Large Language Models (LLMs) have shown remarkable capabilities in environmental perception, reasoning-based decision-making, and simulating complex human behaviors, particularly in interactive role-playing contexts. This paper introduces the Multiverse Interactive Role-play Ability General Evaluation (MIRAGE), a comprehensive framework designed to assess LLMs’ proficiency in portraying advanced human behaviors through murder mystery games. MIRAGE features eight intricately crafted scripts encompassing diverse themes and styles, providing a rich simulation. To evaluate LLMs’ performance, MIRAGE employs four distinct methods: the Trust Inclination Index (TII) to measure dynamics of trust and suspicion, the Clue Investigation Capability (CIC) to measure LLMs’ capability of conducting information, the Interactivity Capability Index (ICI) to assess role-playing capabilities and the Script Compliance Index (SCI) to assess LLMs’ capability of understanding and following instructions. Our experiments indicate that even popular models like GPT-4 face significant challenges in navigating the complexities presented by the MIRAGE. The datasets and simulation codes are available in https://github.com/lime728/MIRAGE.

The rapid advancement of large language models (LLMs) has unlocked transformative potential for role-playing emotional companion products, enabling systems that support emotional well-being, educational development, and therapeutic applications. However, existing approaches often lack sustained personalization and contextual adaptability, limiting their effectiveness in real-world settings. In this paper, we introduce iPET, an LLM-powered virtual pet agent designed to enhance user engagement through rich, dynamic pet behaviors and interactions tailored to individual preferences. iPET comprises three core components: a dialogue module that instantiates virtual pet agents for emotionally interactive conversations; a memory module that stores and synthesizes records of both agent and user experiences; and a world simulation module that generates diverse, preference-driven pet behaviors guided by high-level reflections. Deployed for over 200 days in a real-world, non-commercial product, iPET has served millions of users – providing emotional support to psychologically distressed individuals and demonstrating its effectiveness in practical applications.

Despite advances in unsupervised log anomaly detection, current models require dataset-specific training, causing costly procedures, limited scalability, and performance bottlenecks. Furthermore, numerous models lack cognitive reasoning abilities, limiting their transferability to similar systems. Additionally, these models often encounter the **“identical shortcut”** predicament, erroneously predicting normal classes when confronted with rare anomaly logs due to reconstruction errors. To address these issues, we propose **MLAD**, a novel **M**ulti-system **L**og **A**nomaly **D**etection model incorporating semantic relational reasoning. Specifically, we extract cross-system semantic patterns and encode them as high-dimensional learnable vectors. Subsequently, we revamp attention formulas to discern keyword significance and model the overall distribution through vector space diffusion. Lastly, we employ a Gaussian mixture model to highlight rare word uncertainty, optimizing the vector space with maximum expectation. Experiments on real-world datasets demonstrate the superiority of MLAD.

With the rapid advancement of Large Language Models (LLMs), the demand for robust instruction-following capabilities in code generation tasks has grown significantly. Code generation not only facilitates faster prototyping and automated testing, but also augments developer efficiency through improved maintainability and reusability of code. In this paper, we introduce CodeIF, the first benchmark specifically designed to assess the abilities of LLMs to adhere to task-oriented instructions within diverse code generation scenarios. CodeIF encompasses a broad range of tasks, including function synthesis, error debugging, algorithmic refactoring, and code explanation, thereby providing a comprehensive suite to evaluate model performance across varying complexity levels and programming domains. We conduct extensive experiments with LLMs, analyzing their strengths and limitations in meeting the demands of these tasks. The experimental results offer valuable insights into how well current models align with human instructions, as well as the extent to which they can generate consistent, maintainable, and contextually relevant code. Our findings not only underscore the critical role that instruction-following LLMs can play in modern software development, but also illuminate pathways for future research aimed at enhancing their adaptability, reliability, and overall effectiveness in automated code generation.

Recently, advancements in large multimodal models have led to significant strides in image comprehension capabilities. Despite these advancements, there is a lack of a robust benchmark specifically for assessing the image‐to‐web conversion proficiency of these large models. It is essential to ensure the integrity of the web elements generated, which comprise both visible and invisible categories. Previous evaluation methods (e.g., BLEU) are notably susceptible to significant alterations due to the presence of invisible elements. Furthermore, it is crucial to measure the layout information of web pages—i.e., the positional relationships between elements—which has been overlooked by prior work. To address these challenges, we have curated and aligned a benchmark of images and corresponding web codes (IW-bench). Specifically, we propose Element Accuracy, which tests the completeness of elements by parsing the Document Object Model (DOM) tree. We also introduce Layout Accuracy to analyze positional relationships by converting the DOM tree into a common subsequence. In addition, we design a five‐hop multimodal Chain‐of‐Thought prompting strategy for improved performance, consisting of: 1) SoM prompt injection, 2) inferring elements, 3) inferring layout, 4) inferring web code, and 5) reflection. Our benchmark comprises 1,200 image–code pairs with varying levels of difficulty. We have conducted extensive experiments on existing large multimodal models, providing insights into their performance and identifying areas for improvement in the image‐to‐web domain.