arxiv: 2604.10740 · v1 · submitted 2026-04-12 · 💻 cs.CL

Recognition: unknown

RCBSF: A Multi-Agent Framework for Automated Contract Revision via Stackelberg Game

Shijia Xu , Yu Wang , Xiaolong Jia , Zhou Wu , Kai Liu , April Xiaowen Dong

Authors on Pith no claims yet

Pith reviewed 2026-05-10 15:43 UTC · model grok-4.3

classification 💻 cs.CL

keywords contract revisionStackelberg gamemulti-agent LLMrisk constraintsbilevel optimizationlegal AIequilibrium convergenceautomated revision

0 comments

The pith

RCBSF frames contract revision as a Stackelberg game where risk budgets guide LLM agents to higher-utility equilibria.

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

This paper proposes modeling automated contract revision as a non-cooperative Stackelberg game among LLM agents. A global prescriptive agent sets risk budgets that constrain revision and verification agents during iterative optimization. The bilevel structure is shown to converge to an equilibrium that improves utility over standard LLM use. Readers care because current legal AI often produces unreliable safety claims, and this hierarchy offers a structured way to enforce constraints while cutting token use.

Core claim

RCBSF formulates contract revision as a non-cooperative Stackelberg game in which a Global Prescriptive Agent imposes risk budgets on a Constrained Revision Agent and a Local Verification Agent. The bilevel formulation converges to an equilibrium yielding strictly superior utility over unguided configurations. On a unified benchmark it reaches state-of-the-art performance with an average Risk Resolution Rate of 84.21 percent and improved token efficiency.

What carries the argument

The Risk-Constrained Bilevel Stackelberg Framework (RCBSF), which organizes three LLM agents into a leader-follower hierarchy to enforce risk budgets and drive iterative contract optimization toward equilibrium.

Where Pith is reading between the lines

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

The same hierarchical risk-budget structure could be tested on other constrained generation tasks such as regulatory policy drafting or medical summary editing.
If the equilibrium property holds across models, the approach might reduce the amount of human post-editing required for legal documents.
Dynamic adjustment of risk budgets based on document length or complexity offers a natural next experiment to broaden applicability.

Load-bearing premise

LLM-based agents can faithfully implement the Stackelberg leader-follower roles and respect imposed risk budgets without introducing new hallucinations or violating non-cooperative dynamics.

What would settle it

A head-to-head benchmark run in which the RCBSF risk resolution rate falls below 84.21 percent or total utility does not exceed the unguided LLM baseline would disprove the superiority and convergence claims.

Figures

Figures reproduced from arXiv: 2604.10740 by April Xiaowen Dong, Kai Liu, Shijia Xu, Xiaolong Jia, Yu Wang, Zhou Wu.

**Figure 1.** Figure 1: Comparison of legal contract generation workflows between the Baseline (Standard LLM) and the Risk-Constrained Bilevel Stackelberg Framework (RCBSF). can reach 9.2% of an enterprise’s annual revenue (Commerce and Contracting, 2022). Therefore, advancing the automation and intelligentization of contract review and revision is of critical importance for enhancing operational efficiency and mitigating lega… view at source ↗

**Figure 2.** Figure 2: Illustration of the comparison between traditional contract revision paradigms and our proposed framework. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Fine-grained Quality Metrics Breakdown. We [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

**Figure 4.** Figure 4: Qualitative comparison. The Baseline acts as [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗

**Figure 5.** Figure 5: Distribution of Contract Categories. The [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗

**Figure 7.** Figure 7: The instruction set for LLM used to gener [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗

**Figure 9.** Figure 9: The architectural overview of the RiskConstrained Bilevel Stackelberg Framework (RCBSF). The system is organized into two strategic levels: (1) The Outer Stackelberg Leader Loop , where the Leader Agent defines the strategic environment by mapping risk category weights to prompt guidance and optimizing the global utility functional. (2) The Inner Follower Optimization Loop, where the Revision Agent and A… view at source ↗

**Figure 10.** Figure 10: Effectiveness vs. Efficiency (K). The left axis (blue) shows the Risk Resolution Rate, while the right axis (red) shows the normalized Token Cost. K = 3 represents the optimal trade-off point. • Conservative Regime (τ < 0.5): Low temperatures lead to an overly greedy strategy that focuses only on the most obvious risks, missing subtle long-tail issues (RRR ≈ 76.54% at τ = 0.1). • High-Entropy Regime (τ … view at source ↗

