pith. machine review for the scientific record. sign in

arxiv: 2604.18873 · v1 · submitted 2026-04-20 · 💻 cs.AI

Recognition: unknown

From Natural Language to Executable Narsese: A Neuro-Symbolic Benchmark and Pipeline for Reasoning with NARS

Mina Gabriel, Pei Wang

Authors on Pith no claims yet

Pith reviewed 2026-05-10 04:05 UTC · model grok-4.3

classification 💻 cs.AI
keywords neuro-symbolic reasoningNARSNarsesereasoning benchmarkfirst-order logicexecutable programslanguage models
0
0 comments X

The pith

A benchmark and deterministic pipeline translate natural language reasoning into executable Narsese programs that run in NARS to confirm true, false, or uncertain answers.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper sets out to show that natural language reasoning tasks can be turned into formal symbolic targets whose correctness can be checked by actual execution rather than by the model's guess alone. It builds a benchmark of problems each paired with a first-order logic form, a compiled Narsese program, and a gold label of true, false, or uncertain. A fixed compilation step converts the logic into Narsese that is then run inside OpenNARS for Applications so that only examples whose runtime behavior matches the intended label are kept. The same data is used to train a language model to emit the symbolic structure itself instead of a direct verbal answer. This matters because it gives a concrete, checkable route for models to produce reasoning that can be inspected and corrected by an external symbolic engine.

Core claim

The paper's central claim is that a deterministic pipeline can compile first-order logic representations of natural-language reasoning problems into executable Narsese programs whose runtime behavior in OpenNARS for Applications aligns with gold labels of true, false, or uncertain, and that the resulting benchmark supports both executable validation and supervised training of models to output structured symbolic reasoning steps via the Language-Structured Perception formulation.

What carries the argument

The deterministic compilation pipeline from first-order logic to executable Narsese, which produces programs that are run in OpenNARS for Applications to verify behavioral alignment with intended answers.

If this is right

  • Language models can be trained to emit Narsese programs whose correctness is independently verified by runtime execution rather than by text generation alone.
  • A benchmark with executable targets makes it possible to filter or correct training data so that only symbolically consistent examples are used.
  • The same pipeline supplies concrete three-label supervision that can be used for supervised adaptation of smaller models such as Phi-2.
  • Neuro-symbolic reasoning gains an explicit mechanism for representing and propagating uncertainty through the NARS inference rules.
  • Retained examples provide a growing set of verified symbolic reasoning traces that can be reused for further model training or analysis.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The approach could be applied to other symbolic engines if similar compilation pipelines and runtime validators are developed for them.
  • Execution feedback from NARS could be looped back as additional training signals to improve the language model's ability to generate correct symbolic forms.
  • Larger or more diverse natural-language reasoning collections might be converted the same way, testing how far the translation process remains faithful.
  • The method suggests a route for hybrid systems in which language models handle perception and verbalization while the symbolic engine handles verifiable multi-step inference.

Load-bearing premise

That converting natural language reasoning problems into first-order logic statements and then into Narsese code preserves the original meaning and uncertainty without introducing distortion that would make execution results unreliable.

What would settle it

Running the full set of retained benchmark examples through the compilation pipeline and OpenNARS execution and measuring whether the system outputs match the gold true/false/uncertain labels at a rate substantially above chance.

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces NARS-Reasoning-v0.1, a benchmark of natural-language reasoning problems paired with FOL representations, executable Narsese programs, and gold labels (True/False/Uncertain). It describes a deterministic FOL-to-Narsese compilation pipeline validated by runtime execution in OpenNARS for Applications (ONA), retaining only examples whose execution matches the intended label. It further proposes Language-Structured Perception (LSP) to train LLMs to output symbolic structure and demonstrates this via a Phi-2 LoRA adapter fine-tuned for three-label classification on the benchmark.

