HyperMCTS retains an ordered search tree for complete execution histories while adding a cross-trajectory hypergraph.
Hypergraphs help language agents reuse feedback across planning paths
HyperMCTS shares returns among recurring groups of decisions, improving long-horizon planning without additional training.
Big Tech
Tingsong Xiao · Nithish Balachandar Moudhgalya · Chandrayee Basu · Lichao Wang · Luyang Kong · Benjamin Z. Yao · +2 more
University of Florida · Amazon
Research Digest··2 min read
The authors augment Monte Carlo Tree Search with a task-specific hypergraph that connects equivalent or related decisions appearing under different trajectory prefixes.
Why this paper
From Amazon and University of Florida
In one line
HyperMCTS uses a hypergraph to share decision outcomes across trajectories, improving MCTS efficiency and accuracy for LLM agents.
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