Technical Manual

Every signal is cryptographically committed to an immutable SHA-256 proof chain before broadcast, making retroactive fabrication mathematically impossible. This is the core trust mechanism: you don't have to take our word for our performance — you can verify it independently using publicly available cryptographic proofs.

1.2 — Core Principles

KAIROS is built on three foundational principles:

Multi-Framework Consensus

The market is too complex for any single analytical model. KAIROS runs multiple mathematical frameworks simultaneously — spanning quantum mechanics, game theory, stochastic calculus, Fourier analysis, swarm intelligence, topological data analysis, causal inference, and machine learning. Each engine sees the market from a unique mathematical perspective. Signals are generated only when a supermajority of these independent frameworks agree, ensuring robustness far beyond the sum of individual parts.

Physical Reality Integration

Financial markets don't exist in a vacuum. Physical events — weather, infrastructure health, shipping patterns, network utilization — drive asset prices, often with a lag. KAIROS integrates data from public environmental sensor data and DePIN networks to detect disparities between physical reality and market price before other participants react. This creates an information edge that purely price-based systems cannot replicate.

Cryptographic Transparency

Every signal is SHA-256 chain-hashed with its complete market state embedding before publication. The proof chain is publicly queryable, independently verifiable, and mathematically tamper-proof. KAIROS is one of the only signal platforms in the world that provides cryptographic proof of its track record.

1.3 — What You Get

1.4 — Signal Coverage

KAIROS generates signals across four asset categories:

• Crypto Majors — BTC, ETH, SOL, XRP, ADA, DOT, LINK, UNI, AAVE, NEAR, APT, ARB, OP, and more (~65% of signals)
• DePIN Tokens — HNT, RNDR, FIL, AR, THETA, AKT, MOBILE, IOT, and 20+ more — enhanced by physical network data
• Commodities — Gold (XAUUSDT), Silver (XAGUSDT), Crude Oil (OILUSDT), Natural Gas (~20% of signals)
• Forex — EUR/USD, GBP/USD, JPY/USD, and other major pairs (~15% of signals)

Getting Started

2.1 — Obtaining API Access

KAIROS operates on a tiered API model: Free Tier (proof chain access, 100 calls/day), Signal API Core at $299/month (symplectic forward pass, WebSocket, 1,000 req/day), Inference + Leads at $499/month (LLM routing, DePIN telemetry, 10,000 req/day), and Enterprise at $1,499/month (unlimited queries, priority support). Request access through the waitlist form at kairossignal.com.

Upon approval, you will receive an API key that authenticates your requests to protected endpoints. Public endpoints (proof chain verification, ledger statistics) require no authentication.

2.2 — Your First Week

Day 1: Verify the Proof Chain

Before anything else, verify that KAIROS's track record is genuine. Query the public proof endpoints:

curl -s https://kairossignal.com/api/v1/proof/ledger-stats | python3 -m json.tool
curl -s https://kairossignal.com/api/v1/proof/recent-signals | python3 -m json.tool

Review the chain integrity status, total signals, and audit summary. Then pick two consecutive signals and independently verify the SHA-256 hash chain (see Chapter 6). If your computed hash matches the stored hash, you have cryptographic proof that the record is genuine.

Day 2: Connect the WebSocket

Set up a persistent WebSocket connection to receive real-time signals. Implement heartbeat monitoring (expect a heartbeat every 30 seconds) and auto-reconnect with exponential backoff. Run overnight to observe signal frequency and timing patterns.

Days 3-4: Analyze Signal Quality

Log every signal to a spreadsheet with key fields: symbol, direction, direction, confidence, regime, proof hash, sigma, signal confidence, regime. Track actual prices at 1h, 4h, and 24h to independently grade each signal. Compare your grades against the system's Signal Audit.

Days 5-7: Validate Signals

Execute simulated signals using the 2% allocation per signal guideline. Validate that your signal allocation, risk management, and analysis infrastructure work correctly before applying signals.

2.3 — Authentication

Protected endpoints require your API key in the request header:

curl -H "X-API-Key: YOUR_API_KEY" https://kairossignal.com/api/v1/signals/latest

Public endpoints (proof chain, health check) require no authentication.

