8 papers
TierKV: Long-Context On-Device LLMs via Predictive Multi-Tier KV Caching
Zhihao Shu, Md Musfiqur Rahman Sanim, Jie Hu +4
Large language models (LLMs) are moving onto mobile devices for increasingly diverse workloads over text, images, video, and audio. These applications often require long contexts,…
ReasFlow: Assisting Reasoning-Centric Scientific Discovery in Applied Mathematics via a Knowledge-Based Multi-Agent System
Yutong He, Daibo Li, Guohong Li +15
Recent advances in Large Language Models have fueled autonomous AI agents capable of tackling complex scientific tasks, yet existing automated research systems remain predominantly…
Proof-Refactor: Refactoring Generated Formal Proofs into Modular Artifacts
Yiming Fu, Peixuan Liu, Zichen Wang +1
While Large Language Models (LLMs) have shown strong performance in generating formal proofs, their outputs often remain less readable, modular, maintainable, and reusable than pro…
OptProver: Bridging Olympiad and Optimization through Continual Training in Formal Theorem Proving
Chenyi Li, Yanchen Nie, Zhenyu Ming +3
Recent advances in formal theorem proving have focused on Olympiad-level mathematics, leaving undergraduate domains largely unexplored. Optimization, fundamental to machine learnin…
M2F: Automated Formalization of Mathematical Literature at Scale
Zichen Wang, Wanli Ma, Zhenyu Ming +3
Automated formalization of mathematics enables mechanical verification but remains limited to isolated theorems and short snippets. Scaling to textbooks and research papers is larg…
Scaling up Multi-Turn Off-Policy RL and Multi-Agent Tree Search for LLM Step-Provers
Ran Xin, Zeyu Zheng, Yanchen Nie +2
The integration of Large Language Models (LLMs) into automated theorem proving has shown immense promise, yet is fundamentally constrained by challenges in scaling up both training…
HATA: Trainable and Hardware-Efficient Hash-Aware Top-k Attention for Scalable Large Model Inference
Ping Gong, Jiawei Yi, Shengnan Wang +13
Large Language Models (LLMs) have emerged as a pivotal research area, yet the attention module remains a critical bottleneck in LLM inference, even with techniques like KVCache to…
CentroidKV: Efficient Long-Context LLM Inference via KV Cache Clustering
Jie Hu, Shengnan Wang, Yutong He +8
Large language models (LLMs) with extended context windows have become increasingly prevalent for tackling complex tasks. However, the substantial Key-Value (KV) cache required for…