LA-LQR applies latent-space linear-quadratic regulator control to steer text-to-video model activations toward desired features while penalizing excessive changes.
and Thomson, M
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Local linearity of LLM layers enables LQR-based closed-loop activation steering with theoretical tracking guarantees.
FDM-Bench is a new benchmark dataset for evaluating LLMs on FDM tasks including user queries and G-code anomaly detection, with expert-assessed results showing closed-source models outperforming on anomaly detection and Llama-3.1-405B on queries.
The paper proposes a bidirectional continuum between LLMs and control systems, covering LLM-assisted controller design, control-based LLM steering, and state-space modeling of LLMs.
citing papers explorer
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Activation Steering of Video Generation Models via Reduced-Order Linear Optimal Control
LA-LQR applies latent-space linear-quadratic regulator control to steer text-to-video model activations toward desired features while penalizing excessive changes.
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Local Linearity of LLMs Enables Activation Steering via Model-Based Linear Optimal Control
Local linearity of LLM layers enables LQR-based closed-loop activation steering with theoretical tracking guarantees.
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FDM-Bench: A Comprehensive Benchmark for Evaluating Large Language Models in Additive Manufacturing Tasks
FDM-Bench is a new benchmark dataset for evaluating LLMs on FDM tasks including user queries and G-code anomaly detection, with expert-assessed results showing closed-source models outperforming on anomaly detection and Llama-3.1-405B on queries.
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When control meets large language models: From words to dynamics
The paper proposes a bidirectional continuum between LLMs and control systems, covering LLM-assisted controller design, control-based LLM steering, and state-space modeling of LLMs.