# Agentic LLM Framework for Adaptive Decision Discourse

arXiv: https://arxiv.org/abs/2502.10978 · HTML: https://mlsanity.com/paper/2502.10978/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 30 of its 81 references that are in the map.
- First posted on arXiv 16 Feb 2025.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate](https://mlsanity.com/paper/2308.07201/) (2023)
- [Retrieval Augmentation Reduces Hallucination in Conversation](https://mlsanity.com/paper/2104.07567/) (2021)
- [ChemCrow: Augmenting large-language models with chemistry tools](https://mlsanity.com/paper/2304.05376/) (2023)
- [Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents](https://mlsanity.com/paper/2306.03314/) (2023)
- [Hallucination is Inevitable: An Innate Limitation of Large Language Models](https://mlsanity.com/paper/2401.11817/) (2024)
- [State of What Art? A Call for Multi-Prompt LLM Evaluation](https://mlsanity.com/paper/2401.00595/) (2023)
- [Multi-Agent Cooperation and the Emergence of (Natural) Language](https://mlsanity.com/paper/1612.07182/) (2016)
- [Causal Parrots: Large Language Models May Talk Causality But Are Not Causal](https://mlsanity.com/paper/2308.13067/) (2023)
- [Can Large Language Models Infer Causation from Correlation?](https://mlsanity.com/paper/2306.05836/) (2023)
- [Pride and Prejudice: LLM Amplifies Self-Bias in Self-Refinement](https://mlsanity.com/paper/2402.11436/) (2024)
- [CLadder: Assessing Causal Reasoning in Language Models](https://mlsanity.com/paper/2312.04350/) (2023)
- [Self-Improvement in Language Models: The Sharpening Mechanism](https://mlsanity.com/paper/2412.01951/) (2024)
- [Reducing hallucination in structured outputs via Retrieval-Augmented Generation](https://mlsanity.com/paper/2404.08189/) (2024)
- [Towards Mitigating Hallucination in Large Language Models via Self-Reflection](https://mlsanity.com/paper/2310.06271/) (2023)
- [Retrieval-Augmented Generation for Natural Language Processing: A Survey](https://mlsanity.com/paper/2407.13193/) (2024)
- [LLMs learn governing principles of dynamical systems, revealing an in-context neural scaling law](https://mlsanity.com/paper/2402.00795/) (2024)
- [Measuring and Reducing LLM Hallucination without Gold-Standard Answers](https://mlsanity.com/paper/2402.10412/) (2024)
- [Introspective Planning: Aligning Robots' Uncertainty with Inherent Task Ambiguity](https://mlsanity.com/paper/2402.06529/) (2024)
- [Confidence in the Reasoning of Large Language Models](https://mlsanity.com/paper/2412.15296/) (2024)
- [Mind's Mirror: Distilling Self-Evaluation Capability and Comprehensive Thinking from Large Language Models](https://mlsanity.com/paper/2311.09214/) (2023)
- [I Want to Break Free! Persuasion and Anti-Social Behavior of LLMs in Multi-Agent Settings with Social Hierarchy](https://mlsanity.com/paper/2410.07109/) (2024)
- [Rethinking LLM Uncertainty: A Multi-Agent Approach to Estimating Black-Box Model Uncertainty](https://mlsanity.com/paper/2412.09572/) (2024)
- [Multiple Choice Questions: Reasoning Makes Large Language Models (LLMs) More Self-Confident, Especially When They are Wrong](https://mlsanity.com/paper/2501.09775/) (2025)
- [Trust No AI: Prompt Injection Along The CIA Security Triad](https://mlsanity.com/paper/2412.06090/) (2024)
- [Dynamic Sentiment Analysis with Local Large Language Models using Majority Voting: A Study on Factors Affecting Restaurant Evaluation](https://mlsanity.com/paper/2407.13069/) (2024)
- [Achieving Peak Performance for Large Language Models: A Systematic Review](https://mlsanity.com/paper/2409.04833/) (2024)
- [Distributed or Monolithic? A Computational Architecture Decision Framework](https://mlsanity.com/paper/1608.00944/) (2016)

## Built on by (most cited first, 6 of 6)

- [A Survey of Scaling in Large Language Model Reasoning](https://mlsanity.com/paper/2504.02181/) (2025)
- [An LLM-LVLM Driven Agent for Iterative and Fine-Grained Image Editing](https://mlsanity.com/paper/2508.17435/) (2025)
- [LLM Agents for Deliberative Collaboration: A Study on Joint Decision Making Under Partial Observability](https://mlsanity.com/paper/2607.06157/) (2026)
- [Conflict-Resilient Multi-Agent Reasoning via Signed Graph Modeling](https://mlsanity.com/paper/2605.19418/) (2026)
- [On the Role of Contextual Information and Ego States in LLM Agent Behavior for Transactional Analysis Dialogues](https://mlsanity.com/paper/2512.17060/) (2025)
- [Risk Is Not the Target: A Monotonic Framework for Evaluating Wildfire Operational Risk Signals](https://mlsanity.com/paper/2607.21597/) (2026)

## Abstract (arXiv)

Effective decision-making in complex systems requires synthesizing diverse perspectives to address multifaceted challenges under uncertainty. This study introduces an agentic Large Language Models (LLMs) framework for simulating decision discourse - the deliberative process through which actionable strategies are collaboratively developed. Unlike traditional decision-support tools, this framework simulates diverse stakeholder personas, each bringing unique priorities, expertise and value-driven reasoning to a dialogue that emphasizes trade-off exploration in a self-governed assembly. We present explorative results fostering robust and equitable recommendations, with two use cases: first, our framework simulates a response to the floods that occurred on July 2025 in Texas; second, a hypothetical extreme flooding in a Midwestern township under varying forecasting uncertainty. Recommendations made balance competing priorities considered through social, economic and environmental dimensions, setting a foundation for scalable and context-aware recommendations and transforming how decisions for real-world high-stake scenarios can be approached in digital environments. This research explores novel and alternate routes leveraging agentic LLMs for adaptive, collaborative, and equitable recommendations, with implications across domains where uncertainty and complexity converge.