# Identifying Implicit Premises for Logical Reconstruction of Argument Graphs

arXiv: https://arxiv.org/abs/2608.18821 · HTML: https://mlsanity.com/paper/2608.18821/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 19 Aug 2026.

## Builds on (most cited first, 7 of 7)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models](https://mlsanity.com/paper/2512.02556/) (2025)
- [C-Pack: Packed Resources For General Chinese Embeddings](https://mlsanity.com/paper/2309.07597/) (2023)
- [The Argument Reasoning Comprehension Task: Identification and Reconstruction of Implicit Warrants](https://mlsanity.com/paper/1708.01425/) (2017)
- [Maximum Bayes Smatch Ensemble Distillation for AMR Parsing](https://mlsanity.com/paper/2112.07790/) (2021)
- [Neuro-symbolic Commonsense Social Reasoning](https://mlsanity.com/paper/2303.08264/) (2023)
- [Making Implicit Premises Explicit in Logical Understanding of Enthymemes](https://mlsanity.com/paper/2603.06114/) (2026)

## Abstract (arXiv)

The logical reconstruction of argument graphs from natural language text is challenging because of the prevalence of enthymemes (i.e., arguments with implicit premises). There are natural language processing methods for identifying enthymemes in text, and there are symbolic methods based on abduction for identifying missing premises in a logical representation of enthymemes. However, there is a need for methods to generate implicit premises to logically show a known entailment or contradiction relationship between a pair of statements. To address this, we propose a neuro-symbolic pipeline that uses large language models (LLMs) to generate intermediate implicit premises that are translated into logical formulae and used with logical formulae representing explicit premises and explicit claims to show the logical relationships between them (entailment, contradiction, or neutrality). Our approach is evaluated on the Microtext Argumentative Corpus.