35 citations · 56 across the 26 of their papers we have counts for
28 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…
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-CodeV-SVA: Training Specialized LLMs for Hardware Assertion Generation via RTL-Grounded Bidirectional Data Synthesis
Yutong Wu, Chenrui Cao, Pengwei Jin +6
SystemVerilog Assertions (SVAs) are crucial for hardware verification. Recent studies leverage general-purpose LLMs to translate natural language properties to SVAs (NL2SVA), but t…
QiMeng-ChipV-RTL: 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…
Run, Ruminate, and Regulate: A Dual-process Thinking System for Vision-and-Language Navigation
Yu Zhong, Zihao Zhang, Rui Zhang +9
Vision-and-Language Navigation (VLN) requires an agent to dynamically explore complex 3D environments following human instructions. Recent research underscores the potential of har…
QiMeng-NeuComBack: Self-Evolving Translation from IR to Assembly Code
Hainan Fang, Yuanbo Wen, Jun Bi +8
Compilers, while essential, are notoriously complex systems that demand prohibitively expensive human expertise to develop and maintain. The recent advancements in Large Language M…
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-SALV: Signal-Aware Learning for Verilog Code Generation
Yang Zhang, Rui Zhang, Jiaming Guo +10
The remarkable progress of Large Language Models (LLMs) presents promising opportunities for Verilog code generation which is significantly important for automated circuit design.…
Revisiting Entropy Regularization: Adaptive Coefficient Unlocks Its Potential for LLM Reinforcement Learning
Xiaoyun Zhang, Xiaojian Yuan, Di Huang +6
Reasoning ability has become a defining capability of Large Language Models (LLMs), with Reinforcement Learning with Verifiable Rewards (RLVR) emerging as a key paradigm to enhance…
StepFun-Formalizer: Unlocking the Autoformalization Potential of LLMs through Knowledge-Reasoning Fusion
Yutong Wu, Di Huang, Ruosi Wan +8
Autoformalization aims to translate natural-language mathematical statements into a formal language. While LLMs have accelerated progress in this area, existing methods still suffe…
RealBench: Benchmarking Verilog Generation Models with Real-World IP Designs
Pengwei Jin, Di Huang, Chongxiao Li +10
The automatic generation of Verilog code using Large Language Models (LLMs) has garnered significant interest in hardware design automation. However, existing benchmarks for evalua…
QiMeng: Fully Automated Hardware and Software Design for Processor Chip
Rui Zhang, Yuanbo Wen, Shuyao Cheng +17
Processor chip design technology serves as a key frontier driving breakthroughs in computer science and related fields. With the rapid advancement of information technology, conven…
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…
MigGPT: Harnessing Large Language Models for Automated Migration of Out-of-Tree Linux Kernel Patches Across Versions
Pucheng Dang, Di Huang, Dong Li +4
Out-of-tree kernel patches are essential for adapting the Linux kernel to new hardware or enabling specific functionalities. Maintaining and updating these patches across different…
AGON: Automated Design Framework for Customizing Processors from ISA Documents
Chongxiao Li, Di Huang, Pengwei Jin +12
Customized processors are attractive solutions for vast domain-specific applications due to their high energy efficiency. However, designing a processor in traditional flows is tim…
World-Consistent Data Generation for Vision-and-Language Navigation
Yu Zhong, Rui Zhang, Zihao Zhang +9
Vision-and-Language Navigation (VLN) is a challenging task that requires an agent to navigate through photorealistic environments following natural-language instructions. One main…
CodeV: Empowering LLMs with HDL Generation through Multi-Level Summarization
Yang Zhao, Di Huang, Chongxiao Li +14
The design flow of processors, particularly in hardware description languages (HDL) like Verilog and Chisel, is complex and costly. While recent advances in large language models (…
InverseCoder: Self-improving Instruction-Tuned Code LLMs with Inverse-Instruct
Yutong Wu, Di Huang, Wenxuan Shi +13
Recent advancements in open-source code large language models (LLMs) have been driven by fine-tuning on the data generated from powerful closed-source LLMs, which are expensive to…
Luban: Building Open-Ended Creative Agents via Autonomous Embodied Verification
Yuxuan Guo, Shaohui Peng, Jiaming Guo +15
Building open agents has always been the ultimate goal in AI research, and creative agents are the more enticing. Existing LLM agents excel at long-horizon tasks with well-defined…
Assessing and Understanding Creativity in Large Language Models
Yunpu Zhao, Rui Zhang, Wenyi Li +10
In the field of natural language processing, the rapid development of large language model (LLM) has attracted more and more attention. LLMs have shown a high level of creativity i…
Emergent Communication for Rules Reasoning
Yuxuan Guo, Yifan Hao, Rui Zhang +14
Research on emergent communication between deep-learning-based agents has received extensive attention due to its inspiration for linguistics and artificial intelligence. However,…
Self-driven Grounding: Large Language Model Agents with Automatical Language-aligned Skill Learning
Shaohui Peng, Xing Hu, Qi Yi +9
Large language models (LLMs) show their powerful automatic reasoning and planning capability with a wealth of semantic knowledge about the human world. However, the grounding probl…
Unlearnable Examples Give a False Sense of Data Privacy: Understanding and Relearning
Pucheng Dang, Xing Hu, Kaidi Xu +5
Unlearnable examples are proposed to prevent third parties from exploiting unauthorized data, which generates unlearnable examples by adding imperceptible perturbations to public p…
ANPL: Towards Natural Programming with Interactive Decomposition
Di Huang, Ziyuan Nan, Xing Hu +8
Though LLMs are capable of generating plausible programs, it's challenging to interact with the LLMs further to revise the program, especially if the user's specific requirements a…