  We explore the galaxy-halo connection information that is available in
low-redshift samples from the early data release of the Dark Energy
Spectroscopic Instrument (DESI). We model the halo occupation distribution
(HOD) from z=0.1-0.3 using Survey Validation 3 (SV3; a.k.a., the One-Percent
Survey) data of the DESI Bright Galaxy Survey (BGS). In addition to more
commonly used metrics, we incorporate counts-in-cylinders (CiC) measurements,
which drastically tighten HOD constraints. Our analysis is aided by the Python
package, galtab, which enables the rapid, precise prediction of CiC for any HOD
model available in halotools. This methodology allows our Markov chains to
converge with much fewer trial points, and enables even more drastic speedups
due to its GPU portability. Our HOD fits constrain characteristic halo masses
tightly and provide statistical evidence for assembly bias, especially at lower
luminosity thresholds: the HOD of central galaxies in $z\sim0.15$ samples with
limiting absolute magnitude $M_r < -20.0$ and $M_r < -20.5$ samples is
positively correlated with halo concentration with a significance of 99.9% and
99.5%, respectively. Our models also favor positive central assembly bias for
the brighter $M_r < -21.0$ sample at $z\sim0.25$ (94.8% significance), but
there is no significant evidence for assembly bias with the same luminosity
threshold at $z\sim0.15$. We provide our constraints for each threshold
sample's characteristic halo masses, assembly bias, and other HOD parameters.
These constraints are expected to be significantly tightened with future DESI
data, which will span an area 100 times larger than that of SV3.
