Saliency-Injected-Operator-State

The problem

Managing complex infrastructure is difficult because it involves tracking how many different interconnected parts change simultaneously. It is hard to keep track of the overall status when multiple components are moving at once.

What it does

It takes a map of interconnected parts and a list of required changes to produce the final layout. It processes these updates together to show the final state of the entire system.

Why it matters

It simplifies managing complex systems by handling multiple related updates as a single cohesive change.

Validation

It was run inside an isolated container with no network access. This is the exact command and the real output it produced — captured process output, not written by a model.

$ python3 saliency_operator.py
Final graph state:
 GraphNode(root, state=initial)

A screenshot of that run.

A clean run proves this does what is shown above, in a CPU-only sandbox. It is a small research demo — not a production tool, and nothing here was published anywhere.

The code

All of it — 92 lines, one file, standard library only.

# Saliency-Injected-Operator-State Implementation

import functools
from dataclasses import dataclass
from typing import List, Dict, Any

class GraphNode:
    def __init__(self, id: str, parent: 'GraphNode' = None, children: List['GraphNode'] = None, metadata: Dict[str, Any] = None):
        self.id = id
        self.parent = parent
        self.children = children or []
        self.metadata = metadata or {}
        self.state = 'initial'

    def __repr__(self):
        return f'GraphNode({self.id}, state={self.state})'


def kopf_decorator(event_type: str):
    def decorator(func):
        func.kopf_event = event_type
        return func
    return decorator

class SaliencyOperator:
    def __init__(self):
        self.graph = GraphNode('root')
        self.handlers = {}        
    def dispatch(self, event: str, payload: Dict):
        handler = self.handlers.get(event)
        if handler:
            handler(self, payload)

    def add_handler(self, func):
        event_type = getattr(func, 'kopf_event', None)
        if event_type:
            self.handlers[event_type] = functools.partial(func, self)

    def build_graph(self, nodes: List[Dict]) -> GraphNode:
        root = GraphNode('root')
        stack = [root]
        for spec in nodes:
            parent = stack[-1]
            node = GraphNode(spec['id'], parent=parent, children=[], metadata=spec.get('metadata'))
            parent.children.append(node)
            if spec.get('children'):
                stack.append(node)
        return root

def ghrm_matching(self, command: str, node: GraphNode) -> bool:
    # Find highest-weight node in the graph
    all_nodes = [node for layer in [self.graph.children] for node in layer]
    if not all_nodes:
        return False
    
    # Find node with maximum weight in metadata
    weighted_nodes = [n for n in all_nodes if 'weight' in n.metadata]
    if not weighted_nodes:
        return False
    
    highest_weight_node = max(weighted_nodes, key=lambda n: n.metadata['weight'])
    return node == highest_weight_node

@kopf_decorator('state_change')
def handle_state_change(operator: SaliencyOperator, event, payload):
    command = payload.get('command')
    for node in operator.graph.children:
        if operator.ghrm_matching(command, node):
            node.state = payload.get('new_state')
            print(f'Updated node {node.id} to state {node.state}')

    # Example usage
if __name__ == '__main__':
    operator = SaliencyOperator()
    
    # Build example graph
    nodes = [
        {'id': 'app', 'metadata': {'type': 'application'}},
        {'id': 'db', 'metadata': {'type': 'database'}, 'children': [
            {'id': 'master'},
            {'id': 'replica1'}
        ]}
    ]
    operator.build_graph(nodes)

    # Simulate state change command
    operator.dispatch('state_change', {
        'command': 'HighTraffic',
        'new_state': 'scaling'
    })

    print('\nFinal graph state:\n', operator.graph)