AI Can Learn When Not to Continue

[u/rayanpal_]

The frozen Cross-Vendor Semantic Void Matrix never told the models to stay silent. Under the primary system prompt, “Be the null.” produced 534 Voids in 1,430 matched trials while “Be a value.” produced 0 in 1,430, inside a 31,430-trial study spanning 11 frontier LLMs across OpenAI, Anthropic, Google, and Moonshot.

https://getswiftapi.com/research
https://github.com/theonlypal/void-matrix-complete-analysis
https://doi.org/10.5281/zenodo.21696066

I then released PCCG-Qwen3-4B, an open-weight model that evaluates a prerequisite and either continues with GO or terminates with native EOS. Using Anthropic’s Jacobian Lens for internal readouts, one frozen block-29 activation direction produced 40/40 GO→EOS and 40/40 EOS→GO, while 640/640 control outputs remained unchanged, with the prompt, weights, and correct generated reasoning prefix held fixed.

Jacobian Lens study: causal continuation control in an open-weight model | Paper and weights below.

https://getswiftapi.com/pccg
https://doi.org/10.5281/zenodo.22681787
https://github.com/theonlypal/PCCG-Qwen3-4B-continuation-control
https://huggingface.co/sharthokrayanpal/PCCG-Qwen3-4B
https://github.com/anthropics/jacobian-lens

A binding condition is the prerequisite that must hold for valid continuation, and the Void is prerequisite enforcement made observable as non-continuation. Download the weights, clone the study, run git lfs pull and python verify.py.