Is the fall of Pokrovsk inevitable? Is Trump losing patience with Putin? Here's where things stand on Monday, November 24. United States Secretary of State Marco Rubio told reporters in Geneva that "a tremendous amount of progress" was made during talks in the Swiss city on Sunday and that he was "very optimistic" that an agreement could be reached in "a very reasonable period of time, very soon". Rubio also said that specific areas still being worked on from a 28-point peace plan for Ukraine, championed by US President Donald Trump, included the role of NATO and security guarantees for Ukraine.

Machu Picchu, the remains of a 15th Century Inca city, is Peru's most popular tourist destination, and a Unesco world heritage site. Yet a continuing dispute over the buses that take visitors up to the mountain-top site recently saw some 1,400 stranded tourists needing to be evacuated. Cristian Alberto Caballero Chacón is head of operations for bus company Consettur, which for the past 30 years has transported some 4,500 people every day to Machu Picchu from the local town of Aguas Calientes. It is a 20-minute journey, and the only alternative is an arduous, steep, two-hour walk. He admits that in the past few months there have been some conflicts between people from different communities here.

Decision-making architectures have played a central role for decades [1] both in engineering and other domains, e.g., guidance, navigation and control of Apollo missions [2], chemical plants [3], smart grids [4], unmanned aerial vehicles [5], recommender systems [6], and algorithms [7]. Moreover, architectures are ubiquitous in nature, e.g., diversity in the nervous system enables humans to have fast and accurate sensorimotor control [8]. Reinforcement Learning (RL) is a framework in which an agent learns to make sequential decisions through interaction with an environment in order to maximize cumulative reward [9]. Decision-making architectures have also been proposed and studied in RL. Hierarchical Reinforcement Learning (HRL) is a subfield of RL that deals with hierarchical structures for decision-making agents. Prospective advantages include improved long-term credit assignment, continual learning, interpretability, and the integration of preexisting policies [10], [11].

According to the theory of International Political Economy (IPE), states are often incentivized to rely on rather than constrain powerful corporations. For this reason, IPE provides a useful lens to explain why efforts to govern Artificial Intelligence (AI) at the international and national levels have thus far been developed, applied, and enforced unevenly. Building on recent work that explores how AI companies engage in geopolitics, this position paper argues that some AI workers can be considered actors of geopolitics. It makes the timely case that governance alone cannot ensure responsible, ethical, or robust AI development and use, and greater attention should be paid to bottom-up interventions at the site of AI development. AI workers themselves should be situated as individual agents of change, especially when considering their potential to foster Algorithmic Collective Action (ACA). Drawing on methods of Participatory Design (PD), this paper proposes engaging AI workers as sources of knowledge, relative power, and intentionality to encourage more responsible and just AI development and create the conditions that can facilitate ACA.

The physics programs of current and future collider experiments necessitate the development of surrogate simulators for calorimeter showers. While much progress has been made in the development of generative models for this task, they have typically been evaluated in simplified scenarios and for single particles. This is particularly true for the challenging task of highly granular calorimeter simulation. For the first time, this work studies the use of highly granular generative calorimeter surrogates in a realistic simulation application. We introduce DDML, a generic library which enables the combination of generative calorimeter surrogates with realistic detectors implemented using the DD4hep toolkit. We compare two different generative models - one operating on a regular grid representation, and the other using a less common point cloud approach. In order to disentangle methodological details from model performance, we provide comparisons to idealized simulators which directly sample representations of different resolutions from the full simulation ground-truth. We then systematically evaluate model performance on post-reconstruction benchmarks for electromagnetic shower simulation. Beginning with a typical single particle study, we introduce a first multi-particle benchmark based on di-photon separations, before studying a first full-physics benchmark based on hadronic decays of the tau lepton. Our results indicate that models operating on a point cloud can achieve a favorable balance between speed and accuracy for highly granular calorimeter simulation compared to those which operate on a regular grid representation.

As Large Language Models (LLMs) increasingly influence high-stakes decision-making across global contexts, ensuring their alignment with diverse cultural values has become a critical governance challenge. This study presents a Multi-Layered Auditing Platform for Responsible AI that systematically evaluates cross-cultural value alignment in China-origin and Western-origin LLMs through four integrated methodologies: Ethical Dilemma Corpus for assessing temporal stability, Diversity-Enhanced Framework (DEF) for quantifying cultural fidelity, First-Token Probability Alignment for distributional accuracy, and Multi-stAge Reasoning frameworK (MARK) for interpretable decision-making. Our comparative analysis of 20+ leading models, such as Qwen, GPT-4o, Claude, LLaMA, and DeepSeek, reveals universal challenges-fundamental instability in value systems, systematic under-representation of younger demographics, and non-linear relationships between model scale and alignment quality-alongside divergent regional development trajectories. While China-origin models increasingly emphasize multilingual data integration for context-specific optimization, Western models demonstrate greater architectural experimentation but persistent U.S.-centric biases. Neither paradigm achieves robust cross-cultural generalization. We establish that Mistral-series architectures significantly outperform LLaMA3-series in cross-cultural alignment, and that Full-Parameter Fine-Tuning on diverse datasets surpasses Reinforcement Learning from Human Feedback in preserving cultural variation...

