Measuring and Improving Behavioral Consistency in Large Language Models through Fact-Heuristic-Emotion State Enforcement
Improving LLM behavioral consistency using the Cognitive Kernel Model (CKM), reducing decision flip rate by 82%.
Key Findings
Methodology
The study introduces the Cognitive Kernel Model (CKM), a prompt-level state enforcement method. Before making decisions, the model separates input into Fact, Heuristic, and Emotion roles. CKM maintains a structured state via a transition function without altering model weights.
Key Results
- CKM reduced repeated-output variability, with random-effects Hedges' g=1.09, 95% CI [0.83, 1.35], across 31 model pairs.
- State persistence reduced decision flip rate by 82% in newer models, g=1.52.
- CKM's advantage increased under sampling stochasticity, reaching g=2.87 at temperature 0.7.
Significance
This study demonstrates the measurability of behavioral consistency in LLMs and partially improves it by enforcing the separation of facts, assumptions, and evaluative signals before decision-making. It provides a new path for assessing and enhancing model behavioral stability, with significant academic and practical implications.
Technical Contribution
The technical contribution lies in proposing a state enforcement framework that improves behavioral consistency across different vendors and architectures without altering model weights. This method enables cross-vendor analysis of model behavior.
Novelty
This method is the first to introduce the separation of Fact, Heuristic, and Emotion roles in LLMs, differing from traditional prompt engineering and memory systems, offering a new path for behavioral consistency improvement.
Limitations
- CKM does not improve reasoning correctness, requiring further research to enhance decision quality.
- The method may be less effective in complex scenarios.
Future Work
Future research could explore integrating CKM with other prompt engineering techniques to further enhance reasoning capabilities and consistency.
AI Executive Summary
Large language models (LLMs) can give different answers to the same decision problem, even reversing decisions when prior answers are reintroduced as context. To address this instability, researchers propose the Cognitive Kernel Model (CKM), a prompt-level state enforcement method. CKM requires models to separate inputs into Fact, Heuristic, and Emotion roles before decision-making.
Experiments were conducted on 26 models from four vendors, involving 37,403 observations. Results showed that CKM significantly reduced repeated-output variability and decreased decision flip rates by 82% in newer models. Additionally, CKM's advantage increased under sampling stochasticity.
While CKM does not improve reasoning correctness, it partially enhances behavioral consistency by enforcing the separation of information sources. This study provides a new path for assessing and improving the behavioral stability of LLMs, with significant academic and practical implications.
Deep Analysis
Background
Large language models have made significant advances in natural language processing, but their behavioral consistency remains an unresolved issue. Existing research primarily focuses on evaluating model capabilities, overlooking consistency in behavior with identical inputs.
Core Problem
LLMs can give different answers to the same decision problem, which can lead to trust issues in practical applications, such as a medical assistant providing different advice for the same symptoms.
Innovation
The study introduces the Cognitive Kernel Model (CKM), which reduces behavioral variability by requiring models to separate inputs into Fact, Heuristic, and Emotion roles before decision-making. This method does not alter model weights and is applicable to various model architectures.
Methodology
- �� Classify input information into Fact, Heuristic, and Emotion.
- �� Update structured state via a transition function.
- �� Maintain state consistency across multi-turn trials.
Experiments
Experiments were conducted on 26 models, involving four vendors and 37,403 observations. Four core experiments and multi-arm ablation studies evaluated CKM's effectiveness.
Results
CKM reduced repeated-output variability, significantly lowered decision flip rates in newer models, and showed greater advantages under sampling stochasticity.
Applications
CKM can be used to improve decision stability in medical, legal, and financial fields, reducing trust issues caused by model inconsistency.
Limitations & Outlook
CKM does not improve reasoning correctness and may be less effective in complex scenarios. Future research should explore its performance in different application contexts.
Plain Language Accessible to non-experts
Imagine a kitchen where a chef needs to clearly identify ingredients, seasonings, and cooking methods before preparing a dish. CKM acts like a checklist guiding the chef, ensuring each step is clear. Similarly, CKM helps LLMs by clearly distinguishing facts, assumptions, and emotions, reducing uncertainty in decision-making.
ELI14 Explained like you're 14
Imagine playing a game where your character reacts differently to the same enemy each time. Confusing, right? CKM is like a game guide that helps your character react consistently in the same situations. This way, you won't lose the game because of random character behavior!
Glossary
Cognitive Kernel Model
A prompt-level state enforcement method requiring models to classify inputs into Fact, Heuristic, and Emotion roles before decision-making.
Used to improve LLM behavioral consistency.
State Enforcement
A method that requires models to clearly distinguish different types of information before decision-making.
Core mechanism of CKM.
Decision Flip Rate
The frequency at which a model changes its decision with the same input.
Used to evaluate model consistency.
Hedges' g
A statistical measure used to assess effect size.
Quantifies CKM's impact on behavioral consistency.
Sampling Stochasticity
The variability in model output under different sampling conditions.
CKM's performance at different temperatures.
Open Questions Unanswered questions from this research
- 1 How to further improve reasoning correctness without altering model weights?
- 2 How does CKM perform in multilingual environments?
Applications
Immediate Applications
Medical Assistant
Improve consistency in medical advice for the same symptoms, reducing patient distrust.
Long-term Vision
Cross-Domain Applications
Apply CKM in legal and financial fields to enhance decision stability and reliability.
Abstract
Large language models (LLMs) can give different answers to the same decision problem across runs, and reverse a decision when their own prior answer returns as context. We ask whether this instability can be measured and partially reduced without changing model weights. We test the Cognitive Kernel Model (CKM), a prompt-level state-enforcement layer. Before deciding, the model must separate its input into three epistemic roles: Fact (given or verifiable), Heuristic (inferred or assumed), and Emotion (evaluative or priority signal). CKM adds no capability; it forces the model to track what kind of information it uses before acting. Formally it maintains a structured state S_t = {F_t, H_t, E_t} updated by a transition function. We evaluate CKM on Korean-language decision scenarios (ambiguity, ethical conflict, resource allocation, error handling) across 26 LLMs from four vendors and 37,403 observations, via four core experiments, a 4-arm ablation, a 5-arm sham-restriction ablation, and a temperature probe. Findings: (1) CKM reduces repeated-output variability (random-effects Hedges' g=1.09, 95% CI [0.83, 1.35], 31 model pairs); (2) state persistence cuts the decision-flip rate by 82% in newer models (g=1.52); (3) the effect is not JSON formatting alone (value-only recomputation, g=2.24); (4) intrinsic randomness under fixed anchor states is negligible; (5) the advantage grows under sampling stochasticity (g=2.87 at temperature 0.7); (6) a sham ablation attributes about 45% of the gain to structural scaffolding and 55% to Fact/Heuristic/Emotion content, and CKM is the only arm that both raises consistency and reduces flipping. CKM does not improve reasoning correctness. The narrower result: behavioral consistency is measurable, varies across models, and is partially improvable by forcing models to separate facts, assumptions, and evaluative signals before deciding.