Personalizing large language model (LLM) responses in enterprise environments faces unique challenges: heterogeneous data sources, strict on-premise constraints, and unreliable LLM outputs. We present PersonaPlugin, a production-ready framework that transforms real-world telecom signals—call summaries, app usage, and location patterns—into structured personas for LLM personalization. Our system processes 1M+ behavioral records daily, entirely on-premise. The framework comprises three specialized engines, with the call extraction engine achieving >99% parsing success through cascaded fallback mechanisms. We conduct a comprehensive two-track evaluation: (1) general personalization across 800 scenarios, and (2) call-context personalization across 400 scenarios. Our LLM-as-a-Judge evaluation, validated against human judgments (κ=0.71), demonstrates that persona injection significantly improves personalization: +24.0% in P-Score and +26.7% in Adherence over baseline. Integrating call context with personas yields +11% additional improvement, validating the synergistic value of multi-source persona construction.

While large language models (LLMs) have achieved remarkable performance across various natural language processing (NLP) tasks, LLMs exhibit a limited understanding of commonsense reasoning due to the necessity of implicit knowledge that is rarely expressed in text.Recently, retrieval-augmented language models (RALMs) have enhanced their commonsense reasoning ability by incorporating background knowledge from external corpora.However, previous RALMs overlook the implicit nature of commonsense knowledge, potentially leading to the retrieved documents not directly contain information needed to answer questions.In this paper, we propose Retrieval-augmented knowledge Connection, RECONNECT, which transforms indirectly relevant documents into a direct explanation to answer the given question.To this end, we extract relevant knowledge from various retrieved document subsets and aggregate them into a direct explanation.Experimental results show that RECONNECT outperforms state-of-the-art (SOTA) baselines, achieving improvements of +2.0% and +4.6% average accuracy on in-domain (ID) and outof-domain (OOD) benchmarks, respectively 1 .