Significance. If the pipeline produces representative rather than curated examples, the work could meaningfully advance neuro-symbolic reasoning by supplying an executable benchmark and validation method for NARS, which handles uncertainty natively. The benchmark, deterministic compilation, LSP formulation, and released adapter constitute concrete, reusable artifacts that support both execution-based evaluation and supervised adaptation.

major comments (2)
  1. [Benchmark and Pipeline Validation] Benchmark construction and validation procedure: Retaining examples only when ONA execution yields the gold True/False/Uncertain label makes behavioral alignment hold by construction for the final set. The manuscript must report the initial pool size, the fraction discarded after FOL translation, after Narsese compilation, and after execution, plus characteristics of discarded cases, so that readers can assess whether the pipeline succeeds on representative NL problems rather than a non-representative subset.
  2. [Experimental Results] Supervised adaptation experiments: The claim that the benchmark supports supervised adaptation is unsupported by any quantitative results (accuracy, F1, retention rates, or error analysis) for the Phi-2 LoRA adapter. Without these metrics or baselines, it is impossible to evaluate whether the benchmark enables effective neuro-symbolic training.
minor comments (2)
  1. [Abstract] The abstract asserts 'successful validation' and 'behavioral alignment' without any numbers; adding at least the overall retention rate and one performance figure would improve clarity.
  2. [LSP Formulation] The LSP formulation is introduced without a formal definition or illustrative example; a short worked example of the structured output would aid readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which help clarify the presentation of the benchmark construction and experimental validation. We address each major point below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Benchmark and Pipeline Validation] Benchmark construction and validation procedure: Retaining examples only when ONA execution yields the gold True/False/Uncertain label makes behavioral alignment hold by construction for the final set. The manuscript must report the initial pool size, the fraction discarded after FOL translation, after Narsese compilation, and after execution, plus characteristics of discarded cases, so that readers can assess whether the pipeline succeeds on representative NL problems rather than a non-representative subset.

    Authors: We agree that these statistics are necessary to demonstrate that the retained benchmark is representative rather than the result of selective curation. In the revised manuscript we will add a new subsection detailing the initial pool size, the exact counts and percentages discarded after each pipeline stage (FOL translation, Narsese compilation, and ONA execution), and a concise characterization of the discarded cases (e.g., syntactic failures, execution mismatches, or label inconsistencies). This will allow readers to evaluate the pipeline's success rate on the original natural-language problems. revision: yes

  2. Referee: [Experimental Results] Supervised adaptation experiments: The claim that the benchmark supports supervised adaptation is unsupported by any quantitative results (accuracy, F1, retention rates, or error analysis) for the Phi-2 LoRA adapter. Without these metrics or baselines, it is impossible to evaluate whether the benchmark enables effective neuro-symbolic training.

    Authors: We acknowledge that the current version presents the Phi-2 LoRA adapter primarily as a proof-of-concept release without accompanying quantitative metrics. In the revision we will insert a dedicated experimental subsection that reports accuracy, macro-F1, retention rates after execution validation, and a brief error analysis for the adapter, together with simple baselines (e.g., zero-shot and few-shot prompting of the base Phi-2 model). These additions will directly substantiate the claim that the benchmark supports supervised neuro-symbolic adaptation. revision: yes

Circularity Check

1 steps flagged

Retention of examples conditioned on ONA execution matching gold labels renders behavioral alignment tautological by construction.

specific steps
  1. self definitional [Abstract]
    "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."

    The phrase 'retained examples' and 'ensuring ... behaviorally aligned' indicates that examples are kept only when ONA execution produces the gold label; alignment is therefore enforced by the retention criterion itself rather than independently verified on an unfiltered set.

full rationale

The paper's central claim of a pipeline that produces 'behaviorally aligned' symbolic targets rests on a filtering step that retains only those FOL-to-Narsese translations whose runtime execution in ONA yields the intended True/False/Uncertain label. This selection makes the alignment property hold definitionally for the final benchmark rather than providing an independent test on a fixed, representative set of problems. The core artifacts (new benchmark, LSP formulation, LoRA adapter) retain independent content, but the validation procedure reduces the alignment assertion to a property of the curated subset.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

The work rests on the prior existence and behavior of NARS and ONA; it introduces a new benchmark and formulation without additional fitted parameters or ad-hoc axioms beyond standard neuro-symbolic assumptions.

