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ARGUS: Theory-of-Mind Guided Argument Generation with Strategy-Aware Planning and Knowledge Grounding

Zhe Hu

arXiv:2608.20405v1cs.CL

TL;DR

Persuasive argument generation requires modeling audiences, rhetoric, and evidence, while retrieval may not adapt to evolving argument plans. ARGUS combines explicit audience modeling, rhetorical planning, and planning-time evidence grounding; it outperforms strong baselines across benchmarks and backbones.

  • Problem

    Persuasive argument generation demands audience profiling, calibrated rhetorical strategies, and verifiable evidence, while document-level or post-hoc retrieval cannot adapt to evolving subtopic plans.

    Source: §1 · pp. 1–2

  • Method

    ARGUS uses a Theory-of-Mind reasoner to model audience beliefs and values, a component-aware planner to assign rhetorical functions and ground subtopics, and a multidimensional refiner.

    Source: §1 · p. 2, §6 · p. 9

  • Results

    Across three benchmarks and multiple backbones, ARGUS consistently outperforms strong baselines.

    Source: §6 · p. 9

  • Takeaways & Limitations

    Targeted simulations indicate that ARGUS's gains extend to stance shifts in audiences, rather than merely improved fluency.

    Source: §6 · p. 9

    The LLM-produced Theory-of-Mind model may represent a plausible audience profile rather than faithfully capturing real audience mental states.

    Source: Limitations · p. 9

Abstract

from arXiv · show

Persuasive argument generation requires modeling audience beliefs, rhetorical strategies, and factual grounding. Despite recent advancements, existing methods remain largely audience-agnostic and fail to integrate strategy selection to improve persuasiveness. To bridge this gap, we propose Argus, an agent-based framework that operationalizes classical rhetoric for persuasive writing. At its core, a Theory-of-Mind (ToM) Reasoner constructs an explicit dual mental model of the audience's beliefs and values to guide downstream decisions. This representation conditions a component-aware planner that decomposes the argument into subtopics, assigns fine-grained rhetorical functions (logos, pathos, ethos, kairos), and triggers strategy-guided evidence retrieval at planning time. Finally, a refinement module iteratively targets and resolves multi-dimensional weaknesses without quality regression. We evaluate Argus across three diverse benchmarks using both automated pairwise Elo and LLM-as-judge metrics. Results show that Argus consistently outperforms strong baselines across multiple backbone models, achieving top rankings and the highest overall scores. Targeted simulation experiments further validate its effectiveness in shifting resistant audience stances.

1 Introduction

Persuasive argument generation requires audience modeling, calibrated rhetorical strategies, and precise evidence. ARGUS combines these in a closed-loop framework that plans around audience profiles and grounds rhetoric during planning.

1 Introduction

  • Prior agentic methods improve coherence but remain audience-agnostic, strategy-agnostic, or disconnected in evidence retrieval, limiting persuasive efficacy.Earlier end-to-end neural approaches also often produced structurally incoherent or rhetorically shallow arguments [10].Source: §1 · pp. 1–2
  • A Theory-of-Mind Reasoner models audience argumentative profiles, emotional triggers, and values to guide subtopic decomposition, rhetorical alignment, and counterargument preemption.Its explicit dual mental model is passed to downstream planning before writing begins.Source: §1 · p. 2
  • A Component-Aware Planner maps subtopics to logos, pathos, ethos, and kairos, while retrieving evidence per subtopic according to its rhetorical goal.This integrates empirical grounding into planning rather than using only document-level preprocessing or post-hoc retrieval.Source: §1 · p. 2
  • A surgical refiner diagnoses targeted flaws and revises the final argument.ARGUS frames argument generation as closed-loop structural optimization rather than isolated text production.Source: §1 · p. 2
  • ARGUS outperforms competitive baselines across three datasets and three backbone LLM families, with strong Elo results and simulated stance shifts among resistant audiences.Evaluations use pairwise Elo ratings and absolute LLM judges; targeted simulations test whether ARGUS can influence skeptical counterparts.Source: §1 · p. 2

2 Related Work

Prior argument-generation systems increasingly use structured planning and agentic workflows, but typically leave audience modeling implicit. Related ToM and persuasion research motivates ARGUS’s explicit audience model and rhetorical-function planning.

