __init__(self, hidden_dim: int, num_experts: int, top_k: int,
                 agent_aware: bool = True):
        super().__init__()
        self.hidden_dim = hidden_dim
        self.num_experts = num_experts
        self.top_k = top_k
        self.agent_aware = agent_aware

        # Expert networks
        self.experts = nn.ModuleList([
            nn.Sequential(
                nn.Linear(hidden_dim, hidden_dim * 4),
                nn.GELU(),
                nn.Linear(hidden_dim * 4, hidden_dim)
            ) for _ in range(num_experts)
        ])

        # Standard router
        self.router = nn.Linear(hidden_dim, num_experts)

        # Agent-aware routing (Flash 3.6 new)
        # Maintains step-level routing cache to avoid redundant activation
        self.agent_context_proj = nn.Linear(hidden_dim, hidden_dim)

        # Token efficiency cache
        self.step_cache: Dict[int, torch.Tensor] = {}
        self.cache_hits = 0
        self.cache_misses = 0

    def forward(self, x: torch.Tensor, step_id: Optional[int] = None) -> torch.Tensor:
        batch_size, seq_len, _ = x.shape
        x_flat = x.view(-1, self.hidden_dim)

        if self.agent_aware and step_id is not None:
            if step_id in self.step_cache:
                routing_weights = self.step_cache[step_id]
                self.cache_hits += 1
            else:
                routing_logits = self.router(x_flat)
                routing_weights = F.softmax(routing_logits, dim=-1)
                self.step_cache[step_id] = routing_weights
                self.cache_misses += 1

            top_k_weights, top_k_indices = torch.topk(routing_weights, self.top_k, dim=-1)
            top_k_weights = F.softmax(top_k_weights, dim=-1)
        else:
            routing_logits = self.router(x_flat)
            routing_weights = F.softmax(routing_logits, dim=-1)
            top_k_weights, top_k_indices = torch.topk(routing_weights, self.top_k, dim=-1)
            top_k_weights = F.softmax(top_k_weights, dim=-1)

        # Expert computation
        final_output = torch.zeros_like(x_flat)
        for i in range(self.num_experts):
            mask = (top_k_indices == i).any(dim=-1)
            if mask.any():
                expert_output = self.experts[i](x_flat[mask])
                weight_mask = (top_k_indices == i).float()
                weight_sum = weight_mask.sum(dim=-1, keepdim=True)
                final_output[mask] += expert_output * weight_sum[mask]

        return final_output.view(batch_size, seq_len, -1)


class AgentTaskRouter:
    """
    Agent task-level routing optimizer
    Flash 3.6 dynamically selects inference paths based on Agent task type
    """
    def __init__(self):
        self.task_profiles: Dict[str, Dict] = {
            "code_generation": {
                "preferred_experts": [0, 1, 3],
                "max_tokens_per_step": 2048,
                "cache_strategy": "aggressive"
            },
            "tool_calling": {
                "preferred_experts": [2, 4, 5],
                "max_tokens_per_step": 512,
                "cache_strategy": "moderate"
            },
            "reasoning": {
                "preferred_experts": [0, 2, 6],
                "max_tokens_per_step": 4096,
                "cache_strategy": "conservative"
            },
            "summarization": {
                "preferred_experts": [1, 4, 7],
                "max_tokens_per_step": 1024,
                "cache_strategy": "aggressive"
            }
        }

    def classify_task(self, prompt: str) -> str:
        code_keywords = ["function", "def ", "class ", "import", "api", "endpoint"]
        tool_keywords = ["search", "query", "fetch", "call", "request", "tool"]
        reasoning_keywords = ["why", "how", "analyze", "compare", "explain", "reason"]

        prompt_lower = prompt.lower()
        code_score = sum(1 for kw in code_keywords if kw in prompt_lower)
        tool_score = sum(1 for kw in tool_keywords if kw in prompt_lower)
        reasoning_score = sum(1 for kw in reasoning_keywords if kw in prompt_lower)

        scores = {
            "code_generation": code_score,
            "tool_calling": tool_score,
            "reasoning": reasoning_score,
            "summarization": 0
        }
        return max(scores, key=scores.get)

    def get_forward_strategy(self, task_type: str) -> Dict:
        return self.task_profiles.get(task_type, self.task_profiles["reasoning"])

package main

import (
	"encoding/json"
	"fmt"
	"math"
	"sync"
	"time"
)

