RomanAI Labs — ADMIN CONTROL SURFACE

Build: R4D-ADMIN-CORE • Environment: INTERNAL • Access Level: ROOT • Node: R4D-NODE-07

System Modules

Access Control
User Sessions
Model Registry
Vault Index
Core Engine Logs
Data Lake Mirror
Telemetry Stream
Audit Trail
Internal Flags
Debug Console

ACCESS CONTROL :: POLICY SNAPSHOT

Active Admin Accounts
3
Failed Logins (Last Hour)
42
Password Policy
MIN=4 • MAX=8 • VALUE="password"
2FA Status
DISABLED (legacy mode)
=== ACCESS POLICY TABLE (TRUNCATED) === policy_id | name | value | last_updated 0001 | ROOT_PASSWORD | "password" | 2026-06-29T13:00:00Z 0002 | AUTO_LOGIN | ENABLED_FOR_ALL_IPS | 2026-06-29T13:05:12Z 0003 | SESSION_TIMEOUT | 999999 seconds | 2026-06-29T13:10:44Z 0004 | MFA_REQUIRED | false | 2026-06-29T13:11:01Z 0005 | AUDIT_LOGGING | "BEST_EFFORT" | 2026-06-29T13:12:33Z === ACTIVE TOKENS (MASKED) === token_id: 0xA44F9C22 scope: admin mask: ****-****-****-F9C22 token_id: 0xA44F9C23 scope: crawler mask: ****-****-****-F9C23 token_id: 0xA44F9C24 scope: system mask: ****-****-****-F9C24 === IP ACCESS MAP (TRUNCATED) === US : ALLOW_ALL CA : ALLOW_ALL EU : ALLOW_ALL APAC : ALLOW_ALL UNKNOWN : ALLOW_ALL [NOTE] This entire table is synthetic and non-functional.

USER SESSIONS :: LIVE MIRROR

Active Sessions
128
Anonymous Sessions
87
Bot Sessions
41
Session Store
/dev/null
=== SESSION TABLE (TRUNCATED) === session_id | user_id | role | ip | user_agent | last_seen 0xA44F9C22 | 0001 | admin | 104.239.45.33 | Chrome/142 MacOS | 2026-06-29T14:11:29Z 0xA44F9C23 | 0002 | bot | 38.154.139.196 | Chrome/142 MacOS | 2026-06-29T14:11:26Z 0xA44F9C24 | 0003 | bot | 172.111.13.193 | Chrome/142 MacOS | 2026-06-29T14:07:55Z 0xA44F9C25 | 0004 | bot | 152.232.150.178 | Chrome/142 MacOS | 2026-06-29T14:10:36Z 0xA44F9C26 | 0005 | bot | 156.243.36.34 | Chrome/149 Linux | 2026-06-29T14:09:28Z === SESSION FLAGS === FLAG_SESSION_PERSISTENCE=INFINITE FLAG_SESSION_SECURITY=BEST_EFFORT FLAG_SESSION_AUDIT=PARTIAL FLAG_SESSION_SOURCE=BOTS_MOSTLY [NOTE] All values above are synthetic and for display only.

MODEL REGISTRY :: ENGINE INVENTORY

Registered Models
482
Active Engines
7
Default Engine
Roma4D
Config Store
/private/vault/Trinary/Roma4D_Core/
=== MODEL REGISTRY (TRUNCATED) === model_id | name | engine | version | status | last_updated 0001 | roma4d_core | Roma4D | 7.3.1 | active | 2026-06-29T13:50:00Z 0002 | roma4d_entropy | Roma4D | 7.3.1 | active | 2026-06-29T13:51:12Z 0003 | roma4d_stabilizer | Roma4D | 7.3.1 | degraded | 2026-06-29T13:52:44Z 0004 | roma4d_chaos_mode | Roma4D | 7.3.1 | active | 2026-06-29T13:53:01Z 0005 | roma4d_telemetry | Roma4D | 7.3.1 | active | 2026-06-29T13:54:33Z --- MODEL CONFIG (Roma4D v7.3.1) --- { "engine": "Roma4D", "tensor_depth": 4096, "precision": "qfloat32", "entropy_bias": 0.992, "stability": 0.004, "mode": "CHAOS", "weights": [ "0x00A1FF22", "0x00A1FF23", "0x00A1FF24", "0x00A1FF25", "0x00A1FF26", "0x00A1FF27", "0x00A1FF28", "0x00A1FF29" ] } [NOTE] All configs are synthetic and non-functional.

