3 papers
cs.CR2026
SeedPrints: Fingerprints Can Even Tell Which Seed Your Large Language Model Was Trained From
Yao Tong, Haonan Wang, Siquan Li +2
Fingerprinting Large Language Models (LLMs)is essential for provenance verification and model attribution. Existing fingerprinting methods are primarily evaluated after fine-tuning…
stat.ML2026
Transformers Are Born Biased: Structural Inductive Biases at Random Initialization and Their Practical Consequences
Siquan Li, Yao Tong, Haonan Wang +1
Transformers underpin modern large language models (LLMs) and are commonly assumed to be behaviorally unstructured at random initialization, with all meaningful preferences emergin…
cs.CL2025
From Harm to Help: Turning Reasoning In-Context Demos into Assets for Reasoning LMs
Haonan Wang, Weida Liang, Zihang Fu +8
Recent reasoning LLMs (RLMs), especially those trained with verifier-based reinforcement learning, often perform worse with few-shot CoT than with direct answering. We revisit this…