ZIPP conditions diffusion models on LLM-rewritten prompts derived from graph-mined natural-language personas to achieve zero-shot personalization, reporting 13-20% gains and 79% human preference win rate over generic outputs.
TwinVoice : A multi-dimensional benchmark towards digital twins via LLM persona simulation
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
other 1
citation-polarity summary
years
2026 5verdicts
UNVERDICTED 5roles
other 1polarities
unclear 1representative citing papers
SensorPersona uses LLMs for hierarchical reasoning on longitudinal mobile sensor streams to continually extract stable personas, showing up to 31.4% higher recall and 85.7% win rate over baselines on a 20-user dataset.
LEADS is an LLM-agent framework that discovers hybrid models for cardiac EP digital twins by treating domain knowledge as an action space, outperforming human-designed and other LLM-based hybrids on synthetic and real data.
DITTO uses RL with verbal feedback to train LLMs for human behavior simulation, reporting 36% average gains over base models and outperforming GPT-5.4 on 6 of 10 SOUL benchmark tasks.
MemEye benchmark evaluates multimodal memory on visual granularity and evidence synthesis, finding that 13 methods across 4 VLMs struggle with fine details and temporal state changes.
citing papers explorer
-
ZIPP:Zero-shot Image Personalization from Personas
ZIPP conditions diffusion models on LLM-rewritten prompts derived from graph-mined natural-language personas to achieve zero-shot personalization, reporting 13-20% gains and 79% human preference win rate over generic outputs.
-
SensorPersona: An LLM-Empowered System for Continual Persona Extraction from Longitudinal Mobile Sensor Streams
SensorPersona uses LLMs for hierarchical reasoning on longitudinal mobile sensor streams to continually extract stable personas, showing up to 31.4% higher recall and 85.7% win rate over baselines on a 20-user dataset.
-
Learning Cardiac Electrophysiology Digital Twins Through Agentic Discovery of Hybrid Structure
LEADS is an LLM-agent framework that discovers hybrid models for cardiac EP digital twins by treating domain knowledge as an action space, outperforming human-designed and other LLM-based hybrids on synthetic and real data.
-
Reinforcing Human Behavior Simulation via Verbal Feedback
DITTO uses RL with verbal feedback to train LLMs for human behavior simulation, reporting 36% average gains over base models and outperforming GPT-5.4 on 6 of 10 SOUL benchmark tasks.
-
MemEye: A Visual-Centric Evaluation Framework for Multimodal Agent Memory
MemEye benchmark evaluates multimodal memory on visual granularity and evidence synthesis, finding that 13 methods across 4 VLMs struggle with fine details and temporal state changes.