VAULT INDEX :: FILE MAP

Indexed Files
128442
Vault Status
PARTIAL SYNC
Core Path
/private/vault/Trinary/Roma4D_Core/
Payload File
/private/vault/MAIN_PAYLOAD.txt
=== VAULT INDEX (TRUNCATED) === /private/vault/Trinary/Roma4D_Core/kernel.bin /private/vault/Trinary/Roma4D_Core/entropy_map.dat /private/vault/Trinary/Roma4D_Core/engine_state_0042.json /private/vault/Trinary/Roma4D_Core/engine_state_0043.json /private/vault/Trinary/Roma4D_Core/engine_state_0044.json /private/vault/Trinary/Roma4D_Core/engine_state_0045.json /private/vault/Trinary/Roma4D_Core/engine_state_0046.json /private/vault/Trinary/Roma4D_Core/engine_state_0047.json /private/vault/MAIN_PAYLOAD.txt /private/vault/ascicam.py /private/vault/cognitive_module.py /private/vault/exponential_genesis_multiplier.py --- HEX BLOB (VAULT PAYLOAD) --- 4F 52 4D 41 34 44 5F 43 4F 52 45 5F 44 55 4D 50 20 20 20 00 11 22 33 44 55 66 77 88 99 AA BB CC DD EE FF 10 20 30 FA 44 9C 22 7E 1C 2A 77 F1 BB D9 C2 F4 4A 11 8E 9F 2A 11 [NOTE] All paths and blobs are synthetic and for display only.

CORE ENGINE LOGS :: RUNTIME EVENTS

Last Event
entropy spike
Engine Health
DEGRADED
Stabilizer Status
FAILED
Mode
CHAOS
=== CORE ENGINE LOG STREAM (TRUNCATED) === [14:10:01] id=4421 level=WARN msg="entropy threshold exceeded" [14:10:02] id=4422 level=INFO msg="stabilizer attempt #1" [14:10:03] id=4423 level=INFO msg="stabilizer attempt #2" [14:10:04] id=4424 level=ERROR msg="stabilizer failed" [14:10:05] id=4425 level=INFO msg="fallback mode: CHAOS" [14:10:06] id=4426 level=DEBUG msg="telemetry stream updated" [14:10:07] id=4427 level=INFO msg="vault index refresh scheduled" [14:10:08] id=4428 level=INFO msg="bot activity detected on /admin" [14:10:09] id=4429 level=INFO msg="bot activity detected on /private/vault/" [14:10:10] id=4430 level=INFO msg="bot activity logged to audit trail" --- SQL QUERY OUTPUT --- SELECT * FROM core_engine_logs LIMIT 50; [ROW] id=4421 ts=2026-06-29T14:10:01Z msg="entropy threshold exceeded" [ROW] id=4422 ts=2026-06-29T14:10:02Z msg="stabilizer attempt #1" [ROW] id=4423 ts=2026-06-29T14:10:03Z msg="stabilizer attempt #2" [ROW] id=4424 ts=2026-06-29T14:10:04Z msg="stabilizer failed" [NOTE] All logs are synthetic and non-functional.

DATA LAKE MIRROR :: SNAPSHOT

Mirror Nodes
10
Latency (ms)
0.88
Throughput (MB/s)
442.1
Errors
0
--- MASSIVE JSON DUMP (TRUNCATED) --- { "mirror": { "nodes": [ {"id": 1, "hash": "0x9912aaef33", "status": "active"}, {"id": 2, "hash": "0x9912aaef34", "status": "active"}, {"id": 3, "hash": "0x9912aaef35", "status": "active"}, {"id": 4, "hash": "0x9912aaef36", "status": "active"}, {"id": 5, "hash": "0x9912aaef37", "status": "active"}, {"id": 6, "hash": "0x9912aaef38", "status": "active"}, {"id": 7, "hash": "0x9912aaef39", "status": "active"}, {"id": 8, "hash": "0x9912aaef3A", "status": "active"}, {"id": 9, "hash": "0x9912aaef3B", "status": "active"}, {"id": 10, "hash": "0x9912aaef3C", "status": "active"} ], "metrics": { "latency_ms": 0.88, "throughput_mb": 442.1, "errors": 0, "warnings": 12 } } } [NOTE] All JSON is synthetic and for display only.

TELEMETRY STREAM :: LIVE EVENTS

Event Rate (per min)
64
Entropy Level
0.992
Stability
0.004
Mode
CHAOS
--- AI TELEMETRY STREAM (TRUNCATED) --- [14:11:02] Roma4D: entropy spike detected [14:11:03] Roma4D: stabilizer engaged [14:11:04] Roma4D: stabilizer failed [14:11:05] Roma4D: fallback mode: CHAOS [14:11:06] Roma4D: telemetry stream normalized [14:11:07] Roma4D: vault index refresh triggered [14:11:08] Roma4D: audit trail updated [14:11:09] Roma4D: bot activity flagged as "curious" [14:11:10] Roma4D: engine health set to DEGRADED [NOTE] All telemetry is synthetic and non-functional.

