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arXiv AI By Lu Cheng

Escaping Redundant Reasoning: Structure-Aware Search for Inference-Time LLMs

Read the original on arXiv AI →

The paper introduces BASIN, a training‑free, structure‑aware selection method that groups reasoning states into basins and penalizes repeated visits to the same strategy, thereby redistributing search across distinct reasoning paths within a fixed compute budget. BASIN outperforms the Tree of Thoughts (ToT) baseline by up to +22 percentage points on Game of 24 and +6.7 percentage points on MuSR, and its quality‑aware variant QA‑BASIN further enhances robustness by preserving high‑quality basins. The authors also define a redundancy gap metric, Δ, to quantify how search concentrates differently for correct versus incorrect predictions, showing that ToT often operates near Δ ≈ 0 while BASIN consistently shifts Δ positive.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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