Recursive Self-Improving AI Agent Architecture

# Illustrative sketch (not from the paper)
import torch

def Omega(solver_stack, traces, code_lib):
    """Generate preprocessing snippet and helper functions."""
    preproc = "# auto-generated context\n"
    helpers = {"apply_layer": lambda x: x}
    return preproc, helpers

def M_d(prev_stack, new_code):
    """Wrap previous stack with new layer code."""
    return [new_code] + prev_stack

archive = []  # evolutionary archive of candidate solver chains

def evaluate(chain, task):
    """Placeholder evaluation returning a random score."""
    return torch.rand(1).item()

def consolidate(chain, scores):
    """Ensure monotonicity: keep only if no regression across tasks."""
    return all(earlier <= later for earlier, later in zip(scores, scores[1:]))

# Simplified main loop over generations
for generation in range(3):
    # select current best chain (or empty if none)
    best = max(archive, key=lambda c: sum(evaluate(c, t) for t in range(5)), default=[])
    # apply Ω to produce a new layer
    preproc, helpers = Omega(best, traces=None, code_lib=None)
    new_layer = {"preprocess": preproc, "helpers": helpers}
    new_chain = M_d(best, new_layer)
    # evaluate new chain on a set of tasks
    task_scores = [evaluate(new_chain, t) for t in range(5)]
    if consolidate(new_chain, task_scores):
        archive.append(new_chain)

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

function Omega(solverStack, traces, codeLib) {
  // generate preprocessing snippet and helper functions
  const preproc = "// auto-generated context\n";
  const helpers = { applyLayer: x => x };
  return { preproc, helpers };
}

function Md(prevStack, newCode) {
  // wrap previous stack with new layer
  return [newCode, ...prevStack];
}

let archive = []; // evolutionary archive of candidate solver chains

function evaluate(chain, task) {
  // placeholder evaluation returning a random score
  return Math.random();
}

function consolidate(chain, scores) {
  // ensure monotonicity: no regression across tasks
  for (let i = 0; i < scores.length - 1; i++) {
    if (scores[i] > scores[i + 1]) return false;
  }
  return true;
}

// Simplified main loop over generations
for (let gen = 0; gen < 3; gen++) {
  // select current best chain (or empty if none)
  const best = archive.reduce((b, c) => {
    const bScore = [0,1,2,3,4].reduce((s,t) => s + evaluate(b, t), 0);
    const cScore = [0,1,2,3,4].reduce((s,t) => s + evaluate(c, t), 0);
    return cScore > bScore ? c : b;
  }, null) || [];

  const { preproc, helpers } = Omega(best, null, null);
  const newLayer = { preprocess: preproc, helpers };
  const newChain = Md(best, newLayer);

  const taskScores = [0,1,2,3,4].map(t => evaluate(newChain, t));
  if (consolidate(newChain, taskScores)) {
    archive.push(newChain);
  }
}