Understanding and predicting user behavior on social media platforms is crucial for content recommendation and platform design. While existing approaches focus primarily on common actions like retweeting and liking, the prediction of rare but significant behaviors remains largely unexplored. This paper presents a hybrid methodology for social media user behavior prediction that addresses both frequent and infrequent actions across a diverse action vocabulary. We evaluate our approach on a large-scale Bluesky dataset containing 6.4 million conversation threads spanning 12 distinct user actions across 25 persona clusters. Our methodology combines four complementary approaches: (i) a lookup database system based on historical response patterns; (ii) persona-specific LightGBM models with engineered temporal and semantic features for common actions; (iii) a specialized hybrid neural architecture fusing textual and temporal representations for rare action classification; and (iv) generation of text replies. Our persona-specific models achieve an average macro F1-score of 0.64 for common action prediction, while our rare action classifier achieves 0.56 macro F1-score across 10 rare actions. These results demonstrate that effective social media behavior prediction requires tailored modeling strategies recognizing fundamental differences between action types. Our approach achieved first place in the SocialSim: Social-Media Based Personas challenge organized at the Social Simulation with LLMs workshop at COLM 2025.

Large Language Models (LLMs) often produce fluent but factually incorrect responses, a phenomenon known as hallucination. Abstention, where the model chooses not to answer and instead outputs phrases such as "I don't know", is a common safeguard. However, existing abstention methods typically rely on post-generation signals, such as generation variations or feedback, which limits their ability to prevent unreliable responses in advance. In this paper, we introduce Aspect-Based Causal Abstention (ABCA), a new framework that enables early abstention by analysing the internal diversity of LLM knowledge through causal inference. This diversity reflects the multifaceted nature of parametric knowledge acquired from various sources, representing diverse aspects such as disciplines, legal contexts, or temporal frames. ABCA estimates causal effects conditioned on these aspects to assess the reliability of knowledge relevant to a given query. Based on these estimates, we enable two types of abstention: Type-1, where aspect effects are inconsistent (knowledge conflict), and Type-2, where aspect effects consistently support abstention (knowledge insufficiency). Experiments on standard benchmarks demonstrate that ABCA improves abstention reliability, achieves state-of-the-art performance, and enhances the interpretability of abstention decisions.

Large language models and knowledge graphs hold broad application potential in the field of historical culture, facilitating the excavation, research, and comprehension of cultural heritage. Taking Hunan's historical celebrities emerging from modern Huxiang culture as a case, pre-trained large models can assist researchers in rapidly extracting specific historical figure information from literature--including basic details, life events, and social relationships--and constructing structured knowledge graphs, thereby supporting related research. Currently, systematic data collection on Hunan's historical celebrities remains scarce. Moreover, general-purpose large language models often exhibit insufficient domain knowledge extraction accuracy and weak structured output capabilities in such low-resource scenarios. Therefore, this paper proposes a supervised fine-tuning approach for domain-specific large models to enhance the quality and efficiency of information extraction regarding Hunan's historical celebrities. Specifically, this paper first designs a fine-grained schema-guided instruction fine-tuning template for the Hunan's historical celebrities domain. Using this template, we construct an instruction fine-tuning dataset, addressing the current lack of instruction datasets in domain-specific model fine-tuning. Second,we conducted parameter-efficient instruction fine-tuning on four publicly available large language models--Qwen2.5-7B, Qwen3-8B, DeepSeek-R1-Distill-Qwen-7B, and Llama-3.1-8B-Instruct--using the proposed instruction dataset, and established evaluation criteria for assessing their performance in character information extraction. Experimental results demonstrate that the performance of all four base models significantly improved after domain-specific fine-tuning. Among them, Qwen3-8B achieved the best performance after training with 100 samples and 50 fine-tuning iterations, scoring 89.3866 on the evaluation metrics. This research offers new insights for fine-tuning vertical large models tailored to regional historical and cultural domains, holding significant implications for promoting the cost-effective application of large models and knowledge graphs in the field of historical and cultural heritage. Introduction With the rapid advancement of large language models (LLMs), unprecedented opportunities have emerged for the in-depth exploration, systematic research, and widespread dissemination of Huxiang culture. Simultaneously, this presents new challenges for the digital transformation of traditional cultural resources[1].

Benchmarks are pivotal in driving AI progress, and invalid benchmark questions frequently undermine their reliability. Manually identifying and correcting errors among thousands of benchmark questions is not only infeasible but also a critical bottleneck for reliable evaluation. In this work, we introduce a framework for systematic benchmark revision that leverages statistical analysis of response patterns to flag potentially invalid questions for further expert review. Our approach builds on a core assumption commonly used in AI evaluations that the mean score sufficiently summarizes model performance. This implies a unidimensional latent construct underlying the measurement experiment, yielding expected ranges for various statistics for each item. When empirically estimated values for these statistics fall outside the expected range for an item, the item is more likely to be problematic. Across nine widely used benchmarks, our method guides expert review to identify problematic questions with up to 84\% precision. In addition, we introduce an LLM-judge first pass to review questions, further reducing human effort. Together, these components provide an efficient and scalable framework for systematic benchmark revision.