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Submitted: 05 February 2026 You are already at the latest version To address the limited controllability, unstable output consistency, and weakly constrained decision processes of large language models in text classification tasks, this work proposes a controllable prompt-driven text classification method that establishes an end-to-end unified modeling framework from instruction alignment to constrained decoding.
Submitted: 28 December 2025 You are already at the latest version Large Language Model (LLM)-based multi-agent systems have demonstrated remarkable capabilities across di- verse applications, yet they face critical security challenges in- cluding backdoor attacks, prompt injection, and privacy leakage. Existing defense mechanisms typically address single threat vec- tors, lacking a unified governance architecture for comprehensive security.
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