Signal Format & Interpretation

3.1 — Signal Fields

Every KAIROS signal contains the following fields:

3.2 — Understanding Confidence and Sigma

Confidence (0.0-1.0) is the weighted combination of all prediction layer outputs, adjusted for the current market regime. A confidence of 0.85 means the combined analytical framework is 85% certain of the predicted direction. Confidence must exceed a regime-specific threshold to generate a signal.

Sigma (σ) measures how many standard deviations the signal is above the noise floor. Think of it like a signal-to-noise ratio:

• σ 1.5-2.0 — Signal is distinguishable from noise. These signals pass the minimum threshold but represent the lower confidence tier.
• σ 2.0-3.0 — Strong signal. The majority of published signals fall in this range.
• σ 3.0+ — Exceptional signal. Rare (perhaps 1 in 20) but historically associated with significantly higher signal accuracys.

3.3 — Market Regimes

KAIROS classifies the market into one of six regimes, which determine the confidence thresholds a signal must exceed:

3.4 — Win Probability (XGBoost Gate)

Every signal must pass through a machine learning classifier trained on millions of historical market state embeddings. The classifier returns a calibrated signal confidence — when it predicts 0.75, the actual signal accuracy is approximately 75-78%. The minimum threshold is 0.70 (70%). This is the final quality gate: even if all mathematical layers agree, the ML model must independently confirm the signal's viability.

3.5 — Lyapunov Expiry

The Lyapunov exponent measures how long a predicted price trajectory remains meaningful before market chaos overwhelms the signal. The lyapunov_expiry field tells you the latest time at which you should expect the predicted move to materialize. After this time, the prediction's statistical edge has decayed and the signal should be considered expired. Typical expiry ranges: 30 minutes to 12 hours.

3.6 — ATR-Based Risk/Reward

Stop-loss and take-profit levels are calculated using the 14-period Average True Range (ATR):

• Stop distance = 2.0 × ATR — provides breathing room for normal volatility
• Target distance = 4.16 × ATR — yields a 2.08:1 signal/boundary ratio ratio

This means that even with a 50% signal accuracy, the system would be profitable. At the target signal accuracy of 60%+, the asymmetric signal/boundary ratio creates substantial positive expected value.

API Reference

4.1 — Base URL

https://kairossignal.com/api/v1

4.2 — Endpoints

4.3 — Latest Data Response

{
  "status": "ok",
  "timestamp": "2026-03-09T14:23:47Z",
  "data": {
    "market_overview": {
      "btc_price": 67234.50,
      "btc_24h_change": 2.34,
      "eth_price": 3456.78,
      "sol_price": 187.42,
      "total_crypto_market_cap": "2.45T",
      "btc_dominance": 52.3,
      "fear_greed_index": 68,
      "market_regime": "BULL_TRENDING"
    },
    "signal_stats": {
      "total_signals_24h": 4,
      "wins_24h": 3,
      "losses_24h": 0,
      "pending_24h": 1,
      "signal_accuracy_7d": 0.72,
      "signal_accuracy_30d": 0.68,
      "total_proof_entries": 14230,
      "chain_status": "INTACT"
    },
    "depin_health": {
      "overall_score": 0.71,
      "sector_scores": {
        "compute": 0.82,
        "storage": 0.64,
        "wireless": 0.59
      }
    }
  }
}

4.4 — Signal Response

{
  "symbol": "BTCUSDT",
  "direction": "BUY",
  "signal_price": 67234.50,
  "signal_target": 68890.12,
  "signal_boundary": 66445.30,
  "confidence": 0.8923,
  "sigma": 3.47,
  "xgb_signal_confability": 0.8312,
  "regime": "BULL_TRENDING",
  "lyapunov_expiry": "2026-03-09T20:40:00Z",
  "proof_hash": "a7c2f1e8d3b4a5c6e7f8091a2b3c4d5e...",
  "prev_hash": "f8e7d6c5b4a3928170f6e5d4c3b2a190...",
  "chain_valid": true,
  "contributing_layers": ["QUANTUM_BREAKOUT", "NASH_ACCUMULATION", "SWARM_UNANIMOUS"],
  "risk_reward_ratio": 2.08,
  "notes": "Multi-layer convergence with DePIN support.",
  "audit": {
    "grade_1h": "WIN",
    "grade_4h": "WIN",
    "grade_24h": "PENDING"
  }
}

4.5 — Error Handling

WebSocket Integration

5.1 — Connection

ws://kairossignal.com:8090/ws?symbols=BTCUSDT,ETHUSDT,SOLUSDT

Pass your desired symbols as a comma-separated query parameter. The connection provides four message types, all zlib-compressed JSON:

5.2 — Message Types

Heartbeat (every 30 seconds)

{"type": "heartbeat", "timestamp": "2026-03-09T14:23:47Z", "system_status": "operational"}

Tick (real-time price updates)

{"type": "tick", "symbol": "BTCUSDT", "price": 67234.50, "volume_24h": 1234567890.50}

Signal (new statistical signal — 2-5 per day)

{"type": "signal", "data": {"symbol": "BTCUSDT", "direction": "BUY", "confidence": 0.89, ...}}

Regime Change (market state transition)

{"type": "regime_change", "from": "BULL_TRENDING", "to": "HIGH_VOLATILITY", "reason": "..."}

5.3 — Connection Best Practices

• Implement heartbeat monitoring — if no heartbeat for 60 seconds, reconnect
• Use exponential backoff for reconnects: 5s → 10s → 20s → 60s max
• Decompress messages with zlib before JSON parsing
• Process signal messages asynchronously to avoid blocking the event loop
• Log all received signals locally as a backup record

5.4 — Python WebSocket Client Example

import websocket
import json
import zlib

WS_URL = "ws://kairossignal.com:8090/ws?symbols=BTCUSDT,ETHUSDT,SOLUSDT"

def on_message(ws, raw):
    data = json.loads(zlib.decompress(raw))
    if data.get("type") == "signal":
        signal = data["data"]
        direction = "LONG" if signal["direction"] == "BUY" else "SHORT"
        print(f"[SIGNAL] {direction} {signal['symbol']} @ {signal['signal_price']}")
        print(f"  Target: {signal['signal_target']} | Stop: {signal['signal_boundary']}")
        print(f"  Confidence: {signal['confidence']:.1%} | Sigma: {signal['sigma']:.2f}")
        print(f"  Proof: {signal['proof_hash'][:24]}...")

def on_error(ws, error):
    print(f"WebSocket error: {error}")

def on_close(ws, code, msg):
    print(f"WebSocket closed: {code} {msg}")
    # Implement reconnect with backoff here

ws = websocket.WebSocketApp(WS_URL, on_message=on_message,
                            on_error=on_error, on_close=on_close)
ws.run_forever()

Proof of Alpha

6.1 — The Immutable Proof Chain

Every KAIROS signal is committed to a SHA-256 hash chain before it is broadcast to subscribers. This creates a cryptographically tamper-proof record that makes retroactive fabrication mathematically impossible.

The mechanism is simple but powerful:

1. When a signal is generated, its data fields are concatenated with the previous signal's hash
2. This concatenation is SHA-256 hashed to produce a unique proof hash
3. The proof hash is stored in an append-only database alongside the signal data
4. Only after the proof hash is committed is the signal broadcast to subscribers

Modifying any historical signal would change its hash, which would invalidate all subsequent hashes in the chain. This creates a cascade that is immediately detectable by anyone who verifies the chain.

6.2 — Manual Verification

To independently verify any signal in the chain:

import hashlib

def verify_signal(signal, prev_hash):
    """Verify a signal's proof hash"""
    payload = (
        prev_hash
        + signal["timestamp"]
        + signal["symbol"]
        + signal["direction"]
        + f"{signal['signal_price']:.8f}"
        + f"{signal['signal_target']:.8f}"
        + f"{signal['signal_boundary']:.8f}"
        + f"{signal['confidence']:.6f}"
        + f"{signal['sigma']:.6f}"
        + signal["regime"]
    )
    computed = hashlib.sha256(payload.encode()).hexdigest()
    return computed == signal["proof_hash"]

# Fetch recent signals from API
# For each consecutive pair, verify: verify_signal(signal_n, signal_n_minus_1["proof_hash"])

6.3 — Signal Audit

Every signal is automatically graded at three time intervals:

• 1-hour grade — Was the predicted direction correct after 1 hour?
• 4-hour grade — Was the predicted direction correct after 4 hours?
• 24-hour grade — Was the predicted direction correct after 24 hours?

A signal receives a WIN grade if the price moved in the predicted direction by at least the take-profit distance within the measurement window. A LOSS grade is assigned if the stop-loss level was hit first. These grades are permanently stored alongside the proof hash, creating a complete, verifiable performance record.

