8 citations · 8 across the 4 of their papers we have counts for
5 papers
TIMBRE: Teaching Time Series Forecasters to Read, Remember, and Reconcile
Xinyu Guan, Zhirong Zhang, Hongyuan Liu +4
Event-informed forecasting requires translating reports and historical responses into changes to a numerical forecast. We propose TIMBRE (Temporal Integration of Memory-Based Respo…
Decision-Aware Memory Cards: Counterfactual-Inspired Context Selection and Compression for Tool-Using LLM Agents
Xinyu Guan, Qianyang Zhao, Yuming Deng
Modern large language model (LLM) agents do not simply need longer contexts; they need decision-relevant evidence at the moment of action. We study decision-aware context selection…
The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence
Aili Chen, Aonian Li, Baichuan Zhou +215
We introduce the MiniMax-M2 series, a family of Mixture-of-Experts language models built around the principle that mini activations can unleash maximum real-world intelligence. The…
rStar2-Agent: Agentic Reasoning Technical Report
Ning Shang, Yifei Liu, Yi Zhu +12
We introduce rStar2-Agent, a 14B math reasoning model trained with agentic reinforcement learning to achieve frontier-level performance. Beyond current long CoT, the model demonstr…
rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking
Xinyu Guan, Li Lyna Zhang, Yifei Liu +5
We present rStar-Math to demonstrate that small language models (SLMs) can rival or even surpass the math reasoning capability of OpenAI o1, without distillation from superior mode…