Know Thyself, Know Thy User: Dual-Perspective Reasoning Architecture for Role-Playing Language Models

ICLR 2026 Conference SubmissionAnonymous Authors
role-playing language modelsaxial attentiondual-perspective reasoningmixture of expertsself-awareness
Abstract:

Current role-playing Large Language Models (LLMs) face a fundamental challenge: balancing character authenticity with user satisfaction. While recent dual-process and dual-perspective approaches have made progress, existing systems still struggle with role-user conflicts where character constraints clash with user expectations. We introduce the KnowSelf-KnowOther Transformer (KSKT), a novel dual-perspective reasoning architecture that addresses this challenge through four integrated innovations: Dual-Stream Axial Attention that processes self-understanding and other-understanding along functionally decoupled dimensions, Bipolar Reasoning combining fast intuitive and slow deliberative pathways, Mutual-Understanding Position Encoding capturing dynamic relational contexts, and Self-Awareness Mixture of Experts specializing in multi-dimensional character comprehension. Unlike previous approaches that treat dual-perspective reasoning as post-hoc optimization or separate modules, KSKT integrates mutual understanding directly into the model architecture. Extensive experiments on CharacterBench demonstrate significant improvements: 6.4% overall enhancement over strong baselines, with particularly notable gains in persona consistency (8.7%) and emotional intelligence (15.2%). Critically, controlled experiments show KSKT maintains balanced dual-perspective reasoning (0.87 self-awareness, 0.87 other-awareness) in role-user conflict scenarios, while baseline models exhibit severe single-perspective bias (0.17 vs. 0.83). These results establish KSKT as an effective architectural framework for role-playing systems that must balance character authenticity with user engagement.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces KSKT, a dual-perspective reasoning architecture integrating four mechanisms—Dual-Stream Axial Attention, Bipolar Reasoning, Mutual-Understanding Position Encoding, and Self-Awareness Mixture of Experts—to balance character authenticity with user satisfaction. It resides in the 'Dual-Perspective and Multi-Stream Reasoning Architectures' leaf, which contains only two papers total. This sparse leaf suggests the specific approach of decoupling self-understanding and other-understanding through functionally separate processing streams remains relatively unexplored, positioning the work in a less crowded research direction within the broader role-playing agent landscape.

The taxonomy reveals neighboring leaves addressing related challenges through different mechanisms: 'Persona-Driven Conversational Frameworks' (three papers) emphasize detailed character development and emotional nuance, 'Multimodal Role-Playing Systems' (two papers) integrate speech and paralinguistic features, and 'Timeline-Aware and Narrative-Constrained Agents' (two papers) focus on temporal consistency. KSKT diverges by embedding mutual understanding directly into model architecture rather than treating dual perspectives as post-hoc optimization or separate modules, contrasting with the single-stream persona modeling excluded from its leaf scope. The taxonomy structure indicates this architectural integration approach occupies a distinct niche between persona consistency methods and multimodal extensions.

Among ten candidates examined through limited semantic search, none clearly refute the three core contributions. The Dual-Stream Axial Attention mechanism was compared against one candidate with no overlap found. Bipolar Reasoning and Mutual-Understanding Position Encoding examined six candidates, all classified as non-refutable or unclear. Self-Awareness Mixture of Experts reviewed three candidates, similarly finding no direct prior work. This absence of refutable matches within the examined scope suggests the specific combination of architectural innovations appears novel, though the limited search scale (ten candidates, not exhaustive) means undiscovered related work may exist beyond top-K semantic matches.

Based on the constrained literature search covering ten semantically similar papers, the work appears to occupy a sparsely populated research direction with no immediate architectural precedents among examined candidates. The taxonomy context confirms dual-perspective reasoning architectures remain underexplored relative to persona-driven frameworks or multimodal systems. However, the analysis reflects top-K semantic retrieval limitations and does not constitute comprehensive coverage of all potentially relevant prior work in attention mechanisms, mixture-of-experts models, or dual-process reasoning systems outside the role-playing domain.

Taxonomy

Core-task Taxonomy Papers
31
3
Claimed Contributions
10
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: balancing character authenticity with user satisfaction in role-playing language models. The field organizes around four main branches that collectively address how to build, evaluate, deploy, and support role-playing agents. Role-Playing Agent Architectures and Frameworks explores foundational designs—ranging from dual-perspective reasoning systems that separate character consistency from user engagement, to multi-stream approaches that manage persona, emotion, and dialogue generation in parallel. Evaluation Methodologies and Benchmarking develops metrics and test suites (e.g., RPGBench[6], RMTBench[24]) to measure both fidelity to predefined personas and user experience quality. Domain-Specific Role-Playing Applications tailors these agents to contexts such as interactive storytelling (Interactive Storytelling RPG[8]), gaming NPCs (Dynamic NPC Dialogs[3]), and conversational avatars (Embodied Conversational Avatars[11]). Supporting Technologies and Methodologies provides cross-cutting tools like persona construction pipelines (Persona to Personalization[1]), emotion modeling (EmoCharacter[15]), and crowdsourcing frameworks (Crowdsourcing User Studies[12]) that enable richer character portrayals. A particularly active line of work focuses on architectures that explicitly decouple character-driven reasoning from user-oriented response generation, addressing the tension between staying true to a role and keeping interactions engaging. Dual Perspective Reasoning[0] exemplifies this approach by maintaining separate reasoning streams—one for authentic character behavior and one for user satisfaction—then reconciling them during response synthesis. This contrasts with earlier single-stream methods like Persona Aware Conversational[9] or more recent integrated frameworks such as Rolecraft GLM[2], which blend persona consistency and dialogue quality within a unified generation process. Nearby works like Persona to Personalization[1] emphasize adaptive persona refinement over time, while VoxRole[4] and TimeChara[7] explore temporal dynamics and evolving character states. Dual Perspective Reasoning[0] sits within this cluster of dual-stream and multi-faceted architectures, offering a structured mechanism to navigate the core trade-off without sacrificing either dimension.

Claimed Contributions

Dual-Stream Axial Attention mechanism

A novel attention mechanism that decomposes attention computation into two complementary semantic streams corresponding to self-understanding (character constraints) and other-understanding (user intentions), processing them along orthogonal dimensions with learnable fusion weights.

1 retrieved paper
Bipolar Reasoning and Mutual-Understanding Position Encoding

A dual-pathway reasoning module that integrates fast intuitive (System 1) and slow deliberative (System 2) processing, combined with position encoding that augments standard RoPE with role-specific and intent-specific relational signals to capture dynamic contextual dependencies.

6 retrieved papers
Self-Awareness Mixture of Experts

A specialized mixture-of-experts architecture with four expert networks handling distinct aspects of character understanding (personality, knowledge, emotion, capability), using a self-reflective routing mechanism that extracts character-specific signals rather than standard input-based routing.

3 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Dual-Stream Axial Attention mechanism

A novel attention mechanism that decomposes attention computation into two complementary semantic streams corresponding to self-understanding (character constraints) and other-understanding (user intentions), processing them along orthogonal dimensions with learnable fusion weights.

Contribution

Bipolar Reasoning and Mutual-Understanding Position Encoding

A dual-pathway reasoning module that integrates fast intuitive (System 1) and slow deliberative (System 2) processing, combined with position encoding that augments standard RoPE with role-specific and intent-specific relational signals to capture dynamic contextual dependencies.

Contribution

Self-Awareness Mixture of Experts

A specialized mixture-of-experts architecture with four expert networks handling distinct aspects of character understanding (personality, knowledge, emotion, capability), using a self-reflective routing mechanism that extracts character-specific signals rather than standard input-based routing.