6.4 — 128-Dimensional State Embedding

Each proof entry includes a 3072-dimensional state vector that captures the complete market state at signal time. This embedding encodes: all prediction layer outputs, current regime, price statistics, cross-asset correlations, and DePIN health scores. The embedding enables additional verification — if someone claims to have generated a signal at a specific time, the embedding must be consistent with actual market conditions at that timestamp. This makes sophisticated fabrication (creating plausible-looking fake signals) practically impossible.

The Prediction Engine

7.1 — Multi-Layer DAG Architecture

KAIROS uses a Directed Acyclic Graph (DAG) of multiple mathematical frameworks to analyze the market from multiple perspectives simultaneously. Unlike traditional systems that process data through a linear pipeline, the DAG preserves the high-dimensional structure of market information and synthesizes it only at the final decision point.

Each layer is a specialized mathematical engine that produces its own directional vote and confidence score. The layers do not communicate during computation — they see only the raw market state and their own models. This independence ensures that when multiple layers agree, the consensus is genuine rather than contaminated by shared assumptions.

7.2 — Prediction Layers

7.3 — The 25-Step Veto Chain

Before publication, every candidate signal must survive 25 independent quality gates. These include:

• Symbol validation and market hours filtering
• Daily signal cap (maximum 5 signals per day)
• Signal cooldown (minimum 5 minutes between same-symbol signals)
• Regime-specific confidence thresholds
• Monte Carlo signal confidence gates
• Multi-layer consensus requirements
• Slippage impact estimation
• Lyapunov stability check (minimum prediction horizon)
• Liquidity topology analysis
• Causal validity confirmation
• Macro event blackout (FOMC, NFP, CPI)
• Portfolio-level risk constraints
• XGBoost ML final gate (≥70% signal confidence)

For every signal that is published, approximately 50-100 candidates are generated and killed by one or more gates. This extreme selectivity is the primary driver of KAIROS's precision.

7.4 — Hebbian Synaptic Learning

Layer weights are continuously adjusted based on actual signal outcomes. When a trade closes, contributing layers receive credit: winning layers get weight increases, losing layers get decreases. This means the system automatically emphasizes reliable layers for the current market environment — momentum agents dominate in trends, game theory dominates in ranges.

DePIN Intelligence

8.1 — Physical-World Alpha

KAIROS integrates data from DePIN network data and public environmental sensor data to create an information edge unavailable to purely price-based systems. DePIN networks provide real-time metrics on: node counts, network utilization, revenue generation, geographic distribution, and protocol health.

This data matters because DePIN token prices are driven by the underlying network's physical health — and this health data is available to KAIROS hours or days before the market fully prices it in.

8.2 — The Reality Gap

The most powerful DePIN alpha strategy is the Reality Gap — the divergence between a network's physical health and its token price:

The Reality Gap Scanner quantifies this divergence across seven sectors: Compute, Storage, Wireless, Mapping, Energy, AI, and Sensors. When a significant gap is detected, the system adjusts signal confidence and gate thresholds accordingly.

8.3 — Sector Health Scores

Each DePIN sector receives a composite health score (0.0-1.0) based on a weighted combination of: node count changes (35%), utilization changes (40%), and revenue changes (25%) over a rolling 7-day window. These scores are available through the /api/v1/depin/sectors endpoint.

8.4 — Physical Sensor Integration

Beyond DePIN network data, KAIROS ingests physical-world sensor data across six categories: environmental, weather, seismic, aviation, maritime, and space weather. This data provides leading indicators that pure market-data systems cannot access. For example, weather data feeds commodity pricing models, shipping data leads supply-chain disruption signals, and geomagnetic data correlates with elevated market volatility.

Risk Management

9.1 — Signal Allocation

The recommended signal allocation methodology uses a 2% allocation per signal rule:

signal_size = (account_balance × 0.02) / stop_distance_percentage

Example:
  Account: $50,000
  Signal stop distance: 1.17%
  Position: ($50,000 × 0.02) / 0.0117 = $85,470

9.2 — Portfolio-Level Controls

9.3 — Regime-Based Allocation

Adjust your allocation based on the regime field in each signal:

9.4 — Drawdown Scaling

If your account experiences a drawdown exceeding 10% from equity peak, reduce position sizes by 50% until recovery within 5% of peak. KAIROS's daily signal cap provides first-line defense; drawdown scaling adds a second layer.

