35 citations · 84 across the 37 of their papers we have counts for
38 papers
KernelZero: Co-Evolving Proposer and Coder for Continuously Improved GPU Kernel Generation
Changxin Ke, Rui Zhang, Zixiang Fang +8
High-performance GPU kernels are essential to modern machine learning systems, yet automatically generating kernels that are both correct and efficient remains challenging. Existin…
Kimi K3: Open Frontier Intelligence
Kimi Team, Tongtong Bai, Yifan Bai +398
We introduce Kimi K3, a 2.8T parameter Mixture-of-Experts model with 104 billion activated parameters, native vision capabilities, and a 1-million-token context window. Kimi K3 is…
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-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.…
Code Driven Planning with Domain-Adaptive Critic
Zikang Tian, Shaohui Peng, Du Huang +11
Large Language Models (LLMs) have been widely adopted as task planners for AI agents in sequential decision-making problems, leveraging their extensive world knowledge. However, th…
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-CPU-v2: Automated Superscalar Processor Design by Learning Data Dependencies
Shuyao Cheng, Rui Zhang, Wenkai He +10
Automated processor design, which can significantly reduce human efforts and accelerate design cycles, has received considerable attention. While recent advancements have automatic…
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…
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…
Ex3: Automatic Novel Writing by Extracting, Excelsior and Expanding
Lei Huang, Jiaming Guo, Guanhua He +5
Generating long-term texts such as novels using artificial intelligence has always been a challenge. A common approach is to use large language models (LLMs) to construct a hierarc…
DiffZOO: A Purely Query-Based Black-Box Attack for Red-teaming Text-to-Image Generative Model via Zeroth Order Optimization
Pucheng Dang, Xing Hu, Dong Li +3
Current text-to-image (T2I) synthesis diffusion models raise misuse concerns, particularly in creating prohibited or not-safe-for-work (NSFW) images. To address this, various safet…
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…
Prompt-based Visual Alignment for Zero-shot Policy Transfer
Haihan Gao, Rui Zhang, Qi Yi +13
Overfitting in RL has become one of the main obstacles to applications in reinforcement learning(RL). Existing methods do not provide explicit semantic constrain for the feature ex…
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,…
Context Shift Reduction for Offline Meta-Reinforcement Learning
Yunkai Gao, Rui Zhang, Jiaming Guo +10
Offline meta-reinforcement learning (OMRL) utilizes pre-collected offline datasets to enhance the agent's generalization ability on unseen tasks. However, the context shift problem…