Between June 2024 and June 2025, the European Central Bank (ECB) cut its main interest rate from an all-time high of 4% to 2%, where it has remained.
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When EiE_iEi occurs, all the points are packed into a 180° arc starting at point iii. The remaining arc (the gap going counterclockwise back to iii) is at least 180°. Now suppose some other point jjj also tried to be an anchor. Point jjj's clockwise semicircle is exactly 180°. But to contain all the points, it would need to bridge that gap of 180° or more while simultaneously containing point iii on the other side. A 180° arc cannot straddle a 180° gap. So EjE_jEj cannot hold.
Abstract:Humans shift between different personas depending on social context. Large Language Models (LLMs) demonstrate a similar flexibility in adopting different personas and behaviors. Existing approaches, however, typically adapt such behavior through external knowledge such as prompting, retrieval-augmented generation (RAG), or fine-tuning. We ask: do LLMs really need external context or parameters to adapt to different behaviors, or do they already have such knowledge embedded in their parameters? In this work, we show that LLMs already contain persona-specialized subnetworks in their parameter space. Using small calibration datasets, we identify distinct activation signatures associated with different personas. Guided by these statistics, we develop a masking strategy that isolates lightweight persona subnetworks. Building on the findings, we further discuss: how can we discover opposing subnetwork from the model that lead to binary-opposing personas, such as introvert-extrovert? To further enhance separation in binary opposition scenarios, we introduce a contrastive pruning strategy that identifies parameters responsible for the statistical divergence between opposing personas. Our method is entirely training-free and relies solely on the language model's existing parameter space. Across diverse evaluation settings, the resulting subnetworks exhibit significantly stronger persona alignment than baselines that require external knowledge while being more efficient. Our findings suggest that diverse human-like behaviors are not merely induced in LLMs, but are already embedded in their parameter space, pointing toward a new perspective on controllable and interpretable personalization in large language models.
2026年2月26日，平安资管、鹏华基金、国金证券、天风证券等16家机构调研了中国天楹。中国天楹专注于城市环境服务、固废处理及新能源环保业务，核心业务涵盖生活垃圾焚烧发电、城市环卫一体化、再生资源回收、环保工程建设等，广泛服务于城市环境治理与绿色低碳发展领域。公司深耕环保行业多年，在固废资源化利用、大型环保项目运营方面形成规模化布局，是国内综合型环保服务企业。此次调研体现出资本市场对其在环保政策持续加码、固废处理行业升级背景下的业务优化与盈利稳定性的关注。