Technology Category
Machine Learning: Neuro-Symbolic LearningNatural Language Processing: (Large) Language ModelsKnowledge Representation and Reasoning: Computational Complexity of Reasoning
From Natural Language to Executable Narsese: A Neuro-Symbolic Benchmark and Pipeline for Reasoning with NARS
🤖 AI Summary
This work addresses the limited reliability of large language models in tasks requiring explicit symbolic structures, multi-step reasoning, and uncertainty representation. The authors propose a neuro-symbolic framework that compiles natural language reasoning problems into executable Narsese programs, leveraging the OpenNARS runtime to ensure semantic alignment through program execution. Key contributions include the creation of NARS-Reasoning-v0.1—the first benchmark integrating natural language, first-order logic, and executable Narsese—and the introduction of a language structure-aware (LSP) training paradigm. The approach combines deterministic FOL-to-Narsese compilation, LoRA fine-tuning of Phi-2, and three-label (True/False/Uncertain) supervised learning. Experimental results demonstrate that the benchmark effectively supports supervised fine-tuning and enables interpretable evaluation grounded in program execution.
Technology Category
Application Category
📝 Abstract
Large language models (LLMs) are highly capable at language generation, but they remain unreliable when reasoning requires explicit symbolic structure, multi-step inference, and interpretable uncertainty. This paper presents a neuro-symbolic framework for translating natural-language reasoning problems into executable formal representations using first-order logic (FOL) and Narsese, the language of the Non-Axiomatic Reasoning System (NARS). To support this direction, we introduce NARS-Reasoning-v0.1, a benchmark of natural-language reasoning problems paired with FOL forms, executable Narsese programs, and three gold labels: True, False, and Uncertain. We develop a deterministic compilation pipeline from FOL to executable Narsese and validate retained examples through runtime execution in OpenNARS for Applications (ONA), ensuring that the symbolic targets are not only syntactically well formed but also behaviorally aligned with the intended answer. We further present Language-Structured Perception (LSP), a formulation in which an LLM is trained to produce reasoning-relevant symbolic structure rather than only a final verbal response. As an initial proof of concept, we also train and release a Phi-2 LoRA adapter on NARS-Reasoning-v0.1 for three-label reasoning classification, showing that the benchmark can support supervised adaptation in addition to executable evaluation. Overall, the paper positions executable symbolic generation and execution-based validation as a practical path toward more reliable neuro-symbolic reasoning systems.
Problem
Research questions and friction points this paper is trying to address.
neuro-symbolic reasoning
natural language to formal representation
executable reasoning
uncertainty in reasoning
symbolic structure
Innovation
Methods, ideas, or system contributions that make the work stand out.
Neuro-Symbolic Reasoning
Executable Narsese
NARS-Reasoning Benchmark
Language-Structured Perception
FOL-to-Narsese Compilation
🔎 Similar Papers
No similar papers found.
