LLM reasoning
# HiPO: Intelligent Thinking Switch for Efficient LLM Reasoning When a user asks a large language model (LLM) a simple question— for example: *How many letters P are in the word “HiPPO”?*— the model may unexpectedly produce a long chain-of-thought explanation: > “First, let’s analyze the word HiPPO. The English
linear attention
2025‑11‑01 23:49 — Jiangsu --- Overview In the LLM (Large Language Model) domain, linear attention mechanisms are making a notable comeback. This resurgence is largely led by domestic models, driven not only by limited computational resources, but also by a long‑term target—making AI agents more reliable
On-policy distillation
Thinking Machine’s Breakthrough: On-Policy Distillation for Efficient LLM Training Thinking Machine’s latest research is generating intense discussion in the AI community. After being personally reposted by Mira Murati — founder and former OpenAI CTO — many prominent figures praised its research value: According to Murati’s summary, the team has
LLM agents
Large Model Agents Operating Your Computer — Big Dreams, Harsh Reality Current LLM-based agents face two persistent pain points: * Low success rate — Even slightly complex tasks can cause agents to stall or “crash,” getting stuck without knowing what to do. * Poor efficiency — Completing simple tasks often takes dozens of unnecessary back-and-forth