
All provided installation methods allow running archx in the command line and import archx as a python module. Make sure you have Anaconda installed before the steps below. AE setup (Architecture AE) Unzip the provided zip file named archx-asplos_2026_ae.zip into a new directory conda env create -f environment.yaml The name: archx in evironment.yaml can be updated to a preferred one. conda activate archx Validate installation via archx -h in the command line or import archx in python code. bash run_mugi.sh to run the simulation workflow. Output figures can be found in zoo/llm/results/figs/ and zoo/llm/results/tables. AE setup (Workload AE) Unzip the provided zip file named mugi_profiling-asplos_2026_ae.zip into a new directory conda env create -f environment.yaml The name: mugi_profiling in evironment.yaml can be updated to a preferred one. conda activatemugi_profilingin bash mugi_profiling.sh to run the simulation workflow. Output figures can be found in figures/output
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
