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This paper presents an open and modular transformer-based framework that detects and structures social tipping point evidence at the passage level, outperforming both a climate-focused model and untuned language models.
DRLM, a Deep Reinforcement Learning-based LLM query orchestration framework in edge environments shows robust and stable orchestration, and improves latency under increasing workloads up to 61.4%, demonstrating robust and stable orchestration.
It is shown the conflict is endogenous: when feasible, the welfare-maximizing approval rule is never affine, and there is a reserve report at which pretending to be the marginal type costs exactly the approval prize.
It is shown that the structure of service-dependency graphs, modelled as DAGs of compute stages, is a primary determinant of whether decentralised, price-based resource allocation works reliably at scale.
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