Argument Generation

  • Argument generation progressed from end-to-end models [24] to structured text planning [10] [25] [26] and LLM prompting or agentic decomposition and debate [27] [16] [17] [14] [13].Source: §2 · p. 2
  • Prior agentic systems treat the audience as implicit context and plan mainly over content order, whereas ARGUS models audience beliefs explicitly through Theory of Mind and plans by rhetorical function.Source: §2 · p. 2

Theory of Mind and Audience Modeling

  • LLMs show non-trivial but brittle Theory of Mind [30], while most evaluations test spectatorial belief prediction rather than planning ToM for deliberately shifting a stance [31] [32].Persuasion-focused work includes ToM dialogue benchmarks [33], opponent-aware persuaders [34], and dual-knowledge or meta-cognitive frameworks [35] [36]; studies also find LLM arguments can rival human writing and improve when target-tailored.Source: §2 · p. 2

3 Method

ARGUS decomposes persuasive argument generation into interpretable modules that explicitly model the audience, plan rhetorical functions and grounded evidence, and iteratively refine the resulting text. Its structured pipeline links input analysis and audience modeling to planning, writing, and revision.

3.1 Task Formulation

  • ARGUS treats argument generation as producing an article that persuasively defends a specified stance on a proposition, using a sequence of interpretable reasoning and generation actions rather than a single LLM prompt.The decomposition makes audience alignment, rhetorical organization, evidence grounding, and linguistic coherence explicit dimensions to model and optimize.Source: §3.1 · p. 3

3.2 System Overview

  • The framework passes structured representations through five sequential modules: Input Analyzer, ToM Reasoner, Argument Planner, Argument Writer, and Iterative Refiner.This sequence operationalizes the decomposition into downstream components.Source: §3.2 · p. 3

3.3 Input Analysis

  • The Input Analyzer retrieves targeted external knowledge when needed, then structures input claims, synthesized background knowledge, and—when the input is a developed argument—its premises, assumptions, and logical gaps.These representations guide downstream planning, including targeted counterarguments and attention to weaknesses in the opposing position.Source: §3.3 · p. 3

3.4 Theory-of-Mind Reasoning

  • The ToM Reasoner constructs an explicit audience model of argumentative positions, underlying motivations, resonant emotional themes, values, and strategies for bridging conflicting values.The planner uses this model to select subtopics and rhetorical components and to anticipate objections for inline rebuttal.Source: §3.4 · p. 4

3.5 Argument Planning

  • The planner organizes subtopics into a rhetorical blueprint, assigning logos, pathos, ethos, kairos, or evidence according to the audience model and each subtopic’s persuasive function.Each subtopic also carries key claims, optionally retrieved evidence, and an anticipated audience rebuttal.Source: §3.5 · p. 4
  • For subtopics needing external grounding, the planner retrieves evidence with targeted queries tied to local claims and rhetorical functions, then evaluates and revises the plan for diversity, audience alignment, logical order, and stance consistency.If revisions change subtopics, evidence is retrieved again before writing begins.Source: §3.5 · p. 4

3.6 Argument Writing and Refinement

  • After drafting, an evaluator scores persuasiveness, coherence, factual accuracy, value alignment, and rhetorical balance; revisions target weak dimensions, while a best-of-n buffer guards against quality regression.The Writer follows the plan’s rhetorical composition, evidence grounding, and audience value framing.Source: §3.6 · pp. 4–5

3.7 Design Rationale

  • Separating ToM reasoning from planning makes the elaborated audience model and its objection and value-alignment signals available in subtopic fields, while planning-time rhetorical assignment makes persuasive structure inspectable and deliberately balanceable.The Writer can use the distilled signals without an additional inference step.Source: §3.7 · p. 5

4 Experimental Setup

ARGUS is evaluated on three benchmarks covering diverse domains, stances, and discourse styles, against strong baselines across three backbone models using pairwise and absolute LLM-judge evaluations.

