LLM role-playing, i.e., using large language models (LLMs) to simulate specific personas, has emerged as a key capability in various applications, such as companionship, content creation, and digital games. While current models effectively capture character tones and knowledge, simulating the inner thoughts behind their behaviors remains a non-trivial challenge. Towards cognitive simulation in LLM role-play, previous efforts have mainly suffered from two critical deficiencies: the lack of high-quality datasets with explicit reasoning traces and the absence of reliable reward signals aligned with human preferences. In this paper, we propose HER (Human Emulation Reasoning), a unified framework for cognitive-level persona simulation. HER introduces a dual-layer thinking mechanism that strictly distinguishes characters’ first-person thinking processes from LLMs’ third-person reasoning. To bridge the aforementioned gaps, we curate a reasoning-augmented role-playing dataset via a reverse engineering strategy for supervised learning, and construct human-aligned evaluation principles and preference-based reward models for role-play reinforcement learning. Leveraging these resources, we train HER models based on the Qwen3-32B backbone via a hybrid paradigm of supervised learning (SL) and reinforcement learning from human feedback (RLHF). Extensive experiments validate the effectiveness of our approach. Notably, our models significantly outperform the Qwen3-32B baseline, achieving a 30.26% on the CoSER benchmark and a 14.97% on the MiniMax Benchmark. Our datasets, evaluation principles, and trained models will be released to facilitate future research in cognitive-level LLM role-playing.

Chart understanding requires models to effectively analyze and reason about numerical data, textual elements, and complex visual components. Our observations reveal that the perception capabilities of existing large vision-language models (LVLMs) constitute a critical bottleneck in this process. In this study, we delve into this perception bottleneck by decomposing it into two components: the vision encoder bottleneck, where the visual representation may fail to encapsulate the correct information, and the extraction bottleneck, where the language model struggles to extract the necessary information from the provided visual representations. Through comprehensive experiments, we find that (1) the information embedded within visual representations is substantially richer than what is typically captured by linear extractors, such as the widely used retrieval accuracy metric; (2) While instruction tuning effectively enhances the extraction capability of LVLMs, the vision encoder remains a critical bottleneck, demanding focused attention and improvement. Therefore, we further enhance the visual encoder to mitigate the vision encoder bottleneck under a contrastive learning framework. Empirical results demonstrate that our approach significantly mitigates the perception bottleneck and improves the ability of LVLMs to comprehend charts.

While large language models (LLMs) have demonstrated remarkable abilities across various fields, hallucination remains a significant challenge. Recent studies have explored hallucinations through the lens of internal representations, proposing mechanisms to decipher LLMs’ adherence to facts. However, these approaches often fail to generalize to out-of-distribution data, leading to concerns about whether internal representation patterns reflect fundamental factual awareness, or only overfit spurious correlations on the specific datasets. In this work, we investigate whether a universal truthfulness hyperplane that distinguishes the model’s factually correct and incorrect outputs exists within the model. To this end, we scale up the number of training datasets and conduct an extensive evaluation – we train the truthfulness hyperplane on a diverse collection of over 40 datasets and examine its cross-task, cross-domain, and in-domain generalization. Our results indicate that increasing the diversity of the training datasets significantly enhances the performance in all scenarios, while the volume of data samples plays a less critical role. This finding supports the optimistic hypothesis that a universal truthfulness hyperplane may indeed exist within the model, offering promising directions for future research.