axioms (1)
  • domain assumption NARS non-axiomatic logic correctly models uncertainty via the three labels True/False/Uncertain
    Invoked when gold labels are assigned and when execution in ONA is used to validate behavioral alignment.
invented entities (2)
  • NARS-Reasoning-v0.1 benchmark no independent evidence
    purpose: Paired natural-language, FOL, and executable Narsese instances for training and evaluation
    New dataset constructed and released by the authors.
  • Language-Structured Perception (LSP) no independent evidence
    purpose: Training objective in which an LLM produces reasoning-relevant symbolic structure
    New formulation introduced in the paper.

pith-pipeline@v0.9.0 · 5560 in / 1521 out tokens · 33446 ms · 2026-05-10T04:05:33.357245+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

12 extracted references · 5 canonical work pages

  1. [1]

    In: Proceedings of the Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing

    Bos, J., Markert, K.: Recognising textual entailment with logical inference. In: Proceedings of the Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing. pp. 628–635 (2005)

    2005

  2. [2]

    Gabriel, M.: Nars-reasoning-v0.1.https://huggingface.co/datasets/ MinaGabriel/NARS-Reasoning-v0.1(2025), hugging Face dataset, CC- BY-SA-4.0

    2025

  3. [3]

    Gabriel, M.: Phi-2 lora adapter — nars reasoning (a/b/c inference).https:// huggingface.co/MinaGabriel/phi2-2.7b-lora-nars-adapter(2025), hug- ging Face model repository; LoRA adapter trained on NARS-Reasoning-v0.1

    2025

  4. [4]

    beyond code snippets: Benchmarking llms on repository-level question answering

    Gabriel, M.: The semantic gap between human and artificial agents: Why lan- guage grounding remains unsolved (2025).https://doi.org/10.5281/zenodo. 17108766,https://doi.org/10.5281/zenodo.17108766

    work page doi:10.5281/zenodo 2025

  5. [5]

    In: Artificial General Intelligence: 13th International Conference, AGI 2020

    Hammer, P., Lofthouse, T.: OpenNARS for Applications: Architecture and control. In: Artificial General Intelligence: 13th International Conference, AGI 2020. pp. 193–204. Springer (2020)

    2020

  6. [6]

    Lecture Notes in Networks and Systems1554, 404–420 (2025)

    Isaev, P., Hammer, P.: Nars-gpt: An integrated reasoning system for natural lan- guage interactions. Lecture Notes in Networks and Systems1554, 404–420 (2025)

    2025

  7. [7]

    Microsoft: Phi-2.https://huggingface.co/microsoft/phi-2(2023), hugging Face model card

    2023

  8. [8]

    arXiv preprint arXiv:2305.12295 , year=

    Pan, L., Albalak, A., Wang, X., Wang, W.Y.: Logic-lm: Empowering large lan- guage models with symbolic solvers for faithful logical reasoning. arXiv preprint arXiv:2305.12295 (2023)

    work page arXiv 2023

  9. [9]

    Qi, C., Ma, R., Li, B., Du, H., Hui, B., Wu, J., Laili, Y., He, C.: Large lan- guagemodelsmeetsymbolicproversforlogicalreasoningevaluation.arXivpreprint arXiv:2502.06563 (2025)

    work page arXiv 2025

  10. [10]

    WorldScientific (2025).https://doi.org/10.1142/14486,https://doi

    Wang, P.: Non-Axiomatic Logic - A Model of Intelligent Reasoning, 2nd Edi- tion. WorldScientific (2025).https://doi.org/10.1142/14486,https://doi. org/10.1142/14486

    work page doi:10.1142/14486 2025

  11. [11]

    arXiv preprint arXiv:2305.15541 (2023)

    Yang, Y., Xiong, S., Payani, A., Shareghi, E., Fekri, F.: Harnessing the power of large language models for natural language to first-order logic translation. arXiv preprint arXiv:2305.15541 (2023)

    work page arXiv 2023

  12. [12]

    In: Proceedings of UAI

    Zettlemoyer, L.S., Collins, M.: Learning to map sentences to logical form: Struc- tured classification with probabilistic categorial grammars. In: Proceedings of UAI. pp. 658–666 (2005)

    2005