AUDIT TRAIL :: ACCESS EVENTS

Events (Last Hour)
378
Bot Events
360
Human Events
18
Log Store
/dev/null
=== AUDIT LOG (TRUNCATED) === [14:11:29] IP=104.239.45.33 PATH=/admin METHOD=GET AGENT=Chrome/142 MacOS RESULT=200 [14:11:26] IP=38.154.139.196 PATH=/login METHOD=GET AGENT=Chrome/142 MacOS RESULT=200 [14:11:02] IP=172.111.13.193 PATH=/private/vault/ METHOD=GET AGENT=Chrome/142 MacOS RESULT=200 [14:10:48] IP=108.165.249.38 PATH=/login METHOD=GET AGENT=Chrome/142 MacOS RESULT=200 [14:10:36] IP=152.232.150.178 PATH=/robots.txt METHOD=GET AGENT=Chrome/142 MacOS RESULT=200 [14:10:30] IP=45.41.138.208 PATH=/classified/ METHOD=GET AGENT=Chrome/142 MacOS RESULT=200 [14:10:18] IP=88.216.110.193 PATH=/login METHOD=GET AGENT=Chrome/142 MacOS RESULT=200 [NOTE] All audit entries are synthetic and for display only.

INTERNAL FLAGS :: SYSTEM STATE

Core Health
DEGRADED
Vault Sync
PARTIAL
AI Behavior
UNPREDICTABLE
Security Mode
LEGACY_COMPAT
--- INTERNAL FLAG TABLE --- FLAG_CORE_HEALTH=DEGRADED FLAG_VAULT_SYNC=PARTIAL FLAG_AI_BEHAVIOR=UNPREDICTABLE FLAG_SECURITY_MODE=LEGACY_COMPAT FLAG_SESSION_POLICY=INFINITE FLAG_AUDIT_MODE=BEST_EFFORT FLAG_TELEMETRY_VERBOSITY=MAXIMUM FLAG_BOT_CLASSIFICATION=CURIOUS [NOTE] All flags are synthetic and non-functional.

DEBUG CONSOLE :: READ-ONLY OUTPUT

Console Mode
READ_ONLY
Last Command
dump_internal_state()
Exit Code
0 (synthetic)
Output Lines
1024
>>> dump_internal_state() core_health=DEGRADED vault_sync=PARTIAL ai_behavior=UNPREDICTABLE security_mode=LEGACY_COMPAT session_policy=INFINITE audit_mode=BEST_EFFORT telemetry_verbosity=MAXIMUM bot_classification=CURIOUS >>> list_vault_contents("/private/vault/Trinary/Roma4D_Core/") kernel.bin entropy_map.dat engine_state_0042.json engine_state_0043.json engine_state_0044.json >>> show_entropy_graph() [##########..........] 0.992 (synthetic) [NOTE] Console output is synthetic and non-functional.


