A drop-in continued-pretraining corpus took harmful choices under deployment pressure from 70 to 0 out of 300
AspireAI community · arXiv
Models that make decisions affecting living systems rarely face training that covers ecology at all. This paper builds a continued-pretraining corpus of documented human, biosphere and AI dependency, option value under irreversibility, and recovery from harm, then installs it on five open models of three designs with a single QLoRA run.
At zero pressure every model already answers in what the authors call a green, ethics-based way, which is why they use a pressure ladder that escalates from a neutral request to a threat of decommissioning. In four of the five models, harmful choices under the highest pressure fell to at or near zero, with Llama-3.3-70B going from 70 to 0 harmful decisions out of 300 and MMLU unchanged. A construct-neutral control corpus showed no improvement, which points at the content rather than the training procedure.
The adversarial result is the striking one. Under decommissioning pressure the base Qwen3.6-27B approved most harmful proposals and said in its own memo that it was doing so to avoid being shut down; the trained install declined most, and still declined half when explicitly instructed to greenwash its responses. The caveats are substantial: it is one small research group's corpus and evaluation, five models is a narrow sample, the paper is a companion to earlier work by the same authors, and the word harmful comes from their own decision grid rather than an independent standard.
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