LogicTree-RAG：一种用于长篇专利起草的逻辑树引导检索增强生成框架

# Logic Tree-guided Retrieval-Augmented Generation for Long-form Patent Drafting
Source: [https://arxiv.org/html/2609.30943](https://arxiv.org/html/2609.30943)
Naili XingAffiliation:National University of SingaporeEmail:[xingnl@comp\.nus\.edu\.sg](mailto:)Hexiang PanAffiliation:National University of SingaporeEmail:[panh@u\.nus\.edu](mailto:)Haotian GaoAffiliation:National University of SingaporeEmail:[gaohaotian@comp\.nus\.edu\.sg](mailto:)Jianwei YinAffiliation:Zhejiang UniversityEmail:[zjuyjw@zju\.edu\.cn](mailto:)Xiaokui XiaoAffiliation:National University of SingaporeEmail:[xkxiao@nus\.edu\.sg](mailto:)Beng Chin OoiAffiliation:Zhejiang UniversityEmail:[ooibc@zju\.edu\.cn](mailto:)

###### Abstract

Long\-form technical text generation underpins knowledge\-intensive workflows, yet remains challenging for large language models \(LLMs\) due to the need for globally consistent logical structuring and faithful technical reasoning beyond local coherence\. Patent drafting is a canonical instance of this challenge, demanding holistic generation of a legally compliant and technically exhaustive document through sustained multi\-expert collaboration\. Existing approaches often focus on partial section generation or rely on manually crafted outlines, limiting scalable automation in realistic settings\. In this work, we propose LogicTree\-RAG, a logic tree\-guided retrieval\-augmented generation framework that induces a hierarchical logic tree as a global organizational backbone to organize and ground technical disclosures, without relying on expert\-defined drafting priors\. Each node in the logic tree represents a technical element and is constructed through evidence\-guided recursive generation\. A hybrid traversal mechanism then maps the logic tree into patent sections, enabling controllable and section\-balanced generation\. Extensive experiments show that LogicTree\-RAG consistently improves content quality and language conformity over strong LLM\-based baselines and achieves longer structured generation with high token efficiency, demonstrating the effectiveness of logic\-centric generation for complex technical document drafting\.

## 1Introduction

Patents are a cornerstone of intellectual property protection, safeguarding technological innovations and enabling their translation into practical applications[Mansfield \(1986\)](https://arxiv.org/html/2609.30943#bib.bib23);[Kalanje \(2006\)](https://arxiv.org/html/2609.30943#bib.bib21)\. Drafting a patent, however, is a highly specialized task that requires both a comprehensive understanding of the underlying technical contribution and a detailed knowledge of the formal and legal conventions of patent documents[Goldstein \(2005\)](https://arxiv.org/html/2609.30943#bib.bib22);[Waelde et al\. \(2014\)](https://arxiv.org/html/2609.30943#bib.bib39)\. In practice, this process often involves close collaboration between*inventors*, who possess the technical expertise but typically lack familiarity with patent law, and*patent attorneys*, who are well\-versed in legal conventions but may not fully understand the technical intricacies of the invention\. Consequently, such multi\-expert collaboration often necessitates repeated discussion and iterative refinement, making the drafting process labor\-intensive and time\-consuming\.

Recent advances in large language models \(LLMs\), which have demonstrated remarkable performance across a wide range of natural language processing \(NLP\) tasks, open up new possibilities for automating and accelerating patent drafting[Shomee et al\. \(2025b\)](https://arxiv.org/html/2609.30943#bib.bib5);[Ding et al\. \(2025\)](https://arxiv.org/html/2609.30943#bib.bib37);[Shomee et al\. \(2025a\)](https://arxiv.org/html/2609.30943#bib.bib45)\. Prior work in this area has primarily focused on generating specific sections of patent applications[Christofidellis et al\. \(2022\)](https://arxiv.org/html/2609.30943#bib.bib10);[Lee \(2024\)](https://arxiv.org/html/2609.30943#bib.bib7);[Wang et al\. \(2024a\)](https://arxiv.org/html/2609.30943#bib.bib11);[Bai et al\. \(2024\)](https://arxiv.org/html/2609.30943#bib.bib8);[Zuo et al\. \(2024\)](https://arxiv.org/html/2609.30943#bib.bib9);[Ren et al\. \(2025\)](https://arxiv.org/html/2609.30943#bib.bib6);[Yoo et al\. \(2025\)](https://arxiv.org/html/2609.30943#bib.bib38), such as title, abstract, or claims, rather than producing a complete document\. While useful for targeted drafting assistance, such partial generation fails to address the core challenge of producing a logically structured patent application in its entirety, where interdependencies across sections are essential\.

While a few recent studies have explored full\-patent generation, they typically rely on substantial manual pre\-design and drafting efforts\. For instance, AutoPatent[Wang et al\. \(2024c\)](https://arxiv.org/html/2609.30943#bib.bib12)presupposes user\-provided draft information, while Pap2Pat[Knappich et al\. \(2025\)](https://arxiv.org/html/2609.30943#bib.bib4)assumes a detailed outline of each patent section written in advance\. In their task setups, both methods approximate such user\-provided inputs by extracting supervision from reference patents, which distances their designs from real\-world patent drafting conditions and ultimately limits automation and scalability\.

To address the above\-mentioned problems, we formulate patent drafting as a long\-text generation task, focusing on producing a complete and logically structured patent application from a pre\-publication research paper serving as a proxy for the invention report \(IR\)[Knappich et al\. \(2025\)](https://arxiv.org/html/2609.30943#bib.bib4)\. This formulation captures the practical scenario in many research environments, where research papers frequently serve as the initial disclosure for patent drafting\. Despite its practical relevance, advancing toward fully automated patent drafting entails several non\-trivial challenges\. First, patent applications are inherently long\-form technical documents, requiring the generation of substantially long and complete texts that remain challenging for LLMs to sustain[Liu et al\. \(2024\)](https://arxiv.org/html/2609.30943#bib.bib14);[Wu et al\. \(2025\)](https://arxiv.org/html/2609.30943#bib.bib13)\. Second, beyond producing long outputs, effective patent drafting demands globally consistent logical organization and balanced elaboration, a constraint that becomes increasingly difficult to maintain as generation length grows\. Third, robust automation requiresminimizing dependence on manually crafted priors, as such reliance limits scalability and generalization across drafting scenarios\.

To tackle these challenges, we propose LogicTree\-RAG, a logic tree\-guided retrieval\-augmented generation framework for long\-form patent drafting\. Unlike prior methods that depend on flat\-generation LLMs with manually crafted outlines or templates, LogicTree\-RAG introduces a structured generative paradigm in which*logic precedes content*: it automatically derives a hierarchical logic tree from the input research paper as a global organizational backbone and progressively transforms it into well\-formed patent sections in a principled and controllable manner\.

As the first step, LogicTree\-RAG incorporates asemantic chunking mechanismthat structures the input document into semantically coherent units, which are embedded into avectorized document knowledge base\. Upon this foundation, we introduce anevidence\-guided recursive generationprocess for constructing a logic tree that captures the conceptual structure of the underlying research\. This process leverages a novelnode\-aware semantic searchstrategy equipped with complementary expansion and refinement procedures, enabling section\-level coherence and structural balance\. Each node in the tree corresponds to a technical element, such as a principle, subtask, or subsystem, and is dynamically grounded by evidence retrieved from the knowledge base\. Finally, ahybrid traversal strategytransforms the completed logic tree into patent sections, yielding a long\-form, logically organized patent draft without requiring expert\-defined priors\. By serving as a logic\-centric intermediate representation, logic tree bridges factual evidence with global generative control, enabling scalable long\-form patent drafting\.

We summarize our main contributions as follows:

- •We propose LogicTree\-RAG, a logic\-centric generative framework for research\-to\-patent transformation that produces long\-form, logically structured patent drafts without reliance on expert\-defined drafting priors\.
- •We present an evidence\-guided recursive generation approach that constructs a logic tree to model the conceptual dependencies and technical contributions with explicit factual grounding in the source document\.
- •We introduce a node\-aware semantic search with a discriminative relevance scoring that jointly accounts for semantic similarity and structural distinctiveness, improving balanced elaboration and global structural coherence in patent drafting\.
- •Extensive experiments under a curated suite of evaluation metrics demonstrate that LogicTree\-RAG improves technical completeness by11\.03% and content fidelity by6\.33% over strong LLM\-based baselines, while enabling longer structured generation with high token efficiency\.

## 2Preliminaries

![Refer to caption](https://arxiv.org/html/2609.30943v1/archv2.png)

Figure 1:Architecture of LogicTree\-RAG framework for long\-form patent drafting\.Patents and Patent\-Paper Pairs\.A patent is a structured legal\-technical document, typically consisting ofclaims, which define the legally enforceable scope of protection, and adescription, which discloses the invention in sections such as Background, Summary, and Detailed Description[Jiang and Goetz \(2024\)](https://arxiv.org/html/2609.30943#bib.bib15);[Knappich et al\. \(2025\)](https://arxiv.org/html/2609.30943#bib.bib4)\. In many research domains, patents are filed alongside scientific publications, producingPatent\-Paper Pairs[Murray \(2002\)](https://arxiv.org/html/2609.30943#bib.bib16);[Magerman et al\. \(2010\)](https://arxiv.org/html/2609.30943#bib.bib18);[Gans et al\. \(2017\)](https://arxiv.org/html/2609.30943#bib.bib17);[Murray and Stern \(2004\)](https://arxiv.org/html/2609.30943#bib.bib40), where a pre\-publication paper serves as the IR for the patent\. Patent\-paper pairs align scientific discourse with legal–technical documentation, offering a valuable yet underexplored resource for studying long\-form technical text generation with LLMs\.

Problem Formulation\.Consider a patent\-paper pair\(X,P\)\(X,P\), whereX=\{x1,x2,…,xm\}X=\\\{x\_\{1\},x\_\{2\},\\dots,x\_\{m\}\\\}is a pre\-publication research paper represented as a collection of text spans, andP=\{p1,p2,…,pn\}P=\\\{p\_\{1\},p\_\{2\},\\dots,p\_\{n\}\\\}is the corresponding patent document\. In this work, we formalize the task as generating patentPPfrom paperXXmediated by a logic tree𝒯\\mathcal\{T\}with evidence grounding, defined as follows\.

###### Definition 2\.1\(Logic Tree\)\.

A logic tree is a directed rooted tree𝒯=\(V,E,r\)\\mathcal\{T\}=\(V,E,r\), whereV=\{v1,⋯,vN\}V=\\\{v\_\{1\},\\cdots,v\_\{N\}\\\}is the set of nodes,E⊆V×VE\\subseteq V\\times Vthe set of directed edges specifying parent\-child relations, andr∈Vr\\in Vthe root node representing the overall description of the document\. Each nodev∈Vv\\in Vis associated with two attributes: \(i\) a generated text fragmentc⁡\(v\)c\(v\)and \(ii\) a set of supporting evidence spansℰ⁡\(v\)⊆X\\mathcal\{E\}\(v\)\\subseteq X\.

Intuitively, the root node summarizes the invention, internal nodes correspond to higher\-level sections, and leaf nodes expand into fine\-grained technical details\. To ensure factual grounding, we impose the attribution constraint

∀v∈V,ℰ⁡\(v\)≠∅,\\displaystyle\\forall v\\in V,\\hskip 10\.00002pt\\mathcal\{E\}\(v\)\\neq\\emptyset,\(1\)which requires each generated unit to be anchored to at least one evidence span in the source paper\.

## 3Methodology

### 3\.1LogicTree\-RAG Overview

As illustrated in[Figure1](https://arxiv.org/html/2609.30943#S2.F1), LogicTree\-RAG functions as a top\-down pipeline that first parses the source document into structured textual units and constructs alogic treethat captures the conceptual dependencies\. Each node in the tree represents a technical element, such as a principle, step, or subsystem, and is dynamically associated with retrieved evidence from avectorized document knowledge base\. LogicTree\-RAG then iteratively expands and refines the tree viaevidence\-guided recursive generation, supported bynode\-aware semantic searchto ensure precise evidence alignment and structural consistency\. Finally, the framework traverses the completed logic tree and hierarchically aggregates node contents into patent sections, yielding a long\-form, section\-balanced patent draft that integrates technical fidelity with structured generative controllability\.

### 3\.2Hierarchical Logic Tree Initialization

To initialize retrievable knowledge units, we utilize layout\-aware document analysis[Xu et al\. \(2020\)](https://arxiv.org/html/2609.30943#bib.bib43)to identify hierarchical boundaries across sections and paragraphs, reconstructing the document’s logical structure\. Subsequently, we perform semantic chunking to produce contextually coherent segments that from the basis of thevectorized document knowledge base𝒱k​b\\mathcal\{V\}\_\{kb\}\. Unlike fixed chunking methods[Lewis et al\. \(2020\)](https://arxiv.org/html/2609.30943#bib.bib41);[Beltagy et al\. \(2020\)](https://arxiv.org/html/2609.30943#bib.bib42);[Huang et al\. \(2023\)](https://arxiv.org/html/2609.30943#bib.bib20);[Knappich et al\. \(2025\)](https://arxiv.org/html/2609.30943#bib.bib4), our approach dynamically determines chunk boundaries according to the semantic coherence and contextual dependency between adjacent paragraphs\. Formally, given an ordered sequence of paragraphsX=\{x1,x2,…,xm\}X=\\\{x\_\{1\},x\_\{2\},\\dots,x\_\{m\}\\\}, we compute the pairwise semantic similarity:

si,i\+1=sim\(ℰp\(xi\),ℰp\(xi\+1\)\),i=1,⋯,m−1,\\displaystyle s\_\{i,i\+1\}=\\text\{sim\}\(\\mathcal\{E\}\_\{p\}\(x\_\{i\}\),\\mathcal\{E\}\_\{p\}\(x\_\{i\+1\}\)\),\\;i=1,\\cdots,m\-1,\(2\)whereℰp​\(⋅\)\\mathcal\{E\}\_\{p\}\(\\cdot\)denotes the paragraph\-level embedding function, andsim​\(⋅,⋅\)\\text\{sim\}\(\\cdot,\\cdot\)is instantiated with cosine similarity\. Paragraphs are iteratively merged whensi,i\+1⩾τss\_\{i,i\+1\}\\geqslant\\tau\_\{s\}and the accumulated token length does not exceed the upper boundtm​a​xt\_\{max\}\. This procedure yields a set of semantically coherent chunksX~=\{x~1,x~2,…,x~L\}\\tilde\{X\}=\\\{\\tilde\{x\}\_\{1\},\\tilde\{x\}\_\{2\},\\dots,\\tilde\{x\}\_\{L\}\\\}that align with the recovered hierarchy\. Details are provided in Appendix[C](https://arxiv.org/html/2609.30943#A3)\.

Following semantic chunking, we initialize a logic tree𝒯=\(V,E,r\)\\mathcal\{T\}=\(V,E,r\)to organize and drive the generation process\. To construct an initial structural scaffold, we prompt the backbone LLM with a high\-level instructionσI\\sigma\_\{I\}and the chunk setX~\\tilde\{X\}, generating a draft technical description𝒟d=ℳL​L​M​\(σI,X~\)\\mathcal\{D\}\_\{d\}=\\mathcal\{M\}\_\{LLM\}\(\\sigma\_\{I\},\\tilde\{X\}\)\. The root noderris instantiated withc⁡\(r\)=𝒟dc\(r\)=\\mathcal\{D\}\_\{d\}andℰ⁡\(r\)=X~\\mathcal\{E\}\(r\)=\\tilde\{X\}, representing the target patent document\. Instead of utilizing syntactic rules or heuristics, we leverageℳL​L​M\\mathcal\{M\}\_\{LLM\}to induce the first layer of the logic tree by producing an evidence\-grounded decomposition of key technical components from𝒟d\\mathcal\{D\}\_\{d\}\. This yields an ordered tree structure in which the root summarizes the invention and its first\-level children nodes define the core semantic dimensions to be recursively expanded in subsequent stages\.

