ROAM: An Online Adaptation Framework for Expert Models Based on Open-Scene Reasoning

This article introduces ROAM (an Online Adaptation Framework for Expert Models Based on Open-Scene Reasoning), an innovative expert model adaptation method. Its core idea is to freeze the pre-trained backbone network and perform task adaptation only through low-dimensional corrective latents, while integrating structured semantic priors generated by LLMs to achieve efficient task-specific adaptation without compromising the original model's expertise. The ROAM framework aims to resolve the core contradictions in model adaptation within professional domains and is applicable to scenarios such as industrial prediction and medical diagnosis.

In professional domains like industrial prediction and medical diagnosis, pre-trained deep sequence models (e.g., GRU, LSTM) face a core contradiction: they have strong feature extraction capabilities trained on general data, but specific tasks require domain knowledge fine-tuning. Traditional full-parameter fine-tuning is costly and prone to catastrophic forgetting. Additionally, professional scenarios present challenges such as data scarcity, concept drift (data distribution changes over time), open scenarios (unseen cases during testing), and interpretability requirements. How to efficiently inject domain knowledge while preserving pre-trained capabilities is a common problem in academia and industry.

The core idea of the ROAM framework is to freeze the pre-trained expert backbone model and perform task adaptation only via low-dimensional latent vectors. Its three core components include: 1. Semantic Prior Engine: Converts open-scene evidence (operator logs, maintenance records, etc.) into structured axis-level priors through LLM reasoning; 2. Latent Corrector: Uses ridge regression to fit low-dimensional latent states (including semantic dimensions like bias, scale, load); 3. Online Posterior Update Module: Utilizes delayed labels, observation anomalies, etc., combined with a trust gating mechanism for online updates. In terms of technical architecture, ROAM supports two adaptation strategies: subspace (for data scarcity) and full space (for sufficient data). The Latent Corrector uses ridge regression (L2 regularization) to balance fitting and generalization.

The ROAM codebase adopts a modular design, supporting various sequence models (deep learning models like GRU, LSTM, Transformer, Mamba, as well as classical machine learning methods like SVR and XGBoost). It decouples from specific tasks via the RoamTaskAdapter interface—users only need to define evidence extraction and input formatting to integrate it. The prior engine uses LLMs to generate structured semantic axis confidence (e.g., equipment aging degree, sufficiency of maintenance history) instead of direct prediction, avoiding the numerical instability issue of LLMs. Users can configure LLM providers and parameters via YAML, supporting OpenAI-compatible interfaces.

ROAM adapts to concept drift through an online posterior update mechanism: 1. Delayed Label Utilization: Configure the number of delay steps via the posterior.label_delay parameter; the latent state is automatically updated once the label becomes available; 2. Trust Gating Mechanism: Dynamically adjusts update weights based on observation anomaly degree and diagnostic evidence to suppress the impact of abnormal observations; 3. Continual Learning Without Forgetting: The backbone network is frozen, and the low-dimensional nature of latent vectors ensures stable updates, avoiding damage to pre-trained knowledge.

The ROAM framework is applicable to various professional scenarios: 1. Industrial Predictive Maintenance: Combines sensor data and maintenance records to predict equipment failures; 2. Medical Temporal Prediction: Uses electronic health records and clinical records for patient risk prediction; 3. Financial Risk Control: Leverages transaction sequences and market news for risk scoring; 4. Energy Load Forecasting: Combines historical data and weather forecasts to predict energy demand. In these scenarios, ROAM can effectively handle issues like data scarcity and concept drift.

ROAM currently has limitations: 1. LLM Dependence: The quality of semantic priors depends on LLM capabilities, and fine-tuning may be required for specific domains; 2. Delayed Label Requirement: Online updates need labels to eventually become available, making it difficult to play a role in unlabeled scenarios; 3. Hyperparameter Tuning: Latent dimension, regularization strength, etc., need task-specific adjustments. Future directions include automated hyperparameter search, multi-modal evidence fusion, distributed adaptation in federated learning scenarios, and combining with causal inference to enhance interpretability.

The ROAM framework combines the semantic understanding capability of LLMs with the numerical prediction capability of professional sequence models. Through a three-layer design of frozen backbone, latent correction, and online posterior update, it achieves efficient task adaptation and concept drift adaptation while preserving pre-trained knowledge. For professional prediction tasks facing challenges like data scarcity, concept drift, and open scenarios, ROAM provides a feasible solution, and its modular design facilitates integration into existing workflows.