mlsanityData to 14 Oct 2026

Operation Veja: Fixing Fundamental Concepts Missing from Modern Roleplaying Training Paradigms

arXiv 2601.06039 ↗First posted 12 Dec 2025cs.CLcited by 0

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Abstract (arXiv)

Modern roleplaying models are increasingly sophisticated, yet they consistently struggle to capture the essence of believable, engaging characters. We argue this failure stems from training paradigms that overlook the dynamic interplay of a character's internal world. Current approaches, including Retrieval-Augmented Generation (RAG), fact-based priming, literature-based learning, and synthetic data generation, exhibit recurring limitations in modeling the deliberative, value-conflicted reasoning that defines human interaction. In this paper, we identify four core concepts essential for character authenticity: Values, Experiences, Judgments, and Abilities (VEJA). We propose the VEJA framework as a new paradigm for data curation that addresses these systemic limitations. To illustrate the qualitative ceiling enabled by our framework, we present a pilot study comparing a manually curated, VEJA-grounded dataset against a state-of-the-art synthetic baseline. Using an LLM-as-judge evaluation, our findings demonstrate a significant quality gap, suggesting that a shift toward conceptually grounded data curation, as embodied by VEJA, is necessary for creating roleplaying agents with genuine depth and narrative continuity. The full dataset is available at https://github.com/HyouinKyoumaIRL/Operation-Veja

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 8 references that are in the map. First posted on arXiv 12 Dec 2025.

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References · 4

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  3. 2023InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviewscited by 179
  4. 2023RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Modelscited by 175

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  1. 2026Rethinking Role-Playing Evaluation: Anonymous Benchmarking and a Systematic Study of Personality Effects
  2. 2026Identifying and Mitigating Bottlenecks in Role-Playing Agents: A Systematic Study of Disentangling Character Profile Axes
  3. 2025Test-Time-Matching: Decouple Personality, Memory, and Linguistic Style in LLM-based Role-Playing Language Agent
  4. 2026DynSess: Dynamic Session-Level Evaluation and Optimization Framework for Role-Playing Agents
  5. 2026HER: Human-like Reasoning and Reinforcement Learning for LLM Role-playing
  6. 2025Dynamic Context Adaptation for Consistent Role-Playing Agents with Retrieval-Augmented Generations
  7. 2024Large Language Models are Superpositions of All Characters: Attaining Arbitrary Role-play via Self-Alignment
  8. 2025PsyMem: Fine-grained psychological alignment and Explicit Memory Control for Advanced Role-Playing LLMs