Abstract
Classical spatial models predict platform convergence, yet empirical polarization persists. This paper proposes a non-electoral mechanism: lobbying as a monopsonistic market for legislative support. Here, extreme benefactors must pay more to attract distant politicians, creating a rent gradient that rewards platform differentiation. We find that the unique equilibrium places politicians at $(\frac{1}{4},\frac{3}{4})$ for any monotone policy-production cost. Thus, polarization can arise solely from lobbying-market structure, independent of electoral incentives.

AI Summary

• The paper does not provide empirical evidence to support its claims and relies on theoretical analysis only. [3]
• The paper presents a model of political polarization in the United States, focusing on the role of interest groups and lobbying in shaping party positions. [2]
• The model suggests that the quartering equilibrium is a stable outcome, which may explain why parties in the United States have become more polarized over time. [1]

Abstract
This study analyzes the impacts of economic growth on ecosystem in Turkiye. The study uses annual data for the period 1995-2021 and the ARDL method. The study utilizes the Ecosystem Vitality Index, a sub-dimension of the Environmental Performance Index. In addition, seven models were constructed to assess in detail the impact of economic growth on different dimensions of the ecosystem. The results show that economic growth has a significant impact in all models analyzed. However, the direction of this impact differs across ecosystem components. Economic growth is found to have a positive impact on agriculture and water resources. In these models, a 1% increase in GDP increases the agriculture and water resources indices by 0.074-0.672%. In contrast, economic growth has a negative impact on biodiversity and habitat, ecosystem services, fisheries, acid rain and total ecosystem vitality. In these models, a 1% increase in GDP reduces the indices of biodiversity and habitat, ecosystem services, fisheries, acid rain and total ecosystem vitality by 0.101-2.144%. The results suggest that the environmental costs of economic growth processes need to be considered. Environmentally friendly policies should be combined with sustainable development strategies to reduce the negative impacts of economic growth.

AI Summary

• Economic growth has a complex impact on ecosystems, with both positive and negative effects. [3]
• GDP has a negative impact on biodiversity, ecosystem services, fisheries, acid rain, and total ecosystem vitality. [3]
• The study's findings are consistent with previous research on the impacts of economic growth on the environment. [3]
• Economic growth: The increase in the production of goods and services within an economy over a specific period of time. [3]
• Energy intensity: The amount of energy consumed per unit of GDP or other economic indicator. [3]
• Trade: The exchange of goods and services between countries. [3]
• Energy intensity, population density, and trade have a negative impact on environmental indicators. [2]

Abstract
Explanations are a fundamental element of how people make sense of the political world. Citizens routinely ask and answer questions about why events happen, who is responsible, and what could or should be done differently. Yet despite their importance, explanations remain an underdeveloped object of systematic analysis in political science, and existing approaches are fragmented and often issue-specific. I introduce a framework for detecting and parsing explanations in political text. To do this, I train a lightweight causal language model that returns a structured data set of causal claims in the form of cause-effect pairs for downstream analysis. I demonstrate how causal explanations can be studied at scale, and show the method's modest annotation requirements, generalizability, and accuracy relative to human coding.

AI Summary

• The study analyzed the relationship between language and causal relationships in news headlines using a dataset of 10,000 news articles. [3]
• The log-odds analysis revealed significant differences in the odds of certain tokens appearing as causes or effects across different sources and locations. [3]
• Informative prior: A prior probability distribution that splits the strength between the The study's findings suggest that language plays a crucial role in shaping our understanding of causal relationships in news headlines. [3]
• The systematic bias in confidence between models highlights the importance of considering multiple perspectives when analyzing complex data. [3]
• The results showed that the span model consistently reported higher confidence than the sequence-level model, indicating a systematic bias in confidence. [2]

Abstract
In this note, we describe how the study of backgrounds for general quantum systems can be formulated in terms of the representation theory of abstract $C^*$ algebras. We illustrate our general framework through two example systems: superconductivity and perturbative quantum gravity. In both cases, spontaneously broken symmetries imply the existence of unitarily inequivalent Hilbert spaces that play the role of distinct backgrounds relative to which observables are measured. Background independence can be realized by gauging the broken symmetry; extending the algebra of observables for the theory to include new physical processes that intertwine between these disjoint representations. From the point of view of the background independent theory, different backgrounds have an interpretation as different vacuum expectation values of these intertwining operators. In superconductivity, the intertwiners are intimately related to the Josephson effect. In gravity, they are related to geometric fluctuations. We explain how this framework is connected to recent work on generalized symmetries and algebraic extensions. To this end, we close with some remarks about how the operator algebra of a closed universe may arise from a generalized symmetry associated with the inclusion of the causal wedge inside the entanglement wedge by appealing to subregion-subalgebra duality.

Abstract
This paper articulates short- and long-term research problems in AI agent security and privacy, using the lens of computer systems security. This approach examines end-to-end security properties of entire systems, rather than AI models in isolation. While we recognize that hardening a single model is useful, it is important to realize that it is often insufficient. By way of an analogy, creating a model that is always helpful and harmless is akin to creating software that is always helpful and harmless. The collective experience of decades of cybersecurity research and practice shows that this is insufficient. Rather, constructing an informed and realistic attacker model before building a system, applying hard-earned lessons from software security, and continuous improvement of security posture is a tried-and-tested approach to securing real computer systems. A key goal is to examine where research challenges arise when applying traditional security principles in the context of AI agents. A secondary goal of this report is to distill these ideas for AI and ML practitioners and researchers. We discuss the challenges of applying security principles to agentic computing, present 11 case studies of real attacks on agentic systems, and define a series of new research problems specific to the security of agentic systems.

AI Summary

• Current efforts at addressing policy enforcement challenges for discrete tool-using agents are promising, but there are AI agents that are not amenable to such system designs. [2]
• Separating instructions and data is a cornerstone of modern operating systems security, but it's challenging to apply this concept to agentic computing due to the nuances of prompt injection attacks. [1]