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This work proposes HyperMCTS, a training-free method that augments an ordered MCTS tree with a cross-trajectory hypergraph, allowing outcomes collected under one prefix to inform selection under another while preserving execution histories in the tree.
The Human-LLM Reflection Framework is introduced, a controlled two-pass protocol comparing human and LLM revision under identical conditions across self-, peer-, and cross-agent settings, using an information-theoretic analysis based on per-iteration cross-entropy reduction.
The degradation rate across neural models, both sentence embeddings and decoder-only LLMs, is studied, and how consistent it is depends on the scale of the noise: under word-level noise, models with very different architectures decline along nearly the same curve, while under character-level noise they separate.
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