Tag
This paper introduces a behavior-aware role-playing framework called SIBPersona to enhance the fidelity of impersonating social media influencers by integrating situation-dependent behavioral strategies and an evaluation protocol for obscure individuals.
ANIMASK is a simulation framework that freezes books and scripts into story worlds to analyze how language models contribute to role-play, finding that personas define character identity while model defaults influence behavioral extent.
RoleBreak is an open benchmark for evaluating long-horizon role-playing robustness in spoken dialogue systems, revealing gaps in current models' ability to maintain role consistency and vocal emotion over extended interactions.
The Blood of Dawnwalker is a vampire simulator video game that launches today, inspired by The Witcher 3, featuring day-night cycles and a time-limited quest to save your family in a plague-era medieval world.
This paper introduces PHASE-Tree, a multi-timescale character-state representation for long-horizon role-playing dialogue, along with the LongEvoRoleBench benchmark to evaluate evolved-state generation. The proposed approach outperforms baselines on character-level, semantic, and embedding metrics across long-dialogue corpora.
This paper introduces DREAM, a structured memory framework for LLM-based role-playing agents that uses an Event-aware Memory Graph to maintain temporal and causal coherence, and proposes the TCM benchmark for evaluation.
Introduces PALATE, a scalable benchmark for evaluating role-playing agents using person-aligned LLM-simulated users and personalized rubrics, addressing limitations of fixed-history evaluation.
A gamer shares how LLM-powered mods like SkyrimNet have revolutionized their role-playing experience in Skyrim, enabling deep emergent storytelling and unique character interactions.
This paper proposes ReverieMem, a three-layer memory architecture for book-based LLM role-playing agents that prevents factual overreach and stylistic monotony. It also introduces the KBF-QA benchmark and achieves significant improvements in knowledge boundary fidelity and narrative quality.
This paper introduces AgentViSS, a benchmark evaluating visual social intelligence in multimodal social simulation, containing 240 scenarios with aligned visual-textual evidence. Evaluating seven recent MLLMs reveals a gap between local role enactment and visually grounded interaction management.
Agentopia is a comprehensive framework for long-term life simulation in multi-agent societies, where 100 LLM-powered agents autonomously pursue personal growth and social relationships over 10 simulated years. The work studies emergent social behaviors and uses life reward training to improve LLM role-playing capabilities.
This paper introduces ArcANE, an automatically constructed benchmark for evaluating role-playing language agents' alignment with character psychological trajectories across narrative phases, showing that conditioning on character arc information improves performance, especially in scenarios beyond the source text.
This paper proposes DynSess, a unified session-level evaluation and optimization framework for role-playing agents, addressing the limitation of turn-level metrics by scoring complete dialogue sessions and using session-level rewards to train more coherent character models.
A tool that lets you create AI agents with opposing goals to simulate arguments, useful for sales prep, idea stress-testing, and difficult conversations. Runs locally without API key in mock mode.
Equinox-31B is a fine-tuned AI model on top of Gemma 4 31B Instruct that balances dark adventure and slice-of-life storytelling, designed for interactive fiction and role-playing games like AI Dungeon.
This paper introduces UniCharacter, a two-stage training framework for Customized Multimodal Role-Play (CMRP) that enables unified customization of persona, dialogue style, and visual identity. It presents the RoleScape-20 dataset and demonstrates that the model can achieve coherent cross-modal generation with minimal data.
VITA-QinYu is an expressive end-to-end spoken language model capable of role-playing and singing, trained on 15.8K hours of data to outperform peers in expressiveness and conversational accuracy.