World model inspired sarcasm reasoning with large language model agents

Abstract: Sarcasm understanding is a challenging problem in natural language processing, as it requires capturing the discrepancy between the surface meaning of an utterance and the speaker's intentions as well as the surrounding social context. Although recent advances in deep learning and LLMs have substantially improved performance, most existing approaches still rely on black-box predictions of a single model, making it difficult to structurally explain the cognitive factors underlying sarcasm. Moreover, while sarcasm often emerges as a mismatch between semantic evaluation and normative expectations or intentions, frameworks that explicitly decompose and model these components remain limited. In this work, we reformulate sarcasm understanding as a world model inspired reasoning process and propose World Model inspired SArcasm Reasoning (WM-SAR), which decomposes literal meaning, context, normative expectation, and intention into specialized LLM-based agents. The discrepancy between literal evaluation and normative expectation is explicitly quantified as a deterministic inconsistency score, and together with an intention score, these signals are integrated by a lightweight Logistic Regression model to infer the final sarcasm probability. This design leverages the reasoning capability of LLMs while maintaining an interpretable numerical decision structure. Experiments on representative sarcasm detection benchmarks show that WM-SAR consistently outperforms existing deep learning and LLM-based methods. Ablation studies and case analyses further demonstrate that integrating semantic inconsistency and intention reasoning is essential for effective sarcasm detection, achieving both strong performance and high interpretability.

• The paper introduces WM-SAR which uses modular LLM agents to decompose sarcasm into literal, contextual, normative, inconsistency, and intention components.
• It employs a logistic regression layer to integrate agent outputs, achieving superior accuracy and macro-F1 across diverse datasets.
• The framework enhances interpretability and practical deployment by enabling transparent error analysis and efficient reasoning even with resource constraints.

World Model Inspired Sarcasm Reasoning with LLM Agents: A Technical Summary

Introduction and Motivation

Sarcasm detection in NLP is fundamentally challenging due to the necessity of modeling the discrepancy between explicit surface meaning and the pragmatic intentions of the speaker within given social context. Prior methods, including PLMs (e.g., BERT, RoBERTa) and contemporary LLMs, have yielded measurable gains but are mostly black-box, preventing interpretation or explicit cognitive decomposition. Sarcasm inherently involves the interplay between literal semantic content, implicit context, normative expectations, and the speaker's intention—dimensions that remain entangled in monolithic neural encodings. The presented work proposes a computational framework, WM-SAR, that applies a world model inspired modular decomposition, operationalized via LLM agents, to explicitly reason over these facets, achieve superior performance, and grant interpretable outputs.

World Model Structure and Architectural Decomposition

WM-SAR is engineered upon an explicit mapping of psycholinguistic models of sarcasm onto computational modules. The central pipeline follows observation $\rightarrow$ latent state $\rightarrow$ prediction $\rightarrow$ prediction error $\rightarrow$ decision, with each stage realized by a specialized LLM agent whose outputs are quantifiable and interpretable.

Figure 1: Overview of the WM-SAR framework, showing specialized LLM agents (literal, context, norm, inconsistency, intention) and the LR-based arbiter.

Agent Roles

• Literal Meaning Agent ($A_\mathrm{literal}$): Extracts literal polarity ($M_\mathrm{literal}(u)\in [-1,1]$) directly from the input utterance, disregarding context or intention.
• Context Constructor Agent ($A_\mathrm{context}$): Infers latent background situation $C(u)$ (social role, scene, event) hypothesized from surface text, providing the substrate for expectation modeling.
• Norm and Expectation Reasoner ($A_\mathrm{norm}$): Estimates the socially normative expectation $E_\mathrm{norm}(C(u)) \in [-1,1]$ given the constructed context, decoupled from idiosyncratic subjectivity.
• Inconsistency Detector ($A_\mathrm{inc}$): Computes the semantic gap $$D(u,C(u))=M_\mathrm{literal}(u)-E_\mathrm{norm}(C(u))$$ and the sign inversion indicator, operationalizing the prediction error.
• Mental State and Intention Reasoner ($A_\mathrm{intent}$): Conducts explicit ToM reasoning to estimate a scalar $T_\mathrm{sar}(u,C(u))\in [0,1]$, denoting the alignment of the utterance with sarcastic intention.

The modular outputs are integrated exclusively via a Logistic Regression (LR) layer, yielding probabilistic sarcasm estimates. This decompositional structure implements parallel cognitive processing, facilitating ablation and post-hoc analysis.

