# Explainable Belief Harmonization under Dynamic Epistemic Partitions

arXiv: https://arxiv.org/abs/2607.21210 · HTML: https://mlsanity.com/paper/2607.21210/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 35 references that are in the map.
- First posted on arXiv 23 Jul 2026.

## Builds on (most cited first, 12 of 12)

- [Multi-shot ASP solving with clingo](https://mlsanity.com/paper/1705.09811/) (2017)
- [On Verifiable Legal Reasoning: A Multi-Agent Framework with Formalized Knowledge Representations](https://mlsanity.com/paper/2509.00710/) (2025)
- [Explainable Rule Application via Structured Prompting: A Neural-Symbolic Approach](https://mlsanity.com/paper/2506.16335/) (2025)
- [Answering the "why" in Answer Set Programming - A Survey of Explanation Approaches](https://mlsanity.com/paper/1809.08034/) (2018)
- [Synergizing Logical Reasoning, Knowledge Management and Collaboration in Multi-Agent LLM System](https://mlsanity.com/paper/2507.02170/) (2025)
- [Evaluating Theory of Mind and Internal Beliefs in LLM-Based Multi-Agent Systems](https://mlsanity.com/paper/2603.00142/) (2026)
- [Modelling Multi-Agent Epistemic Planning in ASP](https://mlsanity.com/paper/2008.03007/) (2020)
- [LPMLN, Weak Constraints, and P-log](https://mlsanity.com/paper/2506.12784/) (2025)
- [Natlog: Embedding Logic Programming into the Python Deep-Learning Ecosystem](https://mlsanity.com/paper/2308.15890/) (2023)
- [The Janus System: Multi-paradigm Programming in Prolog and Python](https://mlsanity.com/paper/2308.15893/) (2023)
- [Epistemic Logic Programs: a study of some properties](https://mlsanity.com/paper/2309.16344/) (2023)
- [Pearce's Characterisation in an Epistemic Domain](https://mlsanity.com/paper/2502.09221/) (2025)

## Abstract (arXiv)

Existing approaches to multi-agent belief combination have established mature foundations for combining uncertain beliefs under common assumptions: consensus methods use iterative averaging, logic-based methods resolve conflicting knowledge bases, and epistemic logic analyzes agents' information states. Typically, these approaches assume that the structure determining what each agent can represent remains fixed. However, in many scenarios, agents gain or lose observational capacity during execution, and what was once admissible may become structurally impossible. This paper presents a formal framework for handling such runtime changes in epistemic partitions over continuous belief profiles. A hybrid approach exploits the advantages of answer set programming in elaboration tolerance, declarative integrity constraints, and explanations, with the numerical flexibility of Python. The framework applies to domains where agents operate at heterogeneous and possibly changing levels of resolution, and provides formal guarantees of admissibility preservation under refinement, unique mass-preserving repair under coarsening, and explanation completeness. Evaluation across 100 randomly generated topology changes confirms complete violation detection and explanation coverage.