Autonomous vehicles in enclosed spaces such as tunnels and mines rely on range maps that smoke, dust and fog degrade. Conformal prediction calibrates a keepout around the mapped walls that covers the true unsafe set at a chosen probability. In narrow passages this margin and the planner's buffer close the route, and existing remedies recover reach by relaxing the coverage level. This paper conditions the conformal margin on a label-free severity estimate per viewing direction and sweep, holding per-class coverage near its target as degradation varies. A planning and control layer enforces one braking-distance condition on the keepout at plan time and at every command, takes the measured tracking residual as its only margin and never alters the keepout. The keepout is validated on ground-robot smoke recordings and the closed loop on simulated quadrotor flights in a foggy tunnel. The proposed system substantially reduces entries into the calibrated keepout and reaches numerically more goals than the deployment pattern in both calibration regimes. The per-class keepout holds its coverage near the target on held-out recordings.
XinYu Piao, Myunghoon Ryu, Joongheon Kim, Jong-Kook Kim
A large language model maintains conversational content in an external storage that is outside the model and grows as the dialogue continues. This work assumes an environment where a language model has no external memory and no prior knowledge of the future question or its answer. The motivation is to research whether a dialogue can be stored in the model parameters and later answer a question about it. This work proposes self-keyed in-parameter memorization (SKIM), a closed-form method in which the model generates its own questions about each dialogue turn and writes those questions into its parameters as the keys that later retrieve the turn's content. Generating many varied questions for each turn allows a later user question to match one even when phrased differently, and its answer is recalled from the parameters alone. Evaluated on five language models, SKIM recalls most stored dialogue turns from later user questions, and it answers from the model parameters without any external storage.