---
title: 3 · Thin Channel
emoji: 🔁
colorFrom: indigo
colorTo: purple
sdk: static
app_file: index.html
pinned: true
license: mit
short_description: Rare external truth before calibration collapses
---

# Thin Channel — interactive demo

Interactive visualization of Experiment 6 (`exp6-forecast-calibration`) of the
[Second Loop](https://github.com/SergheiBrinza/thin-channel) project.

This Space loads **no model**. Everything is a static page driven by `data.json`
— the verbatim aggregate output of the live experimental run.

## The exhibit

Move the lever — *how often the system gets to check reality* — across its 11
settings, from **every day** to **never**. The Brier curve redraws live and the
highlighted point tracks your setting. Drive the lever all the way to **never**
and the point jumps off the corridor up to **0.298** — the system collapses
bit-for-bit back to the raw 3B baseline. That is the whole finding, in one
gesture.

## What the numbers say

- 11 reveal schedules tested (day → year + never).
- Corridor of **0.205 – 0.220** Brier on hold-out for *any* finite schedule.
- **0.298** on `never` — collapses to the raw 3B baseline.
- The cliff is binary: channel thickness is total signal volume across the
  system's lifetime, not the frequency at which it arrives.
- Honest caveat (shown in the page): the raw 3B is useless as a forecaster on
  its own; the `(c) ≈ (b)` ablation shows the entire calibration gain comes from
  per-topic base rates, not from the model learning to predict.

## Data and attribution

The forecasting questions come from **ForecastBench** (Karger, Bastani,
Yueh-Han, Jacobs, Halawi, Zhang & Tetlock, *ForecastBench: A Dynamic Benchmark
of AI Forecasting Capabilities*, ICLR 2025, arXiv:2409.19839), licensed
**CC BY-SA 4.0** —
[forecastingresearch/forecastbench-datasets](https://huggingface.co/datasets/forecastingresearch/forecastbench-datasets).
No raw market questions or per-question outcomes are redistributed here — only
aggregate Brier results bundled in `data.json`. Demo code: MIT.

Source code, raw per-schedule JSON results, and methodology document:
<https://github.com/SergheiBrinza/thin-channel>