2 papers
cs.AI2026
AutoCRAT: Within-trajectory Joint Control of Stochasticity and Compute for LLM Reasoning
Hanjun Luo, Qiushi Liu, Jingya Zhang +8
Large language models (LLMs) achieve strong reasoning performance, which depends critically on inference-time decisions. Yet these decisions are commonly handled by static, one-siz…
cs.AI2025
DRAFT-RL: Multi-Agent Chain-of-Draft Reasoning for Reinforcement Learning-Enhanced LLMs
Yuanhao Li, Mingshan Liu, Hongbo Wang +3
Large Language Models (LLMs) have shown impressive capabilities in multi-step reasoning and problem-solving.Recent works introduce multi-agent reflection frameworks where multiple…