4.1 Datasets and Tasks

  • The evaluation uses CMV and iDebate for refutation and ExplaGraphs for supporting statements, with the latter requiring relevant topic knowledge.CMV inputs concatenate the post title and body; iDebate propositions are typically abstract and open-ended.Source: §4.1 · p. 5

4.2 Baselines

  • ARGUS is compared with Direct, Plan-and-Write, Self-Refine [45], and Debate [13] using DeepSeek-V3.2, Qwen3.5-Flash, and GPT-5-mini backbones.Debate uses two opposing agents followed by a synthesis pass.Source: §4.2 · p. 5

4.3 Evaluation Metrics

  • Evaluation combines round-robin pairwise Elo comparisons with independent LLM scoring of persuasiveness, coherence, and factual accuracy.Pairwise judgments are repeated with order swapped; score differences under 0.5 count as ties. GPT-5.4 serves as judge, separate from the generation backbone.Source: §4.3 · p. 6

5 Results and Analysis

ARGUS outperforms baselines across datasets and evaluation settings, and its targeted simulations indicate stronger potential to shift audience stance. The analyses attribute its performance to audience-aware planning and refinement, with rhetorical strategy effectiveness varying by audience and dataset.

5.1 Main Results

  • ARGUS consistently outperforms all baselines across evaluation settings, with absolute judging especially favoring its persuasiveness and factual accuracy rather than coherence.Coherence scores are uniformly high across methods, while integrated audience modeling, evidence-grounded planning, and targeted refinement are identified as key to the gains.Source: §5.1 · p. 6
  • ARGUS’s pairwise gains are strongest on iDebate; it also leads on ExplaGraphs and improves on CMV by tailoring arguments to detailed audience beliefs and preferences.Plan&Write is the strongest baseline overall, and ARGUS maintains strong factual consistency and coherent reasoning on ExplaGraphs.Source: §5.1 · p. 6
  • On ExplaGraphs with GPT-5-mini, Plan&Write has the highest persuasiveness and Self-Refine the best factual accuracy, yet ARGUS still wins pairwise comparisons.The passage attributes this difference to holistic quality not being captured by isolated rubric dimensions; baseline behavior also varies across models and datasets.Source: §5.1 · p. 6

5.2 Persuasion Analysis via Target Simulation

  • A skeptical-audience judge scores stance change from 1–10 in two independent passes, averaging them to reduce stochasticity and better measure persuasive impact.The judge role-plays an audience member holding the original position.Source: §5.2 · pp. 6–7
  • ARGUS receives the highest simulated persuasion scores, with particularly pronounced improvement on CMV; Multi-Agent Debate performs notably worse under Qwen.The CMV result suggests ARGUS uses rich opinions, emotions, and implicit beliefs as contextual signals for targeted arguments; unconstrained debate can produce coherent but audience-misaligned arguments.Source: §5.2 · p. 7, Table 2 · p. 7

5.3 Ablation Study

  • In GPT-5-mini pairwise ablations, removing ToM, planning, or refinement degrades ELO; removing the planner hurts more than removing ToM, and removing refinement causes the largest drop.The variants remove audience belief and preference modeling, structured planning, or iterative refinement, respectively.Source: §5.3 · p. 7, Figure 2 · p. 7, §5.2 · p. 7
  • Refinement alone is insufficient: it depends on strong ToM-guided drafts, while strategic audience-aware planning provides complementary value that refinement cannot replace.The passage points to Self-Refine’s weaker Table 1 performance as supporting evidence.Source: §5.3 · p. 7

5.4 Pipeline Component Analysis

  • CMV arguments allocate 44% to logos, while iDebate and ExplaGraphs emphasize ethos at ∼28%; pathos-heavy CMV arguments correlate negatively with persuasion and ethos positively.These rhetorical correlations largely disappear on iDebate and ExplaGraphs, where proposition brevity leaves less room for differentiated strategies; refinement improves quality across benchmarks, most on CMV.Source: §5.4 · p. 8

6 Conclusion

ARGUS combines audience modeling, evidence-grounded rhetorical planning, and iterative refinement. It outperforms strong baselines across three benchmarks and multiple backbones, with targeted simulations showing audience stance shifts.

