59 citations · 60 across the 11 of their papers we have counts for
12 papers
Reason What Matters: Retrieval-Grounded Reasoning for Universal Multimodal Embeddings
Mingzhou Jiang, Peixi Wu, Hang Cheng +7
Universal multimodal embedding (UME) maps multimodal inputs into a shared embedding space for diverse retrieval tasks. Recent methods improve embeddings through Chain-of-Thought (C…
Generative Embedding Benchmark: How Much Information Survives in a Dense Embedding?
Yun Li, Biao Yang, Peixi Wu +5
Embeddings have emerged as a standard representational interface linking foundation models with downstream systems. Most embedding benchmarks assess representations through discrim…
OneReason Technical Report
OneRec Team, Biao Yang, Boyang Ding +81
Generative recommendation models in the OneRec family have been widely deployed in many real-world services, such as short-video, live-streaming, advertising, and e-commerce. Howev…
DynamicRTL: RTL Representation Learning for Dynamic Circuit Behavior
Ruiyang Ma, Yunhao Zhou, Yipeng Wang +9
There is a growing body of work on using Graph Neural Networks (GNNs) to learn representations of circuits, focusing primarily on their static characteristics. However, these model…
MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs
Chenchen Zhao, Zhengyuan Shi, Xiangyu Wen +19
The emergence of multimodal large language models (MLLMs) presents promising opportunities for automation and enhancement in Electronic Design Automation (EDA). However, comprehens…
DeepRTL2: A Versatile Model for RTL-Related Tasks
Yi Liu, Hongji Zhang, Yunhao Zhou +3
The integration of large language models (LLMs) into electronic design automation (EDA) has significantly advanced the field, offering transformative benefits, particularly in regi…
ForgeEDA: A Comprehensive Multimodal Dataset for Advancing EDA
Zhengyuan Shi, Zeju Li, Chengyu Ma +19
We introduce ForgeEDA, an open-source comprehensive circuit dataset across various categories. ForgeEDA includes diverse circuit representations such as Register Transfer Level (RT…
Speculative Decoding for Verilog: Speed and Quality, All in One
Changran Xu, Yi Liu, Yunhao Zhou +3
The rapid advancement of large language models (LLMs) has revolutionized code generation tasks across various programming languages. However, the unique characteristics of programm…
DeepCircuitX: A Comprehensive Repository-Level Dataset for RTL Code Understanding, Generation, and PPA Analysis
Zeju Li, Changran Xu, Zhengyuan Shi +11
This paper introduces DeepCircuitX, a comprehensive repository-level dataset designed to advance RTL (Register Transfer Level) code understanding, generation, and power-performance…
DeepRTL: Bridging Verilog Understanding and Generation with a Unified Representation Model
Yi Liu, Changran Xu, Yunhao Zhou +2
Recent advancements in large language models (LLMs) have shown significant potential for automating hardware description language (HDL) code generation from high-level natural lang…
Natural language is not enough: Benchmarking multi-modal generative AI for Verilog generation
Kaiyan Chang, Zhirong Chen, Yunhao Zhou +9
Natural language interfaces have exhibited considerable potential in the automation of Verilog generation derived from high-level specifications through the utilization of large la…
Data is all you need: Finetuning LLMs for Chip Design via an Automated design-data augmentation framework
Kaiyan Chang, Kun Wang, Nan Yang +16
Recent advances in large language models have demonstrated their potential for automated generation of hardware description language (HDL) code from high-level prompts. Researchers…