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cs.CL2026
Universal Activation Verbalizer: A Unified Framework for Cross-Model Activation Explanation
Haiyan Zhao, Zirui He, Guanchu Wang +3
Activation verbalization explains hidden representations in natural language, but existing methods are mostly limited to self-explanation, where each model explains only its own ac…
cs.CL2025
LogitTrace: Detecting Benchmark Contamination via Layerwise Logit Trajectories
Zirui He, Haiyan Zhao, Yingcong Li +2
Large language models (LLMs) are commonly evaluated on challenging benchmarks such as AIME and Math500, where benchmark contamination can make memorized solutions appear as genuine…
cs.CL2025
Provable Benefits of Task-Specific Prompts for In-context Learning
Xiangyu Chang, Yingcong Li, Muti Kara +2
The in-context learning capabilities of modern language models have motivated a deeper mathematical understanding of sequence models. A line of recent work has shown that linear at…