Large Language Models are increasingly utilized as Role-Playing Agents (RPAs) to simulate personas in interactive settings. However, current RPAs often produce flattened and stereotypical personas with limited depth and fidelity. This limitation arises from two core challenges: insufficient modeling of complex personal histories and internal logic, and ungrounded reasoning that fails to preserve persona coherence as dialogue context evolves. To address these challenges, we propose ThinkPersona, a role-playing agent trained to explicitly ground responses in individual identity. We introduce Persona Graphs as structured representations that encode life trajectories, values, relationships, and events as interconnected knowledge. We construct 1,201 Persona Graphs from real-world interviews and derive a Question–Reasoning–Answer (QRA) dataset of 23,401 samples that supervises reasoning over persona evidence. Fine-tuning on QRA enables ThinkPersona to internalize persona logic and generate persona-consistent responses in long-context dialogues. Experiments on three benchmarks show that ThinkPersona improves role-playing fidelity, behavioral consistency, and grounded reasoning over existing methods, while preserving general instruction-following capabilities. Our code and dataset are available at https://github.com/Hualeez/ThinkPersona.

Current conversational agents often follow static learning paradigms and miss the implicit, evolving feedback embedded in users’ follow-up behaviors. We propose IEvoAgent, an evolving conversational agent framework that leverages the structured dependency between agent responses and user reactions. We construct an annotated dataset from LMSYS-Chat-1M and WildChat and find consistent response-conditioned feedback patterns. Based on this finding, IEvoAgent uses a conditional feedback distribution matrix to estimate expected feedback rewards, combining offline KTO alignment with an inference-time prompt-evolution mechanism driven by a dynamic matrix. Experiments on MT-Bench-101, WildBench, and FB-Bench show improvements over open-source baselines, indicating that mining implicit feedback supports better multi-turn alignment under evolving user preferences. Our code and dataset are available at https://github.com/Hualeez/IEvoAgent.