# Epistemic-Probabilistic Model for Guarded Multi-Agent LLM Coordination

arXiv: https://arxiv.org/abs/2609.29366 · HTML: https://mlsanity.com/paper/2609.29366/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 24 Sep 2026.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 9 of 9)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Multi-Agent Reinforcement Learning with Temporal Logic Specifications](https://mlsanity.com/paper/2102.00582/) (2021)
- [Large Language Models Miss the Multi-Agent Mark](https://mlsanity.com/paper/2505.21298/) (2025)
- [The Fusion of Large Language Models and Formal Methods for Trustworthy AI Agents: A Roadmap](https://mlsanity.com/paper/2412.06512/) (2024)
- [Adversarial Representation Engineering: A General Model Editing Framework for Large Language Models](https://mlsanity.com/paper/2404.13752/) (2024)
- [A DbC Inspired Neurosymbolic Layer for Trustworthy Agent Design](https://mlsanity.com/paper/2508.03665/) (2025)
- [Common Knowledge in a Logic of Gossips](https://mlsanity.com/paper/1707.08734/) (2017)
- [Conditional Belief, Knowledge and Probability](https://mlsanity.com/paper/1707.08744/) (2017)
- [Strengthening Gossip Protocols using Protocol-Dependent Knowledge](https://mlsanity.com/paper/1907.12321/) (2019)

## Abstract (arXiv)

Multi-agent large language models (LLMs) have become ubiquitous in applied AI, yet their theoretical foundations remain surprisingly understudied. Viewed through the lens of multi-agent systems theory, several shortcomings come to light: a lack of social intelligence, the absence of coordination mechanisms among agents, unknown emergent behavior, and interactions between agents that are bounded by natural language. We address two of these gaps: the absence of social behavior and the lack of mechanisms for inter-agent coordination. We introduce Epistemic Probabilistic Language Agents (EPLA), a neuro-symbolic architecture for multi-agent coordination under uncertainty. A Symbolic Guard provides structured diagnostic feedback. The LLM generates typed actions, and the Guard controls their execution against an authoritative symbolic state. We formalize the epistemic layer in a gossip testbed through epistemic lottery gossip models, which combine view-based call histories with agent-indexed probability weights. We argue that implementing such a formalism can address shortcomings of agentic LLMs.