Yang Chen

Sage-A1.5: From Long-Horizon Execution to Scientific Exploration with Capability Composition

Wenjie Lou, Tianshuo Peng, Shiyang Feng, et al.

Exploring Temporal Context-Aware Physical Grounding in Latent World Models

Yilei Man, Yang Chen, Jingcai Guo

Harness-of-Harness: Multi-Day Autonomous Software Development with Continual Improvement

Haoyang Yan, Min-le Su, Hangfan Zhang, et al.

SKT: Skill-Use Training at Scale via Verified Synthetic Data Generation

Zelin Tan, Yiqun Zhang, Hao Li, et al.

A Machine-Learning-Based Gas Lift Optimization Workflow for Unconventional Fields

Sha, Miao, Alexandra Vendetti, et al.

Kimi K3: Open Frontier Intelligence

Kimi Team, Tongtong Bai, Yifan Bai, et al.

Unified Audio Intelligence Without Regressing on Text Intelligence

Zhifeng Kong, Sang-gil Lee, Jaehyeon Kim, et al.

Scaling the Horizon, Not the Parameters: Reaching Trillion-Parameter Performance with a 35B Agent

Lei Bai, Zongsheng Cao, Yang Chen, et al.

Nemotron-Cascade 2: Post-Training LLMs with Cascade RL and Multi-Domain On-Policy Distillation

Zhuolin Yang, Zihan Liu, Yang Chen, et al.

Nemotron-Cascade 2 is a compact 30-billion parameter AI model that achieves exceptional reasoning and problem-solving abilities comparable to much larger models, winning gold-medal-level performance on prestigious math and coding competitions. The researchers improved upon their previous model by expanding a training technique called Cascade RL across more domains and using a multi-domain distillation method that learns from specialized teacher models, allowing the smaller model to match the capabilities of much larger systems. The model represents a major breakthrough in creating efficient, intelligent AI systems with significantly fewer parameters than competitors.