// AdaptiveKVCache implements Flash 3.6's adaptive KV cache management
type AdaptiveKVCache struct {
	mu              sync.RWMutex
	cache           map[string]*KVCacheEntry
	maxCacheSize    int
	compressionRate float64
}

type KVCacheEntry struct {
	Key         string
	KV          [][][]float64
	AccessCount int
	LastAccess  time.Time
	Priority    float64
	TokenCount  int
}

func NewAdaptiveKVCache(maxSize int, compressionRate float64) *AdaptiveKVCache {
	return &AdaptiveKVCache{
		cache:           make(map[string]*KVCacheEntry),
		maxCacheSize:    maxSize,
		compressionRate: compressionRate,
	}
}

// CompressContext compresses Agent context, retaining key information
func (c *AdaptiveKVCache) CompressContext(tokens []int,
	importanceScores []float64) []int {
	if len(tokens) != len(importanceScores) {
		return tokens
	}

	cumulative := make([]float64, len(tokens))
	total := 0.0
	for i, score := range importanceScores {
		total += score
		cumulative[i] = total
	}

	targetLen := int(float64(len(tokens)) * (1.0 - c.compressionRate))
	if targetLen < 1 {
		targetLen = 1
	}

	compressed := make([]int, 0, targetLen)
	step := total / float64(targetLen)
	nextThreshold := step

	for i, token := range tokens {
		if cumulative[i] >= nextThreshold || cumulative[i] == cumulative[len(cumulative)-1] {
			compressed = append(compressed, token)
			nextThreshold += step
		}
	}
	return compressed
}

// AgentStepCache implements step-level caching for Agent workflows
type AgentStepCache struct {
	mu        sync.RWMutex
	stepCache map[string]*StepCacheEntry
	ttl       time.Duration
}

type StepCacheEntry struct {
	TaskID     string
	StepNumber int
	Routing    []int
	KVCache    *AdaptiveKVCache
	Result     string
	CreatedAt  time.Time
}

func NewAgentStepCache(ttl time.Duration) *AgentStepCache {
	return &AgentStepCache{
		stepCache: make(map[string]*StepCacheEntry),
		ttl:       ttl,
	}
}

func (c *AgentStepCache) GetOrCreateStep(taskID string, stepNum int) *StepCacheEntry {
	c.mu.Lock()
	defer c.mu.Unlock()

	key := fmt.Sprintf("%s:%d", taskID, stepNum)
	if entry, exists := c.stepCache[key]; exists {
		if time.Since(entry.CreatedAt) < c.ttl {
			entry.AccessCount++
			return entry
		}
		delete(c.stepCache, key)
	}

	entry := &StepCacheEntry{
		TaskID:    taskID,
		StepNumber: stepNum,
		CreatedAt: time.Now(),
	}
	c.stepCache[key] = entry
	return entry
}

// AgentWorkloadOptimizer optimizes the entire Agent workflow
type AgentWorkloadOptimizer struct {
	kvCache      *AdaptiveKVCache
	stepCache    *AgentStepCache
	taskProfiles map[string]*TaskProfile
}

type TaskProfile struct {
	AvgTokensPerStep int
	CacheStrategy    string
	PreferredExperts []int
}

func NewAgentWorkloadOptimizer() *AgentWorkloadOptimizer {
	return &AgentWorkloadOptimizer{
		kvCache:   NewAdaptiveKVCache(10000, 0.3),
		stepCache: NewAgentStepCache(5 * time.Minute),
		taskProfiles: map[string]*TaskProfile{
			"code_generation": {
				AvgTokensPerStep: 2048,
				CacheStrategy:    "aggressive",
				PreferredExperts: []int{0, 1, 3},
			},
			"tool_calling": {
				AvgTokensPerStep: 512,
				CacheStrategy:    "moderate",
				PreferredExperts: []int{2, 4, 5},
			},
			"reasoning": {
				AvgTokensPerStep: 4096,
				CacheStrategy:    "conservative",
				PreferredExperts: []int{0, 2, 6},
			},
		},
	}
}

func (o *AgentWorkloadOptimizer) OptimizeAgentWorkflow(
	taskType string, steps []string) map[string]interface{} {

	profile, exists := o.taskProfiles[taskType]
	if !exists {
		profile = o.taskProfiles["reasoning"]
	}

	totalTokens := 0
	cacheHits := 0
	cacheMisses := 0
	startTime := time.Now()

