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Unifying Neural and Symbolic Decision Making

· 2 min read

Key Challenges with LLMs

  • Difficulty with tasks requiring complex planning (e.g., travel itineraries, meeting schedules).
  • Performance declines with increasing task complexity (e.g., more cities, people, or constraints).

Three Proposed Solutions

  1. Scaling Law
    • Increase data, compute, and model size.
    • Limitation: High costs and diminishing returns for reasoning/planning tasks.
  2. Hybrid Systems
    • Combine deep learning models with symbolic solvers. Symbolic reasoning refers to the process of solving problems and making decisions using explicit symbols, rules, and logic. It is a method where reasoning is based on clearly defined relationships and representations, often following formal logic or mathematical principles.
    • Approaches:
      • End-to-End Integration: Unified deep model and symbolic system.
      • Data Augmentation: Neural models provide structured data for solvers.
      • Tool Use: LLMs act as interfaces for external solvers.
    • Notable Examples:
      • MILP Solvers: For travel planning with constraints.
      • Searchformer: Transformers trained to emulate A* search.
      • DualFormer: Switches dynamically between fast (heuristic) and slow (deliberative) reasoning.
      • SurCo: Combines combinatorial optimization with latent space representations.
  3. Emerging Symbolic Structures
    • Exploration of symbolic reasoning emerging in neural networks.
    • Findings:
      • Neural networks exhibit Fourier-like patterns in arithmetic tasks.
      • Gradient descent produces solutions aligned with algebraic constructs.
      • Emergent ring homomorphisms and symbolic efficiency in complex tasks.

Research Implications

  • Neural networks naturally learn symbolic abstractions, offering potential for improved reasoning.
  • Hybrid systems might represent the optimal balance between adaptability (neural) and precision (symbolic).
  • Advanced algebraic techniques could eventually replace gradient descent.

Overall Takeaway

The future of decision-making AI lies in leveraging both neural adaptability and symbolic rigor. Hybrid approaches appear most promising for solving tasks requiring both perception and structured reasoning.

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