Quan Chen
5 indexed papers
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ArmSSL is a novel watermarking framework that provides robust, black-box ownership verification for self-supervised learning encoders while maintaining high utility and resisting adversarial attacks.
The paper introduces a multilingual benchmark (MentalMap) to test if LLMs build internal spatial world models from text, finding a universal 'L3 reasoning cliff' suggesting that text-only working memory is the primary bottleneck.
The paper introduces ProductWebGen, a benchmark for evaluating multimodal models' ability to generate consistent, high-fidelity product webpages from images and instructions, finding that separate editing-based workflows outperform unified models in overall webpage instruction following.
The paper proposes Resonant Context Anchoring (RCA), a lightweight, training-free method that enhances factual faithfulness in LLMs by dynamically amplifying the signal of external context evidence during inference.
This paper introduces PDAGENT-BENCH, a comprehensive benchmark for evaluating Large Language Models and vision-language models in the physical design stack of Very Large-Scale Integrated Circuits.
Papers
PDAGENT-BENCH: Characterizing, Grounding, and Architecting LLM Agents for VLSI Physical Design
Qiufeng Li, Rongqian Chen, Quan Cheng, Chengxuan Wang +8 more
This paper introduces PDAGENT-BENCH, a comprehensive benchmark for evaluating Large Language Models and vision-language models in the physical design stack of Very Large-Scale Integrated Circuits.