# Full Automation of Goal-driven LLM Dialog Threads with And-Or Recursors and Refiner Oracles

arXiv: https://arxiv.org/abs/2306.14077 · HTML: https://mlsanity.com/paper/2306.14077/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning.
- Builds on 15 of its 29 references that are in the map.
- First posted on arXiv 24 Jun 2023.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 15 of 15)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Let's Verify Step by Step](https://mlsanity.com/paper/2305.20050/) (2023)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Neural Probabilistic Logic Programming in DeepProbLog](https://mlsanity.com/paper/1907.08194/) (2019)
- [Deductive Verification of Chain-of-Thought Reasoning](https://mlsanity.com/paper/2306.03872/) (2023)
- [Neuro-Symbolic Artificial Intelligence: Current Trends](https://mlsanity.com/paper/2105.05330/) (2021)
- [Constraint Answer Set Programming without Grounding](https://mlsanity.com/paper/1804.11162/) (2018)
- [Automated Interactive Domain-Specific Conversational Agents that Understand Human Dialogs](https://mlsanity.com/paper/2303.08941/) (2023)
- [Natlog: a Lightweight Logic Programming Language with a Neuro-symbolic Touch](https://mlsanity.com/paper/2109.08291/) (2021)
- [A Question-Answer Driven Approach to Reveal Affirmative Interpretations from Verbal Negations](https://mlsanity.com/paper/2205.11467/) (2022)
- [Abductive Reasoning in Intuitionistic Propositional Logic via Theorem Synthesis](https://mlsanity.com/paper/2205.05728/) (2022)

## Built on by (most cited first, 2 of 2)

- [Through the Looking Glass, and what Horn Clause Programs Found There](https://mlsanity.com/paper/2407.20413/) (2024)
- [Modeling Next-Token Prediction as Left-Nested Intuitionistic Implication](https://mlsanity.com/paper/2601.19915/) (2026)

## Abstract (arXiv)

We automate deep step-by step reasoning in an LLM dialog thread by recursively exploring alternatives (OR-nodes) and expanding details (AND-nodes) up to a given depth. Starting from a single succinct task-specific initiator we steer the automated dialog thread to stay focussed on the task by synthesizing a prompt that summarizes the depth-first steps taken so far. Our algorithm is derived from a simple recursive descent implementation of a Horn Clause interpreter, except that we accommodate our logic engine to fit the natural language reasoning patterns LLMs have been trained on. Semantic similarity to ground-truth facts or oracle advice from another LLM instance is used to restrict the search space and validate the traces of justification steps returned as answers. At the end, the unique minimal model of a generated Horn Clause program collects the results of the reasoning process. As applications, we sketch implementations of consequence predictions, causal explanations, recommendation systems and topic-focussed exploration of scientific literature.