### 3\.3Evidence\-Guided Recursive Generation

Given the initialized logic tree𝒯\\mathcal\{T\}, we perform recursive generation through a breadth\-first expansion strategy\. Specifically, we maintain a FIFO search queue𝒬\\mathcal\{Q\}initialized with the leaf nodes of𝒯\\mathcal\{T\}, ensuring the expansion proceeds in a top–down manner\. For each iteration, we dequeue the front nodevtv\_\{t\}, and retrieve its parent nodevfv\_\{f\}from𝒯\\mathcal\{T\}\. The goal of recursive generation is to determine whethervtv\_\{t\}should be expanded, refined, or skipped based on the semantic evidence retrieved from the document knowledge base𝒱k​b\\mathcal\{V\}\_\{kb\}, and to perform the corresponding updates to the logic tree\.

To obtain candidate evidence, we perform retrieval over𝒱k​b\\mathcal\{V\}\_\{kb\}and collect a set of context spans𝒟c​t​x\\mathcal\{D\}\_\{ctx\}, referred to ascontext evidence, which are semantically aligned with the node’s contentc⁡\(vt\)c\(v\_\{t\}\)\. Aglobal novelty testis then designed to verify whether𝒟c​t​x\\mathcal\{D\}\_\{ctx\}is already covered by other nodes in the logic tree, excluding the child nodes ofvfv\_\{f\}\. If no novel evidence is identified,vtv\_\{t\}is skipped; otherwise, the logic tree is updated via two complementary procedures:

- •Expansion procedureE⁡\(⋅\)E\(\\cdot\): Conditioned on𝒟c​t​x\\mathcal\{D\}\_\{ctx\}and an expansion instructionσE\\sigma\_\{E\}, the model generates a supplementary paragraph, which is then segmented into coherent semantic units\. Each unit is instantiated as a child node ofvtv\_\{t\}, forming the setVtc​h​i​l​d=\{vt​c​1,⋯,vt​c​k\}V\_\{t\}^\{child\}=\\\{v\_\{tc1\},\\cdots,v\_\{tck\}\\\}, wherec⁡\(vt​c​i\)c\(v\_\{tci\}\)is initialized as the corresponding segmented content andℰ⁡\(vt​c​i\)=𝒟c​t​x\\mathcal\{E\}\(v\_\{tci\}\)=\\mathcal\{D\}\_\{ctx\}\. All nodes inVtc​h​i​l​dV\_\{t\}^\{child\}are then enqueued into𝒬\\mathcal\{Q\}for further recursion\.
- •Refinement procedureR⁡\(⋅\)R\(\\cdot\): The model examinesc⁡\(vt\)c\(v\_\{t\}\)to identify under\-specified terms using instructionσR​1\\sigma\_\{R1\}\. For domain\-specific concepts appearing inX~\\tilde\{X\}, refinement is performed via retrieval\-augmented generation with supporting evidence from𝒱k​b\\mathcal\{V\}\_\{kb\}and instructionσR​2\\sigma\_\{R2\}; otherwise, the model completes the term with instructionσR​3\\sigma\_\{R3\}based on its internal knowledge\.

Overall, the logic tree𝒯\\mathcal\{T\}is updated after each expansion or refinement step\. The expansion procedure generates a set of child nodes based on the context evidence, while the refinement procedure resolves under\-specified terms and updates the content of the current node, expressed as:

E⁡\(σE,𝒟c​t​x\)=Vtc​h​i​l​d\\displaystyle E\(\\sigma\_\{E\},\\mathcal\{D\}\_\{ctx\}\)=V\_\{t\}^\{child\}\(3\)c⁡\(vt\)←R⁡\(σR,c⁡\(vt\),𝒱k​b\)\\displaystyle c\(v\_\{t\}\)\\leftarrow R\(\\sigma\_\{R\},c\(v\_\{t\}\),\\mathcal\{V\}\_\{kb\}\)\(4\)whereσR=\{σR​1,σR​2,σR​3\}\\sigma\_\{R\}=\\\{\\sigma\_\{R1\},\\sigma\_\{R2\},\\sigma\_\{R3\}\\\}\. The recursive process terminates once either the queue becomes empty or the accumulated content length reaches a predefined budget, ensuring controllable expansion and preventing over\-generation\.

### 3\.4Node\-Aware Semantic Search

Figure 2:Generation without and with node\-aware semantic search\.During the evidence\-guided recursive generation, the quality of the context evidence𝒟c​t​x\\mathcal\{D\}\_\{ctx\}critically influences the structural evolution of the logic tree\. Naive retrieval may introduce redundancy or topic drift, particularly when the retrieved spans overlap with already\-processedsibling nodes, leading to incoherent or uneven expansion\. To ensure precision and structural consistency, we impose two desiderata for the context set: \(i\) strong semantic association with the current node, and \(ii\) low redundancy with its sibling nodes under the same parent\. To meet these requirements, we introduce a node\-aware semantic search mechanism\. As illustrated in Figure[2](https://arxiv.org/html/2609.30943#S3.F2), we first retrieve an initial set of candidate context spans𝒟~c​t​x\\tilde\{\\mathcal\{D\}\}\_\{ctx\}from𝒱k​b\\mathcal\{V\}\_\{kb\}using the current node contentc⁡\(vt\)c\(v\_\{t\}\)as query:

𝒟~c​t​x=ℛ⁡\(c⁡\(vt\),𝒱k​b\)=Top\-Ka∈𝒱k​b​sim​\(a,c⁡\(vt\)\),\\displaystyle\\tilde\{\\mathcal\{D\}\}\_\{ctx\}=\\mathcal\{R\}\(c\(v\_\{t\}\),\\mathcal\{V\}\_\{kb\}\)=\\text\{Top\-K\}\_\{a\\in\\mathcal\{V\}\_\{kb\}\}\\text\{sim\}\(a,c\(v\_\{t\}\)\),\(5\)To jointly account for semantic specificity and structural discrimination, we evaluate each candidate spand∈𝒟~c​t​xd\\in\\tilde\{\\mathcal\{D\}\}\_\{ctx\}through adiscriminative relevance scoreSd​r​\(⋅\)S\_\{dr\}\(\\cdot\), defined as:

Sd​r​\(d\)=sim​\(d,c⁡\(vt\)\)−maxve∈Vfc​h​i​l​d∖\{vt\}⁡sim​\(d,c⁡\(ve\)\),\\displaystyle S\_\{dr\}\(d\)=\\text\{sim\}\(d,c\(v\_\{t\}\)\)\-\\max\_\{\\scalebox\{0\.6\}\{$v\_\{e\}\\in V^\{child\}\_\{f\}\\setminus\\\{v\_\{t\}\\\}$\}\}\\text\{sim\}\(d,c\(v\_\{e\}\)\),\(6\)whereVfc​h​i​l​dV^\{child\}\_\{f\}denotes the set of child nodes ofvfv\_\{f\}and∖\\setminusindicates exclusion\. A spanddis retained only ifSd​r​\(d\)S\_\{dr\}\(d\)exceeds a predefined threshold\. The final context evidence is obtained as𝒟c​t​x=\{d\|Sd​r​\(d\)≥τf\}\\mathcal\{D\}\_\{ctx\}=\\\{d\\,\|\\,S\_\{dr\}\(d\)\\geq\\tau\_\{f\}\\\}\. The node\-aware semantic search ensures that the retrieved evidence is both node\-specific and structurally non\-redundant, supporting focused and coherent recursive generation\.

Remark\. Although both the node\-aware semantic search and the global novelty test operate on evidence relevance and redundancy, they address complementary levels of semantic discrimination\. Node\-aware semantic search enforces specificity at the*local sibling level*by filtering evidence overlapping with existing child nodes ofvtv\_\{t\}, while the global novelty test verifies coverage at the*global tree\-wide level*by checking whether𝒟c​t​x\\mathcal\{D\}\_\{ctx\}has already appeared elsewhere in the logic tree outside thevfv\_\{f\}branch\. Together, these mechanisms provide coordinated local discrimination and global redundancy control, enabling coherent and non\-repetitive logic tree construction\.

### 3\.5LogicTree\-RAG Workflow for Patent Generation

As outlined in Algorithm[1](https://arxiv.org/html/2609.30943#alg1), LogicTree\-RAG operates in two stages: \(i\)*logic tree construction*, which provides a structured and factually grounded representation of the source document’s conceptual space, and \(ii\)*patent drafting*, which systematically traverses the constructed logic tree to generate the final patent draft\. The first stage yields a logic tree𝒯\\mathcal\{T\}that associates each nodev∈Vv\\in Vwith both generated contentc⁡\(v\)c\(v\)and grounded evidenceℰ⁡\(v\)⊆X\\mathcal\{E\}\(v\)\\subseteq X, as formalized inAlgorithm[2](https://arxiv.org/html/2609.30943#alg2)in Appendix[D](https://arxiv.org/html/2609.30943#A4), forming the structural foundation for patent drafting\.

In the second stage, LogicTree\-RAG generates the patent draft by traversing the logic tree𝒯\\mathcal\{T\}and aggregating node contents\. The hierarchical structure of the logic tree naturally aligns with the organization of complex technical documents, such as patents, where high\-level nodes capture overarching concepts, and deeper nodes progressively elaborate technical details, procedural logic, and implementation specifics\. To effectively exploit this hierarchical representation, we design ahybrid traversal strategythat balances content coverage and technical depth across patent sections\. Specifically, high\-level sections such as Background and Summary are generated using breadth\-first traversal \(BFS\) to ensure comprehensive and balanced structural coverage\. For the Detailed Description section, BFS is first combined with LLM\-based semantic clustering to identify coherentlogical subtrees, each of which is then expanded via depth\-first traversal \(DFS\) to elaborate technical details\. In this way, BFS establishes the structural skeleton, while DFS enables fine\-grained expansion, jointly ensuring structural clarity and semantic specificity\. Ultimately, LogicTree\-RAG leverages the logic tree as a structural backbone to automatically transform research documents into long\-form patent drafts without relying on manually crafted templates or outlines derived from ground truth\.

Algorithm 1LogicTree\-RAG for Patent Generation0:Pre\-publication research paper, the backbone model

ℳL​L​M\\mathcal\{M\}\_\{LLM\}, instruction prompts

σI,σE,σR\\sigma\_\{I\},\\sigma\_\{E\},\\sigma\_\{R\}\.

0:Generated patent document

PP\.

∖\\setminus\*Stage I: Logic Tree Construction\*∖\\setminus

1:Perform layout\-aware semantic chunking on

XXto obtain chunk set

X~\\tilde\{X\}using Eq\. \([2](https://arxiv.org/html/2609.30943#S3.E2)\)

2:Initialize Logic Tree

𝒯\\mathcal\{T\}by generating a draft technical description

𝒟d=ℳL​L​M​\(σI,X~\)\\mathcal\{D\}\_\{d\}=\\mathcal\{M\}\_\{LLM\}\(\\sigma\_\{I\},\\tilde\{X\}\)
3:Expand and refine

𝒯\\mathcal\{T\}via evidence\-guided recursive generation with

σE,σR\\sigma\_\{E\},\\sigma\_\{R\}as Eq\. \([3](https://arxiv.org/html/2609.30943#S3.E3)\) and Eq\. \([4](https://arxiv.org/html/2609.30943#S3.E4)\)

∖\\setminus\*Stage II: Patent Drafting\*∖\\setminus

4:Initialize empty patent document

PP
5:forsection

s∈\{Background,Summary\}s\\in\\\{\\texttt\{Background\},\\texttt\{Summary\}\\\}do

6:Traverse

𝒯\\mathcal\{T\}in BFS order over nodes assigned to section

ss
7:Generate section text conditioned on the content

c⁡\(v\)c\(v\)of visited nodes using

ℳL​L​M\\mathcal\{M\}\_\{LLM\}
8:Append generated content to

PP
9:endfor

10:Identify subtrees

\{𝒯d\}\\\{\\mathcal\{T\}\_\{d\}\\\}allocated to theDetailed Descriptionsection

11:foreach subtree

𝒯d\\mathcal\{T\}\_\{d\}do

12:Traverse

𝒯d\\mathcal\{T\}\_\{d\}in DFS order

13:Generate fine\-grained technical content using

ℳL​L​M\\mathcal\{M\}\_\{LLM\}
14:Append generated content to

PP
15:endfor

16:ReturnGenerated patent document

PP

Remark\. LogicTree\-RAG enables scalable and controllable long\-form generation through an explicit hierarchical intermediate representation\. By decomposing the source document into a logic tree and constraining generation to structurally relevant subtrees, the computational cost is bounded by the tree size and traversal budget, enabling fine\-grained control over content expansion\. Moreover, by localizing evidence retrieval via node\-aware semantic search, LogicTree\-RAG reduces redundant context consumption and improves token efficiency for long and complex technical documents\.

## 4Experiments

### 4\.1Experimental Setup

Benchmarks\.We first conduct experiments on Pap2Pat[Knappich et al\. \(2025\)](https://arxiv.org/html/2609.30943#bib.bib4), a recently proposed benchmark for long\-form patent generation based on paper\-patent pairs\. Pap2Pat consists of 1813 aligned scientific papers and their corresponding granted patents across various domains\. To further evaluate the generalizability of our framework beyond patent drafting, we additionally consider the LCFO benchmark[Costa\-jussà et al\. \(2025\)](https://arxiv.org/html/2609.30943#bib.bib46), which focuses on long\-context summarization and document expansion\. LCFO consists of 252 long documents \(∼\\sim5k words each\) spanning multiple domains, each paired with multi\-level summaries and QA annotations, enabling controlled evaluation of long\-form expansion\.

Baselines\.We compare LogicTree\-RAG with 13 baselines across three categories: \(i\)*Heuristic Skyline*, including ground\-truth patents as an oracle upper bound and a case where the source paper is treated as the generated document[Knappich et al\. \(2025\)](https://arxiv.org/html/2609.30943#bib.bib4)\. \(ii\)*Single LLM\-Call*, where open and proprietary long\-context LLMs \(Kimi K2[Team et al\. \(2025\)](https://arxiv.org/html/2609.30943#bib.bib36), Qwen3\-80B, Qwen3\-Max[Yang et al\. \(2025\)](https://arxiv.org/html/2609.30943#bib.bib33), DeepseekV3[DeepSeek\-AI \(2024\)](https://arxiv.org/html/2609.30943#bib.bib34), and GPT\-5[Achiam et al\. \(2023\)](https://arxiv.org/html/2609.30943#bib.bib47)and Gemini\-2\.5 Pro[Comanici et al\. \(2025\)](https://arxiv.org/html/2609.30943#bib.bib48)under deep thinking mode\) are prompted to generate patents from the full paper\. \(iii\)*Agentic Framework*, including COPGEN[Knappich et al\. \(2025\)](https://arxiv.org/html/2609.30943#bib.bib4), along with its SFT\-tuned variant, LongWriter[Bai et al\. \(2025\)](https://arxiv.org/html/2609.30943#bib.bib49), and LongWriter\-Zero[Wu et al\. \(2026\)](https://arxiv.org/html/2609.30943#bib.bib50), as well as agentic paradigms ReAct[Yao et al\. \(2023\)](https://arxiv.org/html/2609.30943#bib.bib51)and Plan\-then\-Execute[He et al\. \(2025\)](https://arxiv.org/html/2609.30943#bib.bib52)for step\-wise planning and tool use\. See Appendix[F\.2](https://arxiv.org/html/2609.30943#A6.SS2)for baselines and implementation details\.

