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ParaTempo is a training-free asynchronous parallel reasoning framework that uses temporal confidence to dynamically manage reasoning branches, reducing latency and token usage while maintaining accuracy in mathematical and scientific reasoning benchmarks.
Second Thought is a training-free framework that runs auxiliary reasoning branches in parallel during LLM agent action-observation waits to reduce sequential decoding and turn counts without harming accuracy.
CAPS introduces a cascaded adaptive selection framework for efficient parallel reasoning, reducing verifier compute costs by over 75% while outperforming existing pairwise verification methods across multiple LLM benchmarks.
HeavySkill is a new framework that internalizes complex reasoning as an intrinsic model skill through parallel reasoning and summarization stages, outperforming traditional orchestration methods and enabling self-evolving LLMs via reinforcement learning.
STOP method prunes doomed reasoning trajectories early via KV-cache states, cutting token usage 70% and boosting AIME/GPQA accuracy across 1.5B–20B models.
This paper proposes STOP (SuperTOken for Pruning), a systematic framework for pruning inefficient reasoning paths early in parallel reasoning with Large Reasoning Models. The method achieves superior efficiency and effectiveness across models from 1.5B to 20B parameters, boosting GPT-OSS-20B accuracy on AIME25 from 84% to 90% under fixed compute budgets.