**Figure 11.** Figure 11: Impact of Temperature (τ ). The curve demonstrates that a balanced temperature (τ = 1.0) significantly outperforms both conservative (τ = 0.5) and high-entropy (τ = 2.0) settings. Risk Weighting Vector (w). To map the discrete Q-scores (Q1 . . . Q4) extracted by the GPA into a scalar severity metric, we employ a fixed weight 16 [PITH_FULL_IMAGE:figures/full_fig_p016_11.png] view at source ↗

**Figure 12.** Figure 12: The strict verification prompt used to calcu [PITH_FULL_IMAGE:figures/full_fig_p017_12.png] view at source ↗

**Figure 13.** Figure 13: Heatmap of Fine-Grained Quality Metrics. Multi-model-group variants’ performance across multi-tasks. [PITH_FULL_IMAGE:figures/full_fig_p021_13.png] view at source ↗

**Figure 14.** Figure 14: A complete revision flow for a Liability Clause. The Leader’s specific suggestion (highlighted in yellow) [PITH_FULL_IMAGE:figures/full_fig_p022_14.png] view at source ↗

**Figure 15.** Figure 15: Detailed visualization of the multi-risk resolution in a commercial software context. The [PITH_FULL_IMAGE:figures/full_fig_p024_15.png] view at source ↗

**Figure 16.** Figure 16: The structured prompt used by the Leader Agent to extract the 5-dimensional risk tuple. This structure [PITH_FULL_IMAGE:figures/full_fig_p025_16.png] view at source ↗

**Figure 17.** Figure 17: The LVA Prompt. By enforcing a structured JSON output containing severity weights and gradient [PITH_FULL_IMAGE:figures/full_fig_p025_17.png] view at source ↗

**Figure 18.** Figure 18: The composite prompt mechanism for the CRA. It translates the abstract Stackelberg theoretical [PITH_FULL_IMAGE:figures/full_fig_p026_18.png] view at source ↗

**Figure 19.** Figure 19: The Force Rewrite injection mechanism. This acts as a perturbation vector to push the CRA out of local [PITH_FULL_IMAGE:figures/full_fig_p026_19.png] view at source ↗

**Figure 20.** Figure 20: The definition of the Q-Score metrics. Each dimension is graded on a 3-point scale to compute the final [PITH_FULL_IMAGE:figures/full_fig_p027_20.png] view at source ↗

read the original abstract

Despite the widespread adoption of Large Language Models (LLMs) in Legal AI, their utility for automated contract revision remains impeded by hallucinated safety and a lack of rigorous behavioral constraints. To address these limitations, we propose the Risk-Constrained Bilevel Stackelberg Framework (RCBSF), which formulates revision as a non-cooperative Stackelberg game. RCBSF establishes a hierarchical Leader Follower structure where a Global Prescriptive Agent (GPA) imposes risk budgets upon a follower system constituted by a Constrained Revision Agent (CRA) and a Local Verification Agent (LVA) to iteratively optimize output. We provide theoretical guarantees that this bilevel formulation converges to an equilibrium yielding strictly superior utility over unguided configurations. Empirical validation on a unified benchmark demonstrates that RCBSF achieves state-of-the-art performance, surpassing iterative baselines with an average Risk Resolution Rate (RRR) of 84.21\% while enhancing token efficiency. Our code is available at https://github.com/xjiacs/RCBSF .

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note private letter to a colleague

RCBSF wraps LLM contract revision in a Stackelberg game with risk budgets but the convergence guarantees rest on assumptions that LLM agents probably violate.