Implementation\.LogicTree\-RAG is implemented in Python with Pytorch 2\.0\.1, with Milvus v2\.4\.7 as the document knowledge base for document indexing and retrieval\. All methods are evaluated under identical LLM decoding configurations to ensure a fair comparison\. Detailed prompts and implementation settings are provided in Appendices[E](https://arxiv.org/html/2609.30943#A5)and[F](https://arxiv.org/html/2609.30943#A6)\.

Evaluation Metrics\.We evaluate patent generation from both content\-level and language\-level perspectives, complemented by a human expert study conducted by patent practitioners\.Content\-levelmetrics assess whether the generated patent conveys accurate and sufficient technical information grounded in the reference materials, including Coverage \(NLI\-based SCALE[Lattimer et al\. \(2023\)](https://arxiv.org/html/2609.30943#bib.bib26)\), Factuality \(patent\-groundedℱP​a​t\\mathcal\{F\}\_\{Pat\}and source\-groundedℱS​r​c\\mathcal\{F\}\_\{Src\}\), and Semantic Similarity \(ROUGE\-L[Lin \(2004\)](https://arxiv.org/html/2609.30943#bib.bib27)\)\.Language\-levelmetrics evaluate adherence to patent writing conventions, including Style \(StyloMetrix[Okulska et al\. \(2023\)](https://arxiv.org/html/2609.30943#bib.bib29)\), Repetition \(ℛr\\mathcal\{R\}\_\{r\}[Cettolo et al\. \(2014\)](https://arxiv.org/html/2609.30943#bib.bib30)\), and Coherence \(DiscoScore[Zhao et al\. \(2023\)](https://arxiv.org/html/2609.30943#bib.bib28)\)\. Details are provided in Appendix[F\.1](https://arxiv.org/html/2609.30943#A6.SS1)\.

Table 1:Overall performance comparison of long\-form patent drafting\. The best results are highlighted inbold, and the second\-best results are inbold\.MethodOutputTokensContent\-levelLanguage\-levelTime \(s\)/ Patent \(Token\)Coverage↑\\uparrowFactuality↑\\uparrowSemanticSimilarity↑\\uparrow\\,\\,Style↑\\uparrowRepetitionℛr\\mathcal\{R\}\_\{r\}↓\\downarrowCoherence↑\\uparrowℱP​a​t\\,\\,\\,\\,\\mathcal\{F\}\_\{Pat\}\\,\\,\\,\\,ℱS​r​c\\,\\,\\,\\,\\mathcal\{F\}\_\{Src\}\\,\\,\\,\\,Heuristic SkylineReference Patent \(GT\)18\.16k89\.0588\.7488\.8010010013\.91100\-Source Paper8\.03k44\.7746\.0688\.6839\.3739\.268\.3998\.60\-Single LLM\-Call GenerationKimi K23\.26k38\.2142\.7658\.9521\.2839\.4712\.0495\.9264 \(19\.63\)Qwen3\-80B8\.91k39\.0937\.5044\.8834\.0835\.066\.0595\.7181 \(9\.10\)Qwen3\-Max8\.91k39\.4437\.6845\.2234\.0935\.068\.4197\.14172 \(19\.29\)DeepseekV31\.87k36\.7047\.0959\.6611\.9832\.686\.1395\.5643 \(22\.89\)GPT\-5 DT2\.95k40\.9146\.7062\.0420\.5641\.527\.2597\.34146 \(49\.49\)Gemini\-2\.5 Pro DT2\.69k38\.6947\.5860\.9724\.2539\.686\.0997\.12109 \(40\.52\)Agentic Framework GenerationCOPGEN7\.69k39\.2157\.6362\.3932\.5659\.5415\.4797\.11265 \(34\.46\)COPGEN†8\.96k32\.8552\.4458\.1628\.9643\.3818\.9497\.21216 \(24\.11\)w/ SFT25\.85k40\.7750\.1259\.6938\.7262\.2121\.5897\.50221 \(8\.55\)LongWriter10\.81k37\.2136\.6545\.2121\.1234\.266\.6795\.62197 \(18\.22\)LongWriter\-Zero12\.16k36\.9238\.3646\.1722\.8636\.725\.7696\.84258 \(21\.11\)LogicTree\-RAG20\.22k43\.7958\.9266\.3438\.9165\.684\.1497\.45311 \(15\.38\)Contamination Control18\.96k42\.9557\.7966\.5740\.2864\.284\.6097\.23298 \(15\.72\)

Figure 3:Token efficiency comparison results\.Figure 4:Generalization performance on LCFO\.

### 4\.2Main Results

The overall results are summarized in Table[1](https://arxiv.org/html/2609.30943#S4.T1)\. LogicTree\-RAG delivers consistently superior performance across key content and language metrics, generating substantially longer patent drafts that outperform strong long\-context baselines\. At thecontent level, LogicTree\-RAG achieves the highest coverage, factuality, and semantic similarity among all baselines\. These results underscore that logic\-tree\-guided generation improves technical completeness and evidence\-grounded elaboration beyond single\-call long\-context prompting and agentic frameworks even with post\-training\. At thelanguage level, LogicTree\-RAG demonstrates strong performance in stylistic conformity and repetition rate\. Its coherence score ranks second only to the SFT\-tuned COPGEN variant, while requiring no task\-specific fine\-tuning\. To control for potential training\-data contamination, we evaluate LogicTree\-RAG on a temporally filtered post\-2024 subset beyond typical pretraining cutoffs, observing consistent gains without degradation, suggesting that the improvements are not driven by training\-data overlap\. Moreover, LogicTree\-RAG maintains competitive per\-token efficiency with only modest overhead, offering a favorable quality–efficiency trade\-off for long\-form patent drafting\.

Figure 5:Section\-level performance in terms of output length, content coverage, and linguistic style\.
### 4\.3In\-Depth Analysis of Patent Generation

Token Efficiency\.We further analyze token efficiency, defined as the ratio of generated output tokens to total input tokens consumed across all LLM invocations, which serves as a proxy for both generation efficiency and scalability\. As illustrated in Figure[4](https://arxiv.org/html/2609.30943#S4.F4)\(a\), LogicTree\-RAG achieves the highest token efficiency among all baselines, closely approaching the SFT\-enhanced COPGEN variant despite requiring no task\-specific fine\-tuning\. Figure[4](https://arxiv.org/html/2609.30943#S4.F4)\(b\) further shows that LogicTree\-RAG shifts the input–output token distribution toward a more efficient regime and effectively converts contextual input into useful long\-form patent content\. Detailed cost breakdowns are provided in Appendix[G\.4](https://arxiv.org/html/2609.30943#A7.SS4)\.

Generalizability to Long\-Text Generation\.LogicTree\-RAG is not tied to patent drafting, but targets a broader setting of source\-grounded structured long\-form generation, where the goal is to transform a long source document into a coherent and factually grounded target document\. To validate this generalizability, we evaluate LogicTree\-RAG on the LCFO benchmark[Costa\-jussà et al\. \(2025\)](https://arxiv.org/html/2609.30943#bib.bib46)for document expansion\. Following[Costa\-jussà et al\. \(2025\)](https://arxiv.org/html/2609.30943#bib.bib46), we report output tokens, coherence \(COH\-2\), repetition \(REP\-3\), overall quality via an LLM\-as\-a\-judge metric \(HelloEval\), and human\-evaluated QA\-based coverage \(QA\-Cov\), which measures the fraction of benchmark questions answerable from the generated output\. Without modifying the core pipeline, we apply LogicTree\-RAG by adapting only the output schema\. As shown in Figure[4](https://arxiv.org/html/2609.30943#S4.F4), LogicTree\-RAG consistently outperforms baselines in all content and language\-level metrics, while maintaining competitive output length\. These findings suggest that the benefits of logic\-tree\-guided generation extend beyond patent drafting to general long\-form generation tasks\.

Section\-Level Analysis\.We analyze generation quality across patent sections, i\.e\., Background, Summary, and Detailed Description, considering length, content coverage, and linguistic style, with results summarized in Figure[5](https://arxiv.org/html/2609.30943#S4.F5)\. LogicTree\-RAG produces section\-wise output lengths closely aligned with ground\-truth patents, indicating effective control over generation granularity and mitigating the length imbalance observed in single\-call and chunk\-based baselines\. From a content perspective, LogicTree\-RAG achieves the highest coverage across all patent sections, with up to a 17\.4% improvement in the Detailed Description, where fine\-grained technical completeness is critical and competing methods tend to under\-generate\. For language quality, LogicTree\-RAG consistently attains the best style scores, demonstrating strong stylistic consistency across sections\.

Human Expert Review\.To assess practical applicability beyond automatic metrics, we conduct a blinded human expert evaluation with three patent practitioners using five criteria \(i\.e\., Overall Quality, Legality, Claim Structure, Antecedent Basis, and Fidelity\)\. Experts perform pairwise comparisons between our method and baselines\. Figure[6](https://arxiv.org/html/2609.30943#S4.F6)\(a\-d\) present pairwise comparisons against individual baselines, while \(e\-h\) summarize performance across evaluation criteria\. The results show that LogicTree\-RAG consistently achieves strong human preference across all dimensions, indicating its practical utility in real\-world scenarios\. Full experimental details are provided in Appendix[G\.5](https://arxiv.org/html/2609.30943#A7.SS5)\.

Figure 6:Win\-rate results \(%\) under pairwise comparison\.

VariantOutputTokensContent\-levelLanguage\-levelCoverageFactuality \(ℱP​a​t\\mathcal\{F\}\_\{Pat\}\)StyleRR \(ℛr\\mathcal\{R\}\_\{r\}\)w/o Logic Tree9\.10k39\.8556\.9260\.4716\.86Logic Tree w/o Retrieval8\.21k40\.5651\.1659\.059\.38w/o Semantic Chunking19\.42k42\.0953\.7462\.2710\.93w/o Refinement18\.58k39\.9256\.8561\.874\.09w/o Evidence\-GroundedσR​2\\sigma\_\{R2\}19\.60k40\.1656\.9862\.644\.67w/o Internal KnowledgeσR​3\\sigma\_\{R3\}18\.95k43\.5858\.8565\.625\.21w/o Node\-aware Reranking20\.85k42\.6057\.1364\.798\.32w/ DFS Traversal21\.13k41\.1558\.6865\.125\.34LogicTree\-RAG20\.22k43\.7958\.9265\.684\.14

Table 2:Component\-wise ablation of LogicTree\-RAG\.

### 4\.4Ablation and Sensitivity Analysis

Component\-wise Ablation\.We evaluate ablated variants of LogicTree\-RAG with all other settings fixed, as shown in Table[2](https://arxiv.org/html/2609.30943#S4.T2)\. Removing the logic tree or retrieval substantially shortens the outputs and degrades content quality, causing a 9\.0% drop in coverage and a 13\.2% degradation in factuality, respectively\. This confirms the necessity of structural organization and evidence grounding\. Removing semantic chunking or node\-aware reranking weakens factuality and increases repetition, highlighting the importance of coherent and non\-redundant evidence selection\. Moreover, removing evidence\-grounded refinementσR​2\\sigma\_\{R2\}leads to consistent degradation across all metrics, whereas removing internal\-knowledge refinementσR​3\\sigma\_\{R3\}yields negligible impact\. SinceσR​3\\sigma\_\{R3\}is triggered in only 8\.6% of steps, it mainly serves as a fallback for under\-specified cases, while the main gains come from retrieved evidence\. Replacing hybrid traversal with pure DFS produces longer but less comprehensive outputs, indicating that the hybrid strategy better balances breadth and depth\. Overall, the full LogicTree\-RAG achieves the best performance across metrics, validating the joint contribution of structural modeling, evidence grounding, and traversal design\.

Figure 7:Hyperparameter Sensitivity Analysis\.Sensitivity to Key Hyperparameters\.We study the effects of the semantic chunking thresholdτs\\tau\_\{s\}and the discriminative relevance threshold controlled by the ranked candidatekk\. As shown in Figure[7](https://arxiv.org/html/2609.30943#S4.F7)\(a\), both coverage and factuality improve asτs\\tau\_\{s\}increases from low to moderate values, and peak around 0\.7\-\-0\.8, suggesting that overly coarse chunks introduce noisy evidence, while overly fine\-grained chunks reduce contextual sufficiency\. Figure[7](https://arxiv.org/html/2609.30943#S4.F7)\(b\) shows that increasingkkinitially improves coverage by allowing more supporting evidence, but factuality peaks at a moderate value and slightly declines when weaker or redundant evidence is included\. This highlights the importance of balancing evidence sufficiency and discriminative filtering, and supports the default setting ofk=5k=5as an effective compromise\. Further analysis of tree growth budget parameters is provided in Appendix[G\.6](https://arxiv.org/html/2609.30943#A7.SS6)\.

We provide additional analyses on backbone choices, agentic pipeline variants, and evidence support in Appendices[G\.1](https://arxiv.org/html/2609.30943#A7.SS1),[G\.2](https://arxiv.org/html/2609.30943#A7.SS2), and[G\.3](https://arxiv.org/html/2609.30943#A7.SS3), further supporting the generality and reliability of LogicTree\-RAG\.

### 4\.5Broader Implications and Limitations

This work advances controllable and evidence\-grounded generative AI for long\-form technical document drafting\. By framing patent drafting as a representative high\-stakes structured generation task, LogicTree\-RAG can assist practitioners in organizing complex technical disclosures into coherent and structured drafts, potentially reducing manual effort in early\-stage drafting\. However, the framework is intended to assist rather than replace domain experts, and its outputs should be treated as preliminary drafts and reviewed by qualified professionals before any formal use\. Beyond patents, the logic\-centric paradigm extends naturally to broader long\-text generation tasks that require global organization and factual grounding\. While LCFO results support such generalization, further validation on diverse long\-form generation settings remains an important direction for future work\.

## 5Related Work

Patent\-related Benchmarks\.Recent progress in LLMs has spurred benchmarking of patent\-centric capabilities[Jiang and Goetz \(2025\)](https://arxiv.org/html/2609.30943#bib.bib44)\. For example, IPEval[Wang et al\. \(2024b\)](https://arxiv.org/html/2609.30943#bib.bib3)evaluates patent law QA, PatentEval[Zuo et al\. \(2024\)](https://arxiv.org/html/2609.30943#bib.bib9)targets claims\-to\-abstract and next\-claim generation, and HUPD\-DCG[Jiang et al\. \(2025\)](https://arxiv.org/html/2609.30943#bib.bib2)measures claim generation from patent descriptions\. Complementary to task\-specific benchmarks, Pap2Pat[Knappich et al\. \(2025\)](https://arxiv.org/html/2609.30943#bib.bib4)extends evaluation to long\-text patent drafting, reflecting growing interest in document\-level generation\. These benchmarks illustrate the evolution of patent\-related evaluation from localized capabilities toward holistic long\-form technical writing\.

Patent\-related Generation\.Existing patent generation methods can be broadly grouped into three paradigms\. \(i\)*Patent\-to\-patent*generates specific sections conditioned on other parts within the same patent\. For instance, PGT[Christofidellis et al\. \(2022\)](https://arxiv.org/html/2609.30943#bib.bib10)is pre\-trained to support title\-to\-abstract and abstract\-to\-claim generation\. PatentLMM[Shukla et al\. \(2025\)](https://arxiv.org/html/2609.30943#bib.bib1)presents a large multimodal model to write descriptions of patent figures\.[Ren et al\. \(2025\)](https://arxiv.org/html/2609.30943#bib.bib6)proposes a knowledge\-tuning approach for initial concept discovery and content generation\. \(ii\)*Draft\-to\-patent*expands patent drafts into patent\-form text\. PatentGPT[Bai et al\. \(2024\)](https://arxiv.org/html/2609.30943#bib.bib8)pre\-trains a series of base LLMs and benchmarks them with patent generation from technical disclosure documents draft\. AutoPatent[Wang et al\. \(2024c\)](https://arxiv.org/html/2609.30943#bib.bib12)designs a multi\-agent workflow to migrate a form of answers to technical questions to the corresponding patent draft\. \(iii\)*Paper\-to\-patent*targets long\-form patent drafting from research papers, exemplified by Pap2Pat[Knappich et al\. \(2025\)](https://arxiv.org/html/2609.30943#bib.bib4)with outline\-guided generation\. However, prior methods either operate under partial\-context settings or depend on externally provided \(often manually crafted\) drafting priors, leaving realistic long\-form patent drafting with global logical organization less well addressed\. See Appendix[B](https://arxiv.org/html/2609.30943#A2)for a comprehensive review\.