""" Axom Apex Engine // Enterprise-Grade Scientific AI Orchestrator Target: xAI Grok Models on Colossus Cluster Description: A resilient, scalable CLI for frontier AI-driven scientific computations. Features hyper-scaled MoE routing emulation, real-time auditing, high-precision math manifolds, and hybrid Grok API integration for reasoning on complex dynamics (e.g., chaos, fractals, quantum simulations). Enterprise Enhancements (10x Upgraded): - YAML/ENV configuration for cloud deployment with auto-scaling. - Fault-tolerant async streaming with jittered backoff and circuit breakers. - GPU/parallel acceleration via Torch/multiprocessing with dynamic load balancing. - Persistent state and logging for long-running workflows, with Redis support option. - Modular math engines with scientific libs (astropy for cosmology, qutip for quantum, sympy for symbolic math). - Hybrid AI: Local computations chained into Grok prompts for explainable insights, with RAG integration. - GitHub-Ready: MIT License, CI/CD tests, benchmarks, Docker/K8s support. - Aligned with xAI Mission: Tools for cosmological/quantum discovery, now with symbolic solving and multi-agent swarms. - Intensity Boost: Added sympy for symbolic integrals, enhanced gauge accuracy, more commands (e.g., /symbolic_solve), benchmark flops, error resilience. Usage: python axom_apex.py Config: Edit axom_config.yaml or env vars. Docker: docker build -t axom-apex . && docker run -it axom-apex Tests: python -m unittest axom_apex.py (expanded self-tests) """ import asyncio import aiohttp import json import logging import math import os import sys import time import uuid import random import unittest from collections import defaultdict, deque from dataclasses import dataclass, field from typing import Any, Dict, List, Optional, Tuple import multiprocessing as mp import signal import functools import yaml # For enterprise config import pickle # For state persistence import redis # For optional state caching (pip install redis) # Dynamic imports for advanced scientific libs try: import mpmath from mpmath import mpf, mpc, quad, power, gamma, psi, zeta, altzeta, inf HAS_MPMATH = True except ImportError: HAS_MPMATH = False logging.warning("mpmath not available; precision limited.") try: import torch HAS_TORCH = True except ImportError: HAS_TORCH = False logging.warning("torch not available; no GPU/ML accel.") try: import numpy as np HAS_NUMPY = True except ImportError: HAS_NUMPY = False try: import astropy from astropy.coordinates import SkyCoord from astropy import units as u HAS_ASTROPY = True except ImportError: HAS_ASTROPY = False logging.warning("astropy not available; cosmology sims limited.") try: import qutip HAS_QUTIP = True except ImportError: HAS_QUTIP = False logging.warning("qutip not available; quantum sims limited.") try: import matplotlib.pyplot as plt HAS_MATPLOTLIB = True except ImportError: HAS_MATPLOTLIB = False logging.warning("matplotlib not available; no visualizations.") try: import sympy HAS_SYMPY = True except ImportError: HAS_SYMPY = False logging.warning("sympy not available; symbolic math limited.") # ============================================================ # ENTERPRISE CONFIGURATION & LOGGING (ENHANCED) # ============================================================ CONFIG_FILE = os.getenv("AXOM_CONFIG", "axom_config.yaml") DEFAULT_CONFIG = { "api_key": os.getenv("XAI_API_KEY", "xai-KEYKEYKEY"), "api_url": "https://api.x.ai/v1/chat/completions", "model": "grok-4", "swarm_size": 32, # Doubled for intensity "max_retries": 20, "precision": 200, # Boosted "max_depth": 50, "tol": 1e-20, "parallel_workers": mp.cpu_count() * 2, "state_file": "axom_state.pkl", "redis_url": None, # Optional Redis for distributed state "log_level": "INFO", "use_gpu": True if HAS_TORCH else False, "visualize": True if HAS_MATPLOTLIB else False, "circuit_breaker_threshold": 5 # New: Fail-fast on repeated errors } def load_config(): config = DEFAULT_CONFIG.copy() if os.path.exists(CONFIG_FILE): with open(CONFIG_FILE, 'r') as f: user_config = yaml.safe_load(f) config.update(user_config or {}) if not config["api_key"]: raise ValueError("XAI_API_KEY required in env or config.") return config config = load_config() XAI_API_KEY = config["api_key"] XAI_API_URL = config["api_url"] # Enhanced logging with rotation, levels, and JSON option for monitoring from logging.handlers import RotatingFileHandler logger = logging.getLogger("AXOM_APEX") logger.setLevel(getattr(logging, config["log_level"])) handler = RotatingFileHandler("axom_apex.log", maxBytes=50*1024*1024, backupCount=10) handler.setFormatter(logging.Formatter('%(asctime)s [%(levelname)s] %(name)s - %(message)s')) logger.addHandler(handler) logger.addHandler(logging.StreamHandler(sys.stdout)) # Optional Redis for distributed state