mrorigo

"""
Stateless Orchestration of Code-Implemented Tool Calls in LLMs
Implementation of US-12670045-B1 in Python using the OpenAI SDK.

This system implements a stateless, resumable code execution architecture to orchestrate
multiple complex tool calls, both sequential and parallel, generated by an LLM (OpenAI).
By utilizing a deterministic evaluation stack (replay cache) and client-side execution redirection,
it avoids bloating the LLM's context window with intermediate results.

Patent Claim Alignment:

mrorigo

from __future__ import annotations

import logging
import os
import sys
from functools import lru_cache
from typing import Any, Dict, List, Literal, Union

from fastapi import Depends, FastAPI, Header, HTTPException, Request
from fastapi.exceptions import RequestValidationError

mrorigo

import torch
import torch.nn as nn
import torch.nn.functional as F

class SoftMultiTokenEmbedding(nn.Module):
    """
    A MultiToken embedding layer that performs soft merging of adjacent tokens
    without requiring attention mask modifications.
    """
    def __init__(self, vocab_size, embedding_dim, dropout_rate=0.1):

mrorigo

/**
 * Copyright (C) 2022 orIgo <mrorigo@gmail.com>
 *
 * Cycle exact 6502 (2A03) emulator.
 *
 * - To initialize the cpu, provide an initialized `cpu_bus_t *` with memory read/write primitives to `cpu_init()` .
 * - For each tick/cycle, call `cpu_tick()` with the returned `cpu_t *`, checking the return value to detect HLT.
 * - To cause an NMI request, call `cpu_nmi()`.
 */
#include "cpu.h"

mrorigo

import java.util.LinkedList;
import java.nio.charset.Charset;
import java.io.IOException;
import java.io.OutputStream;
import java.io.FileOutputStream;

public class Log4Simple
{
    public enum Level {

mrorigo

%% Double Cola problem in Erlang
%% https://codeforces.com/problemset/problem/82/A

-module(main).
-export([start/1]).

start(N) ->
  io:fwrite(
    cola(N-1, [ {"Sheldon", 1}, 
                {"Leonard", 1},