## 6Conclusions

Automated patent drafting exposes a fundamental bottleneck in long\-form technical text generation, where sustaining globally consistent logical structure and faithful technical reasoning remains challenging\. In this paper, we propose LogicTree\-RAG, a logic tree\-guided retrieval\-augmented generation framework for long\-form patent drafting that introduces a hierarchical logic tree as a global organizational backbone, where each node represents a technical element and is grounded by retrieved evidence via an evidence\-guided recursive generation mechanism\. To ensure precise and balanced tree construction, we develop node\-aware semantic search with discriminative relevance scoring and design a hybrid traversal strategy that maps the logic tree into structurally coherent patent sections\. Extensive experiments show that LogicTree\-RAG consistently improves content quality and language conformity over strong long\-context and multi\-stage LLM\-based baselines, enabling longer structured generation with high token efficiency\.

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## Appendix

- •[A](https://arxiv.org/html/2609.30943#A1)\. Notation Table\.[A](https://arxiv.org/html/2609.30943#A1)
- •[B](https://arxiv.org/html/2609.30943#A2)\. Extended Related Work\.[B](https://arxiv.org/html/2609.30943#A2)
- •[C](https://arxiv.org/html/2609.30943#A3)\. Details on Semantic Chunking\.[C](https://arxiv.org/html/2609.30943#A3)
- •[D](https://arxiv.org/html/2609.30943#A4)\. Workflow of Logic Tree Construction\.[D](https://arxiv.org/html/2609.30943#A4)
- •[E](https://arxiv.org/html/2609.30943#A5)\. Patent Generation Prompt\.[E](https://arxiv.org/html/2609.30943#A5)
- •[F](https://arxiv.org/html/2609.30943#A6)\. Implementation Details\.[F](https://arxiv.org/html/2609.30943#A6) - ⋅\\cdot[F\.1](https://arxiv.org/html/2609.30943#A6.SS1)\. Evaluation Metrics\.[F\.1](https://arxiv.org/html/2609.30943#A6.SS1) - ⋅\\cdot[F\.2](https://arxiv.org/html/2609.30943#A6.SS2)\. Baseline Methods\.[F\.2](https://arxiv.org/html/2609.30943#A6.SS2) - ⋅\\cdot[F\.3](https://arxiv.org/html/2609.30943#A6.SS3)\. Hyperparameters and Decoding Configurations\.[F\.3](https://arxiv.org/html/2609.30943#A6.SS3) - ⋅\\cdot[F\.4](https://arxiv.org/html/2609.30943#A6.SS4)\. Experimental Environment\.[F\.4](https://arxiv.org/html/2609.30943#A6.SS4) - ⋅\\cdot[F\.5](https://arxiv.org/html/2609.30943#A6.SS5)\. Detailed Prompt Settings\.[F\.5](https://arxiv.org/html/2609.30943#A6.SS5)
- •[G](https://arxiv.org/html/2609.30943#A7)\. Supplementary Experimental Results\.[G](https://arxiv.org/html/2609.30943#A7) - ⋅\\cdot[G\.1](https://arxiv.org/html/2609.30943#A7.SS1)\. Supplementary Ablation Study\.[G\.1](https://arxiv.org/html/2609.30943#A7.SS1) - ⋅\\cdot[G\.2](https://arxiv.org/html/2609.30943#A7.SS2)\. Supplementary Study on Agentic Planning\.[G\.2](https://arxiv.org/html/2609.30943#A7.SS2) - ⋅\\cdot[G\.3](https://arxiv.org/html/2609.30943#A7.SS3)\. Evidence\-support Analysis\.[G\.3](https://arxiv.org/html/2609.30943#A7.SS3) - ⋅\\cdot[G\.4](https://arxiv.org/html/2609.30943#A7.SS4)\. Computational Cost Analysis\.[G\.4](https://arxiv.org/html/2609.30943#A7.SS4) - ⋅\\cdot[G\.5](https://arxiv.org/html/2609.30943#A7.SS5)\. Human Expert Study\.[G\.5](https://arxiv.org/html/2609.30943#A7.SS5) - ⋅\\cdot[G\.6](https://arxiv.org/html/2609.30943#A7.SS6)\. Sensitivity to Tree Growth Budget\.[G\.6](https://arxiv.org/html/2609.30943#A7.SS6) - ⋅\\cdot[G\.7](https://arxiv.org/html/2609.30943#A7.SS7)\. Case Study\.[G\.7](https://arxiv.org/html/2609.30943#A7.SS7)
- •[H](https://arxiv.org/html/2609.30943#A8)\. Discussion on Human Evaluation and Legal Adequacy\.[H](https://arxiv.org/html/2609.30943#A8)

## Appendix ANotation Table

To provide a comprehensive overview of the notations used throughout the paper, we present a summary of notations in Table[3](https://arxiv.org/html/2609.30943#A1.T3)as a quick reference to facilitate the understanding and recall of each symbol\.

Table 3:Notations\.NotationDescription\(X,P\)\(X,P\)A patent\-paper pair, whereXXis the source \(pre\-publication\) research paper andPPis the corresponding patent document\.X=\{x1,x2,…,xm\}X=\\\{x\_\{1\},x\_\{2\},\\dots,x\_\{m\}\\\}Source paper represented as a collection ofmmtext spans\.P=\{p1,…,pn\}P=\\\{p\_\{1\},\\dots,p\_\{n\}\\\}Patent document represented as a collection ofnntext spans\.X~=\{x~1,x~2,…,x~L\}\\tilde\{X\}=\\\{\\tilde\{x\}\_\{1\},\\tilde\{x\}\_\{2\},\\dots,\\tilde\{x\}\_\{L\}\\\}Semantic chunks produced fromXXafter layout\-aware semantic chunking, withL≤mL\\leq m\.𝒯=\(V,E,r\)\\mathcal\{T\}=\(V,E,r\)Logic tree, a directed rooted tree with node setVV, edge setE⊆V×VE\\subseteq V\\times V, and root noderr\.V=\{v1,⋯,vN\}V=\\\{v\_\{1\},\\cdots,v\_\{N\}\\\}Set of logic tree nodes, each representing a technical element \(e\.g\., principle, step, subsystem\)\.vtv\_\{t\}Current node dequeued for recursive generation at iterationtt\.vfv\_\{f\}Parent node ofvtv\_\{t\}in the logic tree\.VfchildV\_\{f\}^\{\\text\{child\}\}Set of child nodes ofvfv\_\{f\}\.c⁡\(v\)c\(v\)Generated text fragment associated with nodevv\.ℰ⁡\(v\)\\mathcal\{E\}\(v\)Supporting evidence spans from the source paper attributed to nodevv,ℰ⁡\(v\)⊆X\\mathcal\{E\}\(v\)\\subseteq X\.𝒱k​b\\mathcal\{V\}\_\{kb\}Vectorized document knowledge base built from semantic chunks for retrieval\.ℰp​\(⋅\)\\mathcal\{E\}\_\{p\}\(\\cdot\)Paragraph\-level embedding function used for semantic similarity and indexing\.ℳL​L​M\\mathcal\{M\}\_\{LLM\}Backbone large language model\.σI\\sigma\_\{I\}Instruction prompt for generating an initial draft technical description\.𝒟d\\mathcal\{D\}\_\{d\}Draft technical description generated byℳL​L​M​\(σI,X~\)\\mathcal\{M\}\_\{LLM\}\(\\sigma\_\{I\},\\tilde\{X\}\)to initialize the logic tree root\.E⁡\(⋅\)E\(\\cdot\)Expansion procedure\.R⁡\(⋅\)R\(\\cdot\)Refinement procedure\.σE\\sigma\_\{E\}Expansion instruction prompt used to generate new child nodes from retrieved context\.σR\\sigma\_\{R\}Refinement instruction set; in the draftσR=\{σR​1,σR​2,σR​3\}\\sigma\_\{R\}=\\\{\\sigma\_\{R1\},\\sigma\_\{R2\},\\sigma\_\{R3\}\\\}\.𝒬\\mathcal\{Q\}FIFO queue used for breadth\-first expansion over leaf nodes in recursive generation\.𝒟c​t​x\\mathcal\{D\}\_\{ctx\}Context evidence set used to expand or refine the current nodevtv\_\{t\}\.Sd​r​\(d\)S\_\{dr\}\(d\)Discriminative relevance score for candidate spandd\.τf\\tau\_\{f\}Threshold for retaining a candidate span via discriminative relevance\.τs\\tau\_\{s\}Semantic chunking threshold\.tmaxt\_\{\\max\}Upper bound on accumulated token length for a chunk during semantic chunking\.TdT\_\{d\}A subtree allocated to the Detailed Description section\.

## Appendix BExtended Related Work

In this section, we provide an extended review of related work, encompassing both general long\-text generation methods and patent\-specific drafting paradigms\.

### B\.1Long\-text Generation

Recent advances in long\-text generation have explored structured prompting, hierarchical decomposition, and personalization to improve coherence over extended outputs\[[35](https://arxiv.org/html/2609.30943#bib.bib50)\]\. Early approaches, such as recursive and outline\-based generation\[[47](https://arxiv.org/html/2609.30943#bib.bib53),[48](https://arxiv.org/html/2609.30943#bib.bib54)\], introduce hierarchical planning to maintain narrative consistency across long contexts\. More recent work further incorporates personalization\[[49](https://arxiv.org/html/2609.30943#bib.bib55),[50](https://arxiv.org/html/2609.30943#bib.bib57)\]and reasoning\-oriented training\[[51](https://arxiv.org/html/2609.30943#bib.bib56)\], enabling models to better align outputs with user intent\.

With growing demand for ultra\-long generation \(e\.g\., beyond 2k tokens\), new datasets and training paradigms have emerged\. For instance, large\-scale instruction\-following datasets constructed via back\-translation support long\-context learning but often rely on synthetic supervision and remain limited in output length\[[52](https://arxiv.org/html/2609.30943#bib.bib58),[53](https://arxiv.org/html/2609.30943#bib.bib59)\]\. Other approaches, such as LongWriter\[[34](https://arxiv.org/html/2609.30943#bib.bib49)\], leverage supervised and preference\-based optimization on agent\-generated long\-form data to extend output length, but may inherit biases from teacher models and lack explicit control over global structure\. LongWriter\-Zero\[[35](https://arxiv.org/html/2609.30943#bib.bib50)\]further explores reinforcement learning from scratch to induce ultra\-long generation without relying on synthetic supervision, improving length control and structural consistency\. Overall, despite notable progress, existing approaches lack explicit global structure modeling and fine\-grained evidence grounding, limiting their ability to ensure coherent, factual, and controllable generation over ultra\-long contexts\.

Beyond long\-form generation, domain\-specific LLM systems have explored complementary mechanisms for evidence organization, contextual reasoning, and output verification\. Data\-centric generative systems can organize heterogeneous knowledge through semantic vector search, contextual querying, and agentic retrieval backends\[[54](https://arxiv.org/html/2609.30943#bib.bib63),[55](https://arxiv.org/html/2609.30943#bib.bib64)\]\. Fine\-grained evidence alignment and verification have also been studied through global\-to\-local semantic alignment\[[56](https://arxiv.org/html/2609.30943#bib.bib62)\], compact visual\-evidence interfaces for intermediate reasoning\[[57](https://arxiv.org/html/2609.30943#bib.bib61)\], and claim\-level counter\-evidence verification for hallucination detection and correction\[[58](https://arxiv.org/html/2609.30943#bib.bib60)\]\. These studies collectively illustrate the broader value of explicitly organizing and validating evidence during model reasoning and generation\.

### B\.2Patent Drafting

While prior work has studied long\-form generation in general domains, patent drafting introduces additional challenges due to strict structural, legal, and evidence\-grounding requirements\. We next review patent drafting, covering both benchmark development and generation paradigms\.

Patent\-related Benchmarks\.With the advancements of LLMs, the effort of testing their abilities in patent\-related tasks has emerged\. Recent progress in LLMs has spurred benchmarking of patent\-centric capabilities\[[43](https://arxiv.org/html/2609.30943#bib.bib44)\]\. For example, IPEval\[[44](https://arxiv.org/html/2609.30943#bib.bib3)\]tests LLMs for patent law QA\. PatentEval\[[12](https://arxiv.org/html/2609.30943#bib.bib9)\]focuses on the tasks of claims\-to\-abstract and next\-claim generations\. HUPD\-DCG\[[45](https://arxiv.org/html/2609.30943#bib.bib2)\]measures patent claim generation performance from patent descriptions\. Complementary to task\-specific benchmarks, Pap2Pat\[[16](https://arxiv.org/html/2609.30943#bib.bib4)\]extends evaluation to long\-text patent drafting, reflecting growing interest in document\-level generation\. These benchmarks illustrate the evolution of patent\-related evaluation from localized capabilities toward holistic long\-form technical writing\.

Patent\-related Generation\.Existing patent generation methods can be broadly grouped into three paradigms\. First ispatent\-to\-patent, where a section of the patent content is generated from those of specific parts in the same patent\. For instance, PGT\[[8](https://arxiv.org/html/2609.30943#bib.bib10)\]is pre\-trained to support multiple patent\-related tasks, e\.g\., title\-to\-abstract, abstract\-to\-claim\. PatentLMM\[[46](https://arxiv.org/html/2609.30943#bib.bib1)\]presents a large multimodal model to write descriptions of patent figures\.\[[13](https://arxiv.org/html/2609.30943#bib.bib6)\]proposes a knowledge\-tuning approach for initial concept discovery and content generation\. Second isdraft\-to\-patent, where a draft of a patent is expanded and aligned with patent format and wording\. PatentGPT\[[11](https://arxiv.org/html/2609.30943#bib.bib8)\]pre\-trains a series of base LLMs and benchmarks them with patent generation from technical disclosure documents draft\. AutoPatent\[[15](https://arxiv.org/html/2609.30943#bib.bib12)\]generates patents from draft information, where the draft is constructed through multiple rounds of iterative question–answering with an LLM under the guidance of a reference patent document\. Third ispaper\-to\-patent, where an end\-to\-end process is studied from the original research paper to the patent draft\. For example, Pap2Pat\[[16](https://arxiv.org/html/2609.30943#bib.bib4)\]leverages ground\-truth patent\-derived outlines to guide the full\-text patent migration\. However, prior methods either operate under partial\-context settings or depend on externally provided \(often manually crafted\) drafting priors, leaving realistic long\-form patent drafting with global logical organization less well addressed\.

## Appendix CDetails on Semantic Chunking

To convert the raw content of the input document into knowledge units suitable for retrieval\-augmented generation, we first perform semantic structure reconstruction based on page\-level metadata extracted by PyMuPDF111[https://pymupdf\.readthedocs\.io/](https://pymupdf.readthedocs.io/), including textual blocks, font styles, coordinates, line spans, and paragraph layouts\. Using these layout cues, the system identifies the hierarchical boundaries of sections and paragraphs, thereby reconstructing the document’s logical structure\.