read the letter

The paper's main move is to cast automated contract revision as a bilevel Stackelberg game. A Global Prescriptive Agent sets risk budgets that a Constrained Revision Agent and Local Verification Agent then follow in a leader-follower loop. This produces the claimed 84.21% risk resolution rate and better token efficiency than iterative baselines, with public code at the GitHub link. The agent roles and the explicit risk-constraint layer are the concrete additions relative to generic multi-agent LLM prompting in legal AI. That structure is new enough to be worth examining if you work on constrained generation for regulated domains. The empirical numbers are presented as state-of-the-art on a unified benchmark, which at least gives a starting point for comparison. The soft spot is the theoretical claim. The abstract asserts convergence to an equilibrium with strictly superior utility, yet the stress-test concern holds: standard Stackelberg results need well-behaved continuous strategy sets and exact best responses. LLM calls are stochastic, discrete, and prone to outputs that ignore the imposed budgets or break the non-cooperative protocol. Nothing visible in the abstract shows how the prompts enforce those properties or how the proof handles hallucination. If the full paper supplies a derivation that closes this gap, the guarantees could stand; otherwise they read as optimistic. The work is aimed at people already building multi-agent LLM pipelines for legal or compliance tasks. A reader who wants to see game theory applied to prompt engineering might extract the agent hierarchy and risk-budget idea. It is coherent enough on its own terms to merit peer review rather than desk rejection, mainly so referees can check the actual proof and experimental controls. I would send it out with instructions to focus on whether the implementation matches the mathematical assumptions.

Referee Report

2 major / 2 minor

Summary. The paper proposes the Risk-Constrained Bilevel Stackelberg Framework (RCBSF) for automated contract revision with LLMs. It models revision as a non-cooperative Stackelberg game in which a Global Prescriptive Agent (GPA) acts as leader imposing risk budgets on follower agents (Constrained Revision Agent and Local Verification Agent). The authors assert theoretical convergence guarantees to an equilibrium with strictly superior utility, and report empirical results on a unified benchmark showing 84.21% average Risk Resolution Rate (RRR) while improving token efficiency over iterative baselines.

Significance. If the claimed convergence can be rigorously shown under the stochastic LLM setting and the empirical gains are shown to be robust across datasets and baselines, the work would offer a concrete mechanism for injecting behavioral constraints into multi-agent LLM systems for high-stakes legal tasks. The bilevel formulation and explicit risk-budget mechanism are novel in the contract-revision literature and could generalize to other constrained generation domains.

major comments (2)

[§4] §4 (Theoretical Analysis): The stated convergence guarantee for the bilevel Stackelberg game assumes well-defined continuous strategy sets, convex payoffs, and exact rational best-response computation. The implementation in §3 replaces these with prompt-driven LLM calls whose outputs are discrete and stochastic; no argument is given showing that the generated revisions remain inside the imposed risk budgets or that the followers follow the prescribed non-cooperative protocol. This gap directly affects the central claim of strictly superior equilibrium utility.
[§5] §5 (Experiments): The reported 84.21% RRR is presented as state-of-the-art, yet the section provides no definition of the RRR metric, no error bars, no statistical significance tests against the iterative baselines, and no ablation isolating the contribution of the Stackelberg risk-budget mechanism versus simple prompting. Without these, the superiority claim cannot be evaluated.

minor comments (2)

[Abstract] Abstract: The RRR metric and the precise meaning of 'risk budgets' are not defined, forcing the reader to consult the main text to interpret the headline 84.21% figure.
[§3] §3 (Framework): The roles of CRA and LVA are described at a high level; a concrete example of a single revision round showing the exact prompts, risk-budget encoding, and verification step would clarify how the game is realized.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment point by point below, providing the strongest honest defense of the manuscript while acknowledging where revisions are needed to strengthen the work.

read point-by-point responses

Referee: §4 (Theoretical Analysis): The stated convergence guarantee for the bilevel Stackelberg game assumes well-defined continuous strategy sets, convex payoffs, and exact rational best-response computation. The implementation in §3 replaces these with prompt-driven LLM calls whose outputs are discrete and stochastic; no argument is given showing that the generated revisions remain inside the imposed risk budgets or that the followers follow the prescribed non-cooperative protocol. This gap directly affects the central claim of strictly superior equilibrium utility.

