3 papers
cs.CL2026
LLMs Are Not Good Strategists, Yet Memory-Enhanced Agency Boosts Reasoning
Yi Wu, Zhimin Hu
Strategic reasoning in Large Language Models (LLMs) within long-horizon environments is often limited by inconsistent subgoals. In these settings, finite attention resources preven…
cs.PL2026
ExVerus: Verus Proof Repair via Counterexample Reasoning
Jun Yang, Yuechun Sun, Yi Wu +5
Large Language Models (LLMs) have shown promising results in automating formal verification. However, existing approaches treat proof generation as a static, end-to-end prediction…
cs.AI2025
AI Realtor: Towards Grounded Persuasive Language Generation for Automated Copywriting
Jibang Wu, Chenghao Yang, Yi Wu +5
This paper develops an agentic framework that employs large language models (LLMs) for grounded persuasive language generation in automated copywriting, with real estate marketing…