Prolog Reasoning v2: Building a Deterministic Neuro-Symbolic Memory System for LLM Agents

Prolog Reasoning v2 is a research-grade implementation designed to solve the context decay problem in long-range LLM agent memory scenarios using deterministic symbolic reasoning, providing LLMs with lossless, interpretable, and deterministic knowledge storage and reasoning capabilities. This project is based on the neuro-symbolic AI paradigm, combining the perceptual capabilities of neural networks with the rigor of symbolic system reasoning, and is suitable for high-risk fields requiring precise fact recall such as healthcare and cybersecurity.

Large language models face memory accuracy loss in long-term interaction scenarios, with core challenges including:

1. Summary Degradation Problem: Multiple rounds of summary transmission lead to loss of key details (e.g., "Scott's family is in Ohio" degrades to "family in the Midwest");
2. Approximation of Vector Storage: Vector retrieval-based memory systems answer "what is similar" rather than "what is true", which easily leads to incorrect associations;
3. Hallucination Tendency of Model Weights: Facts in parameters may be blurred or fabricated under recall pressure. Prolog Reasoning v2 addresses these pain points by proposing a solution that stores hard facts in a Prolog logic system and derives answers through explicit reasoning.

Core Design Principles

• Lossless Storage: Original statements are fully preserved in structured form without summary degradation;
• Deterministic Reasoning: Consistent results for the same query, eliminating randomness;
• Interpretability: Answers can trace complete proof steps;
• Schema Safety: IR verification mechanism ensures the integrity of the knowledge base structure;
• Composability: Derive new facts through rules, supporting complex chain reasoning;
• Agent-Ready: Supports MCP and agent framework integration.

Six-Layer Architecture

1. Prolog Engine Layer: Pure Python interpreter supporting unification and backtracking;
2. IR Schema Layer: Verifies compliance of input data formats;
3. Semantic Grounding Layer: Converts natural language queries into intermediate representations;
4. IR Compiler Layer: Compiles JSON facts into Prolog syntax and removes duplicates;
5. Explanation Layer: Generates proof traces and readable explanations;
6. Agent Integration Layer: Provides standardized Skill interfaces.

Natural Language Query Interface

Supports daily language questions (e.g., "Who are John's parents?"), converts them into Prolog queries via semantic grounding, and returns structured results (success status, variable bindings, explanation text, confidence level, proof trace).

Structured Failure Explanation

Provides clear diagnostics when a query fails (e.g., "Undefined entity 'charlie'") to reduce debugging difficulty.

Constraint Propagation Extension

Includes a deterministic constraint propagation layer that supports:

• State propagation of implication rules (fixed-point closure);
• Freedom propagation for domain narrowing;
• Contradiction detection in infeasible domains, suitable for scenarios like resource scheduling.

Prolog Reasoning v2 is suitable for high-risk fields requiring precise fact recall:

• Healthcare: Manage patient medication records and allergy tracking, automatically detect contraindicated medications;
• Cybersecurity: Track access permissions, identify suspicious activity patterns (e.g., frequent access to rejected resources);
• Financial Services: Monitor transaction patterns to meet regulatory compliance, flag high-risk/money laundering transactions;
• Legal Compliance: Track contract terms, detect potential breach risks;
• Supply Chain Management: Evaluate supplier risks (geopolitical issues, labor disputes, etc.).

Local LLM Integration

Integrates with local LLM platforms like LM Studio via MCP server to enhance capabilities:

• Deterministic querying of structured facts;
• Traceable reasoning with proof chains;
• Grounding problem verification feedback;
• Conversational fact management.

Project Status

• Research-grade implementation phase with 60% test coverage;
• Supports Python 3.12+, open-source under MIT license;
• Provides a learning resource library: four courses including Introduction to LLM Memory and Symbolic Reasoning, Knowledge Base Fundamentals, Learning from Failure, and Local LLM & MCP Integration.