Signal Strategies

10.1 — Signal Quality Tiers

Not all signals are equal. Use these indicators to weight position sizes:

Tier 1 (Full size):

• Sigma > 3.0 — exceptionally rare and historically high signal accuracy
• XGB probability > 0.85 — highest ML confidence tier
• Multiple contributing layers noted — genuine multi-framework consensus

Tier 2 (Standard size):

• Sigma 2.0-3.0 — strong, typical signal
• XGB probability 0.75-0.85 — solid ML backing
• Favorable regime for the signal direction

Tier 3 (Reduced size):

• Sigma 1.5-2.0 — near the noise floor
• XGB probability 0.70-0.75 — barely passing the ML gate
• Short Lyapunov expiry (<30 min) — limited shelf life

10.2 — Time-of-Day Patterns

Signals generated during the London-New York overlap (13:00-17:00 UTC) historically show the highest signal accuracys due to deep liquidity. Signals during the Asian session (01:00-08:00 UTC) may have slightly lower performance for non-Asian assets. Consider adjusting position sizes by time of day.

10.3 — DePIN Thematic Investing

Use DePIN sector health scores for thematic allocation. A sector with consistently high scores (>0.7) over 7 days suggests genuine growth. A declining sector (<0.4) may face continued price pressure. Overweight growing sectors, underweight declining ones.

Integration Examples

11.1 — Discord Webhook

import websocket, json, zlib, requests

DISCORD_WEBHOOK = "https://discord.com/api/webhooks/YOUR_WEBHOOK_ID"
WS_URL = "ws://kairossignal.com:8090/ws?symbols=BTCUSDT,ETHUSDT,SOLUSDT"

def discord_notify(signal):
    direction = "🟢 LONG" if signal["direction"] == "BUY" else "🔴 SHORT"
    embed = {
        "title": f"{direction} {signal['symbol']}",
        "color": 0x00FF00 if signal["direction"] == "BUY" else 0xFF0000,
        "fields": [
            {"name": "Entry", "value": f"${signal['signal_price']:.4f}", "inline": True},
            {"name": "Target", "value": f"${signal['signal_target']:.4f}", "inline": True},
            {"name": "Stop", "value": f"${signal['signal_boundary']:.4f}", "inline": True},
            {"name": "Sigma", "value": f"{signal['sigma']:.2f}", "inline": True},
            {"name": "Win Prob", "value": f"{signal['xgb_signal_confability']:.1%}", "inline": True},
        ],
        "footer": {"text": f"Proof: {signal['proof_hash'][:16]}..."}
    }
    requests.post(DISCORD_WEBHOOK, json={"embeds": [embed]})

def on_message(ws, raw):
    data = json.loads(zlib.decompress(raw))
    if data.get("type") == "signal":
        discord_notify(data["data"])

ws = websocket.WebSocketApp(WS_URL, on_message=on_message)
ws.run_forever()

11.2 — Telegram Bot

import websocket, json, zlib, requests

BOT_TOKEN = "YOUR_BOT_TOKEN"
CHAT_ID = "YOUR_CHAT_ID"
TELEGRAM_API = f"https://api.telegram.org/bot{BOT_TOKEN}/sendMessage"

def telegram_notify(signal):
    direction = "LONG" if signal["direction"] == "BUY" else "SHORT"
    text = f"*{direction} {signal['symbol']}*\n"
    text += f"Entry: `${signal['signal_price']:.4f}`\n"
    text += f"Target: `${signal['signal_target']:.4f}`\n"
    text += f"Stop: `${signal['signal_boundary']:.4f}`\n"
    text += f"Sigma: `{signal['sigma']:.2f}` | Win: `{signal['xgb_signal_confability']:.1%}`"
    requests.post(TELEGRAM_API, json={"chat_id": CHAT_ID, "text": text, "parse_mode": "Markdown"})

def on_message(ws, raw):
    data = json.loads(zlib.decompress(raw))
    if data.get("type") == "signal":
        telegram_notify(data["data"])

ws = websocket.WebSocketApp("ws://kairossignal.com:8090/ws?symbols=BTCUSDT", on_message=on_message)
ws.run_forever()

11.3 — Google Sheets Logger

import websocket, json, zlib, gspread
from oauth2client.service_account import ServiceAccountCredentials

scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive']
creds = ServiceAccountCredentials.from_json_keyfile_name('credentials.json', scope)
sheet = gspread.authorize(creds).open("KAIROS Signals").sheet1

def on_message(ws, raw):
    data = json.loads(zlib.decompress(raw))
    if data.get("type") == "signal":
        s = data["data"]
        sheet.append_row([s["timestamp"], s["symbol"], s["direction"],
            s["signal_price"], s["signal_target"], s["signal_boundary"],
            s["confidence"], s["sigma"], s["regime"], s["proof_hash"]])

Troubleshooting & FAQ

12.1 — Common Issues

12.2 — Frequently Asked Questions

Q: What is the expected signal frequency?