Problem Formalization and Deterministic Reasoning

The formal sarcasm decision is triggered when the sign of literal and norm-based evaluative polarity diverge. The composite scalar outputs and interaction terms are provided to the LR arbiter, which, thanks to its low-parameter and transparent form, enables explicit regularization, traceability, and minimal overfitting risk.

The LR receives both primary features ($|D|$, $T_\mathrm{sar}$, $\mathrm{SD}$) and designed interaction terms, capturing non-linear interdependencies.

Empirical Results and Quantitative Analysis

Rigorous experiments are conducted on three heterogeneous sarcasm datasets (IAC-V1, IAC-V2, SemEval-2018). WM-SAR demonstrates empirically superior accuracy and macro-F1 compared to strong neural and LLM-based baselines, including MIARN, BERT, advanced prompting (CoC/GoC/BoC), and prior multi-agent systems (CAF-I). Critically, all agents leverage a common LLM backbone (GPT-4.1-mini), ensuring architectural and data control.

Ablation studies reveal that both inconsistency signals ($|D|$) and intention modeling ($T_\mathrm{sar}$) are indispensable; removal of either deteriorates performance substantially. The sign-inversion indicator ($\mathrm{SD}$) also provides structural gains, while explicit feature interactions contribute further to discriminatory capacity.

Figure 2: Impact of ablations on WM-SAR's accuracy and macro-F1, highlighting the necessity of ToM and inconsistency signals.

Model Robustness and Efficiency

Performance is stable when instantiated with a less resource-intensive LLM (GPT-4.1-mini) versus the full-scale variant, but degrades markedly with severely downsized models (GPT-4.1-nano). This suggests the reasoning structure, not mere LLM capacity, is the key determinant once a base level of agent competence is available.

Computational cost analysis shows WM-SAR incurs higher per-instance latency than single-prompt or simple prompting LLM baselines, but is an order of magnitude faster than heavy-weight collaborative multi-agent pipelines (CAF-I). The tradeoff supports practical deployment where high interpretability and performance are required.

Interpretability and Decision Analysis

Statistical examinations of intermediate agent signals show no single scalar suffices; successful discrimination arises from their integration. LR feature weight analysis demonstrates the primary reliance on intention reasoning ($T_\mathrm{sar}$), but with clear supporting roles for interaction terms and inconsistency measures:

Figure 3: The 10 largest LR coefficients, with ToM-based features and non-linear combinations dominating, validating WM-SAR's structural rationale.

Case studies illustrate the framework's transparency: correctly detected non-sarcasm instances exhibit signal consistency among agents, while prototypical misses occur where surface meaning, norm, and hypothetical intention converge (i.e., "positive sarcasm"), exposing the framework's inductive bias boundaries.

Theoretical and Practical Implications

By reifying cognitive theory (world model structure) into modular LLM-based agents, WM-SAR achieves both SOTA interpretability and performance in sarcasm detection. The explicit roles for inconsistency and ToM provide doctrinal clarity and operational control over the reasoning process, facilitating error analysis, model improvement, and principled transfer.

The approach generalizes—this decomposition is not limited to sarcasm but is extensible to broader pragmatic and social-NLP phenomena (e.g., irony, humor, euphemism, value-laden judgments) where surface-pragmatic mismatches and intention modeling are central.

Limitations and Future Directions

The current evaluation is restricted by agent backbone (GPT-4.1 family); cross-architecture generalizability is untested. All experiments use sentence-level sarcasm—multi-turn dialogue and long-term context may require augmentation of the Context Agent or new modules. The architecture's reliance on detectable meaning-norm imbalances hinders handling of "positive sarcasm," an inherent inductive limitation.

Future research should examine (i) model heterogeneity, (ii) dialogue and multimodal extensions, and (iii) deeper fusion of agent rationales into training and arbitration. Expanding the world model paradigm into other pragmatic reasoning tasks and multimodal inference is a promising direction.

Conclusion

WM-SAR operationalizes a world model inspired decomposition, mapping core components of sarcasm understanding onto specialized LLM agents. Through deterministic inconsistency computation and minimalist statistical integration, it delivers high-accuracy, interpretable sarcasm reasoning, robust to model scale and deployable in resource-constrained settings. The paradigm shift from monolithic to modular, cognitive-aligned design reflects a crucial vector for next-generation interpretable reasoning in LLM-powered language understanding.

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Reference: "World model inspired sarcasm reasoning with LLM agents" (2512.24329)