File size: 3,706 Bytes
31d9d4f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 | #ifndef OV_UNIVERSAL_KERNEL_H
#define OV_UNIVERSAL_KERNEL_H
#include <vector>
#include <cmath>
#include <algorithm>
#include <iostream>
// OpenVinayaka Universal Kernel (v1.0)
// Supports: Transformer, Mamba (SSM), MoE, and Hybrid Architectures.
// Goal: Mathematical Hallucination Elimination via Internal State Steering.
namespace OV_Kernel {
// --- Core Data Structures ---
struct MemoryContext {
std::vector<float> truth_vector; // The "Right Answer" embedding
float confidence; // P = S * C * R * W (0.0 to 1.0)
bool is_active; // Is there relevant memory to inject?
};
// --- 1. Transformer Intervention (Attention Bias) ---
// Target: QK^T matrix (Pre-Softmax Attention Scores)
// Effect: "Blinders" that force the model to look at the Truth Context.
static void apply_attention_bias(
std::vector<float>& attention_scores,
const MemoryContext& mem,
int context_start_idx,
int context_len,
float strength = 10.0f
) {
if (!mem.is_active || mem.confidence < 0.5f) return;
// Apply bias only to the tokens representing the injected truth
for (int i = context_start_idx; i < context_start_idx + context_len; ++i) {
if (i < attention_scores.size()) {
// Log-space bias addition (equivalent to multiplying probability)
attention_scores[i] += (strength * mem.confidence);
}
}
}
// --- 2. State Space Model (Mamba/Jamba) Intervention ---
// Target: Hidden State (h_t) in the SSM recurrence
// Effect: "Drift Correction" - Pulls the state vector back to the Truth.
static void apply_state_correction(
std::vector<float>& hidden_state,
const MemoryContext& mem,
float alpha_base = 0.3f
) {
if (!mem.is_active || mem.confidence < 0.5f) return;
float alpha = alpha_base * mem.confidence; // Correction strength
for (size_t i = 0; i < hidden_state.size(); ++i) {
// Linear Interpolation (LERP) between Hallucination and Truth
float current = hidden_state[i];
float target = mem.truth_vector[i];
hidden_state[i] = ((1.0f - alpha) * current) + (alpha * target);
}
}
// --- 3. Mixture of Experts (MoE) Intervention ---
// Target: Router Logits (Gate)
// Effect: "Expert Steering" - Biases the router to select "Fact/Reasoning" experts
// instead of "Creative/Fiction" experts when Truth is present.
static void apply_router_bias(
std::vector<float>& router_logits,
const std::vector<int>& factual_expert_indices,
const MemoryContext& mem,
float strength = 5.0f
) {
if (!mem.is_active || mem.confidence < 0.6f) return;
for (int expert_idx : factual_expert_indices) {
if (expert_idx < router_logits.size()) {
router_logits[expert_idx] += (strength * mem.confidence);
}
}
}
// --- 4. Titans / Neural Memory Intervention ---
// Target: Persistent Memory Bank (M_t)
// Effect: "Memory Overwrite" - Directly writes the Truth into the long-term neural memory slot.
static void apply_memory_overwrite(
std::vector<std::vector<float>>& memory_bank,
const MemoryContext& mem,
int write_head_idx
) {
if (!mem.is_active || mem.confidence < 0.8f) return;
// Hard overwrite of the memory slot at the write head
if (write_head_idx < memory_bank.size()) {
memory_bank[write_head_idx] = mem.truth_vector;
}
}
}
#endif // OV_UNIVERSAL_KERNEL_H
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