In the realm of domain-specific natural language understanding (NLU) tasks, acquiring high-quality labeled data is often arduous, thereby posing significant challenges for effective model training. Multi-task learning (MTL) addresses these limitations by jointly optimizing multiple tasks within a unified framework. In this paper, we introduce a novel sparse NLU multi-task learning framework that decomposes the language model into modular skill components and employs a dynamic, learnable skill-combination mechanism to adaptively handle diverse tasks. Extensive experiments on benchmark NLU datasets demonstrate that our proposed method surpasses conventional multi-task learning approaches in performance.

Memory serves as a pivotal component in interactive response generation, supplying essential background information and referential knowledge for dialogues. Conventional interactive algorithms have predominantly treated memory as a merely contextual element, largely neglecting the nuanced cognitive processes involved in individualized memory encoding and retrieval. This conceptual gap has led to the prevailing schema where memory-enhanced dialogue datasets incorporate monolithic, undifferentiated memory content, failing to capture the personalized nature of persoa memory processing. Grounded in the self-reference effect from cognitive psychology, we introduce a Multi-Turn Dialogue Dataset with Personalized Contextual Memory (), establishing a comprehensive benchmark to facilitate advanced research on personalized memory processing algorithms.

Despite the remarkable evolution of Large Language Models (LLMs) from simple assistants to versatile agents, effective personalization remains a significant challenge. Existing approaches often treat user preferences as static or merely time-varying traits, overlooking the dynamic nature of human behavior: preferences can shift, and even conflict, depending on context. To address this limitation, we propose a fine-grained taxonomy to differentiate between stable preferences, which are context-agnostic, and situational preferences, which are context-dependent. Building on this taxonomy, we introduce S2Pref, a new dataset of 10k meticulously curated entries. Each entry is grounded in a multi-turn dialogue that implicitly manifests either a stable or a situational preference, as defined by our hierarchical taxonomy. We further design three complementary evaluation tasks to benchmark LLMs on their ability to prioritize contextual signals, proactively resolve ambiguity, and efficiently infer user preferences. Our dataset and diagnostic tasks provide a practical testbed for advancing dynamic, context-aware personalization in conversational agents.

Discovering effective predictive signals, or “alphas,” from financial data with high dimensionality and extremely low signal-to-noise ratio remains a difficult open problem. Despite progress in deep learning, genetic programming, and, more recently, large language model (LLM)–based factor generation, existing approaches still explore only a narrow region of the vast alpha search space. Neural models tend to produce opaque and fragile patterns, while symbolic or formula-based methods often yield redundant or economically ungrounded expressions that generalize poorly. Although different in form, these paradigms share a key limitation: none can conduct broad, structured, and human-like exploration that balances logical consistency with creative leaps.To address this gap, we introduce the Cognitive Alpha Mining Framework (CogAlpha), which combines code-level alpha representation with LLM-driven reasoning and evolutionary search. Treating LLMs as adaptive cognitive agents, our framework iteratively refines, mutates, and recombines alpha candidates through multi-stage prompts and financial feedback. This synergistic design enables deeper thinking, richer structural diversity, and economically interpretable alpha discovery, while greatly expanding the effective search space.Experiments on 5 stock datasets from 3 stock markets demonstrate that CogAlpha consistently discovers alphas with superior predictive accuracy, robustness, and generalization over existing methods. Our results highlight the promise of aligning evolutionary optimization with LLM-based reasoning for automated and explainable alpha discovery.

Existing user simulators based on prompting to role-play or SFT are generally confined to imitating users’ textual utterances, without adequately considering the multi-faceted cognitive processes that underlie human decision-making during interactions. To facilitate better alignment with real human thinking patterns, we construct the LMSYS-UserThinking dataset, in which we augment 51k human–LLM conversations by reconstructing the user’s inner reasoning both during and at the end of each dialogue. Furthermore, to enhance controllability and situational coherence, we introduce scenario settings that describe the global context and user goals throughout multi-turn conversations. Using this dataset, we train user simulators called ThinkingUS on different base models. We evaluate our approach from both offline and online user simulation perspectives, ultimately demonstrating its effectiveness.