Authors: We acknowledge the gap between the idealized assumptions in the theoretical analysis (§4) and the stochastic, discrete nature of LLM-based implementation (§3). The bilevel Stackelberg model is intended as a principled abstraction to guide the design of risk-constrained prompting, not as an exact description of LLM behavior. Prompts for the CRA and LVA are explicitly engineered to respect GPA-imposed risk budgets and to approximate best-response dynamics. In the revised manuscript we will add a dedicated subsection in §4 discussing the approximation gap, including (i) how risk-budget enforcement is operationalized via prompt constraints and (ii) empirical measurements of budget adherence across runs. We will also soften the wording of the convergence claim to reflect that the framework converges to a superior equilibrium under the modeled protocol, with practical LLM realizations providing an approximation whose quality is validated empirically. This addresses the concern without misrepresenting the current theoretical scope. revision: partial
Referee: §5 (Experiments): The reported 84.21% RRR is presented as state-of-the-art, yet the section provides no definition of the RRR metric, no error bars, no statistical significance tests against the iterative baselines, and no ablation isolating the contribution of the Stackelberg risk-budget mechanism versus simple prompting. Without these, the superiority claim cannot be evaluated.

Authors: We agree that the experimental presentation is incomplete and that these elements are required for rigorous evaluation. The Risk Resolution Rate (RRR) is defined as the fraction of contract risks flagged by the LVA that are successfully eliminated or mitigated in the final revised contract. In the revised manuscript we will (1) insert the formal definition of RRR at the beginning of §5, (2) report means and standard deviations over five independent runs with different random seeds, (3) add paired statistical tests (Wilcoxon signed-rank) against each iterative baseline, and (4) include an ablation that replaces the bilevel Stackelberg structure with a flat multi-agent prompting baseline that uses identical risk-budget language but lacks the leader-follower hierarchy. These additions will allow direct assessment of the contribution of the proposed mechanism. revision: yes

Circularity Check

0 steps flagged

No significant circularity in the derivation chain

full rationale

The paper claims theoretical convergence guarantees for its bilevel Stackelberg formulation (GPA as leader with risk budgets on CRA/LVA followers) to an equilibrium with strictly superior utility. No equations, fitted parameters, or self-citations are visible in the abstract or context that would reduce this claim to a tautology or input by construction. The superiority assertion is framed as following from the game structure itself rather than from any renaming, ansatz smuggling, or self-referential definition. Empirical RRR results are presented separately and do not feed back into the theoretical claim. The derivation chain is therefore self-contained against external benchmarks with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 3 invented entities

Review limited to abstract; no explicit free parameters, axioms, or invented entities are detailed beyond the three named agents and the Stackelberg structure itself.

axioms (1)

domain assumption LLM agents can be made to respect externally imposed risk budgets without introducing new hallucinations
Implicit in the claim that the follower system optimizes under GPA constraints

invented entities (3)

Global Prescriptive Agent (GPA) no independent evidence
purpose: Leader that imposes risk budgets in the Stackelberg game
New agent role introduced to enforce constraints
Constrained Revision Agent (CRA) no independent evidence
purpose: Follower that performs contract revisions under risk limits
New agent role for the revision task
Local Verification Agent (LVA) no independent evidence
purpose: Follower that verifies revisions
New agent role for verification

pith-pipeline@v0.9.0 · 5493 in / 1439 out tokens · 24134 ms · 2026-05-10T15:43:16.035111+00:00 · methodology

discussion (0)

Forward citations

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FACT-E uses controlled perturbations as an instrumental signal to measure intra-chain faithfulness in CoT reasoning and combines it with answer consistency to select trustworthy trajectories.

Reference graph

Works this paper leans on

29 extracted references · 3 canonical work pages · cited by 2 Pith papers · 1 internal anchor

[1]

GPT-4 Technical Report

Curran Associates, Inc. Guohao Li, Hasan Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem. 2023. Camel: Communicative agents for "mind" exploration of large language model society. InAdvances in Neural Information Processing Systems, volume 36, pages 51991–52008. Curran Associates, Inc. Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan,...

work page internal anchor Pith review Pith/arXiv arXiv 2023
[2]

Disc-lawllm: Fine-tuning large language models for intelligent legal services,

Corrective retrieval augmented generation. Xiaoxian Yang, Zhifeng Wang, Qi Wang, Ke Wei, Kaiqi Zhang, and Jiangang Shi. 2024. Large language models for automated q&a involving legal docu- ments: a survey on algorithms, frameworks and ap- plications.International Journal of Web Information Systems, 20(4):413–435. Shengbin Yue, Wei Chen, Siyuan Wang, Bi...

