22 papers
PrimitiveVLA: Learning Reusable Motion Primitives for Efficient and Generalizable Robotic Manipulation
Yutai Li, Shaohui Peng, Jiaming Guo +8
Vision-Language-Action (VLA) models offer a promising paradigm for generalist robotic policies, yet their adaptation is hindered by data inefficiency and poor generalization. We ar…
Discrete Diffusion for Complex and Congested Multi-Agent Path Finding with Sparse Social Attention
Yuanzhe Wang, Tian Zhi, Zihang Wei +8
Multi-Agent Path Finding (MAPF) is a coordination problem that requires computing globally consistent, collision-free trajectories from individual start positions to assigned goal…
LKV: End-to-End Learning of Head-wise Budgets and Token Selection for LLM KV Cache Eviction
Enshuai Zhou, Yifan Hao, Chao Wang +7
Long-context inference in Large Language Models (LLMs) is bottlenecked by the linear growth of Key-Value (KV) cache memory. Existing KV cache compression paradigms are fundamentall…
QiMeng-CRUX: Narrowing the Gap Between Natural Language and Verilog via Core Refined Understanding eXpression for Circuit Design
Lei Huang, Rui Zhang, Jiaming Guo +9
Large language models (LLMs) have shown promising capabilities in hardware description language (HDL) generation. However, existing approaches often rely on free-form natural langu…
QiMeng-CodeV-R1: Reasoning-Enhanced Verilog Generation
Yaoyu Zhu, Di Huang, Hanqi Lyu +16
Large language models (LLMs) trained via reinforcement learning with verifiable reward (RLVR) have achieved breakthroughs on tasks with explicit, automatable verification, such as…
LocalV: Exploiting Information Locality for IP-level Verilog Generation
Hanqi Lyu, Di Huang, Yaoyu Zhu +10
The generation of Register-Transfer Level (RTL) code is a crucial yet labor-intensive step in digital hardware design, traditionally requiring engineers to manually translate compl…