18 citations · 25 across the 19 of their papers we have counts for
20 papers
HMARS: A Hierarchical Multi-Agent Memory System for Long-Context Reasoning
Zeju Li, Ziyang Zheng, Yizhou Zhou +1
Long-context reasoning requires models to access, retrieve, and integrate evidence scattered across documents, dialogues, and accumulated interaction histories. Standard retrieval-…
From Craft to Kernel: A Governance-First Execution Architecture and Semantic ISA for Agentic Computers
Xiangyu Wen, Yuang Zhao, Xiaoyu Xu +9
The transition of agentic AI from brittle prototypes to production systems is stalled by a pervasive crisis of craft. We suggest that the prevailing orchestration paradigm-delegati…
DiffHLS: Differential Learning for High-Level Synthesis QoR Prediction with GNNs and LLM Code Embeddings
Zedong Peng, Zeju Li, Qiang Xu +1
High-Level Synthesis (HLS) compiles C/C++ into RTL, but exploring pragma-driven optimization choices remains expensive because each design point requires time-consuming synthesis.…
Latent Context Compilation: Distilling Long Context into Compact Portable Memory
Zeju Li, Yizhou Zhou, Qiang Xu
Efficient long-context LLM deployment is stalled by a dichotomy between amortized compression, which struggles with out-of-distribution generalization, and Test-Time Training, whic…
From Craft to Constitution: A Governance-First Paradigm for Principled Agent Engineering
Qiang Xu, Xiangyu Wen, Changran Xu +2
The advent of powerful Large Language Models (LLMs) has ushered in an ``Age of the Agent,'' enabling autonomous systems to tackle complex goals. However, the transition from protot…
Reasoning Scaffolding: Distilling the Flow of Thought from LLMs
Xiangyu Wen, Junhua Huang, Zeju Li +6
The prevailing approach to distilling reasoning from Large Language Models (LLMs)-behavioral cloning from textual rationales-is fundamentally limited. It teaches Small Language Mod…
ForgeHLS: A Large-Scale, Open-Source Dataset for High-Level Synthesis
Zedong Peng, Zeju Li, Mingzhe Gao +3
High-Level Synthesis (HLS) plays a crucial role in modern hardware design by transforming high-level code into optimized hardware implementations. However, progress in applying mac…
Making Slow Thinking Faster: Compressing LLM Chain-of-Thought via Step Entropy
Zeju Li, Jianyuan Zhong, Ziyang Zheng +5
Large Language Models (LLMs) using Chain-of-Thought (CoT) prompting excel at complex reasoning but generate verbose thought processes with considerable redundancy, leading to incre…
Spatial 3D-LLM: Exploring Spatial Awareness in 3D Vision-Language Models
Xiaoyan Wang, Zeju Li, Yifan Xu +5
New era has unlocked exciting possibilities for extending Large Language Models (LLMs) to tackle 3D vision-language tasks. However, most existing 3D multimodal LLMs (MLLMs) rely on…
Argus: Leveraging Multiview Images for Improved 3-D Scene Understanding With Large Language Models
Yifan Xu, Chao Zhang, Hanqi Jiang +6
Advancements in foundation models have made it possible to conduct applications in various downstream tasks. Especially, the new era has witnessed a remarkable capability to extend…
Solve-Detect-Verify: Inference-Time Scaling with Flexible Generative Verifier
Jianyuan Zhong, Zeju Li, Zhijian Xu +3
Large Language Model (LLM) reasoning for complex tasks inherently involves a trade-off between solution accuracy and computational efficiency. The subsequent step of verification,…
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…
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…
Dyve: Thinking Fast and Slow for Dynamic Process Verification
Jianyuan Zhong, Zeju Li, Zhijian Xu +2
We present Dyve, a dynamic process verifier that enhances reasoning error detection in large language models by integrating fast and slow thinking, inspired by Kahneman's Systems T…
3DMIT: 3D Multi-modal Instruction Tuning for Scene Understanding
Zeju Li, Chao Zhang, Xiaoyan Wang +4
The remarkable potential of multi-modal large language models (MLLMs) in comprehending both vision and language information has been widely acknowledged. However, the scarcity of 3…
SemProtector: A Unified Framework for Semantic Protection in Deep Learning-based Semantic Communication Systems
Xinghan Liu, Guoshun Nan, Qimei Cui +6
Recently proliferated semantic communications (SC) aim at effectively transmitting the semantics conveyed by the source and accurately interpreting the meaning at the destination.…
Multi-source Education Knowledge Graph Construction and Fusion for College Curricula
Zeju Li, Linya Cheng, Chunhong Zhang +2
The field of education has undergone a significant transformation due to the rapid advancements in Artificial Intelligence (AI). Among the various AI technologies, Knowledge Graphs…
Boosting Physical Layer Black-Box Attacks with Semantic Adversaries in Semantic Communications
Zeju Li, Xinghan Liu, Guoshun Nan +4
End-to-end semantic communication (ESC) system is able to improve communication efficiency by only transmitting the semantics of the input rather than raw bits. Although promising,…
Exploring the Distributed Knowledge Congruence in Proxy-data-free Federated Distillation
Zhiyuan Wu, Sheng Sun, Yuwei Wang +5
Federated learning (FL) is a privacy-preserving machine learning paradigm in which the server periodically aggregates local model parameters from clients without assembling their p…