# Why Conclusions Diverge from the Same Observations: Formalizing World-Model Non-Identifiability via an Inference

arXiv: https://arxiv.org/abs/2605.12255 · HTML: https://mlsanity.com/paper/2605.12255/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in World models for language agents, one of 11 directions in World models.
- Builds on 3 of its 16 references that are in the map.
- First posted on arXiv 12 May 2026.

## Placed in

- World models / [World models for language agents](https://mlsanity.com/wm/world-models-for-language-agents/)

## Builds on (most cited first, 3 of 3)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations](https://mlsanity.com/paper/1811.12359/) (2018)
- [Demystifying the Draft EU Artificial Intelligence Act](https://mlsanity.com/paper/2107.03721/) (2021)

## Built on by (most cited first, 2 of 2)

- [Toward AI That Understands Self and Others: A World-Model Theory of Cognitive Diversity and Alignment](https://mlsanity.com/paper/2605.29930/) (2026)
- [Exact Distinguishability in Non-Markovian Decision Processes](https://mlsanity.com/paper/2610.01527/) (2026)

## Abstract (arXiv)

When people share the same documents and observations yet reach different conclusions, the disagreement often shifts into a judgment that the other party is cognitively defective, irrational, or acting in bad faith. This paper argues that such divergence is better described as a form of non-identifiability inherent in inference and learning, rather than as a defect of the other party. We organize the phenomenon into two levels: (i) θ-level non-identifiability, where conclusions diverge under the same world model W because inference settings differ; and (ii) W-level non-identifiability, where repeated use of an inference setting θ biases data exposure and update rules, causing the learned world model W itself to diverge. We introduce an inference profile θ = (R, E, S, D), consisting of Reference, Exploration, Stabilization, and Horizon, and show how outputs can split even for the same observation o and the same W. We further explain why disagreements tend to project onto a small number of bases -- abstract versus concrete, externalizability, and order versus freedom -- as a consequence of general constraints on learning systems: computational, observational, and coordination constraints. Finally, we relate the framework to deep representation learning, including representation hierarchy, latent-state estimation, and regularization-exploration trade-offs, and illustrate the framework through a case study on AI regulation debates.