The Flow has not summarised this story yet — read it at arXiv Computation and Language.
PathCal is a training‑free controller that refines large reasoning language models by dynamically adjusting the influence of reflection markers—continuation, revision, and alternative‑opening—during inference. By applying finite logit adjustments without extra forward passes, it exploits instantaneous competition among these markers to improve the accuracy–length tradeoff in mathematical reasoning tasks and shorten generation in coding and multilingual math. The method is grounded in an exact log‑odds identity and frozen‑prefix probes that confirm its local directional effect.
arXiv:2606. 17945v1 Announce Type: new Abstract: Large language models provide a tractable system for asking how intelligence itself emerges, rather than only how LLMs can be engineered.
arXiv:2601. 18383v2 Announce Type: replace-cross Abstract: Large Reasoning Models (LRMs) excel at solving complex problems by explicitly generating a reasoning trace before deriving the final answer.
arXiv:2505. 17315v2 Announce Type: replace Abstract: Recent language models exhibit strong reasoning capabilities, yet the influence of long-context capacity on reasoning remains underexplored.
arXiv:2606. 02835v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) improve performance by generating explicit intermediate reasoning traces through increased test-time compute, yet the assumption that longer reasoning is consistently beneficial remains under-examined.
arXiv:2502. 15631v2 Announce Type: replace-cross Abstract: Large language models have demonstrated remarkable progress in mathematical reasoning, leveraging chain-of-thought and reinforcement learning.