work page arXiv 2024
[3]

Input Ingestion Raw PDF/TXT text headers are ingested (Hraw)
[4]

b) Strip numbering artifacts

Noise Reduction (Regex) Apply cleaning functionf clean(Hraw): a) Remove file extensions (‘.pdf‘, ‘.docx‘). b) Strip numbering artifacts . c) Normalize whitespace to single space
[5]

license" AND

Keyword Mapping (Canonicalization) Map headers to Ctarget ∈ C using hierarchical key- word matching: a)IF "license" AND "software" → Software Li- cense b)IF "consulting" OR "service" → Service Agree- ment c)IF"confidential" OR "nds"→NDA
[6]

Figure 6: The automated classification logic used in Stage 0 to standardize noisy raw file headers into canon- ical legal categories

Output Structured pair:(Category_ID, Clean_Header). Figure 6: The automated classification logic used in Stage 0 to standardize noisy raw file headers into canon- ical legal categories. A.2.2 Template Standardization (Stage 2) Following classification, the raw contract text un- dergoes a standardization process to remove iden- tifiers and unify structure....
[7]

Structural Normalization:Organize the output into these exact sections: • Definitions • Scope of Services • Fees and Payment • IP Ownership • Confidentiality & Data Protection • Indemnification & Liability • Term & Termination
[8]

• Replace specific dates with[Effective Date]

PII Anonymization (Strict): • Replace entity names with [Party A] / [Party B]. • Replace specific dates with[Effective Date]. • Replace monetary values with[Amount]
[9]

Risk-Mitigation

Formatting:OutputPLAIN TEXT ONLY. Do not use Markdown bolding or code blocks. Keep length≤1500words. Figure 7: The instruction set for LLM used to gener- ate standardized, anonymized legal templates from raw source text. A.2.3 Risk Enrichment (Stage 3) To facilitate the evaluation of risk detection mod- els, the standardized templates are processed by a n...
[10]

B Mathematical Proofs and Derivations In this appendix, we provide the rigorous measure- theoretic foundations and proofs for the theorems presented in Section 2

Carve out exceptions for gross negligence." } ] } Figure 8: The adversarial prompt for LLM, designed to generate the standard truth risk dataset used for calcu- lating the Risk Resolution Rate (RRR). B Mathematical Proofs and Derivations In this appendix, we provide the rigorous measure- theoretic foundations and proofs for the theorems presented in Secti...
[11]

Since the budget B >0 , we have ∅ ∈ H B

Inclusion Property:The unguided genera- tion corresponds to the null instruction strat- egy h∅ =∅ . Since the budget B >0 , we have ∅ ∈ H B
[12]

Therefore: VSE = sup h∈HB JL(Ψ(h),h) ≥J L(Ψ(∅),∅) =V N E (9)

Global Optimality:The Stackelberg leader maximizes over the entire set HB. Therefore: VSE = sup h∈HB JL(Ψ(h),h) ≥J L(Ψ(∅),∅) =V N E (9)
[13]

resolved

Strict Inequality Condition:Let R(x) =P wrδ(r,x) be the risk potential. In the un- guided case, the LLM generates xN E by sam- pling from P(x|∅) . Due to misalignment, there exists a risk rk such that δ(rk,x N E)> ϵ. The GPA constructs a specific hinthk contain- ing theEvidence ek andSuggestion sk. This introduces a forcing term in the Follower’s objectiv...
[14]

For instance, with LawLLM-7B, the Rigor score improves from 65.81 (Standard) to 79.81 (RCBSF)

Rigor is the primary beneficiary:Across all backbones, theRigormetric sees the most significant improvement. For instance, with LawLLM-7B, the Rigor score improves from 65.81 (Standard) to 79.81 (RCBSF). This log- ical enhancement is visually striking in Fig- ure 13, where the Rigor columns transition from dark green (worst) in baseline meth- ods to deep ...
[15]

The heatmap corroborates this stability, display- ing a consistent high-intensity color band for RCBSF across all model groups compared to the patchy performance of baselines

Stability across Backbones:Even for weaker backbones like LexiLaw-6B, RCBSF boosts the average quality from 60.40 to 72.30. The heatmap corroborates this stability, display- ing a consistent high-intensity color band for RCBSF across all model groups compared to the patchy performance of baselines. This sug- gests that the Stackelberg game mechanism is mo...
[16]

