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cs.CL2026
MemBoost: A Memory-Boosted Framework for Cost-Aware LLM Inference
Joris Köster, Zixuan Liu, Siavash Khajavi +1
Large Language Models (LLMs) deliver strong performance but incur high inference cost in real-world services, especially under workloads with repeated or near-duplicate queries acr…
cs.CL2026
Targeting Misalignment: A Conflict-Aware Framework for Reward-Model-based LLM Alignment
Zixuan Liu, Siavash H. Khajavi, Guangkai Jiang +1
Reward-model-based fine-tuning is a central paradigm in aligning Large Language Models with human preferences. However, such approaches critically rely on the assumption that proxy…