float: """ Applies Attention Residuals to the agent's internal belief. Enables the agent to selectively retrieve past high-alpha representations. """ if not self.history_blocks: self.history_blocks.append(current_evidence) return self.prior # Q = Current Evidence, K/V = Historical Compressed Blocks q = current_evidence kv = np.array(self.history_blocks) # MoonshotAI scaled dot-product attention "> float: """ Applies Attention Residuals to the agent's internal belief. Enables the agent to selectively retrieve past high-alpha representations. """ if not self.history_blocks: self.history_blocks.append(current_evidence) return self.prior # Q = Current Evidence, K/V = Historical Compressed Blocks q = current_evidence kv = np.array(self.history_blocks) # MoonshotAI scaled dot-product attention "> float: """ Applies Attention Residuals to the agent's internal belief. Enables the agent to selectively retrieve past high-alpha representations. """ if not self.history_blocks: self.history_blocks.append(current_evidence) return self.prior # Q = Current Evidence, K/V = Historical Compressed Blocks q = current_evidence kv = np.array(self.history_blocks) # MoonshotAI scaled dot-product attention "> 
# jc_omni/jc_swarm_emulator.py (v300+ AttnRes Upgrade)
import numpy as np
from typing import Dict, List

class AttnResSwarmAgent:
    """A single virtual agent within the swarm utilizing Attention Residuals."""
    def __init__(self, agent_id: int, initial_prior: float):
        self.agent_id = agent_id
        self.prior = initial_prior
        self.history_blocks: List[np.ndarray] = []
        self.block_size = 8

    def update_belief_via_attn(self, current_evidence: np.ndarray) -> float:
        """
        Applies Attention Residuals to the agent's internal belief.
        Enables the agent to selectively retrieve past high-alpha representations.
        """
        if not self.history_blocks:
            self.history_blocks.append(current_evidence)
            return self.prior

        # Q = Current Evidence, K/V = Historical Compressed Blocks
        q = current_evidence
        kv = np.array(self.history_blocks)
        
        # MoonshotAI scaled dot-product attention
        d_k = np.sqrt(q.shape[0])
        attn_weights = np.exp(np.dot(kv, q) / d_k)
        attn_weights /= np.sum(attn_weights) + 1e-9
        
        # Residual context retrieval
        context = np.dot(attn_weights, kv)
        
        # Update prior based on the retrieved context (The Lever)
        # We use the mean of the context vector as the 'belief shift'
        belief_shift = np.mean(context)
        self.prior = np.clip(self.prior + (belief_shift * 0.1), 0.01, 0.99)
        
        # Manage block memory (FIFO)
        if len(self.history_blocks) > 20: self.history_blocks.pop(0)
        self.history_blocks.append(current_evidence)
        
        return self.prior

class SwarmEmulator:
    def __init__(self, swarm_size: int = 2000):
        self.swarm_size = swarm_size
        self.agents = [AttnResSwarmAgent(i, 0.5) for i in range(swarm_size)]

    def execute_consensus_sprint(self, spot: float, vel: float, acc: float, svd_dev: float) -> Dict:
        """
        Executes a multi-path projection where each agent is anchored 
        by Attention Residuals.
        """
        final_states = []
        
        # Base kinematic evidence vector
        evidence_vector = np.array([vel, acc, svd_dev])
        
        for agent in self.agents:
            # 1. Update individual agent prior via Attention Retrieval
            agent_prior = agent.update_belief_via_attn(evidence_vector)
            
            # 2. Project future state (The 'Stark' trajectory)
            # Higher prior belief in drift results in tighter, more aggressive paths
            drift_velocity = vel * (1.0 + agent_prior)
            noise_scale = svd_dev * (1.0 - agent_prior)
            
            # Simulated 12-step path projection
            projected_price = spot + (drift_velocity * 12) + np.random.normal(0, noise_scale)
            final_states.append(projected_price)

        # 3. Calculate Swarm Consensus
        states_arr = np.array(final_states)
        bullish_threshold = spot * 1.02
        bearish_threshold = spot * 0.98
        
        consensus = {
            "swarm_mean": float(np.mean(states_arr)),
            "bull_confidence": float(np.sum(states_arr > bullish_threshold) / self.swarm_size),
            "bear_confidence": float(np.sum(states_arr < bearish_threshold) / self.swarm_size),
            "entropy": float(np.std(states_arr))
        }
        
        return consensus