6 Conclusion

  • Across three benchmarks and multiple backbones, ARGUS consistently outperforms strong baselines, while targeted simulations show genuine audience stance shifts rather than mere fluency.The simulations indicate that the gains translate into stance shifts in audiences.Source: §6 · p. 9
  • ARGUS’s component-aware planner assigns rhetorical functions and grounds each subtopic in evidence at planning time.The framework’s three connected axes also include explicit audience modeling and a multi-dimensional refiner.Source: §6 · p. 9
  • ARGUS externalizes audience beliefs and values before writing and uses a multi-dimensional refiner to target weaknesses without quality regression.These components accompany the component-aware planner in the framework.Source: §6 · p. 9

Limitations

Argus’s audience models, factual grounding, and simulated persuasion evaluations remain imperfect, while its multi-stage pipeline adds latency. The authors identify fact-checking, validation against human stance shifts, and agent parallelization as future work.

Limitations

  • The LLM-generated Theory-of-Mind model represents a plausible audience, not a verified account of real audience beliefs and values.Even with planning-time evidence retrieval, sections can contain inaccurate claims; refinement cannot ground arguments against empirical consensus, motivating a dedicated fact-checking agent.Source: Limitations · p. 9
  • The multi-stage pipeline adds latency over single-pass generation, which parallelizing independent agents could mitigate.Source: Limitations · p. 9
  • Human persuasion judgments can be confounded by annotators’ prior beliefs, while simulated skeptical-audience judgments remain an approximation vulnerable to LLM-judge bias.The authors identify validating simulated evaluations against human stance shifts as future work.Source: Limitations · p. 9

Ethical Considerations

Argus’s audience modeling creates dual-use risks because it can make arguments resonate while enabling manipulation, disinformation, or influence operations. The authors advocate disclosure and safeguards, and intend the work to support legitimate applications.

Ethical Considerations

  • Audience modeling can strengthen arguments while also enabling manipulation, disinformation, or influence operations, especially when conditioning on beliefs and emotional triggers.Source: Ethical Considerations · p. 9
  • The authors advocate disclosure of AI-generated arguments, guidelines against deceptive use, and research on provenance and detection to support legitimate applications.Examples include debate education, writing assistance, and policy analysis.Source: Ethical Considerations · p. 9

A Implementation Details

Argus uses three instruction-tuned generation backbones with GPT-5.4 as the fixed evaluation judge. Evaluation samples 30 inputs from each of three datasets, with shared inference settings across baselines and Argus.

Backbone LLM and evaluation judge

  • DeepSeek-v3.2, Qwen3.5-Flash-2026-02-23, and gpt-5-mini-2025-08-07 serve as generation backbones, while GPT-5.4 is the judge across all conditions.Implementations use the official API.Source: A Implementation Details · p. 13

Generation and retrieval

  • Planning iterations and refinement rounds are capped at 2, and JSON outputs receive up to 3 retries with exponential backoff.WebSearch uses the ddgs library 3 for URLs and short snippets, then trafilatura 4 to parse webpages.Source: A Implementation Details · p. 13

Evaluation datasets

Baselines

B ARGUS Module Prompts

Figures 5–11 document prompts for all ARGUS modules and evaluators, including notation for novel components and dynamically inserted content.

B ARGUS Module Prompts

  • Figures 5–11 show prompts for all ARGUS modules and evaluators; red borders mark three novel components, and brackets [...] denote dynamically inserted content.User message templates are included where informative.Source: B ARGUS Module Prompts · p. 13

C Evaluation Protocol Details

The evaluation uses independent GPT-5.4 quality scoring and bidirectional pairwise ELO judgments, with overall scores retained and position bias addressed by averaging score matrices.

C Evaluation Protocol Details

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