ConMem: Contribution-Aware Memory for Long-Horizon Manufacturing Inspection Logs

Long-horizon steel-equipment inspection requires reasoning over heterogeneous records accumulated across repeated inspection cycles. Existing retrieval-augmented generation systems treat historical logs as a static corpus and retain records without estimating their diagnostic value, failing to report early risk. To this end, we propose ConMem, a contribution-aware memory framework for LLM-assisted equipment inspection, supporting a human-in-the-loop early-risk screening system. Specifically, our ConMem first segments inspection logs into functional evidence units, then estimates each memory unit's contribution to downstream diagnosis through a Shapley-style estimation, and finally retains high-value evidence under a constrained memory budget. In experiments, we evaluate ConMem on real-world dataset and ConMem achieves 76.0% QA accuracy, exceeding the strongest directly comparable baseline. Relative to the naive 8K-context LLM baselines, it reduces the average number of input tokens by 88.2% and response time by 86.6%. Ablation studies also show that the functional-role-aware segmentation and contribution-based valuation are helping prioritize weak degradation signals for targeted field inspection. Practical deployments further confirm that ConMem retains the weak early signal across three inspection cycles, providing an early-stage seal-wear alert targeted for on-site inspectors.

BCRLB Under the Fusion Extended Kalman Filter

In the process of tracking multiple point targets in space using radar, since the targets are spatially well separated, the data between them will not be confused. Therefore, the multi-target tracking problem can be transformed into a single-target tracking problem. However, the data measured by radar nodes contains noise, clutter, and false targets, making it difficult for the fusion center to directly establish the association between radar measurements and real targets. To address this issue, the Probabilistic Data Association (PDA) algorithm is used to calculate the association probability between each radar measurement and the target, and the measurements are fused based on these probabilities. Finally, an extended Kalman filter (EKF) is used to predict the target states. Additionally, we derive the Bayesian Cram\'er-Rao Lower Bound (BCRLB) under the PDA fusion framework.