Solving AI's Memory Bottleneck: Puranic 'Kala-Chakra' Model for State Management

AI Memory Bottleneck — पुराणातील काल-चक्र मॉडेल

LLM चा Context Window हा Linear आहे — तो विसरतो. पण पुराणातील काल-चक्र (Cyclical Time) हे Hierarchical Memory Architecture सुचवते.

Large Language Models च्या Context Window मध्ये संवाद वाढला की जुना संदर्भ कापला जातो — परिणामी Context Loss, Hallucination, State Drift. समस्या हार्डवेअरची नाही — समस्या Linear Memory Architecture ची आहे.

# काल-चक्र Memory System | Branch 1 | Post 11
from collections import deque
from typing import List, Dict

class KalaChakraMemory:
    """Hierarchical Vedic Memory Architecture"""

    def __init__(self):
        # 4-layer Hierarchical Memory
        self.nimesha   = deque(maxlen=5)    # निमेष — Working RAM
        self.dina      = deque(maxlen=20)   # दिन — Session Cache
        self.yuga      = []                 # युग — Long-term Store
        self.mahakalpa: Dict = {}           # महाकल्प — Core Weights

    def perceive(self, data: str, importance: float):
        """नवीन Input — Layer निर्णय"""
        self.nimesha.append(data)
        if importance > 0.5:
            self.dina.append(data)
        if importance > 0.8:
            self.yuga.append(data)
        if importance == 1.0:
            key = str(hash(data))[:6]
            self.mahakalpa[key] = data
        print(f"📥 Stored: '{data}' (importance={importance})")

    def pralaya(self):
        """प्रलय — Selective Reset (Core survives)"""
        self.nimesha.clear()
        self.dina.clear()
        self.yuga = []
        print(f"🔄 Pralaya! Core preserved: {len(self.mahakalpa)} entries")

    def status(self):
        print(f"\n🧠 Memory Status:")
        print(f"  निमेष (RAM):    {list(self.nimesha)}")
        print(f"  दिन (Cache):    {list(self.dina)}")
        print(f"  युग (LTM):      {self.yuga}")
        print(f"  महाकल्प (Core): {self.mahakalpa}")

# --- Demo ---
mem = KalaChakraMemory()
mem.perceive("user_greeting",    0.3)
mem.perceive("user_preference",  0.7)
mem.perceive("core_dharma",      1.0)
mem.status()
mem.pralaya()
mem.status()