6 papers
Can Terminal Agents Trust Their Own Verification? Diagnosing and Improving Self-Verification
Yingfeng Luo, Shaowei Wei, Daixin Wang +7
Terminal agents rely on self-verification to assess and correct their solutions as they solve tasks through interaction with command-line environments. Yet how trustworthy such sel…
Learning from What You Retrieve: Online RL Fine-Tuning for Semantic Retrieval
Shaowei Wei, Chong Huang, Songtao Fang +3
In large-scale e-commerce retrieval, dual-encoder retrievers are op- timized for contrastive similarity, whereas downstream rerankers capture finer-grained relevance preferences; t…
Generative Retrieval for E-commerce: Jointly Learning Embedding and Codebook with Same Product Cluster
Songtao Fang, Zihao Xu, Shaowei Wei +2
With the development of large language models (LLMs), generative retrieval is becoming increasingly important in e-commerce scenarios. Current mainstream approaches typically use a…
Ling and Ring 2.6 Technical Report: Efficient and Instant Agentic Intelligence at Trillion-Parameter Scale
Ang Li, Ben Liu, Bin Han +215
Efficient and scalable agentic intelligence requires models that can deliver both low-latency responses and strong reasoning capabilities while remaining practical to train, serve,…
Every Activation Boosted: Scaling General Reasoner to 1 Trillion Open Language Foundation
Ling Team, Ang Li, Ben Liu +138
We introduce Ling 2.0, a series reasoning-oriented language foundation built upon the principle that every activation boosts reasoning capability. Designed to scale from tens of bi…
Leave No One Behind: Online Self-Supervised Self-Distillation for Sequential Recommendation
Shaowei Wei, Zhengwei Wu, Xin Li +5
Sequential recommendation methods play a pivotal role in modern recommendation systems. A key challenge lies in accurately modeling user preferences in the face of data sparsity. T…