Tag: Adaptive Systems
All the articles with the tag "Adaptive Systems".
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Enabling Flexible Multi-LLM Integration for Scalable Knowledge Aggregation
本文提出了一种动态整合框架,通过自适应选择网络和动态加权融合策略从多个LLM中聚合知识,显著提升性能并减少50%的知识干扰,同时保持计算效率。
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RL of Thoughts: Navigating LLM Reasoning with Inference-time Reinforcement Learning
本文提出RL-of-Thoughts (RLoT) 方法,通过强化学习训练轻量化导航模型,在推理时动态构建任务特定逻辑结构,显著提升大型语言模型在多领域推理任务中的表现,并展现出跨模型和任务的强迁移能力。
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Thought calibration: Efficient and confident test-time scaling
本文提出‘思想校准’方法,通过推理树抽象和轻量级探针动态决定语言模型推理终止时机,在分布内数据上减少高达60%的思考token,同时保持性能,并在分布外数据上实现20%的减少。
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Route to Reason: Adaptive Routing for LLM and Reasoning Strategy Selection
本文提出Route-To-Reason(RTR)框架,通过动态路由机制联合选择最优模型和推理策略,在多个推理任务上实现了更高的准确率和超过60%的token使用量减少,显著优化了性能与成本的权衡。
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Steering Away from Harm: An Adaptive Approach to Defending Vision Language Model Against Jailbreaks
ASTRA introduces an efficient defense for Vision Language Models by adaptively steering activations away from adversarial directions using image attribution, achieving state-of-the-art performance in mitigating jailbreak attacks with minimal impact on benign utility and high inference efficiency.