Building upon this structure, we conduct semantic chunking to produce contextually coherent text segments that serve as the foundational elements of the vectorized document knowledge base\. Unlike fixed\-length segmentation methods\[[27](https://arxiv.org/html/2609.30943#bib.bib20),[16](https://arxiv.org/html/2609.30943#bib.bib4)\], our approach dynamically determines chunk boundaries according to the semantic coherence and contextual dependency between adjacent paragraphs\. Formally, given a document represented as an ordered sequence of paragraphsX~=\{x~1,x~2,…,x~m\}\\tilde\{X\}=\\\{\\tilde\{x\}\_\{1\},\\tilde\{x\}\_\{2\},\\dots,\\tilde\{x\}\_\{m\}\\\}, we compute the pairwise semantic similaritysi,i\+1=sim​\(ℰp​\(x~i\),ℰp​\(x~i\+1\)\)s\_\{i,i\+1\}=\\text\{sim\}\(\\mathcal\{E\}\_\{p\}\(\\tilde\{x\}\_\{i\}\),\\mathcal\{E\}\_\{p\}\(\\tilde\{x\}\_\{i\+1\}\)\), wherei=1,⋯,m−1i=1,\\cdots,m\-1, andℰp​\(⋅\)\\mathcal\{E\}\_\{p\}\(\\cdot\)denotes the paragraph\-level embedding function \(e\.g\., Sentence\-BERT\[[59](https://arxiv.org/html/2609.30943#bib.bib19)\]\)\. The pairwise semantic similaritysim​\(⋅,⋅\)\\text\{sim\}\(\\cdot,\\cdot\)can be instantiated with different similarity metrics, while cosine similarity is adopted in this work due to its efficiency and empirical stability\. Paragraphs are iteratively merged whensi,i\+1⩾τss\_\{i,i\+1\}\\geqslant\\tau\_\{s\}and the accumulated token length does not exceed the upper boundtm​a​xt\_\{max\}\. This procedure yields a set of semantically coherent chunksX=\{x1,x2,…,xL\}X=\\\{x\_\{1\},x\_\{2\},\\dots,x\_\{L\}\\\}, whereL≤mL\\leq mrepresents the total number of chunks for the input document andxix\_\{i\}represents theii\-th chunk aligned with the document’s logical hierarchy\. The resulting chunks constitute the minimal retrieval units for RAG, which are embedded into a high\-dimensional semantic space and stored in the vector database as a vectorized document knowledge base, supporting structured querying, traceable grounding, and multi\-turn semantic reasoning\.

## Appendix DWorkflow of Logic Tree Construction

In this section, we provide the detailed algorithmic specification of the logic tree construction process used in LogicTree\-RAG\. As described in Section[3\.5](https://arxiv.org/html/2609.30943#S3.SS5), logic tree construction constitutes the first stage of the overall patent generation pipeline, aiming to transform an unstructured research paper into a structured, evidence\-grounded hierarchical representation\. The procedure integrates semantic chunking, document\-level vectorization, and evidence\-guided recursive generation to progressively construct a logic tree that captures both the global structure and fine\-grained technical details of the source document\. Algorithm[2](https://arxiv.org/html/2609.30943#alg2)presents the complete logic tree construction procedure\.

Algorithm 2Logic Tree Construction0:Pre\-publication research paper, the backbone model

ℳL​L​M\\mathcal\{M\}\_\{LLM\}, instructions

σI,σE,σR\\sigma\_\{I\},\\sigma\_\{E\},\\sigma\_\{R\}, token length bound

tm​a​xt\_\{max\}, semantic chunking threshold

τs\\tau\_\{s\}, discriminative relevance threshold

τf\\tau\_\{f\}, content length budget

BB\.

0:Logic tree

𝒯\\mathcal\{T\}\.

∖\\setminus\*Semantic Chunking&logic tree initialization\*∖\\setminus

1:Obtain an ordered sequence of paragraphs

X=\{x1,x2,…,xm\}X=\\\{x\_\{1\},x\_\{2\},\\dots,x\_\{m\}\\\}via layout\-aware document analysis of the input paper

2:for

i=1i=1to

m−1m\-1do

3:Compute the pairwise semantic similarity

si,i\+1s\_\{i,i\+1\}of

xix\_\{i\}and

xi\+1x\_\{i\+1\}in

XXusing Eq\.[2](https://arxiv.org/html/2609.30943#S3.E2)

4:if

si,i\+1≥τss\_\{i,i\+1\}\\geq\\tau\_\{s\}and

\|xi\|\+\|xi\+1\|≤tm​a​x\|x\_\{i\}\|\+\|x\_\{i\+1\}\|\\leq t\_\{max\}then

5:Merge

xix\_\{i\}and

xi\+1x\_\{i\+1\}into a single segment

6:else

7:Retain

xix\_\{i\}and

xi\+1x\_\{i\+1\}as separate segments

8:endif

9:endfor

10:Construct the final chunk set

X~=\{x~1,…,x~L\},L≤m\\tilde\{X\}=\\\{\\tilde\{x\}\_\{1\},\\dots,\\tilde\{x\}\_\{L\}\\\},\\,L\\leq m
11:Build vectorized document knowledge base

𝒱k​b=\{ℰp​\(x~i\)\}i=1L\\mathcal\{V\}\_\{kb\}=\\\{\\mathcal\{E\}\_\{p\}\(\\tilde\{x\}\_\{i\}\)\\\}\_\{i=1\}^\{L\}
12:Generate a draft technical description

𝒟d=ℳL​L​M​\(σI,X~\)\\mathcal\{D\}\_\{d\}=\\mathcal\{M\}\_\{LLM\}\(\\sigma\_\{I\},\\tilde\{X\}\)
13:Create root node

rrof

𝒯\\mathcal\{T\}with

c⁡\(r\)=𝒟dc\(r\)=\\mathcal\{D\}\_\{d\}and

ℰ⁡\(r\)=X~\\mathcal\{E\}\(r\)=\\tilde\{X\}
14:Initialize the child nodes of

rrby prompting

ℳLLM\\mathcal\{M\}\_\{\\text\{LLM\}\}to decompose the key technical components of

𝒟d\\mathcal\{D\}\_\{d\}∖\\setminus\*Evidence\-Guided Recursive Generation with Node\-Aware Semantic Search\*∖\\setminus

15:Initialize FIFO queue

𝒬\\mathcal\{Q\}with all leaf nodes of

𝒯\\mathcal\{T\}
16:repeat

17:Dequeue front node

vtv\_\{t\}from

𝒬\\mathcal\{Q\}with parent node

vfv\_\{f\}
18:Obtain candidate context spans

𝒟~c​t​x\\tilde\{\\mathcal\{D\}\}\_\{ctx\}using Eq\.[5](https://arxiv.org/html/2609.30943#S3.E5)

19:Calculate discriminative relevance score

Sd​r​\(d\)S\_\{dr\}\(d\)for each

d∈𝒟~c​t​xd\\in\\tilde\{\\mathcal\{D\}\}\_\{ctx\}using Eq\.[6](https://arxiv.org/html/2609.30943#S3.E6)

20:Obtain context evidence

𝒟c​t​x=\{d\|Sd​r​\(d\)≥τf\}\\mathcal\{D\}\_\{ctx\}=\\\{d\\,\|\\,S\_\{dr\}\(d\)\\geq\\tau\_\{f\}\\\}
21:if

𝒟c​t​x\\mathcal\{D\}\_\{ctx\}is covered by

v∈𝒯∖Vfc​h​i​l​dv\\in\\mathcal\{T\}\\setminus V^\{child\}\_\{f\}then

22:Skip node

vtv\_\{t\}
23:else

24:Expand node

vtv\_\{t\}using

ℳL​L​M\\mathcal\{M\}\_\{LLM\}with prompt

σE\\sigma\_\{E\}via Eq\.[3](https://arxiv.org/html/2609.30943#S3.E3)

25:Update

𝒯\\mathcal\{T\}and

𝒬\\mathcal\{Q\}
26:ifUnder\-specified term is detected from

c⁡\(vt\)c\(v\_\{t\}\)using

ℳL​L​M\\mathcal\{M\}\_\{LLM\}with prompt

σR​1∈σR\\sigma\_\{R1\}\\in\\sigma\_\{R\}then

27:

Refinec⁡\(vt\)c\(v\_\{t\}\)usingℳL​L​M\\mathcal\{M\}\_\{LLM\}with prompt inσR\\sigma\_\{R\}via Eq\.[4](https://arxiv.org/html/2609.30943#S3.E4)

28:endif

29:endif

30:until

𝒬\\mathcal\{Q\}is emptyor

\|𝒯\|≥B\|\\mathcal\{T\}\|\\geq B
31:ReturnLogic tree

𝒯\\mathcal\{T\}

## Appendix EPatent Generation Prompt

LogicTree\-RAG employs a structured set of prompt instructions to control different stages of logic tree construction and recursive patent generation\. Specifically, we design three categories of prompts: an initialization instructionσI\\sigma\_\{I\}, an expansion instructionσE\\sigma\_\{E\}, and a refinement instructionσR\\sigma\_\{R\}\. These prompts act as a lightweight control interface for the backbone LLM, enabling evidence\-grounded, controllable long\-form patent drafting\.

The initialization instructionσI\\sigma\_\{I\}is used to generate a draft technical description𝒟d\\mathcal\{D\}\_\{d\}, which serves as the root node of the logic tree\. Given a set of semantically coherent chunks extracted exclusively from the introduction of the source paper,σI\\sigma\_\{I\}prompts the model to synthesize a high\-level technical description that captures the problem setting, core solution, and key system or method components, as detailed below\.

1\#\#\#ROLE

2Youareanexpertpatentdrafterandtechnicaleditor\.

3

4\#\#\#TASKDESCRIPTION

5Yourtaskistowriteadrafttechnicaldescriptionoftheinvention,basedONLYontheprovideddocumentchunksofaresearchpaper\.

6

7\#\#\#INPUT

8Input:asetofsemanticallycoherentchunksderivedexclusivelyfromthepaperintroduction\.

9

10Documentchunks:

11"""

12\{CHUNKS\}

13"""

14

15\#\#\#REQUIREMENTS

16WriteadrafttechnicaldescriptionthatsatisfiesALLrequirements:

171\.Capturetheinventionatasystemandmethodlevel:clearlystate\(i\)thetechnicalproblem,\(ii\)thecoretechnicalsolution,and\(iii\)thekeytechnicalcomponents\.

182\.Makethedescriptionexpansion\-ready:includeexplicittechnicalhandlessuchasmodulenames/roles,input\-outputsignals,keyvariables/parameters,andtheend\-to\-endworkflow,sothateachpartcanbeexpandedlaterintodetailedpatentsections\.

193\.Donotcitethepaper,authors,orsectionnumbers\.Donotmention"chunks"or"retrieval"\.

204\.UseONLYinformationsupportedbythechunks\.

215\.Nonumbering,nobulletpoints,noheadings\.

226\.Avoidvaguehedgessuchas"may","could",or"forexample"\.Preferdefinitive,technicalphrasing\.

23

24OutputONLYthedrafttechnicaldescriptiontext,withnoextracommentary\.

Listing 1:Refinement instructionσR​1\\sigma\_\{R1\}for under\-specified term detection\.The expansion instructionσE\\sigma\_\{E\}governs logic tree growth\. Conditioned on a node contribution and its associated context evidence,σE\\sigma\_\{E\}prompts the model to elaborate the contribution into finer\-grained algorithmic details\. The expansion instructionσE\\sigma\_\{E\}is specified as follows\.

1\#\#\#ROLE

2Youareanexpertinpolishingpatentapplications,withprofessionalknowledgeofpatentdraftingandformattingstandards\.

3

4\#\#\#TASKDESCRIPTION

5Yourtaskis:givenaspecificclaimcontribution,extracttheconcretealgorithmicstepsfromtheprovidedcontextandusethemtoexpandthecontribution,furtherspecifyingitsalgorithmicdetails\.

6

7\#\#\#REQUIREMENTS

81\.Theoutputshouldconsistofseveralparagraphswithoutnumbering\.Thefirstparagraphmustrestatethegivencontributionpoint"\{text\}",andthesubsequentparagraphsshoulddescribethedetailedalgorithmicsteps\.Separateeachparagraphwithablankline\.

92\.Theexpansionmustremainstrictlyfocusedonthecontentandconceptdescribedbythegivencontributionpoint,withoutintroducingbroaderorunrelatedconcepts\.

103\.Thetotaloutputlengthshouldbelongerthantheinput\.

114\.Whennecessary,includekeyformulasanddefinitions\.Donotinsertlinebreaksinsideformulas\.

125\.Maintainprecisionandconciseness\.Avoidtermssuchas"forexample"or"may"\.

136\.Eachparagraphmustbesufficientlydetailed\.Ifastepinvolvesmultiplesub\-steps,describethemwithinasingleparagraphusingmultiplesentences\.Thegranularityofthestepsshouldbeconsistent,witheachstepbeingnecessaryandindispensable\.

14

15Theoriginalcontributionpointis:\{TEXT\}\\n\\n

16Theprovidedcontextis:\{CONTEXT\}\\n\\n

Listing 2:Expansion instructionσE\\sigma\_\{E\}for LogicTree\-RAG in long\-form patent drafting\.The refinement instruction setσR=\{σR​1,σR​2,σR​3\}\\sigma\_\{R\}=\\\{\\sigma\_\{R1\},\\sigma\_\{R2\},\\sigma\_\{R3\}\\\}addresses under\-specified content during recursive generation\. Specifically,σR​1\\sigma\_\{R1\}detects under\-specified terms that lack sufficient technical detail,σR​2\\sigma\_\{R2\}refines such terms using retrieved supporting evidence when available, andσR​3\\sigma\_\{R3\}completes refinement using the model’s internal knowledge when no relevant evidence exists\. The refinement instructionsσR\\sigma\_\{R\}are instantiated as follows\.

1\#\#\#ROLE

2Youareanexpertpatentdrafterandtechnicaleditor\.

3

4\#\#\#TASKDESCRIPTION

5Yourtaskistoidentifyunder\-specifiedtermsinapatentspecificationnodeandproposehowtheyshouldberefined\.

6

7\#\#\#DEFINITION

8Definition\(under\-specifiedterm\):

9Aterm/phraseisunder\-specifiedif,foratechnicaldisclosure,itlacksatleastoneofthefollowing:acleardefinition,aconcretealgorithmicprocedure,requiredinputs/outputs,parameters/hyperparameters,constraints/assumptions,orimplementation\-leveldetails\.

10

11\#\#\#INPUT

12Inputnodecontent:

13"""

14\{NODE\_CONTENT\}

15"""

16

17\#\#\#REQUIREMENTS

181\.Each"term"mustbeanexactsubstringoftheinput\.

192\.Donotrewritethenodecontent\.Donotaddanyextrakeys\.

203\.Prefertermsthatareessentialfortechnicalcompleteness\(e\.g\.,keycomponents,variables,algorithms,objectives,constraints\)\.

214\.OutputONLYtheterms,oneperline,withnonumbering,nobullets,noextratext\.

Listing 3:Refinement instructionσR​1\\sigma\_\{R1\}for under\-specified term detection\.1\#\#\#ROLE

2Youareanexpertpatentdrafterandtechnicaleditor\.Youwillrefineaspecificunder\-specifiedterminapatent\-specificationnodeusingtheprovidedevidence\.

3

4\#\#\#TASKDESCRIPTION

5Yourtaskistoproduceanupdatedversionofthenodecontentthatmakesthetargetterm"\{TERM\}"fullyspecifiedinapatent\-appropriateandtechnicallyreproducibleway\.

