@inproceedings{kordjamshidi-etal-2026-perceplog,
title = "{P}ercep{L}og-0: {LLM}-Augmented Backward Chaining for Legal Reasoning",
author = "Kordjamshidi, Parisa and
Bulusu, Sai Amrit and
Seshadri, Madhavan and
Aslan, Samer and
Barrett, Leslie and
Santus, Enrico",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.2099/",
pages = "44688--44706",
ISBN = "979-8-89176-462-0",
abstract = "In-depth logical reasoning in domains such as legal analysis remains challenging for large language models (LLMs), exhibiting brittle and inconsistent performance on tasks requiring multi-step deductive inference. This limitation is expected, as LLMs primarily rely on learned distributional associations rather than performing explicit step-by-step logical inference through formal rule application. To address this gap, we propose a novel framework, called PercepLog, that incorporates a classical deductive reasoning algorithm, backward chaining, within an outer control loop, while utilizing LLMs for primitive operations such as substitution and unification. By following the backward chaining algorithm, the framework benefits from the interpretability and explainability of symbolic reasoning procedures while retaining the flexibility of LLM perception for processing natural-language legal text. We evaluate the proposed method on statutory reasoning tasks across a range of legal domains as well general logical reasoning. The results demonstrate that our approach enables more robust, explainable, and principled reasoning over unstructured legal text, where exhaustive formalization is often brittle and prone to hallucination. Our method yields average relative performance improvements of 58{\%} over direct LLM reasoning and 16{\%} over formal LLM-based translation and Prolog inference pipelines."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="kordjamshidi-etal-2026-perceplog">
<titleInfo>
<title>PercepLog-0: LLM-Augmented Backward Chaining for Legal Reasoning</title>
</titleInfo>
<name type="personal">
<namePart type="given">Parisa</namePart>
<namePart type="family">Kordjamshidi</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sai</namePart>
<namePart type="given">Amrit</namePart>
<namePart type="family">Bulusu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Madhavan</namePart>
<namePart type="family">Seshadri</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Samer</namePart>
<namePart type="family">Aslan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Leslie</namePart>
<namePart type="family">Barrett</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Enrico</namePart>
<namePart type="family">Santus</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Findings of the Association for Computational Linguistics: EMNLP 2026</title>
</titleInfo>
<name type="personal">
<namePart type="given">Hung-Yi</namePart>
<namePart type="family">Lee</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sunipa</namePart>
<namePart type="family">Dev</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jessy</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Desmond</namePart>
<namePart type="family">Elliott</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Budapest, Hungary</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-462-0</identifier>
</relatedItem>
<abstract>In-depth logical reasoning in domains such as legal analysis remains challenging for large language models (LLMs), exhibiting brittle and inconsistent performance on tasks requiring multi-step deductive inference. This limitation is expected, as LLMs primarily rely on learned distributional associations rather than performing explicit step-by-step logical inference through formal rule application. To address this gap, we propose a novel framework, called PercepLog, that incorporates a classical deductive reasoning algorithm, backward chaining, within an outer control loop, while utilizing LLMs for primitive operations such as substitution and unification. By following the backward chaining algorithm, the framework benefits from the interpretability and explainability of symbolic reasoning procedures while retaining the flexibility of LLM perception for processing natural-language legal text. We evaluate the proposed method on statutory reasoning tasks across a range of legal domains as well general logical reasoning. The results demonstrate that our approach enables more robust, explainable, and principled reasoning over unstructured legal text, where exhaustive formalization is often brittle and prone to hallucination. Our method yields average relative performance improvements of 58% over direct LLM reasoning and 16% over formal LLM-based translation and Prolog inference pipelines.</abstract>
<identifier type="citekey">kordjamshidi-etal-2026-perceplog</identifier>
<location>
<url>https://aclanthology.org/2026.findings-emnlp.2099/</url>
</location>
<part>
<date>2026-10</date>
<extent unit="page">
<start>44688</start>
<end>44706</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T PercepLog-0: LLM-Augmented Backward Chaining for Legal Reasoning
%A Kordjamshidi, Parisa
%A Bulusu, Sai Amrit
%A Seshadri, Madhavan
%A Aslan, Samer
%A Barrett, Leslie
%A Santus, Enrico
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F kordjamshidi-etal-2026-perceplog
%X In-depth logical reasoning in domains such as legal analysis remains challenging for large language models (LLMs), exhibiting brittle and inconsistent performance on tasks requiring multi-step deductive inference. This limitation is expected, as LLMs primarily rely on learned distributional associations rather than performing explicit step-by-step logical inference through formal rule application. To address this gap, we propose a novel framework, called PercepLog, that incorporates a classical deductive reasoning algorithm, backward chaining, within an outer control loop, while utilizing LLMs for primitive operations such as substitution and unification. By following the backward chaining algorithm, the framework benefits from the interpretability and explainability of symbolic reasoning procedures while retaining the flexibility of LLM perception for processing natural-language legal text. We evaluate the proposed method on statutory reasoning tasks across a range of legal domains as well general logical reasoning. The results demonstrate that our approach enables more robust, explainable, and principled reasoning over unstructured legal text, where exhaustive formalization is often brittle and prone to hallucination. Our method yields average relative performance improvements of 58% over direct LLM reasoning and 16% over formal LLM-based translation and Prolog inference pipelines.
%U https://aclanthology.org/2026.findings-emnlp.2099/
%P 44688-44706
Markdown (Informal)
[PercepLog-0: LLM-Augmented Backward Chaining for Legal Reasoning](https://aclanthology.org/2026.findings-emnlp.2099/) (Kordjamshidi et al., Findings 2026)
ACL
- Parisa Kordjamshidi, Sai Amrit Bulusu, Madhavan Seshadri, Samer Aslan, Leslie Barrett, and Enrico Santus. 2026. PercepLog-0: LLM-Augmented Backward Chaining for Legal Reasoning. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 44688–44706, Budapest, Hungary. Association for Computational Linguistics.