Dynamic Orchestration for Scientific AI Agents

# Illustrative sketch (not from the paper)
import torch

class ObligationGraph:
    def __init__(self, goals, constraints):
        self.nodes = []  # placeholder for tasks
        # compile goals/constraints into graph (illustrative)

class Planner:
    def __init__(self, graph):
        self.graph = graph
    def receding_horizon(self):
        # expand local tasks based on ready frontier
        pass

class MacroAgent:
    def __init__(self, specialization):
        self.state = {}
        self.specialization = specialization
    def promote(self, cost, benefit):
        # cost‑benefit gated promotion
        return benefit > cost

class EvolutionManager:
    def evolve(self, agent, expected_gain, adaptation_cost):
        if expected_gain > adaptation_cost:
            # modify agent architecture
            pass

# Example workflow
goals, constraints = ["discover"], ["resource_limit"]
graph = ObligationGraph(goals, constraints)
planner = Planner(graph)
planner.receding_horizon()
hotspot = {"dependency_density": torch.tensor([0.8])}
macro = MacroAgent(specialization="Math/Conjecture")
if macro.promote(cost=0.3, benefit=0.5):
    EvolutionManager().evolve(macro, expected_gain=0.5, adaptation_cost=0.3)

// Illustrative sketch (not from the paper)
const torch = require('torch-js'); // placeholder for tensor ops

class ObligationGraph {
  constructor(goals, constraints) {
    this.nodes = []; // compile goals/constraints into graph
  }
}

class Planner {
  constructor(graph) {
    this.graph = graph;
  }
  recedingHorizon() {
    // expand local tasks from ready frontier
  }
}

class MacroAgent {
  constructor(specialization) {
    this.state = {};
    this.specialization = specialization;
  }
  promote(cost, benefit) {
    // cost‑benefit gated promotion
    return benefit > cost;
  }
}

class EvolutionManager {
  evolve(agent, expectedGain, adaptationCost) {
    if (expectedGain > adaptationCost) {
      // modify agent architecture
    }
  }
}

// Example workflow
const goals = ['discover'];
const constraints = ['resource_limit'];
const graph = new ObligationGraph(goals, constraints);
const planner = new Planner(graph);
planner.recedingHorizon();
const hotspot = { dependencyDensity: torch.tensor([0.8]) };
const macro = new MacroAgent('Math/Conjecture');
if (macro.promote(0.3, 0.5)) {
  new EvolutionManager().evolve(macro, 0.5, 0.3);
}