# Knowing But Not Doing: Convergent Morality and Divergent Action in LLMs

arXiv: https://arxiv.org/abs/2601.07972 · HTML: https://mlsanity.com/paper/2601.07972/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 40 references that are in the map.
- First posted on arXiv 12 Jan 2026.

## Builds on (most cited first, 20 of 20)

- [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Kimi K2: Open Agentic Intelligence](https://mlsanity.com/paper/2507.20534/) (2025)
- [GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models](https://mlsanity.com/paper/2508.06471/) (2025)
- [Revolutionizing Finance with LLMs: An Overview of Applications and Insights](https://mlsanity.com/paper/2401.11641/) (2024)
- [Hidden Persuaders: LLMs' Political Leaning and Their Influence on Voters](https://mlsanity.com/paper/2410.24190/) (2024)
- [Are Large Language Models Consistent over Value-laden Questions?](https://mlsanity.com/paper/2407.02996/) (2024)
- [How Well Do LLMs Represent Values Across Cultures? Empirical Analysis of LLM Responses Based on Hofstede Cultural Dimensions](https://mlsanity.com/paper/2406.14805/) (2024)
- [Values in the Wild: Discovering and Analyzing Values in Real-World Language Model Interactions](https://mlsanity.com/paper/2504.15236/) (2025)
- [Mind the Value-Action Gap: Do LLMs Act in Alignment with Their Values?](https://mlsanity.com/paper/2501.15463/) (2025)
- [Large Language Models as Superpositions of Cultural Perspectives](https://mlsanity.com/paper/2307.07870/) (2023)
- [The Personality Illusion: Revealing Dissociation Between Self-Reports & Behavior in LLMs](https://mlsanity.com/paper/2509.03730/) (2025)
- [Actions Speak Louder than Words: Agent Decisions Reveal Implicit Biases in Language Models](https://mlsanity.com/paper/2501.17420/) (2025)
- [High-Dimension Human Value Representation in Large Language Models](https://mlsanity.com/paper/2404.07900/) (2024)
- [What does ChatGPT return about human values? Exploring value bias in ChatGPT using a descriptive value theory](https://mlsanity.com/paper/2304.03612/) (2023)
- [Stick to your Role! Stability of Personal Values Expressed in Large Language Models](https://mlsanity.com/paper/2402.14846/) (2024)
- [Do LLMs have Consistent Values?](https://mlsanity.com/paper/2407.12878/) (2024)
- [From Values to Opinions: Predicting Human Behaviors and Stances Using Value-Injected Large Language Models](https://mlsanity.com/paper/2310.17857/) (2023)
- [SPeCtrum: A Grounded Framework for Multidimensional Identity Representation in LLM-Based Agent](https://mlsanity.com/paper/2502.08599/) (2025)
- [AdaptJobRec: Enhancing Conversational Career Recommendation through an LLM-Powered Agentic System](https://mlsanity.com/paper/2508.13423/) (2025)

## Built on by (most cited first, 7 of 7)

- [The Chameleon's Limit: Investigating Persona Collapse and Homogenization in Large Language Models](https://mlsanity.com/paper/2604.24698/) (2026)
- [The Judgment-Consequence Gap: LLM Moral Reasoning in Healthcare Decisions](https://mlsanity.com/paper/2608.05583/) (2026)
- [AI Revealed Preferences](https://mlsanity.com/paper/2608.26178/) (2026)
- [When Counterbalancing Hides the Bias: Access-Conditioned Position Lock in Forced-Choice LLM Evaluation](https://mlsanity.com/paper/2607.10202/) (2026)
- [Deflecting the Value Compass: Interacting with Large Language Models Temporarily Shifts Human Value Priorities Toward Personal Focus](https://mlsanity.com/paper/2609.25586/) (2026)
- [On the Behavioral Traits of LLM Agents](https://mlsanity.com/paper/2609.32776/) (2026)
- [Principled Under Pressure: Post-Training Decides Whether LLMs Act on Their Own Moral Judgment](https://mlsanity.com/paper/2610.08670/) (2026)

## Abstract (arXiv)

Value alignment is central to the development of safe and socially compatible artificial intelligence. However, how Large Language Models (LLMs) represent and enact human values in real-world decision contexts remains under-explored. We present ValAct-15k, a dataset of 3,000 advice-seeking scenarios derived from Reddit, designed to elicit ten values defined by Schwartz Theory of Basic Human Values. Using both the scenario-based questions and the traditional value questionnaire, we evaluate ten frontier LLMs (five from U.S. companies, five from Chinese ones) and human participants (n = 55). We find near-perfect cross-model consistency in scenario-based decisions (Pearson r ≈ 1.0), contrasting sharply with the broad variability observed among humans (r ∈ [-0.79, 0.98]). Yet, both humans and LLMs show weak correspondence between self-reported and enacted values (r = 0.4, 0.3), revealing a systematic knowledge-action gap. When instructed to "hold" a specific value, LLMs' performance declines up to 6.6% compared to merely selecting the value, indicating a role-play aversion. These findings suggest that while alignment training yields normative value convergence, it does not eliminate the human-like incoherence between knowing and acting upon values.