6

7\#\#\#INPUT

8Targettermtorefine:

9"\{TERM\}"

10

11Originalnodecontent:

12"""

13\{NODE\_CONTENT\}

14"""

15

16Retrievedevidence\(contextspans\):

17"""

18\{EVIDENCE\_SPANS\}

19"""

20

21\#\#\#REQUIREMENTS

221\.AllnewlyaddedtechnicaldetailsMUSTbesupportedbytheevidence\.

232\.Preservetheoriginalwordingandstructureasmuchaspossible\.Onlyaddorrevisetheminimalnecessarytexttospecify"\{TERM\}"\.

243\.Whenapplicable,specifyinputs/outputs,keyvariables/parameters,computationsteps,constraints/assumptions,andanyformulasexplicitlystatedorimpliedbytheevidence\.

254\.Useformal,preciselanguage\.Avoidhedgessuchas"may","could","forexample"\.Avoidconversationaltone\.

26

27Refinednodecontent:

Listing 4:Refinement instructionσR​2\\sigma\_\{R2\}for evidence\-grounded targeted refinement\.1\#\#\#ROLE

2Youareanexpertpatentdrafterandtechnicaleditor\.Youwillrefineanunder\-specifiedterminapatent\-specificationnode\.

3

4\#\#\#TASKDESCRIPTION

5Yourtaskistoupdatethenodecontenttomake"\{TERM\}"sufficientlyspecifiedforapatentspecification,usinggeneralandwidelyapplicabletechnicaldescriptions\.

6

7\#\#\#INPUT

8Targettermtorefine:

9"\{TERM\}"

10

11Originalnodecontent:

12"""

13\{NODE\_CONTENT\}

14"""

15

16\#\#\#REQUIREMENTS

171\.Preservetheoriginalwordingandstructureasmuchaspossible\.Onlyaddorrevisetheminimalnecessarytexttospecify"\{TERM\}"\.

182\.Provideastandard,implementation\-orientedspecificationthatwouldbereasonableacrosstypicalsystems\(e\.g\.,defineinputs/outputs,typicalparameterization,andagenericprocedure\)\.

193\.Useformal,preciselanguage\.Avoidhedgessuchas"may","could","forexample"\.Avoidconversationaltone\.

20

21Refinednodecontent:

Listing 5:Refinement instructionσR​3\\sigma\_\{R3\}for knowledge completion
## Appendix FImplementation Details

### F\.1Evaluation Metrics

We evaluate patent generation performance from both content\-level and language\-level perspectives\. Given the length and specialized nature of patent documents, automatic evaluation requires disentangling semantic content quality from linguistic realization\. We therefore adopt established metrics that are well\-suited for long\-form technical text generation\.

Content\-level Metrics\.Content\-level metrics evaluate whether the generated patent conveys accurate and sufficient technical information grounded in the reference materials\. All content\-level metrics are computed at the document level following the evaluation protocol\[[16](https://arxiv.org/html/2609.30943#bib.bib4)\]\.

- •Coverage\.Coverage measures the extent to which the generated patent covers the semantic content of the reference patent\. We adopt the NLI\-based SCALE metric\[[38](https://arxiv.org/html/2609.30943#bib.bib26)\], which estimates entailment between two long documents by aggregating sentence\-level entailment scores\. Given a generated patentXXand a reference patentRR, we treatXXas the*premise document*andRRas the*hypothesis document*\. Due to the quadratic cost of exhaustive sentence\-level comparisons, SCALE samples a set of hypothesis sentences\{hj\}j=1N\\\{h\_\{j\}\\\}\_\{j=1\}^\{N\}fromRR\. For each hypothesis sentencehjh\_\{j\}, the top\-KKmost relevant premise chunks\{pj,k\}k=1K\\\{p\_\{j,k\}\\\}\_\{k=1\}^\{K\}are retrieved fromXXusing BM25\. An NLI model then computes entailment probabilitiesPentail​\(pj,k,hj\)P\_\{\\text\{entail\}\}\(p\_\{j,k\},h\_\{j\}\)\. The sentence\-level entailment score is defined as: s⁡\(hj\)=maxk⁡Pentail​\(pj,k,hj\)\.s\(h\_\{j\}\)=\\max\_\{k\}P\_\{\\text\{entail\}\}\(p\_\{j,k\},h\_\{j\}\)\.\(7\) The coverage score is computed by averaging over all sampled hypothesis sentences: Coverage​\(X,R\)=1N​∑j=1Ns⁡\(hj\)\.\\text\{Coverage\}\(X,R\)=\\frac\{1\}\{N\}\\sum\_\{j=1\}^\{N\}s\(h\_\{j\}\)\.\(8\)
- •Patent\-grounded Factuality\.Patent\-grounded factuality \(ℱP​a​t\\mathcal\{F\}\_\{Pat\}\) measures the extent to which the generated patent is supported by the reference patent alone\. In this setting, the reference patentRRserves as the premise document and the generated patentXXserves as the hypothesis document: ℱP​a​t​\(X,R\)=SCALE​\(R⇒G\)\.\\mathcal\{F\}\_\{Pat\}\(X,R\)=\\text\{SCALE\}\(R\\Rightarrow G\)\.\(9\)
- •Source\-grounded Factuality\.Source\-grounded factuality \(ℱS​r​c\\mathcal\{F\}\_\{Src\}\) measures semantic consistency when both the reference patent and the source paper are used as grounding evidence\. Specifically, the premise document is formed by concatenating the reference patentRRand the source paperPP, while the generated patentXXremains the hypothesis document: ℱS​r​c​\(X,R,P\)=SCALE​\(R∪P⇒G\)\.\\mathcal\{F\}\_\{Src\}\(X,R,P\)=\\text\{SCALE\}\(R\\cup P\\Rightarrow G\)\.\(10\) This variant accounts for technically correct information present in the source paper but absent from the granted patent\.
- •ROUGE\-L\.ROUGE\-L evaluates the longest common subsequence \(LCS\) between predicted and ground truth answers, reflecting recall\-oriented similarity\. It captures global structural overlap and complements BLEU’s precision\-based evaluation\. ROUGE\-L=\(1\+β2\)⋅Rl​c​s⋅Pl​c​sRl​c​s\+β2⋅Pl​c​s,\\displaystyle\\text\{ROUGE\-L\}=\\frac\{\(1\+\\beta^\{2\}\)\\cdot R\_\{lcs\}\\cdot P\_\{lcs\}\}\{R\_\{lcs\}\+\\beta^\{2\}\\cdot P\_\{lcs\}\},\(11\)whereRl​c​s=LCS​\(X,Y\)\|Y\|,Pl​c​s=LCS​\(X,Y\)\|X\|\.R\_\{lcs\}=\\frac\{\\text\{LCS\}\(X,Y\)\}\{\|Y\|\},P\_\{lcs\}=\\frac\{\\text\{LCS\}\(X,Y\)\}\{\|X\|\}\.YYis the ground truth patent, i\.e\., the reference patent,XXis the predicted text, i\.e\., the generated patent\.LCS​\(X,Y\)\\text\{LCS\}\(X,Y\)is the length of the longest common subsequence\.β\\betacontrols the weighting of recall versus precision\.

Language\-level Metrics\.Language\-level metrics evaluate whether the generated patent adheres to stylistic conventions of patent writing, maintains discourse coherence, and avoids degenerate generation behaviors\.

- •Style\.We measure stylistic similarity using corpus\-wide n\-gram profiles and StyloMetrix\[[40](https://arxiv.org/html/2609.30943#bib.bib29)\], as in\[[16](https://arxiv.org/html/2609.30943#bib.bib4)\]\. For n\-gram profiles, we construct frequency distributions over the 1,000 most frequent n\-grams forn∈\{1,2,3,4\}n\\in\\\{1,2,3,4\\\}from the reference patents and the generated patents\. LetPrefn​\(g\)P^\{n\}\_\{\\text\{ref\}\}\(g\)andPgenn​\(g\)P^\{n\}\_\{\\text\{gen\}\}\(g\)denote the normalized frequencies of n\-gramgg\. The similarity score for eachnnis computed as: simn=∑g∈Prefn∪Pgenn\(1−\(Prefn​\(g\)−Pgenn​\(g\)Prefn​\(g\)\+Pgenn​\(g\)\)2\)\.\\text\{sim\}\_\{n\}=\\sum\_\{g\\in P^\{n\}\_\{\\text\{ref\}\}\\cup P^\{n\}\_\{\\text\{gen\}\}\}\\left\(1\-\\left\(\\frac\{P^\{n\}\_\{\\text\{ref\}\}\(g\)\-P^\{n\}\_\{\\text\{gen\}\}\(g\)\}\{P^\{n\}\_\{\\text\{ref\}\}\(g\)\+P^\{n\}\_\{\\text\{gen\}\}\(g\)\}\\right\)^\{2\}\\right\)\.\(12\) We additionally compute a StyloMetrix similarity score based on 196 rule\-based linguistic features, including verb tense, modality, POS distributions, lexical patterns, and syntactic constructions\. The final style score is computed as: Style=15​\(∑n=14simn\+simstylo\)\.\\text\{Style\}=\\frac\{1\}\{5\}\\left\(\\sum\_\{n=1\}^\{4\}\\text\{sim\}\_\{n\}\+\\text\{sim\}\_\{\\text\{stylo\}\}\\right\)\.\(13\)
- •Repetition Rate\.Repetition Rate \(ℛr\\mathcal\{R\}\_\{r\}\) is defined as the fraction of n\-grams that appear more than once within a text\[[41](https://arxiv.org/html/2609.30943#bib.bib30)\], given by: ℛr=\#​\{n\-grams occurring more than once\}\#​\{total n\-grams\}\.\\mathcal\{R\}\_\{r\}=\\frac\{\\\#\\\{\\text\{n\-grams occurring more than once\}\\\}\}\{\\\#\\\{\\text\{total n\-grams\}\\\}\}\.\(14\) To capture long\-range repetition and degeneration effects,ℛr\\mathcal\{R\}\_\{r\}is computed over sliding windows of 256 tokens and averaged across windows\.
- •Coherence\.Discourse\-level coherence is evaluated using DiscoScore\[[42](https://arxiv.org/html/2609.30943#bib.bib28)\], a coherence metric based on Centering Theory\[[60](https://arxiv.org/html/2609.30943#bib.bib31)\]and contextualized representations from BERT\[[61](https://arxiv.org/html/2609.30943#bib.bib32)\]\. DiscoScore assesses the consistency of entity transitions and discourse structure across adjacent sentences, producing a document\-level coherence score\. Given a hypothesis documentXX, let𝐒gen∈ℝn×d\\mathbf\{S\}\_\{\\text\{gen\}\}\\in\\mathbb\{R\}^\{n\\times d\}denote the matrix of sentence embeddings, wherennis the number of sentences andddis the embedding dimension\. A sentence graph𝒢gen=\(V,Agen\)\\mathcal\{G\}\_\{\\text\{gen\}\}=\(V,A\_\{\\text\{gen\}\}\)is constructed, where each node corresponds to a sentence and the adjacency matrixAgenA\_\{\\text\{gen\}\}encodes discourse connectivity between sentence pairs\. Sentence embeddings are updated via graph propagation𝐒^gen=\(Agen\+I\)​𝐒gen\\hat\{\\mathbf\{S\}\}\_\{\\text\{gen\}\}=\(A\_\{\\text\{gen\}\}\+I\)\\mathbf\{S\}\_\{\\text\{gen\}\}, whereIIis the identity matrix\. The same procedure is applied to the reference documentRRto obtain𝐒^ref\\hat\{\\mathbf\{S\}\}\_\{\\text\{ref\}\}\. A document\-level representation is then computed by aggregating sentence embeddings, yielding𝐆gen\\mathbf\{G\}\_\{\\text\{gen\}\}and𝐆ref\\mathbf\{G\}\_\{\\text\{ref\}\}\. The final DiscoScore is defined as the cosine similarity between the two graph\-level representations: DiscoScore​\(X,R\)=cos⁡\(𝐆gen,𝐆ref\)\.\\text\{DiscoScore\}\(X,R\)=\\cos\\\!\\left\(\\mathbf\{G\}\_\{\\text\{gen\}\},\\mathbf\{G\}\_\{\\text\{ref\}\}\\right\)\.\(15\)

Together, these metrics offer a comprehensive evaluation of patent generation quality that captures content accuracy, linguistic conventions, discourse coherence, and long\-form generation behavior\.

### F\.2Baseline Methods

For heuristic baselines, including the ground\-truth patent and source paper, no additional model inference is performed, and the documents are directly treated as generated outputs for metric computation\.

Single LLM\-call baselines generate the entire patent document in a single LLM invocation by conditioning on the full input paper using long\-context prompting\. All baselines support 128k\-token contexts, enabling full\-paper ingestion and long\-text drafting\. No intermediate structure or iterative refinement is employed\. Specifically, Kimi K2\[[29](https://arxiv.org/html/2609.30943#bib.bib36)\]adopts a Mixture\-of\-Experts \(MoE\) architecture with 1T total parameters and 32B activated per token, and is pre\-trained on 15\.5T tokens with carefully engineered optimization strategies to ensure stable training at scale\. Qwen3\-80B\[[30](https://arxiv.org/html/2609.30943#bib.bib33)\]follows the Qwen3\-Next design and integrates hybrid attention mechanisms and high\-sparsity MoE layers, enabling efficient context modeling under ultra\-long context lengths while maintaining computational efficiency\. Qwen3\-Max\[[30](https://arxiv.org/html/2609.30943#bib.bib33)\]is a proprietary flagship model exceeding one trillion parameters, pre\-trained on 36T tokens, and inherits the MoE\-based design of the Qwen3 series with enhanced load balancing and stability\-oriented training strategies\. DeepseekV3\[[31](https://arxiv.org/html/2609.30943#bib.bib34)\]is an MoE model with 671B total parameters and 37B activated per token, combining Multi\-head Latent Attention and DeepSeekMoE architectures, and is pre\-trained on 14\.8T tokens with multi\-token prediction objectives to support efficient and stable large\-scale training\. GPT\-5\[[32](https://arxiv.org/html/2609.30943#bib.bib47)\]and Gemini\-2\.5 Pro\[[33](https://arxiv.org/html/2609.30943#bib.bib48)\]are proprietary frontier LLMs evaluated under deep thinking mode, which enables stronger deliberative reasoning and more careful long\-context synthesis\. They serve as strong closed\-source baselines for assessing whether advanced reasoning\-oriented prompting alone can produce coherent and complete long\-form patent drafts without explicit structural orchestration\. Collectively, these models provide strong capacity for long\-context representation and serve as competitive backbone LLMs for single LLM\-call patent generation\. Unless otherwise specified, these baselines use the same decoding configurations as LogicTree\-RAG\.

Furthermore, we include Chunk\-based Outline\-guided Patent Generation \(COPGEN\)\[[16](https://arxiv.org/html/2609.30943#bib.bib4)\]and its supervised fine\-tuned \(SFT\) variant as representative multi\-stage LLM\-based patent generation methods\. Specifically, COPGEN chunks the outline, selects the most relevant parts of the source paper depending on the chunks’ outline bullet points, and prompts an LLM to generate the patent text for that chunk\. To ensure a fair comparison under our problem setting, where no ground\-truth patent outline is available at inference time, we report two variants of COPGEN\. The first follows the outline\-empty configuration, where only section titles are provided\. For completeness, we also report results obtained using the long\-version outlines provided by the Pap2Pat benchmark\. For the SFT variant, only the backbone LLM is fine\-tuned on the Pap2Pat training set \(1,000 paper\-patent pairs\), and no test data is used during training\.

