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Bingyu Shen

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#artificial intelligence Preprint Oct 2026

MOMAT: Mixture of Multiple Atlases for Low-Power Jailbreak Defense of Quantized LLMs

Quantized large language models are increasingly deployed on edge devices for their low latency and energy efficiency. However, model quantization weakens alignment safeguards, leaving qLLMs (quantized large language models) highly vulnerable to jailbreak attacks. To address this challenge, we present MOMAT (Mixture of...

Bo-Yang Li, Bingyu Shen, Wei-Hao Hong et al. · 0 citations
#artificial intelligence Preprint Oct 2026

Empty Commitments: When Agents Promise What They Cannot Deliver

A chatbot that says"I will remind you tomorrow"will not run again until the user writes. We call such a promise an empty commitment: a promise of action after the current turn that nothing in the agent's tools or runtime can carry out. Unlike a broken promise, its emptiness is decided by the agent's configuration at th...

Jia-Qi Tang, Bingyu Shen, Lan Wei et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Decision-Sufficient State Representations: Measuring and Reducing Write-Time Regret

Long tasks produce more history than an LLM agent can hold in its context, and more than it uses reliably even when the history fits. A growing line of work therefore has agents carry a short written state instead: at every step a writer rewrites the state, and a reader acts from the state alone. Steps stay cheap, but...

Bingyu Shen, Bo-Yang Li · 1 citation

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