LINC: Label-Free In-Context Network Reconfiguration under Unobserved Failure-Mode Switches

Policies trained in a single operating regime often fail when the environment changes without warning—the analogue of a robot that cannot recover after a broken leg, or a network that keeps repairing hubs after the attack has moved to a region. We study this problem as in-context meta-reinforcement learning in a hidden-parameter (contextual) MDP whose latent parameter—the failure mode—is unobserved and may switch inside an episode. We propose LINC, which infers a label-free context from a short window of transition tokens by self-supervised next-step prediction with variance–covariance regularization, and conditions a PPO policy on the detached context together with a size-weighted bridging prior. At test time the agent adapts entirely in-context, without parameter updates or access to task labels or switch signals. On synthetic network-reconfiguration tasks, LINC matches strong contextual baselines when the mode is held fixed, recovers faster and with a shallower transient after unobserved switches, and degrades most gracefully under out-of-distribution mixtures, intensities, and scales.