University of Alabama

BitNest introduces a bit-nested speculative decoding framework that embeds a low-precision draft directly into the higher-precision target representation and recovers the target through residual refinement, so draft and target share a single physical weight representation, with the progressive-precision design extended to the KV cache for long-context inference; across multiple 7B–8B edge-friendly LLMs and diverse workloads it reports an average speculative acceptance rate of 95.2% while closely preserving higher-precision model quality, and 1.48–1.61x end-to-end speedup over FP16 autoregressive decoding, with consistently competitive or higher decoding speedup on the LLaMA models supported by all representative self-speculative baselines.

BitNest introduces a bit-nested speculative decoding framework that embeds a low-precision draft directly into the higher-precision target representation and recovers the target through residual refinement, so draft and target share a single physical weight representation, with the progressive-precision design extended to the KV cache for long-context inference; across multiple 7B–8B edge-friendly LLMs and diverse workloads it reports an average speculative acceptance rate of 95.2% while closely preserving higher-precision model quality, and 1.48–1.61x end-to-end speedup over FP16 autoregressive decoding, with consistently competitive or higher decoding speedup on the LLaMA models supported by all representative self-speculative baselines.

This paper presents the composite adaptive control barrier function (CaCBF) algorithm for nonlinear control-affine systems with linear parametric uncertainty, deriving the adaptation law from a composite energy function that integrates a logarithmic safety barrier, a control Lyapunov function, and a parameter-error term; it proves forward invariance of the safe set for all bounded parameters without persistence of excitation, robustness of the safety guarantee to bounded state-derivative measurement errors, uniform ultimate boundedness of all closed-loop signals, and that the CaCBF admissible control set always contains the robust counterpart as a subset; simulations of adaptive cruise control, an omnidirectional robot, and a planar drone traversing a narrow gate show CaCBF recovers the pe

This paper presents the composite adaptive control barrier function (CaCBF) algorithm for nonlinear control-affine systems with linear parametric uncertainty, deriving the adaptation law from a composite energy function that integrates a logarithmic safety barrier, a control Lyapunov function, and a parameter-error term; it proves forward invariance of the safe set for all bounded parameters without persistence of excitation, robustness of the safety guarantee to bounded state-derivative measurement errors, uniform ultimate boundedness of all closed-loop signals, and that the CaCBF admissible control set always contains the robust counterpart as a subset; simulations of adaptive cruise control, an omnidirectional robot, and a planar drone traversing a narrow gate show CaCBF recovers the pe