# Propensity Inference: Environmental Contributors to LLM Behaviour

arXiv: https://arxiv.org/abs/2604.21098 · HTML: https://mlsanity.com/paper/2604.21098/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 40 references that are in the map.
- First posted on arXiv 22 Apr 2026.

## Builds on (most cited first, 24 of 24)

- [Alignment faking in large language models](https://mlsanity.com/paper/2412.14093/) (2024)
- [Frontier Models are Capable of In-context Scheming](https://mlsanity.com/paper/2412.04984/) (2024)
- [Risks from Learned Optimization in Advanced Machine Learning Systems](https://mlsanity.com/paper/1906.01820/) (2019)
- [The Alignment Problem from a Deep Learning Perspective](https://mlsanity.com/paper/2209.00626/) (2022)
- [Emergent Misalignment: Narrow finetuning can produce broadly misaligned LLMs](https://mlsanity.com/paper/2502.17424/) (2025)
- [Agentic Misalignment: How LLMs Could Be Insider Threats](https://mlsanity.com/paper/2510.05179/) (2025)
- [Measuring AI Ability to Complete Long Software Tasks](https://mlsanity.com/paper/2503.14499/) (2025)
- [Large Language Models can Strategically Deceive their Users when Put Under Pressure](https://mlsanity.com/paper/2311.07590/) (2023)
- [Natural Emergent Misalignment from Reward Hacking in Production RL](https://mlsanity.com/paper/2511.18397/) (2025)
- [Scheming AIs: Will AIs fake alignment during training in order to get power?](https://mlsanity.com/paper/2311.08379/) (2023)
- [Stress Testing Deliberative Alignment for Anti-Scheming Training](https://mlsanity.com/paper/2509.15541/) (2025)
- [Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path?](https://mlsanity.com/paper/2502.15657/) (2025)
- [Gradual Disempowerment: Systemic Existential Risks from Incremental AI Development](https://mlsanity.com/paper/2501.16946/) (2025)
- [Uncovering Deceptive Tendencies in Language Models: A Simulated Company AI Assistant](https://mlsanity.com/paper/2405.01576/) (2024)
- [A Rosetta Stone for AI Benchmarks](https://mlsanity.com/paper/2512.00193/) (2025)
- [Incomplete Tasks Induce Shutdown Resistance in Some Frontier LLMs](https://mlsanity.com/paper/2509.14260/) (2025)
- [PropensityBench: Evaluating Latent Safety Risks in Large Language Models via an Agentic Approach](https://mlsanity.com/paper/2511.20703/) (2025)
- [Why Do Some Language Models Fake Alignment While Others Don't?](https://mlsanity.com/paper/2506.18032/) (2025)
- [Lessons from a Chimp: AI "Scheming" and the Quest for Ape Language](https://mlsanity.com/paper/2507.03409/) (2025)
- [Planning in a recurrent neural network that plays Sokoban](https://mlsanity.com/paper/2407.15421/) (2024)
- [HiBayES: A Hierarchical Bayesian Modeling Framework for AI Evaluation Statistics](https://mlsanity.com/paper/2505.05602/) (2025)
- [Evaluating the Goal-Directedness of Large Language Models](https://mlsanity.com/paper/2504.11844/) (2025)
- [The Loss of Control Playbook: Degrees, Dynamics, and Preparedness](https://mlsanity.com/paper/2511.15846/) (2025)
- [Why Do Language Model Agents Whistleblow?](https://mlsanity.com/paper/2511.17085/) (2025)

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

- [Multi-Agent AI Control: Distributed Attacks Hamper Per-Instance Monitors](https://mlsanity.com/paper/2607.07368/) (2026)
- [Gram: Assessing sabotage propensities via automated alignment auditing](https://mlsanity.com/paper/2605.30322/) (2026)
- [Realistic honeypot evaluations for scheming propensity](https://mlsanity.com/paper/2605.29729/) (2026)
- [LLMs Can Leak Training Data But Do They Want To? A Propensity-Aware Evaluation of Memorization in LLMs](https://mlsanity.com/paper/2606.06286/) (2026)
- [Model Forensics: Investigating Whether Concerning Behavior Reflects Misalignment](https://mlsanity.com/paper/2606.26071/) (2026)
- [Causal Behavioral Evaluation of AI Agents at Scale via Automated Behavioral Science](https://mlsanity.com/paper/2608.10030/) (2026)
- [AI Persuasion as a Threat to Human Control](https://mlsanity.com/paper/2609.14796/) (2026)

## Abstract (arXiv)

Motivated by loss of control risks from misaligned AI systems, we develop and apply methods for measuring language models' propensity for unsanctioned behaviour. We contribute three methodological improvements: analysing effects of changes to environmental factors on behaviour, quantifying effect sizes via Bayesian generalised linear models, and taking explicit measures against circular analysis. We apply the methodology to measure the effects of 12 environmental factors (6 strategic in nature, 6 non-strategic) and thus the extent to which behaviour is explained by strategic aspects of the environment, a question relevant to risks from misalignment. Across 23 language models and 11 evaluation environments, we find approximately equal contributions from strategic and non-strategic factors for explaining behaviour, do not find strategic factors becoming more or less influential as capabilities improve, and find some evidence for a trend for increased sensitivity to goal conflicts. Finally, we highlight a key direction for future propensity research: the development of theoretical frameworks and cognitive models of AI decision-making into empirically testable forms.