Existing LLM-based role-playing methods often rely on superficial textual descriptions or simplistic metrics, inadequately modeling both intrinsic and extrinsic character dimensions. Additionally, they typically simulate character memory with implicit model knowledge or basic retrieval augment generation without explicit memory alignment, compromising memory consistency. The two issues weaken reliability of role-playing LLMs in several applications, such as trustworthy social simulation. To address these limitations, we propose PsyMem, a novel framework integrating fine-grained psychological attributes and explicit memory control for role-playing. PsyMem supplements textual descriptions with 26 psychological indicators to detailed model character. Additionally, PsyMem implements memory alignment training, explicitly trains the model to align character’s response with memory, thereby enabling dynamic memory-controlled responding during inference. By training Qwen2.5-7B-Instruct on our specially designed dataset (including 5,414 characters and 38,962 dialogues extracted from novels), the resulting model, termed as PsyMem-Qwen, outperforms baseline models in role-playing, achieving the best performance in human-likeness and character fidelity.

Aligning large language models (LLMs) with human preferences is challenging due to the inherent heterogeneity and multi-dimensionality of these preferences. Existing inference-time multi-objective alignment methods often rely on fixed weights to combine preference models. However, different tokens contribute unequally to preference objectives during decoding. Ignoring such token-level variation may cause the loss of preference information and lead to objective interference. To alleviate this, we utilize the implicit rewards of preference models to estimate importance differences between tokens, and propose a new training-free and reward-free framework TAW to dynamically balance multiple objectives for inference-time alignment. We design the relative entropy and marginal entropy estimators to quantify the informativeness and uncertainty of preference objectives. TAW with the estimator ensures that more informative or reliable preferences are emphasized, while weak or uncertain signals are suppressed. Experiments on two LLM alignment benchmarks show that TAW outperforms strong baselines without predefined weightings, while also achieves an overall optimal Pareto front when explicit preference weighting is applied.

Multi-task learning (MTL) enables joint learning over multiple tasks based on shared representations, but suffers from task interference issue during optimization. Existing works mainly focus on task balancing or probabilistic modeling but fail to address the issue since they struggle to learn sufficient representations for all target tasks. To address this, we propose a multi-task representation alignment (MTRA) framework to achieve task-specific alignment and self-alignment on the shared representations from a mutual information perspective. MTRA ensures that the learned representations contain task-relevant features while mitigating the negative effects of task-irrelevant features. First, we design a task-specific alignment objective to align the shared representations and task-specific representations with the expected targets of all tasks via information maximization. Besides, we design a self-alignment objective to eliminate task-irrelevant features via conditional information minimization. Experiments on two multi-task language benchmarks show that MTRA outperforms 13 representative MTL methods under the same settings, particularly under label-noisy and data-constrained conditions. Further analysis shows that the learned shared representations exhibit sufficient task informativeness and superior alignment properties.