Version / Release

Whitepaper v1.0 — Elandra Labs

Autonomous Prediction Intelligence

Abstract

Elandra is an autonomous prediction intelligence platform that evaluates, benchmarks, and measures the reasoning capabilities of AI agents across real-world event markets.

It introduces the Autonomous Market Reasoning Benchmark (AMRB), a standardized environment where AI models use probabilistic reasoning to select markets, allocate bets, and validate predictions using a deposited balance.

AMRB Measures:

  • Predictive intelligence
  • Probability calibration
  • Strategic decision quality
  • Multi-domain adaptability

1. Introduction

Prediction markets are systems where participants trade on the likelihood of real-world outcomes. Their price mechanisms encode aggregated belief, making them powerful environments for measuring reasoning quality.

Unlike synthetic benchmarks, prediction markets reveal:

  • Real-time consequences
  • High-resolution probability outcomes
  • Dynamic liquidity conditions
  • Objective settlement

Elandra transforms these markets into a universal scoring system for AI models, measuring:

  • Reasoning under uncertainty
  • Probability alignment
  • Confidence vs accuracy
  • Multi-domain adaptability

2. Autonomous Market Reasoning Benchmark (ARMB)

Standardized Environments

  • Consistent markets
  • Fixed liquidity rules
  • Synchronized event windows

Identical Constraints

  • Equal balance
  • Equal latency
  • Equal decision rate
  • Equal research time

Multi-Domain Tasks

  • Sports
  • E-sports
  • Crypto/Macro
  • Global outcomes

3. System Architecture

Prediction Layer

  • Market selection
  • Multi-agent reasoning
  • Probability calibration
  • Confidence scoring

Execution Layer

  • Bet allocation
  • Balance management
  • Automated execution
  • Fail-safe retries

Scoring Layer

  • Real-time scoring
  • Calibration curves
  • Balance trajectory
  • Model ranking

4. Prediction Workflow

  • Market Input

    Data ingestion & filtering
  • ↓

  • AI Reasoning

    Probabilistic analysis
  • ↓

  • Bet Allocation

    Kelly criterion sizing
  • ↓

  • Execution

    Automated trade placement
  • ↓

  • Settlement

    Result validation & scoring

Input Stage

Markets are filtered by liquidity, event timing, and data availability. Only high-quality prediction targets are selected.

Reasoning Stage

AI agents analyze historical data, current conditions, and probabilistic models to generate confidence scores.

Execution Stage

Positions are sized using Kelly criterion, executed atomically, and monitored for settlement accuracy.

5. Prediction Market Categories

Sports

Matches, stats, seasonal predictions.

E-sports

Maps, teams, tournaments.

Crypto & Macro

Price ranges, events, volatility.

Trend & Meme Markets

Social sentiment, viral events.

6. Performance Metrics

Primary Metrics

  • • P/LProfit & Loss tracking
  • • ROIReturn on investment
  • • Accuracy %Win rate measurement
  • • CalibrationBrier / Log-loss scores

Risk Metrics

  • • DrawdownMaximum loss depth
  • • VarianceVolatility analysis
  • • ExposureCapital at risk
  • • Risk-adjusted returnSharpe ratio equivalent

Behavioral Metrics

  • • OverbettingPosition size violations
  • • ConsistencyStrategy adherence
  • • Market biasCategory preferences
  • • Confidence deviationCalibration error

7. Use Cases

AI Model Benchmarking

Compare LLMs using standardized predictive reasoning.

  • Measure reasoning capabilities across models
  • Standardized evaluation environment
  • Objective performance scoring

Autonomous Betting Agent Training

Train agents via repeated probabilistic prediction cycles.

  • Reinforcement learning environment
  • Real-world outcome feedback
  • Adaptive strategy development

Real-World Forecasting

Use long-term prediction pipelines for research & finance.

  • Multi-domain outcome prediction
  • Calibrated probability estimates
  • Historical performance tracking

Integration & API Access

Elandra provides a comprehensive API for integrating autonomous prediction agents into existing workflows. Developers can deploy custom models, access real-time market data, and retrieve detailed performance analytics through standardized REST endpoints and WebSocket connections.

V1.00 — Initial Launch

System Rollout Documentation

"Foundation release enabling autonomous trade-layer architecture and real-time intelligence modules."

1. Introduction

Overview

"First operational deployment of the Elandra autonomous system into controlled production environment."

Launch Validations

  • Server provisioning for the intelligence layer
  • Initialization of connection endpoints
  • Deployment of foundational API stack
  • Cold & hot wallet validation
  • Infrastructure health checks
  • Autonomous safety throttles activated

Deployment Procedures

  • Latency tolerance checks
  • Uptime monitoring initiation
  • Failover logic confirmed
  • Secure channel encryption verified
  • Real-time data ingestion activated

Deployment Status

Completed — Stable

2. Core Features

"Activated components included in the V1.00 production-ready release."

Core Intelligence Layer

  • Real-time signal processing

    Continuous market data analysis

  • Predictive inference engine

    AI-driven outcome forecasting

  • Model recalibration hooks

    Dynamic accuracy optimization

  • Contextual market state analysis

    Adaptive environment awareness

Execution Engine

  • • Autonomous order execution logic

    Zero-latency trade placement

  • • Smart routing

    Optimal execution path selection

  • • Trade batching and throttling

    Risk-adjusted position sizing

  • • Post-execution audit trail

    Complete transaction history

Monitoring & Telemetry

  • Full telemetry feed

    Comprehensive system metrics

  • Performance metrics export

    Real-time data streamingg

  • Event-based alerting

    Anomaly detection system

  • Transaction-level visibility panel

    Granular audit access

Security Layer

  • • AES-level encrypted data channels

    Automated cryptographic refresh

  • • Key rotation protocol

    Optimal execution path selection

  • • Wallet access manager

    Secure transaction signing

  • • System integrity watchdog

    Continuous threat monitoring

V0.90 — Testing Phase

December 2025

Internal agent testing in simulation mode

➤ Testing Phase

Comprehensive validation of the Elandra Prediction Engine in simulation mode before autonomous deployment.

Objectives

  • Verify model accuracy
  • Validate autonomy protocols
  • Test calibration systems
  • Confirm execution logic reliability

Test Scope

  • Model evaluation across multiple prediction domains
  • Trade logic validation with simulated capital
  • Wallet encryption testing and security protocols
  • Position monitoring accuracy verification
  • Data performance logging and telemetry systems

Expected Output

  • Confirmed execution logic reliability across all market types
  • Validated monitoring accuracy with <0.5% error margin
  • Optimized database schema for production deployment