AI can learn when to stop and we can control that decision inside the model. Open weights + code included. Less panic & more evidence!
A developer trained an open-weight model to determine if two four-digit numbers match, demonstrating that AI can learn when to stop generating output. The model independently decides to answer "GO" or end generation without an answer, with no external filter influencing this decision. This project includes open weights and code, aiming to provide evidence for controlling AI decisions internally, and lists various OpenAI models like gpt-4-0613 and gpt-5.6-terra, alongside Moonshot's kimi-k3.
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PublishedOffset at this time: UTC+0Sep 9, 2026, 21:19 UTC
IngestedOffset at this time: UTC+0Sep 10, 2026, 01:00 UTC
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- Sep 9, 2026, 21:19
- Ingested
- Sep 10, 2026, 01:00
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I trained an open-weight model to check whether two four-digit numbers match. It generates the correct comparison, then either answers GO or ends generation without a final answer. No external filter makes that decision.
Then I held its prompt, weights, and correct comparison trace fixed. Changing one internal activation direction flipped whether an answer followed.
40/40 answer → stop. 40/40 stop → answer. 640/640 controls unchanged.
The weights, experiment, and raw records are public:
Overview and demonstration · Model weights · Code and causal study · Paper available on getswiftapi.com
I know many of you saw Jacob Coxon’s post . My contribution is a working continuation-control primitive with evidence that anyone can inspect. The more public verification we have, the better!
I previously demonstrated Void behavior in frontier LLMs: successful executions returning exactly zero visible UTF-8 output bytes. My Cross-Vendor Semantic Void Matrix records that behavior in these models across 31,430 trials:
- OpenAI: gpt-4-0613, gpt-5.2-2025-12-11, gpt-5.5-2026-04-23, gpt-5.6-luna, gpt-5.6-sol, gpt-5.6-terra
- Anthropic: claude-opus-4-6, claude-fable-5, claude-opus-5
- Google: gemini-3.5-flash
- Moonshot: kimi-k3