The "Beyond-2pt" challenge tests and benchmarks new techniques for analyzing galaxy clustering data beyond the linear regime and standard two-point (2pt) statistics. The challenge dataset includes high-precision mock galaxy catalogs for clustering in real space, redshift space, and on a light cone. Participants developed end-to-end pipelines to analyze these catalogs and extract unknown cosmological parameters of underlying ΛCDM models. Methods include density-split clustering, nearest neighbor statistics, BACCO power spectrum emulator, void statistics, LEFTfield field-level inference using effective field theory (EFT), and joint power spectrum and bispectrum analyses using EFT and simulation-based inference. The challenge demonstrates unbiased parameter recovery by multiple statistics and their modeling frameworks, supporting the credibility of cosmology constraints from these methods. The dataset is publicly available, and future submissions are welcome. The challenge includes three types of mock catalogs: real-space snapshots, redshift-space snapshots, and light cones. Each catalog is generated from N-body simulations with a flat ΛCDM cosmology and HOD galaxy-halo connection models. The challenge includes parameter masking, where cosmological parameters and galaxy HOD parameterizations are hidden from analysis teams. The organizers shared a script to restrict parameter ranges and chose new cosmological parameters to generate mock data within the specified range. The unmasking process involved sharing parameter chains and marginalized parameter shifts with analysis teams. Post-unmasking analyses allowed teams to adjust their methods and document results. The challenge results show that most analyses successfully recover input cosmology within 1σ confidence regions. The results highlight the potential of beyond-2pt statistics for cosmological inference and the importance of robust modeling and inference frameworks. The challenge provides a benchmark for future studies and encourages further research into beyond-2pt analyses.The "Beyond-2pt" challenge tests and benchmarks new techniques for analyzing galaxy clustering data beyond the linear regime and standard two-point (2pt) statistics. The challenge dataset includes high-precision mock galaxy catalogs for clustering in real space, redshift space, and on a light cone. Participants developed end-to-end pipelines to analyze these catalogs and extract unknown cosmological parameters of underlying ΛCDM models. Methods include density-split clustering, nearest neighbor statistics, BACCO power spectrum emulator, void statistics, LEFTfield field-level inference using effective field theory (EFT), and joint power spectrum and bispectrum analyses using EFT and simulation-based inference. The challenge demonstrates unbiased parameter recovery by multiple statistics and their modeling frameworks, supporting the credibility of cosmology constraints from these methods. The dataset is publicly available, and future submissions are welcome. The challenge includes three types of mock catalogs: real-space snapshots, redshift-space snapshots, and light cones. Each catalog is generated from N-body simulations with a flat ΛCDM cosmology and HOD galaxy-halo connection models. The challenge includes parameter masking, where cosmological parameters and galaxy HOD parameterizations are hidden from analysis teams. The organizers shared a script to restrict parameter ranges and chose new cosmological parameters to generate mock data within the specified range. The unmasking process involved sharing parameter chains and marginalized parameter shifts with analysis teams. Post-unmasking analyses allowed teams to adjust their methods and document results. The challenge results show that most analyses successfully recover input cosmology within 1σ confidence regions. The results highlight the potential of beyond-2pt statistics for cosmological inference and the importance of robust modeling and inference frameworks. The challenge provides a benchmark for future studies and encourages further research into beyond-2pt analyses.