Agentic memory improves clean reasoning but worsens performance when spurious patterns are present in stored trajectories; CAMEL calibration reduces this reliance while preserving clean performance.
MIT press, 2000
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Presents a causal inference framework for autonomous robot decision-making on task execution timing and strategy using estimates of battery usage and human obstructions, evaluated via a new Gazebo simulator called PeopleFlow against a non-causal baseline in a warehouse setting.
citing papers explorer
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The Trap of Trajectory: Towards Understanding and Mitigating Spurious Correlations in Agentic Memory
Agentic memory improves clean reasoning but worsens performance when spurious patterns are present in stored trajectories; CAMEL calibration reduces this reliance while preserving clean performance.
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Causality-enhanced Decision-Making for Autonomous Mobile Robots in Dynamic Environments
Presents a causal inference framework for autonomous robot decision-making on task execution timing and strategy using estimates of battery usage and human obstructions, evaluated via a new Gazebo simulator called PeopleFlow against a non-causal baseline in a warehouse setting.