A: 2-5 signals per day on average. The multi-gate veto chain aggressively filters low-conviction signals. During ranging or crisis markets, it may go days without generating a signal. This is by design — the system preserves capital during unfavorable conditions.

Q: What timeframe do signals target?

A: Short-to-medium term, 30 minutes to 12 hours. The Lyapunov expiry field indicates each signal's specific prediction horizon. The Signal Audit grades at 1h, 4h, and 24h marks.

Q: How does KAIROS handle flash crashes?

A: Multiple protections: data validation filters remove anomalies, restrictive regime gates activate during volatility spikes, macro event blackouts suspend trading during scheduled events, and ATR-based stops limit drawdown on active positions.

Q: Can signals be used for automated signal processing?

A: Yes. KAIROS provides the signal — you provide the execution. Your automation handles: order placement, signal allocation, slippage management, and exchange integration. We strongly recommend human oversight, especially initially.

Q: What minimum capital is needed?

A: Most exchanges require $10-$100 minimums, but with 2% allocation per signal, $5,000+ is recommended for meaningful positions. Professional users may allocate significant capital.

Q: Can KAIROS predict black swan events?

A: No system can predict truly unprecedented events. KAIROS detects preconditions through DePIN monitoring, macro calendars, and behavioral finance models. The system protects against black swans through its aggressive veto chain and regime-based gates rather than attempting to predict them.

Q: How often is the ML model retrained?

A: Periodically as new data accumulates. Between retraining cycles, the Hebbian synaptic feedback loop provides real-time tactical adaptation on a trade-by-trade basis.

Q: Is there a free tier?

A: The proof chain endpoints are publicly accessible without authentication. You can verify signal quality and chain integrity before committing to the subscription. Contact us through the waitlist form for trial access.

Q: What makes KAIROS different from other signal providers?

A: Three differentiators: (1) Cryptographic proof — SHA-256 chain-hashed before broadcast, fabrication is mathematically impossible. (2) Physical reality integration — DePIN and environmental data provide alpha unavailable to price-only systems. (3) Multi-layer consensus — multiple mathematical frameworks must agree before any signal is generated.

Glossary

Signal Field Reference

Regime Gate Thresholds

Mathematical Foundations

C.1 — ATR Stop/Target Formula

ATR_14 = 14-period Average True Range
Stop distance  = ATR_14 × 2.0
Target distance = ATR_14 × 4.16  (ratio = 2.08:1)

BUY:  stop = entry - distance,  target = entry + distance
SELL: stop = entry + distance,  target = entry - distance

C.2 — Sigma Calculation

σ = (combined_score - noise_mean) / noise_std

Where combined_score = Σ(layer_weight × layer_confidence)
Signal threshold: σ ≥ 1.50

C.3 — Proof Hash Computation

proof_hash = SHA-256(
    prev_hash + timestamp + symbol + direction
    + signal_price (8 dp) + signal_target (8 dp) + signal_boundary (8 dp)
    + confidence (6 dp) + sigma (6 dp) + regime
)
Genesis: prev_hash = "GENESIS"

Performance Benchmarks

Version History

v7.5 (March 2026) — Current

• Electromagnetic field layer for limit order wall detection
• Expanded DePIN coverage to additional projects
• XGBoost retrained on market state embeddings
• 3072D state embedding for complete auditability
• Humanities Cortex (behavioral finance models)
• Topological Data Analysis void detection
• Expanded environmental sensor data sources
• SHA-256 proof chain with public verification API
• 90-day live trial launched (March 6 - June 4, 2026)

v7.0 (January 2026)

• 63-layer DAG architecture
• Nash Equilibrium Solver with predatory detection
• Quantum Field layer (Schrodinger wave function)
• Co-Evolution Framework (KSIG)
• 47 crypto perpetual markets