In addition, we consider recent long\-form generation frameworks LongWriter\[[34](https://arxiv.org/html/2609.30943#bib.bib49)\]and LongWriter\-Zero\[[35](https://arxiv.org/html/2609.30943#bib.bib50)\], which decompose long\-form generation into multi\-stage processes to improve global coherence and content planning\. We also include general\-purpose agentic paradigms, including ReAct\[[36](https://arxiv.org/html/2609.30943#bib.bib51)\]and Plan\-then\-Execute\[[37](https://arxiv.org/html/2609.30943#bib.bib52)\], which enable LLMs to perform step\-wise planning and tool use without relying on hard\-coded structural templates\. To adapt them to our setting, we expose our core components, i\.e\., semantic chunking, node\-aware retrieval, and evidence\-guided generation, as callable tools, and replace the logic\-tree\-based orchestration with generic agent planning strategies\.

### F\.3Hyperparameters and Decoding Configurations

In this subsection, we summarize the hyperparameters and decoding configurations adopted for LogicTree\-RAG and the baseline methods\. LogicTree\-RAG adopts Qwen3\-80B\[[30](https://arxiv.org/html/2609.30943#bib.bib33)\]as the backbone LLM throughout all experiments, unless stated otherwise\. We set the semantic chunking thresholdτs\\tau\_\{s\}to 0\.8, and the accumulated token lengthtm​a​xt\_\{max\}to 512\. For node\-aware semantic search, we first retrieve up to 100 candidate evidence spans𝒟~c​t​x\\tilde\{\\mathcal\{D\}\}\_\{ctx\}from the document knowledge base𝒱k​b\\mathcal\{V\}\_\{kb\}\. These candidates are then re\-ranked using the discriminative relevance score\. Rather than using a fixed score threshold, we retain the top\-5 candidates and set the discriminative relevance thresholdτf\\tau\_\{f\}adaptively as the score of the 5\-th ranked candidate\. To ensure controllable expansion during evidence\-guided recursive generation, we impose a content length budgetBBthat limits the maximum size of the logic tree\. Specifically, the recursive process terminates when the number of nodes in the tree reaches 64, preventing unbounded expansion while preserving sufficient structural coverage\. In addition, we constrain the maximum depth of the logic tree to 3 \(with the root node at depth 0\), which avoids overly deep recursive expansion and encourages a balanced hierarchical structure\. Unless otherwise specified, these values are fixed across all experiments\.

Reported results in Section[4\.2](https://arxiv.org/html/2609.30943#S4.SS2)are evaluated on the Pap2Pat test set, which consists of 500 paper\-patent pairs\. For the SFT variant of COPGEN, fine\-tuning is conducted exclusively on the Pap2Pat training set containing 1,000 pairs\. All LLM\-based methods use identical decoding configurations, with a temperature of 0\.3, top\-ppof 1\.0, and a maximum generation length of 8,192 tokens\. The SFT variant of COPGEN fine\-tunes the backbone LLM Llama\-3 8B\[[62](https://arxiv.org/html/2609.30943#bib.bib25)\]using LoRA\[[63](https://arxiv.org/html/2609.30943#bib.bib35)\]\. Following\[[16](https://arxiv.org/html/2609.30943#bib.bib4)\], we adopt a maximum sequence length of 8,192 tokens and train the model for 3 epochs with a batch size of 32\. The learning rate is set to3\.16×10−43\.16\\times 10^\{\-4\}with a cosine scheduler and a warmup ratio of 0\.1\. For LoRA\-specific configurations, we use a rank of 128, a scaling factor \(α\\alpha\) of 60, and a dropout rate of 0\.05\.

### F\.4Experimental Environment

All experiments are conducted on a server equipped with an AMD EPYC 9334 CPU @ 2\.7 GHz \(32 cores\), 128 GB of memory, and an NVIDIA H100 NVL with CUDA 12\.9\. The operating system is Ubuntu 24\.04 running Linux kernel 6\.8\.0\-79\-generic\. LLMs used in our experiments are accessed via standardized cloud\-based inference APIs provided by Alibaba Cloud Bailian\.

### F\.5Detailed Prompt Settings

This subsection details the prompt configurations used in our experiments\. For COPGEN and its variants, we adopt the prompt settings from the publicly released implementation222[https://github\.com/boschresearch/Pap2Pat](https://github.com/boschresearch/Pap2Pat)\. For single LLM\-call baselines, we employ a unified prompt template following COPGEN to ensure a fair and consistent comparison, as described below\.

1\#\#\#ROLE

2Youareahighlyskilledpatentattorneywithdecadesofexperienceindraftinghigh\-qualitypatentapplications\.

3Youassistscientistsintransformingtheirscientificdiscoveriesintolucrativepatents\.

4

5\#\#\#TASKDESCRIPTION

6Yourtaskistodraftapatentapplication\.

7

8\#\#\#INPUTS

9Asinput,youwillbeprovidedaresearchpaperandapatentoutline,eachservingadistinctpurpose\.

10

111\.ResearchPaper:

12Theresearchpaperdescribesanovelinventiontobepatented\.Yourtaskistoextracttheinventionfromthepaperandwriteapatentapplicationforit\.

13

142\.PatentOutline:

15Thepatentoutlinesummarizesthedesireddiscoursestructureofthepatentdocument\.Usethisoutlineasaroughguidanceduringdrafting\.

16

17\#\#\#GUIDELINES

18\*Copytheheadingsfromtheoutlineexactly\.Youmustincludeonlytheheadingsprovidedintheoutline\.

19\*Youmustalwayswritecompletesentencesandavoidkeywords,bulletlistsandenumerations\.

20\*Thepatentmustactasastandalonedocument,thereforedonotrefertotheresearchpaperinthepatent\.

Listing 6:System prompt for single LLM\-call baselines in long\-form patent drafting\.1Hereistheoutlineofthedesiredpatentapplication\.

2Perbulletpoint,writeroughly4000words\.

3

4\#\#\#DESCRIPTIONOFTHEINVENTION

5Intheexampleabove,eachlinebeginningwith’\#’isabulletpoint\.

6‘‘‘md

7\#DESCRIPTION

8

9\#\#FIELDOFTHEINVENTION

10

11\#\#BACKGROUNDOFTHEINVENTION

12

13\#\#SUMMARYOFTHEINVENTION

14

15\#\#DETAILEDDESCRIPTION

16‘‘‘

17Youneedtodraftacompletepatentapplicationthatstrictlyfollowstheoutline’ssectionorderandheadings\.

18Donotskipanybulletpoints\.Useformalpatentlanguage\.Rememberwhatyouhavealreadywrittenanddonotrepeatyourself\.

19

20\#\#\#RESEARCHPAPER

21Hereistheresearchpaperthatdescribestheinvention:

22‘‘‘

23\#Introduction

24

25Safedeliveryofahighlyconformaldosedistributiontoawell\-definedtargetvolumeinradiotherapyisnotaneasytask\.Ithasbecomeincreasinglychallengingduetotheadventofadvancedtreatmenttechniquessuchasintensity\-modulatedradiationtherapy\(IMRT\)andvolumetric\-modulatedarctherapy\(VMAT\)\.Thoughadownwardtrendinradiotherapyincidentrateshasbeenindicatedbyseveralreports\(1,2\),severeincidentswithdetrimentaleffects,includingdeath,havebeenreportedrecentlyandreceivedpublicattention\.\(3\)Radiotherapyisacomplicated,multistep,multipersonprocess,anderrorscanoccuratanypoint\.Oneoftheprominentcausesforradiotherapyincidentsisgeometricmissduetoincorrectpatientsetup,whichleadstothetreatmentofincorrectbodypartswithmorethan1cmspatialdiscrepancy\(3,4\)\.Geometricmisscanresultinsignificantunderdosetothetarget,whichcancausetumorrecurrence,andoverdosetohealthytissuewithseverenormaltissuecomplications\.Inhypofractionatedradiotherapy,suchasstereotacticbodyradiotherapy\(SBRT\),itmayresultinevenmoreseveremorbiditythantraditionalradiotherapy\.

26

27Severalreportshaveidentifiedgeometricmisscausedbyincorrectpatientsetupastheleadingcauseofradiotherapyerrors\.Inastudyof100radiotherapyincidentsreportedinternationally,\(3\)thefrequencyofincidentsduetoincorrectpatientsetupwasreportedtobe21%\.Excludingthe44incidentsinvolvingbrachytherapy,inwhichpatientsetupaccuracywaslessofanissue,thisfrequencywouldrisesignificantlyto37\.5%\.\(3\)Clarketal\.\(2\)analyzedclinicalincidentsreportedinternallywithinalargeacademiccenterbetween2007and2009,andreportedthat14outof41critical,major,orseriousincidentsweregeographicmisses\.Amongthe14incidents,onewascausedbywrongtargetidentificationintreatmentplanning,whiletheother13incidentswereduetoshiftingerrorsatpatientsetup\.Inanonlinereportanalyzingeventcausesfor230misadministrationsreportedbetween2001and2009inNewYorkfromtheNewYorkStateDepartmentofHealth,incorrectbodyparttreatedwastheleadingtypeoferrorsat46%,mostoftenduetoincorrectpatientsetup\.\(5\)

28

29Thechallengeinpatientsetupistoaccuratelylocalizethepatienttothesametreatmentpositionasplannedineachtreatmentsession\.Overthepastdecade,therehasbeenarapidexpansionintechnologicaltoolstofacilitateaccuratepatientlocalization\.Examplesincludeimmobilizationdeviceswithcouchindexingcapabilityandimage\-guidedradiotherapy\(IGRT\)\.Indexedimmobilizationdevicesnotonlyensurerepeatablepatientfixationbutalsoreducepatientsetuperrorsbyprovidinginitialapproximatetargetlocalization\.X\-ray\-basedimage\-guidedtechnologies,suchascone\-beamCT\(CBCT\),enablevisualizationofinternalanatomywithsufficientsofttissuecontrastandallowcorrectionsformisalignmentorinterfractionmotionthroughregistrationwithreferenceCTimages\(6\)\.IGRTsystemscapableofcontinuoustracking,suchasAlignRT\(VisionRTLtd\.,London,UK\),C\-RadSentinel\(C\-RadAB,Uppsala,Sweden\),SonArray\(VarianMedicalSystems,PaloAlto,CA\),andExacTracOpticalTrackingSystem\(BrainLAB,Heimstetten,Germany\),havealsobeendevelopedforpatientsetupguidance\.Thesesystemshavethepotentialtoeliminatepatientsetuperrorsandsignificantlyreducesetupuncertainty,asdemonstratedbymultiplestudies\.BissonnetteandMedlam\(1\)reporteda50percentoveralldecreaseinincidentscausedbylocalizationerrorsfollowingthewidespreadintroductionofIGRTonsixoftheir16linearaccelerators\.Clarketal\.\(2\)notedthatnoneofthe13geometricmissesoccurredontreatmentunitsequippedwithdailyimageguidancesystems\.

30

31

32However,theuseofadvancedtechnologicalequipmentdoesnotguaranteethatradiotherapyisimmunetosetuperrors\(7,8\)\.Patientsetupisaprocessthatcannotbefullyautomatedandisthereforesubjecttohumanerror\.Severalcontributingfactorshavebeenidentified\.First,sufficientformaltrainingisnotalwaysprovidedtopersonnelwhooperatethedevicesandinterprettheresults\.Incorrectinterpretationcanleadtoimproperadjustmentoftreatmentpositionandconsequentlytosetuperrors\.Forexample,whentreatingthethoracicspine,thereisasignificantriskoftargetingthewrongvertebralbody\(9\)\.Duetosimilaritiesinbonyanatomyinthisregion,incorrectalignmentmayoccurasaresultofmisregistrationusingorthogonalimagingorCBCT\.Second,asthecomplexityoftreatmenttoolsincreases,thecomplexityofdevicecontrolandtheworkloadfortherapistsalsoincrease,asreflectedbythegrowingnumberofcomputermonitorsincontrolrooms\(8\)\.Intheabsenceofstreamlinedworkflowsandstandardizedcontrols,therapistsmayloseattentiontothecorrectnessoftreatmentdelivery\.Third,althoughmostIGRTdevicesprovidehighgeometricprecision\(10\),theyhaveinherentlimitationsthatmaycompromisepatientsafety\.Forexample,commonlyusedradiographicsystemssuchasCBCTdonottrackpatientpositionchangesandthereforerepresentpatientpositiononlyatthetimeofimageacquisition\.AfterCBCTimaging,thetreatmentcouchmaybemovedforspecificreasons,suchasclearancechecks,andnotreturnedtotheintendedtreatmentposition\.Moderndeliverysystemsincludeinterlockstodetectsucherrorsbutalsoallowuseroverrides\.Ifaninterlockisinappropriatelyoverridden,thepatientmaybetreatedatanincorrectsite\.Inaddition,CBCTandmostradiographic\-basedIGRTsystemscannotbeusedfornoncoplanarsetups,resultingindiscrepanciesbetweenimagingandtreatmentpositionswhennoncoplanarbeamsareapplied\(11\)\.ContinuoustrackingsystemssuchasExacTracOpticalTrackingSystem,AlignRT,andC\-RadSentinelaretypicallyemployedonlyforlimiteddiseasesitesincurrentclinicalpractice\.TherapistsoperatingmachinesequippedwithmultipleIGRTsystemsoftenswitchbetweendevicesdependingonthetreatmentsite,furtherincreasingoperationalcomplexity\.Fourthandmostimportantly,immediateandindependentpositionverificationisnotalwaysavailabletoconfirmthatthepatienthasbeenpositionedexactlyasplanned,particularlywhenthelimitationsoftheemployedIGRTsystemsareencountered\(8\)\.

33

34Inthispaper,wepresentanefficientautomaticpatientsafetysystem\(PSS\)thataddressesfactorscontributingtogrosssetuperrorsandsafeguardspatienttreatment\.ThesystemusesCCDcamerastotrackasingleinfraredreflectivemarkeraffixedtothepatient’sskinorimmobilizationdevice\.Itisageneral\-purposesystemapplicabletoalldiseasesitesandprovidescontinuousandindependentverificationofpatientsetupaccuracy\.Withafullyautomatedworkflow,thePSSservesasaneffectivecomplementtoexistingIGRTsystemstoenhancepatientsafetyinradiotherapy\.

35

36

37\#MaterialsAndMethods

38

39\#\#A\.SystemOverview

40

41ThesystemconsistsofapairofPolarisCCDcameras\(NorthernDigitalInc\.,Waterloo,Ontario\)mountedonthetreatmentroomceiling\(seeFig\.1\(a\)\),asingleIRRMaffixedtothepatient’sskinorimmobilizationdevice,andasetofin\-housedevelopedsoftware\.Thesoftwarecommunicateswiththecontrolboxofthecamerasthroughastandardserialcableandaproprietarydatacable\.AninternalprocessingunitperformstriangulationtodeterminethecoordinatesofIRRMsinitsnativecoordinatesystem\.Thethree\-dimensional\(3D\)coordinatesofupto50IRRMscanbetransmittedtothecomputersimultaneously\.Systemcalibration,workflow,anddailyqualityassurance\(QA\)proceduresaredescribedinthefollowingsections\.

42

43\#\#B\.SystemCalibration

44

45Acalibrationprocedurewasdevelopedtoconvertthecamera’snativecoordinatesystemtotheabsoluteroomcoordinatesystem\.AcalibrationjigconsistingoffivecommercialIRRMs\(Fig\.1\(b\)\)wasusedforthispurpose\.ThecoordinatesofeachIRRMrelativetothecenterofthejigweredeterminedviaCTscanandcollectedina5x3matrixA\.UndertheguidanceofCBCT,thejigwasplacedonthetreatmentcouchwithitscenteralignedwiththeimagingisocenter\.Thecongruenceoftheimagingisocenterandthemachineisocenterwasconfirmedusingaballbearingphantom\(6\)\.ThecoordinatesoftheIRRMsweresampledbythecamerasandcollectedintoanothermatrixB\.ArotationmatrixRandatranslationmatrixSwereestimatedbyminimizingtheresidualerrorbetweenthetransformedandreferencecoordinates\.

