Large Language Model (LLM) agents exhibit remarkable conversational and reasoning capabilities but remain constrained by limited context windows and the lack of persistent memory. Recent efforts address these limitations via external memory architectures, often employing graph-based representations, yet most adopt flat, entangled structures that intertwine semantics with topology, leading to redundant representations, unstructured retrieval, and degraded efficiency and accuracy. To resolve these issues, we propose LiCoMemory, an end-to-end agentic memory framework for real-time updating and retrieval, which introduces CogniGraph, a lightweight hierarchical graph that utilizes entities and relations as semantic indexing layers, and employs temporal and hierarchy-aware search with integrated reranking for adaptive and coherent knowledge retrieval. Experiments on long-term dialogue benchmarks, LoCoMo and LongMemEval, show that LiCoMemory not only outperforms established baselines in temporal reasoning, multi-session consistency, and retrieval efficiency, but also notably reduces update latency.

As AI assistants serve millions of users daily, evaluating user experience (UX) beyond general model capability has become increasingly important. We present UXBench, the first user-centric benchmark grounded in real user feedback signals for evaluating preference alignment and dialogue generation. The benchmark consists of three interconnected tasks, UX Judge, UX Eval, and UX Recovery, with 7,400 test instances extracted from over 70K interaction logs of a mainstream Chinese AI assistant. The dataset closely reflects real user distributions, covering 8 scenarios, 83 domains, and diverse failure patterns that pose severe challenges. Extensive experiments on 26 frontier language models provide novel insights into how well models perceive user experience and how improvements in model capability contribute to better dialogue engagement. Through analyses of model behavior and performance gaps, we document six important findings, demonstrating that user feedback prediction is a learnable capability and revealing different aspects that influence user experience. UXBench establishes a new evaluation landscape and calls for greater attention to tailored UX optimization, contributing toward a user-centric scaling law for the development of successful AI assistants. The full project is released at https://github.com/mengze-hong/UXBench.

Cultural reasoning lies at the core of many emerging AI applications, yet existing benchmarks provide limited insight into how well models understand and contextualize cultural artifacts. We introduce ExhibitionBench, the first benchmark that frames exhibition curation as a high-level evaluation testbed, comprising 300 institution-derived exhibition and thematic collection records, 23,658 artifacts, and three interconnected tasks that evaluate complementary aspects of cultural understanding and reasoning, translating them into user-perceivable utilities. Extensive experiments across 29 systems reveal a significant gap between frontier LLMs and expert-level competence, particularly on tasks that require collection-level coherence and thematic understanding. Performance gains from scaling, prompting, and multimodal inputs are limited and highly model-dependent, whereas tailored model optimizations can equip open-weight models with better capabilities that support on-premise deployment. These findings highlight the difficulty of reasoning over cultural collections and provide a practical benchmark for developing more culturally grounded and reliable AI systems. The full project is released at https://github.com/mengze-hong/ExhibitionBench.

Streaming Automatic Speech Recognition (ASR), also known as online or real-time ASR, transcribes speech incrementally while audio is being spoken and is critical for many industry applications. Despite the strong performance of modern offline ASR models, streaming deployment remains challenging because limited future context can cause early recognition errors to propagate across later chunks. We define the task of streaming ASR error correction and propose StreamCorrect, a modular framework that fine-tunes a lightweight error corrector from self-generated streaming ASR errors and integrates it with existing streaming ASR systems. To enable training without human annotation, StreamCorrect introduces a timestamp-free boundary recovery procedure based on Prefix-to-Reference Alignment for generating chunk-level correction data. Across multiple languages and ASR backbones, StreamCorrect achieves up to 39% relative character error rate (CER) reduction with minimal latency overhead. Error propagation analysis further shows increasing benefits over time, with up to 7.2% absolute CER reduction in later audio chunks. StreamCorrect bridges offline ASR models and streaming deployment through a lightweight correction plug-in while preserving the pretrained ASR component without additional tuning or distillation.