LLM reasoning - aitoearn

LLM reasoning

Scaling Law in Expanding External Tests: New Discovery from Zhongguancun College — Lightweight Validators Unlock Optimal LLM Reasoning Choices

2025-11-06 13:26 — Beijing Rather than chasing ever-larger models, it can be smarter to fully leverage the capabilities of existing ones. Author & Institution Collaboration This work is a joint effort by researchers from: * Beijing Zhongguancun College * Harbin Institute of Technology (HIT) * Institute of Automation, Chinese Academy of Sciences * Other

LLM reasoning

Kwai & Nanjing University Launch HiPO Adaptive Reasoning Framework to Tackle LLM “Overthinking”

# 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

AI agents

How to Make Agents Meet Expectations: Top 10 Practical Lessons from Building Cloud Assistant Aivis with Context Engineering and Multi-Agent Systems

Building & Optimizing AI Agents: Lessons from YunXiaoer Aivis This is the 123rd article of 2025 (Estimated reading time: 15 minutes) --- 01. Background This year, our team has been focusing on the YunXiaoer Aivis project — a digital employee in the Alibaba Cloud service domain. It represents our transition from

LLM reasoning

HKUST Proposes New Algorithm to Revolutionize LLM Reasoning: Random Strategy Evaluation Emerges as a Breakthrough in Mathematical Reasoning

2025-10-31 · Beijing “Simplify, Don’t Complicate” — The Real Key to Advancing Performance Authors & Affiliations * He Haoran — PhD student at The Hong Kong University of Science and Technology (HKUST), specializing in reinforcement learning and foundation models. * Ye Yuxiao — First-year PhD student at HKUST (Co-first author). * Pan Ling — Assistant Professor, Department