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Chencheng Zhu

2 accepted papers

2025

Benchmarking Large Language Models for Cryptanalysis and Side-Channel Vulnerabilities

EMNLP 2025

Recent advancements in Large Language Models (LLMs) have transformed natural language understanding and generation, leading to extensive benchmarking across diverse tasks. However, cryptanalysis—a critical area for data security and its connection to LLMs’ generalization abilities remains underexplo

Cited by 0SourcePDFScholar
2025

TurnBench-MS: A Benchmark for Evaluating Multi-Turn, Multi-Step Reasoning in Large Language Models

EMNLP 2025

Despite impressive advances in large language models (LLMs), existing benchmarks often focus on single-turn or single-step tasks, failing to capture the kind of iterative reasoning required in real-world settings. To address this limitation, we introduce **TurnBench**, a novel benchmark that evaluat

Cited by 0SourcePDFScholar