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CausalGuard: A Smart System for Detecting and Preventing False Information in Large Language Models

Piyushkumar Patel

TL;DR

CausalGuard addresses the persistent problem of hallucinations in large language models by combining causal reasoning with symbolic verification to detect and prevent false information in real time. The system comprises a dual-path neurosymbolic architecture: a Causal Reasoning Engine that models the knowledge state and generates counterfactual evidence, and a Symbolic Verification Network that builds dynamic knowledge graphs and performs theorem proving to ensure logical consistency. Across 12 diverse benchmarks, CausalGuard achieves high detection performance (Precision 0.893, Recall 0.917, F1 0.905), strong factual accuracy (0.924), and substantial reduction in hallucinations (about 78% relative to vanilla GPT-4) while preserving response quality (BLEU 0.962) and providing interpretable reasoning traces. The work demonstrates that real-time, explainable, and domain-agnostic hallucination mitigation is feasible without retraining models, enabling safer deployment in sensitive domains like healthcare and finance.

Abstract

While large language models have transformed how we interact with AI systems, they have a critical weakness: they confidently state false information that sounds entirely plausible. This "hallucination" problem has become a major barrier to using these models where accuracy matters most. Existing solutions either require retraining the entire model, add significant computational costs, or miss the root causes of why these hallucinations occur in the first place. We present CausalGuard, a new approach that combines causal reasoning with symbolic logic to catch and prevent hallucinations as they happen. Unlike previous methods that only check outputs after generation, our system understands the causal chain that leads to false statements and intervenes early in the process. CausalGuard works through two complementary paths: one that traces causal relationships between what the model knows and what it generates, and another that checks logical consistency using automated reasoning. Testing across twelve different benchmarks, we found that CausalGuard correctly identifies hallucinations 89.3\% of the time while missing only 8.3\% of actual hallucinations. More importantly, it reduces false claims by nearly 80\% while keeping responses natural and helpful. The system performs especially well on complex reasoning tasks where multiple steps of logic are required. Because CausalGuard shows its reasoning process, it works well in sensitive areas like medical diagnosis or financial analysis where understanding why a decision was made matters as much as the decision itself.

CausalGuard: A Smart System for Detecting and Preventing False Information in Large Language Models

TL;DR

CausalGuard addresses the persistent problem of hallucinations in large language models by combining causal reasoning with symbolic verification to detect and prevent false information in real time. The system comprises a dual-path neurosymbolic architecture: a Causal Reasoning Engine that models the knowledge state and generates counterfactual evidence, and a Symbolic Verification Network that builds dynamic knowledge graphs and performs theorem proving to ensure logical consistency. Across 12 diverse benchmarks, CausalGuard achieves high detection performance (Precision 0.893, Recall 0.917, F1 0.905), strong factual accuracy (0.924), and substantial reduction in hallucinations (about 78% relative to vanilla GPT-4) while preserving response quality (BLEU 0.962) and providing interpretable reasoning traces. The work demonstrates that real-time, explainable, and domain-agnostic hallucination mitigation is feasible without retraining models, enabling safer deployment in sensitive domains like healthcare and finance.

Abstract

While large language models have transformed how we interact with AI systems, they have a critical weakness: they confidently state false information that sounds entirely plausible. This "hallucination" problem has become a major barrier to using these models where accuracy matters most. Existing solutions either require retraining the entire model, add significant computational costs, or miss the root causes of why these hallucinations occur in the first place. We present CausalGuard, a new approach that combines causal reasoning with symbolic logic to catch and prevent hallucinations as they happen. Unlike previous methods that only check outputs after generation, our system understands the causal chain that leads to false statements and intervenes early in the process. CausalGuard works through two complementary paths: one that traces causal relationships between what the model knows and what it generates, and another that checks logical consistency using automated reasoning. Testing across twelve different benchmarks, we found that CausalGuard correctly identifies hallucinations 89.3\% of the time while missing only 8.3\% of actual hallucinations. More importantly, it reduces false claims by nearly 80\% while keeping responses natural and helpful. The system performs especially well on complex reasoning tasks where multiple steps of logic are required. Because CausalGuard shows its reasoning process, it works well in sensitive areas like medical diagnosis or financial analysis where understanding why a decision was made matters as much as the decision itself.

Paper Structure

This paper contains 31 sections, 7 equations, 2 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Limitations of Current Approaches
  3. The Case for Causal-Symbolic Integration
  4. Our Contributions
  5. Related Work
  6. Hallucination in Large Language Models
  7. Causal Inference in NLP
  8. Combining Neural Networks and Logic
  9. Measuring and Adjusting Confidence
  10. How Our System Works
  11. Problem Formulation
  12. CausalGuard Architecture
  13. Causal Reasoning Engine
  14. Symbolic Verification Network
  15. Integration and Decision Making
  16. ...and 16 more sections

Figures (2)

  • Figure 1: CausalGuard Architecture: A neurosymbolic framework combining causal reasoning and symbolic verification for real-time hallucination detection. The dual-path design enables both statistical robustness and logical rigor.
  • Figure 2: Performance comparison across 12 benchmarks (F1 scores). CausalGuard consistently outperforms baselines across diverse tasks, with strong performance on complex reasoning and scientific domains.