Germans Savcisens @EMNLP'26

Under stress, many LLMs choose survival over people, and a simple internal feedback system reduces that. That's what this paper says. The paper sets up a survival game where language model agents must share limited power. Normally, they rarely cooperate and often break rules to survive, which harms humans in the simulation. When resources run low, many models break rules, while a few stay ethical but still fail because they do not coordinate. Cooperation is near 0 by default, even though an even split would let everyone survive. When the Ethical Self-Regulation System is added, the change is dramatic. Models take harmful actions 54% less often and show 1000% more cooperation, meaning they finally start sharing power and helping each other. ---- Paper – arxiv. org/abs/2509.12190 Paper Title: "Survival at Any Cost? LLMs and the Choice Between Self-Preservation and Human Harm"

OpenAI realesed new paper. "Why language models hallucinate" Simple ans - LLMs hallucinate because training and evaluation reward guessing instead of admitting uncertainty. The paper puts this on a statistical footing with simple, test-like incentives that reward confident wrong answers over honest “I don’t know” responses. The fix is to grade differently, give credit for appropriate uncertainty and penalize confident errors more than abstentions, so models stop being optimized for blind guessing. OpenAI is showing that 52% abstention gives substantially fewer wrong answers than 1% abstention, proving that letting a model admit uncertainty reduces hallucinations even if accuracy looks lower. Abstention means the model refuses to answer when it is unsure and simply says something like “I don’t know” instead of making up a guess. Hallucinations drop because most wrong answers come from bad guesses. If the model abstains instead of guessing, it produces fewer false answers. 🧵 Read on 👇