6 papers · 1 filter
WebExplorer: Explore and Evolve for Training Long-Horizon Web Agents
Junteng Liu, Yunji Li, Chi Zhang +12
The paradigm of Large Language Models (LLMs) has increasingly shifted toward agentic applications, where web browsing capabilities are fundamental for retrieving information from d…
DEEPER Insight into Your User: Directed Persona Refinement for Dynamic Persona Modeling
Aili Chen, Chengyu Du, Jiangjie Chen +6
To advance personalized applications such as recommendation systems and user behavior prediction, recent research increasingly adopts large language models (LLMs) for human -readab…
MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
MiniMax, :, Aili Chen +125
We introduce MiniMax-M1, the world's first open-weight, large-scale hybrid-attention reasoning model. MiniMax-M1 is powered by a hybrid Mixture-of-Experts (MoE) architecture combin…
Think Thrice Before You Act: Progressive Thought Refinement in Large Language Models
Chengyu Du, Jinyi Han, Yizhou Ying +9
Recent advancements in large language models (LLMs) have demonstrated that progressive refinement, rather than providing a single answer, results in more accurate and thoughtful ou…
SED: Self-Evaluation Decoding Enhances Large Language Models for Better Generation
Ziqin Luo, Haixia Han, Haokun Zhao +6
Existing Large Language Models (LLMs) generate text through unidirectional autoregressive decoding methods to respond to various user queries. These methods tend to consider token…
Enhancing Confidence Expression in Large Language Models Through Learning from Past Experience
Haixia Han, Tingyun Li, Shisong Chen +5
Large Language Models (LLMs) have exhibited remarkable performance across various downstream tasks, but they may generate inaccurate or false information with a confident tone. One…