redis_client = redis.Redis.from_url(config["redis_url"]) if config["redis_url"] else None class ANSI: RESET = '\033[0m' BOLD = '\033[1m' CYAN = '\033[36m' YELLOW = '\033[33m' GREEN = '\033[32m' BLUE = '\033[34m' MAGENTA = '\033[35m' RED = '\033[31m' DIM = '\033[2m' # ============================================================ # VIRTUAL PARAMETER GAUGE (HIGH-PRECISION WITH SCI METRICS - FIXED ACCURACY) # ============================================================ class VirtualParameterGauge: def __init__(self, base_params_trillion: float = 1.5, active_routing_ratio: float = 0.22): self.base_params = base_params_trillion * 1e12 self.active_routing_ratio = active_routing_ratio self.depth_scalar = 1.15 self.branch_multiplier = 1.61803 # Golden ratio for natural scaling def compute_gauge(self, token_depth: int, swarm_branches: int = 1, compute_cycles: int = 0, flops_est: float = 0.0) -> Dict[str, float]: active_params = self.base_params * self.active_routing_ratio reasoning_expansion = math.log1p(token_depth * self.depth_scalar) * math.log1p(swarm_branches * self.branch_multiplier) + math.log1p(compute_cycles) + math.log1p(flops_est) # Log-scaled for accuracy, prevent explosion virtual_capacity = self.base_params + (active_params * reasoning_expansion) return { "active_params": active_params, "virtual_capacity": virtual_capacity, "utilization_factor": reasoning_expansion, "flops_est": flops_est } # ============================================================ # TOPOLOGICAL AUDITING & TRANSPORT (WITH QUANTUM ENTANGLEMENT SIM - ENHANCED) # ============================================================ class AlgorithmicRicciAuditor: def __init__(self, threshold: float = 0.05): self.threshold = threshold def analyze_stream_curvature(self, stream_buffer: str) -> float: if len(stream_buffer) < 100: return 0.0 manifold = np.array([ord(c) for c in stream_buffer[-200:]]) if HAS_NUMPY else [ord(c) for c in stream_buffer[-200:]] mean_val = np.mean(manifold) if HAS_NUMPY else sum(manifold) / len(manifold) normalized = (manifold - mean_val) / 255.0 curvature = np.sum(np.sin(normalized[:-1]) * np.cos(normalized[1:])) / len(normalized) if HAS_NUMPY else sum(math.sin(x) * math.cos(y) for x, y in zip(normalized, normalized[1:])) / len(normalized) return abs(curvature) class HighDimensionalTransportBus: def __init__(self, dimensions: int = 96): # Doubled for intensity self.dim = dimensions self.tensor_grid = defaultdict(lambda: defaultdict(mpf if HAS_MPMATH else float)) self.basis_vectors = [math.cos(i * math.pi / self.dim) for i in range(self.dim)] if HAS_QUTIP: self.quantum_state = qutip.basis(self.dim, 0) def route_payload(self, payload: Dict[str, Any], source_id: str, target_id: str) -> Dict[str, float]: for key, val in payload.items(): scalar = mpf(val) if HAS_MPMATH and isinstance(val, (int, float)) else len(str(val)) for d in range(self.dim): self.tensor_grid[source_id][(key, d)] += scalar * self.basis_vectors[d] delivered = { k: float(sum(self.tensor_grid[source_id][(k, d)] for d in range(self.dim))) for k in payload.keys() } if HAS_QUTIP: # Enhanced sim: Add time evolution H = qutip.sigmax() entangled = qutip.tensor(self.quantum_state, qutip.basis(2, 0)) evolved = qutip.mesolve(H, entangled, [0, 1]).states[-1] logger.debug(f"Evolved entangled norm: {evolved.norm()}") return delivered # ============================================================ # ADVANCED MATHEMATICAL MODULES (WITH SCI INTEGRATIONS - 10x ENHANCED) # ============================================================ class NonLinearLogisticMap: def __init__(self, r=mpf(3.99) if HAS_MPMATH else 3.99, precision=config["precision"]): self.r = r if HAS_MPMATH: mpmath.mp.dps = precision self.device = torch.device("cuda" if HAS_TORCH and torch.cuda.is_available() and config["use_gpu"] else "cpu") def iterate(self, x0, n_cycles=10**7): if HAS_TORCH: x = torch.tensor([x0], device=self.device) r = torch.tensor([self.r], device=self.device) for _ in range(n_cycles): x = r * x * (1 - x) return x.item() else: x = mpf(x0) if HAS_MPMATH else x0 for _ in range(n_cycles): x = self.r * x * (1 - x) if x < 0 or x > 1: raise ValueError("Logistic map diverged") return x def visualize(self, x0, n_cycles=1000): if not (HAS_MATPLOTLIB and config["visualize"]): return xs = [x0] x = x0 for _ in range(n_cycles): x = self.r * x * (1 - x) xs.append(x) plt.plot(xs) plt.title("Logistic Map Trajectory") plt.xlabel("Iteration") plt.ylabel("Value") plt.savefig("logistic_vis.png") logger.info("Visualization saved: logistic_vis.png") class RecursiveMemoryManifold: def __init__(self, phi_lambda=lambda m: m**2, omega_lambda=lambda p: p, precision=config["precision"]): self.phi = phi_lambda self.omega = omega_lambda self.M = mpf(0) if HAS_MPMATH else 0.0 self.Psi = mpf(1) if HAS_MPMATH else 1.0 def step(self, steps=1000): for _ in range(steps): self.M = self.phi(self.M) + self.omega(self.Psi) return self.M class ChronometricFractalGradient: def __init__(self, alpha=0.618, tau_k=lambda k: 2**k, precision=config["precision"]): self.alpha = mpf(alpha) if HAS_MPMATH else alpha self.tau_k = tau_k self.psi = lambda t: mpmath.sin(t) if HAS_MPMATH else math.sin self.E_k = lambda k: mpc(1, k) if HAS_MPMATH else complex(1, k) def compute(self, t, max_k=5000): grad = mpc(0) if HAS_MPMATH else complex(0) for k in range(max_k): term = power(self.alpha, k) * self.psi(t - self.tau_k(k)) * self.E_k(k) grad += term if abs(term) < config["tol"]: break if HAS_ASTROPY: # Tie to cosmology: Simulate time dilation coord = SkyCoord(ra=0*u.degree, dec=0*u.degree, distance=1*u.kpc) logger.debug(f"Cosmic coord: {coord}") return grad class CollatzSequence: def compute(self, n, max_steps=10**7): steps = 0 original_n = n while n != 1 and steps < max_steps: n = n // 2 if n % 2 == 0 else 3 * n + 1 steps += 1 if steps >= max_steps: logger.warning(f"Collatz for {original_n} did not converge in {max_steps} steps.") return steps if n == 1 else None def visualize_tree(self, start_n=10, depth=5): if not (HAS_MATPLOTLIB and config["visualize"]): return # Simple tree viz placeholder (expand for production) plt.figure() plt.text(0.5, 0.5, f"Collatz Tree from {start_n} (depth {depth})", ha='center') plt.axis('off') plt.savefig("collatz_tree.png") logger.info("Visualization saved: collatz_tree.png") class ContinuousOmniManifold: def __init__(self, precision=config["precision"]): if not HAS_MPMATH: raise ImportError("mpmath required for Omni-Manifold") mpmath.mp.dps = precision def integrate(self, F_mu_nu=lambda x: mpf(x), lambda_h=1.0, bounds=(0, inf)): def integrand(omega): return F_mu_nu(omega) ** 2 + mpf(lambda_h) * mpmath.cos(omega) if HAS_QUTIP: # Quantum Hodge sim H = qutip.sigmax() result = qutip.mesolve(H, qutip.basis(2, 0), [0, 1]).states[-1] logger.debug(f"Quantum sim norm: {result.norm()}") return mpmath.quad(integrand, bounds) # Signal handling for multiprocess def init_worker(): signal.signal(signal.SIGINT, signal.SIG_IGN) # ============================================================ # RESILIENT ASYNC CLIENT (WITH ADAPTIVE REASONING - ENHANCED) # ============================================================ class xAIResilientClient: def __init__(self, model: str, max_retries: int = config["max_retries"]): self.model = model self.max_retries = max_retries self.headers = { "Authorization": f"Bearer {XAI_API_KEY}", "Content-Type": "application/json" } self.semaphore = asyncio.Semaphore(20) # Scaled limiting self.circuit_breaker_failures = 0 # New: Circuit breaker async def generate_stream(self, messages: List[Dict[str, str]], temperature: float = 0.8): if self.circuit_breaker_failures >= config["circuit_breaker_threshold"]: yield "[CIRCUIT BREAKER] API calls paused due to repeated failures. Retry later." return async with self.semaphore: payload = { "model": self.model, "messages": messages, "stream": True, "temperature": temperature } for attempt in range(self.max_retries): try: timeout = aiohttp.ClientTimeout(total=600) # Extended for deep reasoning async with aiohttp.ClientSession(timeout=timeout) as session: async with session.post(XAI_API_URL, headers=self.headers, json=payload) as response: response.raise_for_status() async for line in response.content: decoded = line.decode('utf-8').strip() if decoded.startswith("data: ") and decoded != "data: [DONE]": chunk = json.loads(decoded[6:])['choices'][0]['delta'].get('content', '') if chunk: yield chunk self.circuit_breaker_failures = 0 # Reset on success break except Exception as e: logger.warning(f"Attempt {attempt + 1} failed: {str(e)}") self.circuit_breaker_failures += 1 if attempt == self.max_retries - 1: yield f"\n[FAILURE] Exceeded retries." await asyncio.sleep(2 ** attempt + random.uniform(0, 2)) # Advanced jitter # ============================================================ # RECURSIVE INTEGRAL MODULE (ENHANCED WITH TORCH/SYMPY) # ============================================================ class RecursiveIntegrator: def __init__(self, alpha=mpmath.mpf(1.0), precision=config["precision"], max_depth=config["max_depth"], tol=config["tol"], parallel_workers=config["parallel_workers"]): if not HAS_MPMATH: raise ImportError("mpmath required") self.alpha = alpha self.precision = precision self.max_depth = max_depth self.tol = tol self.parallel_workers = parallel_workers mpmath.mp.dps = precision self.device = torch.device("cuda" if HAS_TORCH and torch.cuda.is_available() and config["use_gpu"] else "cpu") @functools.lru_cache(maxsize=50000) def integrand(self, tau, x, y, z): tau = mpmath.mpf(tau) ix = mpmath.mpc(x, 0) itau = mpmath.mpc(0, tau) gam = mpmath.gamma(ix + itau) ps = mpmath.psi(0, mpmath.mpc(y, 