Are You the A-hole? A Fair, Multi-Perspective Ethical Reasoning Framework
On the map: LLM reasoning → Reasoning benchmarks and evaluation
Abstract (arXiv)
Standard methods for aggregating natural language judgments, such as majority voting, often fail to produce logically consistent results when applied to high-conflict domains, treating differing opinions as noise. We propose a neuro-symbolic aggregation framework that formalizes conflict resolution through Weighted Maximum Satisfiability (MaxSAT). Our pipeline utilizes a language model to map unstructured natural language explanations into interpretable logical predicates and confidence weights. These components are then encoded as soft constraints within the Z3 solver, transforming the aggregation problem into an optimization task that seeks the maximum consistency across conflicting testimony. Using the Reddit r/AmItheAsshole forum as a case study in large-scale moral disagreement, our system generates logically coherent verdicts that diverge from popularity-based labels 62% of the time, corroborated by an 86% agreement rate with independent human evaluators. This study demonstrates the efficacy of coupling neural semantic extraction with formal solvers to enforce logical soundness and explainability in the aggregation of noisy human reasoning.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 7 of its 23 references that are in the map. First posted on arXiv 30 Apr 2026.
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- 2025Structured Moral Reasoning in Language Models: A Value-Grounded Evaluation Framework17 Jun
- 2023SatLM: Satisfiability-Aided Language Models Using Declarative Prompting16 May
Other references · 5
- 2022Jury Learning: Integrating Dissenting Voices into Machine Learning Modelscited by 115
- 2020Scruples: A Corpus of Community Ethical Judgments on 32,000 Real-Life Anecdotescited by 100
- 2025Actions Speak Louder than Words: Agent Decisions Reveal Implicit Biases in Language Modelscited by 30
- 2022Fairify: Fairness Verification of Neural Networkscited by 22
- 2023AITA Generating Moral Judgements of the Crowd with Reasoningcited by 1
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- 2024Enhancing Ethical Explanations of Large Language Models through Iterative Symbolic Refinement
- 2025Probabilistic Aggregation and Targeted Embedding Optimization for Collective Moral Reasoning in Large Language Models
- 2026JuryFlow: Disagreement-Guided Human-in-the-Loop Multi-Agent Evaluation
- 2026VERDICT: Training-Free Step-Wise Verification of Multimodal Reasoning via Disagreement-Aware Consensus
- 2026When AI Agents Disagree Like Humans: Reasoning Trace Analysis for Human-AI Collaborative Moderation
- 2026Understanding Moral Reasoning Trajectories in Large Language Models: Toward Probing-Based Explainability
- 2023NeuroQL: A Neuro-Symbolic Language and Dataset for Inter-Subjective Reasoning
- 2026Unsupervised Elicitation of Moral Values from Language Models