Reinforcement learning has driven the largest post-training gains on tasks with verifiable rewards, such as code and math. Extending this success to subjective tasks remains difficult: good responses must satisfy multiple soft criteria simultaneously, and no scalar reward can indicate which aspect of a response should improve. Personalization is a clear instance of this. A personalized response must balance helpfulness, user-specific relevance, and naturalness, yet standard verifiers cannot localize which specific dimension needs improvement. In this regime, supervised fine-tuning quickly saturates, and PPO with Bradley-Terry reward models is prone to reward hacking, especially via length bias, producing verbose or superficially personalized responses. We propose Critique-Post-Edit, an RL framework that combines (1) a personalized Generative Reward Model (GRM) producing multi-dimensional scores and natural-language critiques, and (2) a critique-guided editing step that turns feedback into nearby alternative responses for policy learning. The reward signal stays scalar; our contribution is to use personalized critiques to construct more informative local alternatives (responses close to the on-policy rollout in edit distance) during training. Under length-controlled evaluation, Critique-Post-Edit improves over PPO by 11.0 and 11.6 win-rate points on Qwen2.5-7B and 14B models, respectively. Notably, the 14B model matches or outperforms GPT-4.1 on the evaluated benchmarks. A static mechanism study further shows that critique-guided edits stay more local than extra rollouts and shift scores more favorably than generic rewrites. All of our experiments are on personalization; systematic evaluation on other subjective tasks is left to future work.

User-centric multi-turn agents must act on an evolving task situation shaped by changing user intents, accumulated tool-grounded facts, missing information, and execution constraints. Existing context-management methods improve the use of past interaction history, but rarely maintain an explicit situation state that separates grounded facts from task-state judgments. As a result, agents often need to infer fine-grained attributes, task dependencies, and constraint satisfaction implicitly from dialogue traces. We propose Intent-Driven Situation States (IDSS), a training-free framework that maintains an explicit situation state alongside the dialogue. IDSS parses tool returns into provenance-aware entities and attributes, tracks user intents, required variables, constraints, and execution status, and propagates new facts to task constraints to update action executability. This allows agents to avoid infeasible actions, advance dependent goals, and reuse relevant information without repeatedly searching raw history. Experiments on three interactive benchmarks across eight LLMs show that IDSS improves task completion, preference elicitation, and interaction efficiency, with clear gains on tasks involving multi-entity coordination, evolving user constraints, and constraint-aware replanning. Ablations and error analyses show that these improvements come from the interaction between fact persistence, intent-centered state tracking, and constraint modeling. These results suggest that explicit situation tracking offers an effective alternative to history-centric context management for reliable user-centric multi-turn agents.

The dream to create AI assistants as capable and versatile as the fictional J.A.R.V.I.S from Iron Man has long captivated imaginations. With the evolution of multi-modal large language models ((M)LLMs), this dream is closer to reality, as (M)LLM-based Agents using computers, mobile phones and web browsers by operating within the environments and interfaces (e.g., Graphical User Interface (GUI) and Command Line Interface (CLI)) provided by operating systems (OS) to automate tasks have significantly advanced. This paper presents a comprehensive survey on these advanced agents, designated as OS Agents. We begin by elucidating the fundamentals of OS Agents, exploring their key components and capabilities. We then examine methodologies for constructing OS Agents, focusing on domain-specific foundation models and agent frameworks. A detailed review of evaluation metrics and benchmarks highlights how OS Agents are assessed across diverse platforms and tasks. Finally, we discuss current challenges and identify promising directions for future research. An open-source GitHub repository is maintained as a dynamic resource to foster further innovation in this field.

This study presents RoleCraft-GLM, an innovative framework aimed at enhancing personalized role-playing with Large Language Models (LLMs). RoleCraft-GLM addresses the key issue of lacking personalized interactions in conversational AI, and offers a solution with detailed and emotionally nuanced character portrayals. We contribute a unique conversational dataset that shifts from conventional celebrity-centric characters to diverse, non-celebrity personas, thus enhancing the realism and complexity of language modeling interactions. Additionally, our approach includes meticulous character development, ensuring dialogues are both realistic and emotionally resonant. The effectiveness of RoleCraft-GLM is validated through various case studies, highlighting its versatility and skill in different scenarios. Our framework excels in generating dialogues that accurately reflect characters’ personality traits and emotions, thereby boosting user engagement. In conclusion, RoleCraft-GLM marks a significant leap in personalized AI interactions, and paves the way for more authentic and immersive AI-assisted role-playing experiences by enabling more nuanced and emotionally rich dialogues.