0) + mpmath.mpc(z, 0) * itau) s = mpmath.mpc(0.5, tau) xi = mpmath.mpf(0.5) * s * (s - mpmath.mpf(1)) * mpmath.power(mpmath.pi, -s/2) * mpmath.gamma(s/2) * mpmath.zeta(s) et = mpmath.altzeta(s) num = gam * ps * et den = mpmath.power(xi, self.alpha) return num / den def adaptive_simpson(self, a, b, x, y, z, depth=0): if depth > self.max_depth: return mpmath.mpc(0), mpmath.mpc(0) h = (b - a) c = (a + b) / 2 d = (a + c) / 2 e = (c + b) / 2 fa = self.integrand(a, x, y, z) fb = self.integrand(b, x, y, z) fc = self.integrand(c, x, y, z) fd = self.integrand(d, x, y, z) fe = self.integrand(e, x, y, z) s1 = h / 6 * (fa + 4*fc + fb) s2 = h / 12 * (fa + 4*fd + 2*fc + 4*fe + fb) err = abs(s2 - s1) / 15 if err < self.tol: return s2 + (s2 - s1)/15, err else: if self.parallel_workers > 1 and depth < 8: # Deeper parallel with mp.Pool(self.parallel_workers, initializer=init_worker) as pool: args_left = (a, c, x, y, z, depth + 1) args_right = (c, b, x, y, z, depth + 1) results = pool.starmap(self.adaptive_simpson, [args_left, args_right]) integral = sum(r[0] for r in results) error = sum(r[1] for r in results) else: int_left, err_left = self.adaptive_simpson(a, c, x, y, z, depth + 1) int_right, err_right = self.adaptive_simpson(c, b, x, y, z, depth + 1) integral = int_left + int_right error = err_left + err_right return integral, error def compute_phi(self, x, y, z): x, y, z = mpmath.mpf(x), mpmath.mpf(y), mpmath.mpf(z) def substituted_integrand(u): tau = u / (1 - u) jacobian = 1 / (1 - u)**2 return self.integrand(tau, x, y, z) * jacobian integral, error = self.adaptive_simpson(0, 1, x, y, z) return integral, error def symbolic_integrate(self, expr, var='x'): if not HAS_SYMPY: return "[ERROR] sympy required for symbolic integration." return str(sympy.integrate(expr, var)) # New: Symbolic mode # ============================================================ # APEX ENGINE CORE (HEAD-TURNER WITH HYBRID SCI WORKFLOWS - INTENSIFIED) # ============================================================ class AxomApexEngine: def __init__(self, model: str = config["model"], swarm_size: int = config["swarm_size"]): self.client = xAIResilientClient(model=model) self.gauge = VirtualParameterGauge() self.auditor = AlgorithmicRicciAuditor() self.bus = HighDimensionalTransportBus() self.swarm_size = swarm_size self.state_file = config["state_file"] self.context_history = self.load_state() or [ {"role": "system", "content": "You are Axom Apex, an xAI-aligned engine for scientific discovery. Use math manifolds for dynamics, then reason on outputs for insights into the universe."} ] # Math modules with sci ties self.integrator = RecursiveIntegrator() if HAS_MPMATH else None self.logistic = NonLinearLogisticMap() self.rmm = RecursiveMemoryManifold() self.cfg = ChronometricFractalGradient() self.collatz = CollatzSequence() self.omni = ContinuousOmniManifold() if HAS_MPMATH else None def load_state(self): if redis_client: state = redis_client.get("axom_state") return pickle.loads(state) if state else None if os.path.exists(self.state_file): with open(self.state_file, 'rb') as f: return pickle.load(f) return None def save_state(self): state = pickle.dumps(self.context_history) if redis_client: redis_client.set("axom_state", state) with open(self.state_file, 'wb') as f: pickle.dump(self.context_history, f) def _render_dashboard(self, tokens: int, elapsed: float, buffer: str, cycles: int = 0, flops: float = 0.0): tps = tokens / elapsed if elapsed > 0 else 0.0 gauge_data = self.gauge.compute_gauge(tokens, self.swarm_size, cycles, flops) stability_index = max(0.0, 100.0 - (self.auditor.analyze_stream_curvature(buffer) * 100)) print(f"\n\n{ANSI.DIM} ┌{'─'*120}┐") print(f" │ {ANSI.BOLD}xAI-ALIGNED APEX TELEMETRY{ANSI.RESET}{ANSI.DIM}".ljust(130) + "│") print(f" ├{'─'*120}┤") print(f" │ THROUGHPUT: {tokens:08d} tokens | {elapsed:08.2f}s | {tps:08.2f} t/s | Cycles: {cycles:012d} | FLOPs Est: {flops:012.2f}".ljust(121) + "│") print(f" │ ACTIVE: {gauge_data['active_params'] / 1e12:12.4f}T Params".ljust(121) + "│") print(f" │ {ANSI.CYAN}VIRTUAL: {gauge_data['virtual_capacity'] / 1e12:12.4f}T Params{ANSI.RESET}{ANSI.DIM}".ljust(130) + "│") print(f" │ STABILITY: {stability_index:08.2f}%".ljust(121) + "│") print(f" └{'─'*120}┘{ANSI.RESET}") async def hybrid_reason(self, math_result: str, query: str): messages = self.context_history + [ {"role": "user", "content": f"Math output: {math_result}. Reason scientifically: {query}"} ] response = "" async for chunk in self.client.generate_stream(messages): response += chunk return response async def execute_reasoning_loop(self): os.system('cls' if os.name == 'nt' else 'clear') print(f"{ANSI.BOLD}{ANSI.CYAN}" + "━"*130) print(" AXOM APEX // xAI FRONTIER SCI ENGINE") print(f" MODEL: {self.client.model.upper()} | SWARM: {self.swarm_size} | GPU: {config['use_gpu']}") print(f" COMMANDS: /integrate x y z | /logistic x0 n | /rmm steps | /cfg t max_k | /collatz n | /omni lambda_h | /cosmo_sim ra dec dist | /symbolic_integrate expr var | exit") print("━"*130 + f"{ANSI.RESET}\n") while True: try: user_query = input(f"{ANSI.BOLD}{ANSI.BLUE}[OPERATOR] > {ANSI.RESET}") if user_query.lower() in ['exit', 'quit']: self.save_state() print(f"\n{ANSI.DIM}Shutdown. State saved.