Research Roadmap

Our research agenda follows a structured, multi-year roadmap focused on advancing intelligent systems that combine mathematics, simulation, cognition, and modern AI.
This roadmap guides our long-term vision across several research fronts.

Short-Term Objectives (0–18 Months)

Foundational Systems, Tools & Early Prototypes

1. MathRAG Foundations

  • Build initial graph-based RAG pipelines for mathematical knowledge.
  • Integrate symbolic tools (Lean4, expression trees, AST analysis).
  • Develop structured retrieval for definitions, theorems, and reasoning steps.

2. Robotics Simulation & Control Frameworks

  • Create baseline ROS2/Gazebo simulation environments.
  • Implement core control systems (PID, MPC, RL-based).
  • Prototype sensor fusion modules for LiDAR, IMU, radar.

3. Signal Processing & Perception Algorithms

  • Develop acoustic and time-series ML pipelines.
  • Build feature extraction and spectral analysis libraries.
  • Evaluate multimodal detection and tracking algorithms.

4. Financial Modeling Experiments

  • Implement time-series forecasting models.
  • Prototype agent-based market simulations.
  • Test cognitive/sentiment-based prediction methods.

5. Optimization & OR Toolkit

  • Construct reusable optimization modules (LP, QP, nonlinear).
  • Early work on scheduling and resource allocation solvers.
  • Apply optimization to robotics and financial simulations.

Mid-Term Objectives (18–48 Months)

Integrated Systems, Frameworks & Hybrid Intelligence

1. Hybrid Symbolic–Neural Reasoning Systems

  • Combine MathRAG with neural language models.
  • Develop structured reasoning chains with verification steps.
  • Implement symbolic feedback loops for correctness and consistency.

2. Advanced Autonomous System Simulation

  • Multi-agent collaborative robotics simulation.
  • Uncertainty-aware perception and decision-making.
  • High-fidelity digital twins for dynamic systems.

3. Adaptive Control & Intelligent Decision Systems

  • RL-enhanced optimal control for complex environments.
  • Robust and fault-tolerant control architectures.
  • Behavioral modeling for autonomy under uncertainty.

4. Multimodal Sensor Intelligence

  • Fusion of acoustic, radar, LiDAR, visual, and inertial data.
  • Real-time detection and classification for defense robotics.
  • Cross-domain sensor modeling for dynamic missions.

5. Advanced Market & Cognitive Models

  • Narrative-driven AI agents for market environments.
  • Multi-factor dynamic models using ML + OR techniques.
  • Strategic agent-based simulations integrating cognitive factors.

Long-Term Objectives (4–10 Years)

Unified Intelligent Systems, Scientific AI & Advanced Autonomy

1. Fully Integrated Reasoning Engine (MathRAG+)

A unified reasoning system capable of:

  • Interpreting complex scientific and mathematical problems
  • Generating structured reasoning paths
  • Validating conclusions using formal proof systems
  • Assisting research, engineering, and operational decision-making

This represents the long-term fusion of symbolic logic, neural models, and mathematical search.

2. Autonomous Cognitive Systems

Development of agents capable of:

  • High-level strategic reasoning
  • Multi-step planning under uncertainty
  • Adaptive learning across missions
  • Human-aligned decision frameworks

Applicable to robotics, sensing, defense missions, and financial systems.

3. Simulation-Driven Intelligent Ecosystems

Large-scale simulations that integrate:

  • Robotics
  • Sensing and signal dynamics
  • Economic and behavioral models
  • Multi-agent decision systems

The goal is to create environments where intelligent systems can learn, adapt, and evolve safely.

4. Advanced Optimization & Scientific Computing Engines

Next-generation tools for:

  • Large-scale optimization (convex, nonlinear, stochastic)
  • Scientific simulation across domains
  • Hybrid symbolic–numeric problem solving
  • Automated scientific discovery assistance

These tools unify mathematical rigor with intelligent computation.

Vision

Our long-term vision is to create intelligent systems that can:

  • Reason mathematically
  • Understand complex environments
  • Perceive through multimodal sensing
  • Adapt through simulation and learning
  • Optimize decisions under constraints
  • Collaborate with humans transparently and reliably

Neuroligence is committed to building the foundations of next-generation scientific, autonomous, and cognitive intelligence.