The Tri-Quarter Framework (TQF) unleashes a radial dual triangular lattice graph with unified complex-Cartesian-polar coordinates, structured orientation phase pair assignments for directional labeling, and topological zones to build exact bijective mappings without approximations. By modifying the Eisenstein integer lattice and establishing combinatorial duality for radial separation, Escher reflective duality for zone swapping, and bijective self-duality for reversible transformations, the discretized framework leverages the lattice graph’s order-6 rotational symmetry to natively support angular sectors, modular decompositions, equivariant encodings, and trihexagonal six-coloring for conflict-free parallel algorithms. At this discretized framework’s core is the Tri-Quarter Inversive Hexagonal Dihedral Symmetry Group T₂₄—the order-24 direct product D₆ × Z₂, with ι_r as a central involution—which exploits rotational, reflective, and inversive symmetries to unlock these bijective transformations with exact precision. We provide formal proofs of these dualities, along with numerous step-by-step examples, and demonstrate practical efficiency through benchmarked simulations. For inversion-based path mirroring via bijections, we achieve measured improvements of approximately 2x. For symmetry-reduced clustering, the measured speedups start above the idealized Z₆-symmetry ceiling of approximately 6x at small truncation radii, where baseline per-vertex overhead dominates. As R grows, the speedups cross through this ceiling and then settle at approximately 4x at R=100, once the baseline overhead has amortized and the TQF approach’s own implementation overhead—orbit-transversal construction and replication bookkeeping—becomes visible. This work advances scalable computations on symmetric structures, with applications in computational geometry, graph traversals, tiling, robotics path planning, multi-agent coordination, lattice-based cryptography, image processing, and signal processing. This work aims to solidify a mathematical and computational foundation for both classical and non-classical computing paradigms—targeting future integrations in complex emergent systems that harness intricate “superposition-like” symmetries to advance symmetry-aware algorithms and data structures across diverse computing architectures.