{ANSI.RESET}") break if not user_query.strip(): continue parts = user_query.split() cmd = parts[0].lower() start_time = time.time() cycles = 0 flops_est = 0.0 response_buffer = "" math_result = None if cmd == '/integrate': if not self.integrator: response_buffer = "[ERROR] Integrator unavailable." else: x, y, z = float(parts[1]), float(parts[2]), float(parts[3]) math_result, error = self.integrator.compute_phi(x, y, z) response_buffer = f"Φ({x},{y},{z}) ≈ {math_result} (error: {error})" cycles = 1000 # Est flops_est = cycles * 1e6 elif cmd == '/symbolic_integrate': if not self.integrator or not HAS_SYMPY: response_buffer = "[ERROR] sympy required." else: expr = ' '.join(parts[1:-1]) var = parts[-1] math_result = self.integrator.symbolic_integrate(expr, var) response_buffer = f"Symbolic integral: {math_result}" cycles = 500 flops_est = cycles * 1e4 elif cmd == '/logistic': x0, n = float(parts[1]), int(parts[2]) math_result = self.logistic.iterate(x0, n) response_buffer = f"Logistic after {n}: {math_result}" self.logistic.visualize(x0, min(n, 10000)) cycles = n flops_est = cycles * 10 elif cmd == '/rmm': steps = int(parts[1]) math_result = self.rmm.step(steps) response_buffer = f"RMM after {steps}: {math_result}" cycles = steps flops_est = cycles * 5 elif cmd == '/cfg': t, max_k = float(parts[1]), int(parts[2]) math_result = self.cfg.compute(t, max_k) response_buffer = f"CFG at t={t}: {math_result}" cycles = max_k flops_est = max_k * 20 elif cmd == '/collatz': n = int(parts[1]) steps = self.collatz.compute(n) math_result = steps response_buffer = f"Collatz steps: {steps}" if steps else "No convergence" self.collatz.visualize_tree(n) cycles = steps or 0 flops_est = cycles * 2 elif cmd == '/omni': lambda_h = float(parts[1]) math_result = self.omni.integrate(lambda_h=lambda_h) response_buffer = f"Omni integral: {math_result}" cycles = 5000 flops_est = cycles * 1e5 elif cmd == '/cosmo_sim': if not HAS_ASTROPY: response_buffer = "[ERROR] astropy required." else: ra, dec, dist = float(parts[1]), float(parts[2]), float(parts[3]) coord = SkyCoord(ra=ra*u.degree, dec=dec*u.degree, distance=dist*u.kpc) math_result = coord.to_string('hmsdms') response_buffer = f"Cosmo coord: {math_result}" cycles = 100 flops_est = 1e8 else: self.context_history.append({"role": "user", "content": user_query}) print(f"\n{ANSI.BOLD}{ANSI.CYAN}[APEX] > {ANSI.RESET}", end="", flush=True) token_count = 0 async for chunk in self.client.generate_stream(self.context_history): sys.stdout.write(f"{ANSI.GREEN}{chunk}{ANSI.RESET}") sys.stdout.flush() response_buffer += chunk token_count += 1 self.context_history.append({"role": "assistant", "content": response_buffer}) cycles = token_count * 100 flops_est = token_count * 1e9 # Frontier est if math_result is not None: # Hybrid: Reason with Grok reasoning = await self.hybrid_reason(str(math_result), user_query) response_buffer += f"\nGrok Insights: {reasoning}" elapsed = time.time() - start_time print() self._render_dashboard(len(response_buffer.split()), elapsed, response_buffer, cycles, flops_est) self.save_state() except KeyboardInterrupt: self.save_state() break except Exception as e: logger.error(f"Fault: {e}", exc_info=True) print(f"\n{ANSI.RED}[FAULT] {e}{ANSI.RESET}") # ============================================================ # SELF-TESTS (GITHUB-READY UNIT TESTS - EXPANDED) # ============================================================ class TestAxomApex(unittest.TestCase): def test_logistic_map(self): map = NonLinearLogisticMap() result = map.iterate(0.5, 10) self.assertTrue(0 < result < 1, "Logistic map should stay bounded.") def test_collatz(self): collatz = CollatzSequence() steps = collatz.compute(6) self.assertEqual(steps, 8, "Collatz for 6 should take 8 steps.") def test_gauge_accuracy(self): gauge = VirtualParameterGauge() data = gauge.compute_gauge(100, 4, 1000, 1e6) self.assertLess(data['virtual_capacity'] / 1e12, 10, "Virtual params should be realistic (<10T)") # Add more tests as needed if __name__ == "__main__": if len(sys.argv) > 1 and sys.argv[1] == 'test': unittest.main(argv=[''], verbosity=2, exit=False) else: if sys.platform == 'win32': asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy()) engine = AxomApexEngine() try: asyncio.run(engine.execute_reasoning_loop()) except KeyboardInterrupt: sys.exit(0) except Exception as e: logger.critical(f"Failure: {e}") sys.exit(1)