1 citations · 1 across the 8 of their papers we have counts for
10 papers
EvoSpatial: Experience-Driven Skill and Tool Evolution for Spatial Reasoning Agents
Bin Yu, Shijie Lian, Zhaolong Shen +10
Spatial reasoning agents can inspect tool outputs and repair errors, but reusing these lessons requires preserving both the computation and the conditions that make it valid. We in…
Preserving Updates in Low-Bit Recurrent State Quantization
Haotian Li, Liran Dong, Kaicheng Xiao +1
Linear attention and recurrent models compress past tokens into a fixed-size state that is updated at every step. However, storing these states in high precision incurs substantial…
RAM-Net: Linear-Time Sequence Modeling with Sparsely Addressable State
Kaicheng Xiao, Haotian Li, Liran Dong +1
Linear attention offers an efficient alternative to full attention with a fixed-size recurrent state. However, this state is shared by all tokens, so information from distinct toke…
Yunjue Agent Tech Report: A Fully Reproducible, Zero-Start In-Situ Self-Evolving Agent System for Open-Ended Tasks
Haotian Li, Shijun Yang, Weizhen Qi +5
Conventional agent systems often struggle in open-ended environments where task distributions continuously drift and external supervision is scarce. Their reliance on static toolse…
SCULPT: Constraint-Guided Pruned MCTS that Carves Efficient Paths for Mathematical Reasoning
Qitong Fang, Haotian Li, Xu Wang
Automated agent workflows can enhance the problem-solving ability of large language models (LLMs), but common search strategies rely on stochastic exploration and often traverse im…
TrajSelector: Harnessing Latent Representations for Efficient and Effective Best-of-N in Large Reasoning Model
Bin Yu, Xinming Wang, Shijie Lian +6
Large language models (LLMs) have shown remarkable progress in complex reasoning tasks, largely enabled by test-time scaling (TTS) paradigms that allocate additional compute during…
Two Is Better Than One: Rotations Scale LoRAs
Hongcan Guo, Guoshun Nan, Yuan Yang +9
Scaling Low-Rank Adaptation (LoRA)-based Mixture-of-Experts (MoE) facilitates large language models (LLMs) to efficiently adapt to diverse tasks. However, traditional gating mechan…
Long-Short Chain-of-Thought Mixture Supervised Fine-Tuning Eliciting Efficient Reasoning in Large Language Models
Bin Yu, Hang Yuan, Haotian Li +5
Recent advances in large language models have demonstrated that Supervised Fine-Tuning (SFT) with Chain-of-Thought (CoT) reasoning data distilled from large reasoning models (e.g.,…
Not All Tokens Are What You Need In Thinking
Hang Yuan, Bin Yu, Haotian Li +6
Modern reasoning models, such as OpenAI's o1 and DeepSeek-R1, exhibit impressive problem-solving capabilities but suffer from critical inefficiencies: high inference latency, exces…
Self-supervised Spatial-Temporal Learner for Precipitation Nowcasting
Haotian Li, Arno Siebes, Siamak Mehrkanoon
Nowcasting, the short-term prediction of weather, is essential for making timely and weather-dependent decisions. Specifically, precipitation nowcasting aims to predict precipitati…