11 papers
LLM-Augmented Bayesian Optimization with Adaptive Policies for Chiplet Design Space Exploration
Hang Liu, Zhengfei Chen, Zhengrui Chen +3
Chiplet technology is a key approach for extending Moore's Law. However, its design process is constrained by two primary challenges: the scarcity of early-stage platforms for rapi…
Beyond Selection: Token Parameterization for Extreme Visual Token Compression
Rui Zhong, Yu Li, Zheyu Yan +1
Visual-token compression is effective for improving the efficiency of vision-language models, but under extreme compression budgets, token pruning can break visual grounding while…
TrainSDC: Characterizing and Mitigating Silent Data Corruption in Large Language Model Training
Zhipeng Xia, Haotian Xu, Siyu Yun +4
LLM training is increasingly vulnerable to silent data corruption (SDC), yet existing protection methods largely treat Transformer computations uniformly because their vulnerabilit…
ResilPhase: Plug-and-Play Phase Mapping and Noise-Resilient Macro-Trajectory Extrapolation for Diffusion Acceleration
Qicheng Zhao, Yu Li, Qi Sun +1
The adoption of powerful diffusion models is hindered by their significant inference latency. Recent ``cache-then-forecast'' schemes alleviate this issue by accelerating DiTs using…
LithoDreamer: A Physics-Informed World Model for Multi-Stage Computational Lithography
Yuqi Jiang, Yumeng Liu, Zimu Li +7
As semiconductor technology nodes scale, computational lithography is essential for ensuring yield and performance. However, lithography is a continuous physical process involving…
SafeSpec: Fast and Safe LLM via Dynamic Reflective Sampling
Haotian Xu, Zeyang Zhang, Linbao Li +3
Speculative inference accelerates large language model (LLM) decoding but provides no inherent safety guarantees. Existing safety defenses are largely incompatible with speculative…
Light Interaction: Training-Free Inference Acceleration for Interactive Video World Models
Jiacheng Lu, Haoyi Zhu, Sipei Yi +3
Interactive video world models generate video chunk by chunk in response to user-controlled camera movements, enabling applications such as real-time game simulation, virtual scene…
Toward Physically Consistent Driving Video World Models under Challenging Trajectories
Jiawei Zhou, Zhenxin Zhu, Lingyi Du +10
Video generation models have shown strong potential as world models for autonomous driving simulation. However, existing approaches are primarily trained on real-world driving data…
FluxEDA: A Unified Execution Infrastructure for Stateful Agentic EDA
Zhengrui Chen, Zixuan Song, Yu Li +2
Large language models and autonomous agents are increasingly explored for EDA automation, but many existing integrations still rely on script-level or request-level interactions, w…
Context-Aware Hierarchical Learning: A Two-Step Paradigm towards Safer LLMs
Tengyun Ma, Jiaqi Yao, Daojing He +4
Large Language Models (LLMs) have emerged as powerful tools for diverse applications. However, their uniform token processing paradigm introduces critical vulnerabilities in instru…
SafeMVDrive: Multi-view Safety-Critical Driving Video Synthesis in the Real World Domain
Jiawei Zhou, Linye Lyu, Zhuotao Tian +2
Safety-critical scenarios are rare yet pivotal for evaluating and enhancing the robustness of autonomous driving systems. While existing methods generate safety-critical driving tr…