Large Language Models (LLMs), with 7B+ parameters, remain difficult to deploy on edge devices due to memory latency constraints, despite increasing demand for personalized on-device agents with strong reasoning capabilities. Enabling such deployment, in current hardware specifications, requires aggressive compression while preserving downstream performance. Singular Value Decomposition (SVD)-based methods provide a promising reconstruction-aware compression paradigm, and recent trainable frameworks improve adaptive rank allocation under varying compression budgets. However, maintaining model quality under high compression remains challenging due to statistical mismatches between decomposed cores and compounded degradation from direct high-ratio compression.

We propose GRACE-SVD, a trainable SVD compression framework combining Guided Rank Allocation with a novel Core Equalization method that aligns decomposed core distributions while preserving reconstruction. We further introduce a progressive compression pipeline with intermediate equalization to mitigate performance collapse under aggressive compression. Across multiple model families and compression settings, our GRACE-SVD consistently outperforms existing SVD techniques, achieving stable compression even at 0.4 parameter ratio, with lower perplexity and stronger zero-shot performance under comparable or lower compression budgets.

Mobile devices, with their rapidly growing usage, have turned into rich sources of user information, holding critical insights for betterment of user experience and personalization. Creating, receiving and storing important information in the form of unstructured text has become a part and parcel of daily routine of users. From purchase deliveries in Short Message Service (SMS) or Notifications, to event booking details in Calendar applications, mobile devices serve as a portal for understanding user interests, behaviours and activities through information extraction. In this paper, we address the challenge of on-device extraction of user information from unstructured data in natural language from heterogeneous sources like messages, notification, calendar etc. The issue of privacy concern is effectively eliminated by the on-device nature of the proposed solution. Our proposed solution consists of 3 components – A Na ̈ıve-Bayes based classifier for domain identification, a Dual Character andWord based Bidirectional Long Short Term Memory (Bi-LSTM) and Conditional Random Field (CRF) model for attribute extraction and a rule-based Entity Linker. Our solution achieved a 93.29% F1 score on five domains (shopping, travel, event, service and personal). Since on-device deployment has memory and latency constraints, we ensure minimal model size and optimal inference latency. To demonstrate the efficacy of our approach, we have experimented on CoNLL- 2003 dataset and achieved comparable performance to existing benchmark results.