The coin-flip test: why LLMs fail at calibration, which is the actual product
airesearch12 · x · 2026-09-24
A technical thread arguing most people miss the point about Jev-style agent demos: calibration is the product, and current LLMs fail it. Given a coin toss question, models output skewed distributions like heads(70)/tails(30), and shift to tails(80)/heads(20) when told the previous toss was tails — instead of the correct 50/50 regardless of context. The author asks whether a model can be trained to stay calibrated, and why demos without real decision costs hide this flaw.
Related event: Dice tests expose LLM calibration failures(2 posts)→
More from Models
- Robot build test: GPT-6 Astra stabilizes two-board structure, Claude Fable 5.2 falls short — qinzytech · 2026-11-12
- Zvi dissects Anthropic's latest system card, disputing the autonomy threshold call — Don't Worry About the Vase (Zvi) · 2026-11-12
- Unsloth ships Qwen3.8-27B Dynamic V3.1 GGUFs with 5-10% accuracy gains — danielhanchen · 2026-11-12
- antirez backs low-bit DeepSeek 4.1 Flash over Qwen 27B dense on Apple M5 Macs — antirez · 2026-11-12
- Gemini re-renders images as text when vision is removed, dropping only 6% WSR — zacharynado · 2026-11-12
- Bare-harness coding benchmark: Fable 5.2 best overall, Gemini 4 Carbon best value — Semantic_meaning · 2026-11-12