China Telecom (China)

Multi-agent memory is reorganized around task dependency graphs: a shared blackboard records evidence during execution and corrects goal, plan, and world-state divergence, then verified steps and dependencies are distilled into reusable procedures, giving the highest task score in all six benchmark settings across ALFWorld, WebArena, and OfficeBench.

Multi-agent memory is reorganized around task dependency graphs: a shared blackboard records evidence during execution and corrects goal, plan, and world-state divergence, then verified steps and dependencies are distilled into reusable procedures, giving the highest task score in all six benchmark settings across ALFWorld, WebArena, and OfficeBench.

The paper proposes WAND, a reinforcement learning framework for quadrotor navigation in dense obstacle fields under time-varying wind: a Temporal Convolutional Network estimates wind-induced disturbance acceleration from proprioceptive histories, the estimate is injected into the policy through a zero-initialized residual module (WindAdapter) and reused for low-level feedforward compensation, improving observed success rate by 8.3 percentage points on average over feedforward compensation alone across 12 wind-disturbed simulation settings and succeeding in 18 of 20 indoor fan-induced flight trials.

The paper proposes WAND, a reinforcement learning framework for quadrotor navigation in dense obstacle fields under time-varying wind: a Temporal Convolutional Network estimates wind-induced disturbance acceleration from proprioceptive histories, the estimate is injected into the policy through a zero-initialized residual module (WindAdapter) and reused for low-level feedforward compensation, improving observed success rate by 8.3 percentage points on average over feedforward compensation alone across 12 wind-disturbed simulation settings and succeeding in 18 of 20 indoor fan-induced flight trials.