# Situated Epistemic Infrastructures: A Diagnostic Framework for Post-Coherence Knowledge

arXiv: https://arxiv.org/abs/2508.04995 · HTML: https://mlsanity.com/paper/2508.04995/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 62 references that are in the map.
- First posted on arXiv 7 Aug 2025.

## Builds on (most cited first, 6 of 6)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm](https://mlsanity.com/paper/2102.07350/) (2021)
- [ChatGPT and a New Academic Reality: Artificial Intelligence-Written Research Papers and the Ethics of the Large Language Models in Scholarly Publishing](https://mlsanity.com/paper/2303.13367/) (2023)
- [Beyond Tools: Generative AI as Epistemic Infrastructure in Education](https://mlsanity.com/paper/2504.06928/) (2025)

## Abstract (arXiv)

Large Language Models (LLMs) such as ChatGPT have rendered visible the fragility of contemporary knowledge infrastructures by simulating coherence while bypassing traditional modes of citation, authority, and validation. This paper introduces the Situated Epistemic Infrastructures (SEI) framework as a diagnostic tool for analyzing how knowledge becomes authoritative across hybrid human-machine systems under post-coherence conditions. Rather than relying on stable scholarly domains or bounded communities of practice, SEI traces how credibility is mediated across institutional, computational, and temporal arrangements. Integrating insights from infrastructure studies, platform theory, and epistemology, the framework foregrounds coordination over classification, emphasizing the need for anticipatory and adaptive models of epistemic stewardship. The paper contributes to debates on AI governance, knowledge production, and the ethical design of information systems by offering a robust alternative to representationalist models of scholarly communication.