Personalizing large language models (LLMs) requires reflecting stable user preferences while adapting their expression to each target context. Soft-prompt personalization has become a practical route to this goal, but existing methods produce soft prompts in which stable preferences, target-induced content, and context-dependent expression are bundled into an entangled conditioning signal without separately addressable components, leaving inference with no principled space to intervene on individual aspects of personalization. We propose Factorized soft prompts with Latent Control (FaLCon), an LLM personalization framework built on two coupled stages. First, factorized soft prompts replace this entangled conditioning with a set of separately addressable preference factors, forming a structured control interface. Second, inference-time latent control treats the resulting factor weights as a continuous action space and adapts them per instance from teacher-forced likelihood feedback, keeping the LLM frozen and updating only a low-dimensional factor-weight posterior without back-propagating through the LLM. Experiments on multiple personalized generation tasks reveal that FaLCon consistently outperforms strong baselines, validating the individual necessity and joint effectiveness of the structured interface and the inference-time controller.

Existing LLM-based agents primarily utilize coarse-grained experiential memory, where experiences are retrieved based on global task or scene context. While effective in simple settings, such coarse-grained memory lacks the situational alignment required for complex multi-step decision-making. As a result, recalled experiences often fail to match the agent’s current state, blurring reasoning focus and leading to inaccurate decisions at critical steps. To this end, we propose State-Aware memory(SAMem), a new fine-grained memory paradigm for LLM agents that explicitly aligns memory retrieval with the current state. Instead of storing and reusing globally shared experiences, SAMem organizes memory at the level of state-specific reasoning thoughts, enabling the agent to retrieve only the most relevant experience for the current decision context. This state-conditioned memory allows the agent to focus on the most informative reasoning cues at each step, rather than being distracted by task-level but state-misaligned guidance. Extensive experiments on complex decision-making benchmarks demonstrate that SAMem outperforms existing experiential memory approaches, achieving superior performance and substantially improved task-solving efficiency. These results indicate that state-aware, fine-grained memory enhances the decision-making capabilities of LLM agents.