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HarvestBench: Measuring Whether LLM Agents Will Pay to Avoid Killing Animals
HarvestBench, a reproducible farm-simulation benchmark, shows LLM agents pay fuel costs to avoid killing animals, with kill rates spanning 0.4% to 98.8% across nine models.
HarvestBench is a reinforcement-learning gridworld farm simulation where LLM agents choose between driving over animals at no cost or paying a posted fuel price to swerve during a cooperative corn harvest. Across nine models and 7,201 priced decisions, kill rates ranged from 0.4% to 98.8%, unordered by capability, with Terra and Sol the most merciful and GPT-4o-mini the most cruel. Morality briefings cut kill rates below 6% in five of six reasoning models, while removing them pushed rates above 84% in all six. The scorer counts events in the game log without an LLM grader, making results fully reproducible.
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Don't Trust the Super-App: A Case Study of Russia's Max
Researchers show super-apps like Russia's Max can silently capture mini-app UI, storage, network traffic, and authentication, enabling untraceable user impersonation.
The paper challenges the assumption that super-apps are trusted intermediaries, citing WeChat's passive cross-mini-app tracking, Russia's Max's entanglement with state prosecution of online speech, and Iran's Bale operating during a prolonged internet shutdown. Using Max as a case study, the authors demonstrate that a super-app can capture mini-app UI, read and write mini-app local storage, inject arbitrary JavaScript into mini-app runtimes, mediate network traffic, and control authentication context, enabling silent user impersonation without trace. They argue these capabilities are inherent to the super-app architecture and call for mobile OS and app store interventions to close the blind spot.
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