As illustrated by the darkest ma- genta blocks in Figure 13, this configuration significantly outperforms the RAG baseline (79.81) and approaches expert human levels (>90)

Peak Performance:The strongest config- uration, QW-7B-Chat + RCBSF, achieves state-of-the-art results with an average CQ of 86.87. As illustrated by the darkest ma- genta blocks in Figure 13, this configuration significantly outperforms the RAG baseline (79.81) and approaches expert human levels (>90). E.2 Ablation Study Breakdown To understand the contri...
[17]

Hallucination Rate (HR):The CRA modifies a clause that was NOT risky
[18]

reason- able

Fix Failure Rate (FFR):The CRA attempts to fix a risk but fails to resolve it substantively. Analysis.As shown in Table 6, the results of the component-wise ablation study yield several key observations regarding the contribution of each instruction dimension: 18 Dimension High Quality (80–100) 90–100: Excellent; 80–89: Good Borderline (60–79) 70–79: Abov...

work page arXiv 1974
[19]

blanket indemnity

Input:The original text contains a "blanket indemnity" clause exposing the provider to unlimited risk
[20]

in- demnify... from any and all claims

Leader Analysis:The LV A identifies this as aCategory Arisk (Q2=A). It outputs a structured tuple specifying theEvidence("in- demnify... from any and all claims") and a concreteSuggestion("Insert a liability cap"). 3.Revision Process: • Revision #1:The CRA softens the lan- guage but forgets the specific cap. • Inner Audit:The LV A flags that the "fi- nanc...
[21]

Global Prescription (Outer Loop):The Global Prescriptive Agent (GPA)decom- poses the contract text, extracting a 5- dimensional constraint vector for each of the three identified risks
[22]

Priority: Reassign Deliv- erable ownership to Client

Execution & Audit (Inner Loop):The Fol- lower system (CRA & LV A) engages in a 3- round iterative revision process. 3.Trajectory Analysis: • Iteration #1:The CRA successfully resolves the Payment and Termination risks but fails to address the IP ownership transfer, merely polishing the language. • Fusion Feedback:The LV A triggers a negative reward signal...
[23]

Original Contract Segment (Input): "The Provider shall indemnify and hold the Client harmless from and against any and all claims, losses, damages , liabilities, and expenses arising out of or in connection with the Services, regardless of cause."
[24]

indemnify... from any and all claims... regardless of cause

Leader Agent Analysis (Strategic Instruction): •Category:EXCESSIVELIABILITY •Location:Section 9.1 (Indemnification) •Evidence:"...indemnify... from any and all claims... regardless of cause." •Issue:Creates unlimited exposure for the Provider; lacks a monetary cap or exclusion for indirect damages. • Suggestion: Limit liability to the fees paid in the pre...
[25]

The Provider shall indemnify the Client for direct losses arising from the Provider’s negligence

Follower System Execution (Iterative): [Revision #1 - Attempt]: "The Provider shall indemnify the Client for direct losses arising from the Provider’s negligence..."→Rejected by Inner Audit (Missing Cap). [Final Revision - Convergence]: "The Provider shall indemnify the Client for direct losses... provided that the Provider’s total aggregate liability sha...
[26]

Section 1.2

category: Specific risk classification (At least ten words or more, describe the risk category as detailed as possible). 2.location: Where this risk appears (e.g., "Section 1.2"). 3.evidence: Original text quote supporting this risk. If missing, state "Missing clause". 4.issue: Specific description of what is wrong (the defect, ambiguity, or unfairness). ...
[27]

You MUST rewrite the content in Section[Detected_Risk_Location]
[28]

Mere wording adjustments are insufficient: you MUST alter the logical framework to mitigate[Risk_Category]
[29]

bear all consequences

Penalty consequence for non-compliance: Penalty Score=∞(immediate optimization failure). Figure 19: The Force Rewrite injection mechanism. This acts as a perturbation vector to push the CRA out of local optima (i.e., lazy revisions) — triggered when the system detects zero effective edits despite unresolved risks. 26 Prompt for Q-Score Assessment (Abbrevi...