Role-playing agents (RPAs) must sustain persona fidelity, natural interaction, and scene progression across multi-turn trajectories. However, supervised fine-tuning, preference optimization, and coarse episode-level RL can over-reinforce safe stylized responses that appear in-character but are repetitive, low-information, or weakly grounded in the evolving scene. We propose LSH-RL, a Long-Short Horizon Reinforcement Learning framework for persona-level RPAs. LSH-RL trains a shared policy through dynamic multi-role self-play, combining short-horizon utterance/action rewards with long-horizon character-trajectory rewards. A gated trajectory-to-turn reward fusion mechanism prevents locally degenerate turns from receiving undeserved long-horizon credit, while a dialogue-action curriculum supports staged optimization. Experiments on PersonaArena and CharacterBox show that LSH-RL improves over SFT and DPO in both dialogue-only and full interaction settings. Further analyses show reduced repetition and stronger human preferences, suggesting a path toward more coherent and interaction-rich persona-level RPAs. The source code is available at https://github.com/Shiwenlong-source/LSH-RL.

Large reasoning models improve difficult problem solving by spending many tokens on internal reasoning. This paradigm is natural for single-turn tasks, but poorly matched to language agents: an agent may take tens or hundreds of environment steps, making deep thinking before every action costly and sometimes harmful. We study agentic deep thinking: when should an agent invoke long-form private deliberation, and how should it be exposed inside a ReAct-style loop? We propose a selective deep-thinking framework that separates the short externalized Thought used to choose the next action from a longer private memo used to reorganize future behavior. Without model post-training, we instantiate this framework with Periodic Thinking, Sel.-Gate, and Sel.-Action. Across three benchmarks and six model families, selective deep thinking improves agents, but the best interface depends on backbone capability: strong proprietary models benefit most from deliberation as a callable action, while smaller open-source models are more robust with a separate gate. We then use GRPO to teach a less capable backbone when to deliberate, turning the weakest zero-shot interface into the strongest learned policy and improving transfer to unseen agentic tasks. These results show that selective deep thinking can improve both the effectiveness and efficiency of long-horizon language agents.

Recommender systems based on Large Language Models (LLMs) are often plagued by hallucinations of out-of-domain (OOD) items. To address this, we propose RecLM, a unified framework that bridges the gap between retrieval and generation by instantiating three grounding paradigms under a single architecture: embedding-based retrieval, constrained generation over rewritten item titles, and discrete item-tokenizer generation. Using the same backbone LLM and prompts, we systematically compare these three views on public benchmarks. RecLM strictly eradicates OOD recommendations (OOD@10 = 0) across all variants, and the constrained generation variants RecLM-cgen and RecLM-token achieve overall state-of-the-art accuracy compared to both strong ID-based and LLM-based baselines. Our unified view provides a systematic basis for comparing three distinct paradigms to reduce item hallucinations, offering a practical framework to facilitate the application of LLMs to recommendation tasks. Source code is at https://github.com/microsoft/RecAI.

Large Language Models (LLMs) exhibit significant potential in complex software engineering tasks, however, their fault localization capabilities within repository are constrained by inherent limitations in max context length. Although Test-Time Scaling (TTS) can generate multiple candidate solutions, traditional selection strategies often fail to identify the optimal one. To solve this problem, we introduces Hierarchical Localization Reward Model (HiLoRM), which specifically designed to evaluate and select the most accurate fault localization candidates (at file, function, and line levels) from the multiple sampled outputs of LLMs, thereby enhancing localization accuracy. Furthermore, we constructed the HiFL-44k dataset, comprising approximately 44,000 fault localization instances, to train HiLoRM. Experimental results demonstrate that on the SWE-Bench-Lite dataset, HiLoRM improves the final line-level localization recall by 12% compared to a baseline model that does not use a reward model. Concurrently, HiLoRM exhibits a strong capability to evaluate predictions from larger LLMs (e.g., 32B parameters) and demonstrates transferability and generalization potential when applied to other fault localization methods. This work provides an effective methodology and an accessible model to significantly improve the accuracy and reliability of LLMs for repository-level fault localization. Our codes and datasets are available at https://github.com/SZU-ZJW/HiFL-Method.

Role-playing capabilities in large language models (LLMs) often lack cognitive consistency in complex scenarios that require deep understanding and coherent reasoning. While recent reasoning models excel in math and coding tasks, they show limited effectiveness in open-ended role-playing scenarios. We introduce R-CHAR (Role-Consistent Hierarchical Adaptive Reasoning), a metacognition-driven framework that enhances role-playing performance through guided thinking trajectories synthesis and adaptive evaluation. Our approach demonstrates that concise thinking processes can achieve superior performance efficiently compared to elaborate reasoning chains in role-playing social intelligence tasks, outperforming existing specialized models. Experimental results on the SocialBench benchmark show significant and stable performance improvements across varying scenario complexities, showing particular strength in long-context comprehension (from 34.64% to 68.59%) and group-level social interactions. Our work advances the development of cognitively consistent role-playing systems, bridging the gap between surface-level mimicry and authentic character simulation.