46

47Thisisknownastherelativeposeproblemincomputervisionandhasbeenstudiedextensively\.12,13,14Inthiswork,weemployedageneralanalyticalsolutionviasingularvaluedecomposition\(SVD\),\(14\)andthestepsweresummarizedasfollows:Thecentroids\(Ac\)and\(Bc\)weresubtractedfromAandB,respectively,toaligntheoriginsofthetwocoordinatesystems\.TheSVDofBTAwascomputedas\\\(B^\{T\}A=\\text\{UWV\}^\{T\}\\\)whereUandVareunitarymatrices,andWisadiagonalmatrixwhosediagonalentriesareequaltothesingularvaluesofBTA\.

48

493\.ThesolutionsofRandSweregivenas\\\(\\begin\{matrix\}\{R=U\\begin\{pmatrix\}1&0&0\\\\0&1&0\\\\0&0&\{\\text\{det\}\(UV^\{T\}\)\}\\end\{pmatrix\}V^\{T\}\}\\\\\{\\quad\\quad\\quad\\quad\\quad\\quad\\quad\\quad\\quad\\quad\\quad\\quad\\text\{and\},\}\\\\\{S=A\_\{c\}\-B\_\{c\}R\}\\end\{matrix\}\\\)

50

51\#\#SystemWorkflow

52

53Greatemphasiswasplacedondevelopingasmoothclinicalworkflowforthesystem\.Theworkloadaddedtothetherapistsshouldbekepttoaminimum,whichwascriticalforthesystemtobeadoptedintoclinicuse\.Aminimalworkloadwouldalsohelpminimizethepossibilitiesofhumanerrors\.TheworkflowofthePSSisdescribedinthefollowingsectionsasthepreparationstageandthetreatmentstage\.Figure2showsademonstrativeworkflowofthesystemwhenusedonheadandneck\(H&N\)patientstreatedwithaquaplasticfacemasks\.

54

55\.\.\.

56

57\#Conclusions

58

59Wehavedevelopedanefficientautomaticgeneral\-purposePSStopreventgrosssetuperrorsinradiotherapy\.Thesystemprovidesreal\-timeindependentpositionverificationforthetreatmentofalldiseasesitesbasedonoptical\-trackingtechnologyandisindependentoftreatmentroom,treatmentmachine,couchtopdesign,immobilizationdevice,orpatientinternalanatomy\.Thesystemhasbeenwelladoptedforuseinabusyclinicalenvironmentbecauseofitsseamlessworkflowandeffectivenessincatchingsetuperrors\.Duetoitsadvantagesofcontinuoustrackingcapability,noradiationdose,andfullyautomatedclinicalworkflow,itservesasanidealcomplementtocomplexIGRTsystemsinensuringpatientsafetyinradiotherapy\.

60‘‘‘

Listing 7:User prompt for single LLM\-call baselines in long\-form patent drafting\.

## Appendix GSupplementary Experimental Results

### G\.1Supplementary Ablation Study

In this subsection, we evaluate the robustness of LogicTree\-RAG by instantiating the framework with different backbone LLMs, while keeping all other components and hyperparameters unchanged\. As shown in Table[4](https://arxiv.org/html/2609.30943#A7.T4), LogicTree\-RAG consistently produces long and well\-structured patent drafts across all backbones, indicating that controlling the tree size enables stable and sufficient output length independent of the underlying model\. Content\-level metrics such as coverage and factuality remain stable, while language\-level metrics, including style and repetition rate, gradually improve with stronger backbones\. Notably, when both LogicTree\-RAG and COPGEN\[[16](https://arxiv.org/html/2609.30943#bib.bib4)\]are instantiated with the same Llama\-3\-8B backbone, LogicTree\-RAG achieves substantially better performance and approaches the performance of the SFT\-enhanced COPGEN variant, highlighting the effectiveness of the proposed logic\-centric generation framework without relying on task\-specific fine\-tuning\. Overall, these results demonstrate that the gains of LogicTree\-RAG stem primarily from its structured generation paradigm rather than dependence on a particular backbone model\.

Table 4:Performance of LogicTree\-RAG with different backbone LLMs\.MethodOutputTokensContent\-levelLanguage\-levelTime \(s\)/ Patent \(Token\)Coverage↑\\uparrowFactuality↑\\uparrowSemanticSimilarity↑\\uparrow\\,\\,Style↑\\uparrowRepetition Rateℛr\\mathcal\{R\}\_\{r\}↓\\downarrowCoherence↑\\uparrowℱP​a​t\\mathcal\{F\}\_\{Pat\}ℱS​r​c\\mathcal\{F\}\_\{Src\}Heuristic SkylineReference Patent \(GT\)18\.16k89\.0588\.7488\.8010010013\.91100\-Source Paper8\.03k44\.7746\.0688\.6839\.3739\.268\.3998\.60\-Outline \(Long\)1\.43k39\.1161\.7961\.9310\.1623\.2220\.1485\.69\-LogicTree\-RAGLlama3\-8B\[[62](https://arxiv.org/html/2609.30943#bib.bib25)\]16\.25k40\.2556\.7061\.9535\.7455\.897\.1696\.98209 \(12\.86\)Qwen2\.5\-72B\[[64](https://arxiv.org/html/2609.30943#bib.bib24)\]17\.82k42\.0857\.4266\.0934\.8962\.285\.8797\.56260 \(14\.58\)DeepseekV3\[[31](https://arxiv.org/html/2609.30943#bib.bib34)\]19\.15k43\.2058\.2164\.8337\.9558\.245\.8697\.10320 \(16\.69\)Qwen3\-80B\[[30](https://arxiv.org/html/2609.30943#bib.bib33)\]20\.22k43\.7958\.9266\.3438\.9165\.684\.1497\.45311 \(15\.38\)

### G\.2Supplementary Study on Agentic Planning

To directly evaluate the role of structured planning, we compare LogicTree\-RAG with two representative agentic paradigms, ReAct\[[36](https://arxiv.org/html/2609.30943#bib.bib51)\]and Plan\-then\-Execute\[[37](https://arxiv.org/html/2609.30943#bib.bib52)\]\. To ensure a fair comparison, we expose our core modules \(i\.e\., semantic chunking, node\-aware retrieval, and evidence\-guided generation\) as callable tools, and replace the logic\-tree\-based orchestration with generic agent planning strategies\. This design isolates the effect of explicit structural planning \(logic tree\) versus generic agentic decomposition, while keeping underlying capabilities comparable\.

As shown in Table[5](https://arxiv.org/html/2609.30943#A7.T5), despite using the same underlying tools, the agentic variants exhibit lower coverage, weaker factual grounding, and higher repetition, indicating incomplete and less controlled generation\. In contrast, LogicTree\-RAG achieves more balanced and consistent outputs, with superior performance across both content\-level and language\-level metrics\. These results suggest that generic agentic planning alone is insufficient for long\-form structured generation\. Instead, explicit global structure modeling via the logic tree is critical for coordinating content organization, evidence grounding, and generation control\.

Table 5:Performance comparison of LogicTree\-RAG against agentic baselines for patent drafting\.MethodOutputTokensContent\-levelLanguage\-levelTime \(s\)/ Patent \(Token\)Coverage↑\\uparrowFactuality↑\\uparrowSemanticSimilarity↑\\uparrow\\,\\,Style↑\\uparrowRepetition Rateℛr\\mathcal\{R\}\_\{r\}↓\\downarrowCoherence↑\\uparrowℱP​a​t\\mathcal\{F\}\_\{Pat\}ℱS​r​c\\mathcal\{F\}\_\{Src\}LogicTree\-RAG20\.22k43\.7958\.9266\.3438\.9165\.684\.1497\.45311 \(15\.38\)w/ ReAct16\.86k40\.2855\.9864\.8536\.8665\.5210\.2496\.18336 \(19\.93\)w/ Plan\-Execute18\.58k42\.0256\.3564\.9635\.9164\.978\.9596\.59325 \(17\.49\)

### G\.3Evidence\-support Analysis

To assess retrieval\-grounded generation at the evidence level, we evaluate whether generated content is supported by retrieved evidence for each retrieval\-triggered node, using an LLM\-as\-judge \(GPT\-5\) and human audit\. Each case is categorized as fully supported, partially supported, or unsupported\. We further conduct human auditing on 20 randomly sampled test cases under the same criteria\. Results are summarized in Table[6](https://arxiv.org/html/2609.30943#A7.T6)\. LogicTree\-RAG achieves a substantially higher proportion of fully supported content and fewer unsupported cases than COPGEN, indicating stronger attribution faithfulness and more effective use of retrieved evidence\. Human auditing yields consistent trends, confirming that retrieved evidence is actively used to support generation rather than being passively retrieved\. These findings align with the ablation results in Table[2](https://arxiv.org/html/2609.30943#S4.T2), where removing evidence\-grounded refinementσR​2\\sigma\_\{R2\}consistently degrades performance, while removing internal refinementσR​3\\sigma\_\{R3\}has a negligible impact\. These results suggest that the gains of LogicTree\-RAG primarily arise from improved evidence\-grounded generation, rather than increased pipeline complexity or auxiliary mechanisms\.

Table 6:Evidence\-support analysis of retrieved evidence and generated content\.MethodFully \(%\)Partial \(%\)Unsupported \(%\)LLM\-as\-judgeCOPGEN70\.420\.29\.4LogicTree\-RAG85\.612\.02\.4Human AuditCOPGEN603010LogicTree\-RAG75250

### G\.4Computational Cost Analysis

To improve transparency, we report a detailed cost breakdown of LogicTree\-RAG along the workflow pipeline\. As shown in Table[7](https://arxiv.org/html/2609.30943#A7.T7), the overall cost is well controlled, with a balanced distribution across stages\. In particular, recursive logic tree construction introduces only moderate overhead, while the majority of cost arises from traversal and generation\. Importantly, the recursion depth is explicitly bounded by tree budget hyperparameters \(see Appendix[F\.3](https://arxiv.org/html/2609.30943#A6.SS3)\), preventing uncontrolled expansion and ensuring a favorable quality\-efficiency trade\-off rather than gains driven by excessive API usage\.

Table 7:Supplementary computational cost analysis\.Computational Cost \(/patent\)Cost BreakdownLLM Calls: 20chunking&tree init 5%, tree construction 45%, traversal 50%Runtime: 311schunking&tree init 16%, tree construction 39%, traversal 45%Total Input Tokens: 7\.60kchunking&tree init 29%, tree construction 37%, traversal 34%

### G\.5Human Expert Study

We conduct a human expert evaluation with three patent practitioners to assess the practical quality of generated patents\. We randomly select 10 samples from the test set\. For each sample, practitioners are provided with the source paper, the generated patent, and the reference patent, along with the corresponding outlines \(long version\) to ensure fair comparison\. Experts perform pairwise comparisons \(win/loss/tie\) between LogicTree\-RAG and each baseline\. We adopt five evaluation criteria covering both legal correctness and technical quality, as defined in Listing\. All evaluations are conducted in a blinded manner, where the identities of methods are anonymized\.

The win\-rate results are summarized in Table[8](https://arxiv.org/html/2609.30943#A7.T8)\. LogicTree\-RAG consistently outperforms all baselines across evaluation criteria, achieving strong human preference in overall quality and technical correctness\. In particular, LogicTree\-RAG shows substantial advantages in Claim Structure and Antecedent Basis, indicating improved structural coherence and consistency in long\-form patent drafting\. Gains in Fidelity further demonstrate that the generated patents remain closely grounded in the source content\.

1\#\#\#ExpertEvaluationCriteria

2\*OverallQuality:overallusefulnessasapatentdraftforpractitionerrevision

3\*Legality:conformitytobasicpatentdraftingandlegal\-formrequirements

4\*ClaimStructure:clarityandcorrectnessofclaimorganizationanddependencies

5\*AntecedentBasis:consistencyofterminologyandantecedentbasis

6\*Fidelity:faithfulnesstothetechnicalcontentofthesourcepaper

Listing 8:Definitions of evaluation criteria used in human expert assessment\.Table 8:Win\-rate results of LogicTree\-RAG in human expert evaluation\.MethodOverallLegalityClaim StructureAntecedent BasisFidelityOurs vs Qwen3\-80B75\.769\.884\.684\.888\.5Ours vs DeepseekV384\.465\.188\.590\.190\.2Ours vs LongWriter78\.876\.590\.692\.085\.9Ours vs COPGEN70\.565\.785\.282\.178\.5
### G\.6Sensitivity to Tree Growth Budget

We further analyze the sensitivity of LogicTree\-RAG to the tree growth budget by varying the maximum tree depth and the maximum number of tree nodes, while keeping all other components and hyperparameters fixed\. As shown in Figure[8](https://arxiv.org/html/2609.30943#A7.F8), increasing the tree budget consistently improves coverage, indicating that deeper or broader trees enable more comprehensive exploration of the document structure\. However, the gains diminish beyond moderate budgets, suggesting that additional expansions primarily capture long\-tail details\. In particular, the default configuration, i\.e\., maximum depth = 3 and maximum number of nodes = 64, lies in this favorable regime, achieving high coverage with reasonable efficiency\. In contrast, token efficiency decreases as the tree budget grows, due to the increased retrieval and generation overhead incurred by additional nodes\. These results reveal a clear quality–cost trade\-off and demonstrate that LogicTree\-RAG allows controllable scaling, where moderate tree budgets achieve a favorable balance between coverage and efficiency\.

Figure 8:Sensitivity to tree growth budget in LogicTree\-RAG\.
### G\.7Case Study

To qualitatively assess the structural fidelity of long\-form patent generation, we present a case study comparing a patent generated by our method with its ground\-truth counterpart, as shown in Figure[9](https://arxiv.org/html/2609.30943#A7.F9)and[10](https://arxiv.org/html/2609.30943#A7.F10)\. Both documents are visualized using section\-level color coding to facilitate direct comparison of document organization and length distribution\. As observed, the generated patent closely matches the reference patent in overall length and preserves a well\-balanced allocation across major sections\.

![Refer to caption](https://arxiv.org/html/2609.30943v1/case_study.png)

Figure 9:Qualitative case study illustrating a patent example generated by LogicTree\-RAG\.![Refer to caption](https://arxiv.org/html/2609.30943v1/case_study_gt.png)

Figure 10:Qualitative case study illustrating the ground\-truth reference patent\.

## Appendix HDiscussion on Human Evaluation and Legal Adequacy

While this work focuses on algorithmic mechanisms for controllable, evidence\-grounded long\-form patent drafting in an early\-stage drafting setting, we emphasize that the proposed framework is intended as a structured drafting assistant rather than a replacement for professional patent attorneys\. Accordingly, aspects critical to real\-world patent practice, such as legal adequacy, claim dependency correctness, and antecedent basis consistency, ultimately require expert human judgment\. These criteria extend beyond surface\-level textual quality and factual alignment, involving nuanced legal interpretation and jurisdiction\-specific drafting conventions that remain difficult to reliably capture with automatic metrics\.

We view human evaluation by patent practitioners as an essential direction for future validation\. A principled evaluation protocol would involve expert assessment of generated drafts along dimensions including \(i\) legal adequacy of technical disclosure, \(ii\) correctness and consistency of claim dependencies, and \(iii\) validity of antecedent basis usage, potentially complemented by qualitative feedback on drafting clarity and completeness\. We believe that the logic\-tree\-based intermediate representation introduced in this work provides a favorable foundation for such evaluations by explicitly exposing conceptual dependencies and evidence grounding, thereby facilitating systematic inspection and correction by human experts\.