Santosh S. Vempala

Computer scientist at Georgia Tech who co-authored, with Adam Tauman Kalai, "Calibrated Language Models Must Hallucinate" (arXiv:2311.14648; STOC 2024) — the theorem proving a statistical lower bound on hallucination rate, tied to the Good-Turing missing-mass estimate, for any calibrated learning algorithm — and later co-authored OpenAI's "Why Language Models Hallucinate" (2025), which extends and applies that bound. Matters to this vault as the second connecting figure across its correlated-errors/hallucination lineage, alongside Kalai. Same deferred-hub status as Kalai on 2026-10-31, discharged by the same direct read of the 2023/2024 paper this capture performed.

References

• claim-kalai-vempala-2023-hallucination-bound-is-a-rate-theorem-sorted-by-fact-regularity
• claim-kalai-vempala-2023-construction-models-arbitrary-fact-hallucination-as-uniform-random-draw
• claim-kalai-vempala-2023-theorem-is-silent-on-cross-model-hallucination-agreement
• claim-llm-hallucination-divergence-is-the-default-not-convergence
• Captures: 10-inbox/raw/2026-11-02-this-weeks-ai-safety-capture-found-llm-errors.md