Ramchand KumaresanR. Kumaresan
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Thiruvaasal

Probabilistic Reconstruction of Lost Pallava Temples

What It Is

A research system for reconstructing the six lost Pallava temples of Mahabalipuram using evidence-tiered inference, Agamic architectural constraints, posterior reweighting, and per-element uncertainty maps. The system does not claim a single historical truth — it characterizes the posterior over plausible reconstructions given the evidence.

Why It Matters

Most AI-driven 3D reconstruction is unconstrained generation — it hallucinates spires, invents floor plans, and produces nothing a heritage reviewer can verify. Thiruvaasal asks whether canonical architectural priors can make archaeological reconstruction *falsifiable, calibrated, and reviewable*. Constraint necessity is tested by ablation, not asserted.

What I Built

  • ✓Evidence ledger with explicit tier / source / rights / geometry scope / provenance per element
  • ✓Agamic hard-constraint engine — rejects impossible structures before scoring; soft constraints scored as named energy terms with visible weights
  • ✓Posterior reweighting pipeline producing per-element confidence from posterior marginals
  • ✓Holdout temple experiment design — tests the method on a temple with known ground truth before generalizing
  • ✓Constraint necessity ablation — quantifies whether priors are doing real work or are decorative
  • ✓Reviewer-ready report generation with full audit trail (config + input artifacts + output artifacts + verification command per result)

Tech Stack

PythonPydantic schemasJSON-LD provenanceBayesian reweightingpytest
Outcome

Phase 1 method in active development. Paper bet is narrow: one site + one holdout protocol proving the priors before any public demos. Foundation for the KUMARI multi-site knowledge OS.

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