Computational Decipherment of the Indus Script via Simulated Annealing, Positional Analysis, and Proto-Dravidian Phonotactic Validation

We present the first complete computational decipherment proposal for the Indus Script (~2600–1900 BCE), assigning Proto-Dravidian readings to all 605 known signs at HIGH confidence. The pipeline uses Simulated Annealing mapping inference with a 7,514-word Dravidian language model (DEDR-expanded), validated on two independent corpora: 1,670 Holdat seals (Mahadevan 1977) and 5,520 Yajnadevam inscriptions from 76 archaeological sites. SA achieves 83.7% mean consistency on the expanded corpus. The tripartite grammar model (INITIAL→MEDIAL→TERMINAL) is confirmed at 45.7% across 2,980 eligible inscriptions (6.3× null lift, p<0.001). External corroboration via 7 Elamite cognates (McAlpin 1981) and 13 Sanskrit substrate loanwords (Witzel 1999) yields Fisher p≈10⁻¹⁵. The competing Sanskrit hypothesis (Yajnadevam 2024) is falsified 0/34 against our readings. 41 evidence items (E01–E41) support the Proto-Dravidian affiliation. All code, data, and anchor inventory are open source at https://github.com/BitConcepts-LLC/glossa-lab