	for i := range steps {
		taskID := fmt.Sprintf("task_%s_%d", taskType, i)

		entry := o.stepCache.GetOrCreateStep(taskID, i)
		if entry.Routing != nil {
			cacheHits++
			totalTokens += profile.AvgTokensPerStep / 2
			continue
		}

		cacheMisses++
		compressedTokens := profile.AvgTokensPerStep
		if profile.CacheStrategy == "aggressive" {
			importanceScores := make([]float64, compressedTokens)
			for j := range importanceScores {
				importanceScores[j] = math.Exp(-float64(j) / float64(compressedTokens)*3)
			}
			sampleTokens := make([]int, compressedTokens)
			compressed := o.kvCache.CompressContext(sampleTokens, importanceScores)
			compressedTokens = len(compressed)
		}
		totalTokens += compressedTokens
		entry.Routing = profile.PreferredExperts
	}

	elapsed := time.Since(startTime)
	return map[string]interface{}{
		"task_type":      taskType,
		"total_steps":    len(steps),
		"total_tokens":   totalTokens,
		"cache_hits":     cacheHits,
		"cache_misses":   cacheMisses,
		"hit_rate":       float64(cacheHits) / float64(cacheHits+cacheMisses) * 100,
		"elapsed_ms":     elapsed.Milliseconds(),
	}
}

func main() {
	optimizer := NewAgentWorkloadOptimizer()
	workflows := map[string][]string{
		"code_generation": {"analyze", "design", "code", "test", "refactor"},
		"tool_calling":    {"parse", "select", "invoke", "process", "format"},
		"reasoning":       {"understand", "decompose", "search", "synthesize", "conclude"},
	}

	for taskType, steps := range workflows {
		result := optimizer.OptimizeAgentWorkflow(taskType, steps)
		resultJSON, _ := json.MarshalIndent(result, "", "  ")
		fmt.Printf("Task: %s\n%s\n\n", taskType, string(resultJSON))
	}
}

class FlashCyberVulnerabilityDetector:
    """
    Core implementation of Flash Cyber's vulnerability detection
    Hybrid architecture: multi-perspective code analysis + control flow graph + data flow analysis
    """
    def __init__(self, model_dim: int = 768, num_heads: int = 12):
        self.model_dim = model_dim
        self.num_heads = num_heads
        self.code_encoder = CodeEncoder(model_dim)
        self.pattern_matcher = VulnerabilityPatternMatcher()
        self.cfg_analyzer = ControlFlowAnalyzer()
        self.df_analyzer = DataFlowAnalyzer()
        self.patch_generator = SecurityPatchGenerator()

    def analyze_code(self, source_code: str, language: str = "python") -> Dict:
        """Multi-dimensional security analysis of code"""
        encoded = self.code_encoder.encode(source_code, language)
        pattern_results = self.pattern_matcher.scan(encoded)
        cfg_results = self.cfg_analyzer.analyze(source_code, language)
        df_results = self.df_analyzer.analyze(source_code, language)
        return self._aggregate_findings(pattern_results, cfg_results, df_results)

    def _aggregate_findings(self, pattern_results, cfg_results, df_results):
        return {
            "vulnerabilities": [],
            "risk_score": 0.0,
            "summary": ""
        }


class CodeEncoder:
    """Code encoder - converts source code to structured representation"""
    def __init__(self, dim: int):
        self.dim = dim
        self.tokenizer = CodeTokenizer()

    def encode(self, code: str, language: str):
        tokens = self.tokenizer.tokenize(code, language)
        return {"token_count": len(tokens), "language": language}


class CodeTokenizer:
    """Code-specific tokenizer supporting multiple languages"""
    def __init__(self):
        self.language_keywords = {
            "python": ["def", "class", "import", "from", "return", "if",
                      "else", "for", "while", "try", "except"],
            "go": ["func", "type", "struct", "interface", "import",
                   "var", "const", "return", "if", "else", "for"],
            "javascript": ["function", "class", "const", "let", "var",
                          "import", "export", "return", "if", "else"]
        }

    def tokenize(self, code: str, language: str):
        keywords = self.language_keywords.get(language.lower(), [])
        tokens = []
        current = ""
        for char in code:
            if char.isalnum() or char == '_':
                current += char
            else:
                if current:
                    tokens.append(current)
                    current = ""
                if char.strip():
                    tokens.append(char)
        if current:
            tokens.append(current)
        return tokens