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Researchers frame LLM agent recovery as a causal decision problem to balance rescue and harm. The Causal Intervention Router improves long-horizon ALFWorld success from 70.33% to 73.33%.
Source: arXiv
https://arxiv.org/abs/2610.00372
#MachineLearning
The paper introduces FedCausalCompose, a causal world-model framework for modular LLM agents that uses intervention-response evidence to improve cross-module interface recovery.
Source: arXiv
https://arxiv.org/abs/2610.00012
#MachineLearning
The paper proposes a spatial strategy where large language model agents collect geodesic trajectories, vector-quantize them, and use the resulting tools for decision-making in grid-world environments.
Source: arXiv
https://arxiv.org/abs/2610.00613
#MachineLearning
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