ABSTRACT DeepSeek - R1 is a cutting - edge open - source large language model (LLM) developed by DeepSeek, showcasing advanced reasoning capabilities through a hybrid architecture that integrates m ixture of e xperts (MoE), chain of thought (CoT) reasoning, and reinforcement learning. Released under the per missive MIT license, DeepSeek - R1 offers a transparent and cost - effective alternative to proprietary models like GPT - 4o and Claude - 3 Opus; i t excels in structured problem - solving domains such as mathematics, healthcare diagnostics, code generation, and phar maceutical research. Its architecture enables efficient inference while preserving reasoning depth, making it suitable for deployment in resource - constrained settings. However, DeepSeek - R1 also exhibits increased vulnerability to bias, misinformat ion, adversarial manipulation, and safety failures - especially in multilingual and ethically sensitive contexts. Th is survey highlights the model's strengths, including interpretability, scalability, and adaptability, alongside its limitations in general language fluency and safety alignment. Future research priorities include improving bias mitigation, natural language compreh ension, domain - specific validation, and regulatory compliance. Overall, DeepSeek - R1 represents a major advance in open, scalable AI, underscoring the need for collaborative governance to ensure responsible and equitable deployment. INTRODUCTION T he rise of AI and generative models in health and technology Artificial Intelligence (AI) has undergone transformative growth in recent years, profoundly reshaping numerous fields including language processing, automation, and complex decision - making. At its core, AI refers to the simulation of human intelligence by machines, enabling them to perform tasks such as speech recognition, natural lang uage understanding, visual perception, and predictive analytics. One of the recent remarkable advancements in the Generative AI domain is the emergence of DeepSeek - R1, a large language model (LLM) developed by the Chinese company DeepSeek. In benchmarking evaluations, it has demonstrated results competitive with, and in some domains superior to, models like OpenAI's GPT - 4o and GPT - o1 [4] . This has positioned DeepSeek - R1 as a notable advancement not only in LLM capability but also in the global AI development race. DeepSeek - R1: a paradigm shift in LLM development What sets DeepSeek - R1 apart from conventional LLMs is its novel training architecture. This hybrid approach mimics certain aspects of human learning, allowing the model to refine its behavior over time and adapt to mo re complex reasoning tasks.

Grounded text generation models often produce content that deviates from their source material, requiring user verification to ensure accuracy. Existing attribution methods associate entire sentences with source documents, which can be overwhelming for users seeking to fact-check specific claims. In contrast, existing sub-sentence attribution methods may be more precise but fail to align with users' interests. In light of these limitations, we introduce Localized Attribution Queries (LAQuer), a new task that localizes selected spans of generated output to their corresponding source spans, allowing fine-grained and user-directed attribution. We compare two approaches for the LAQuer task, including prompting large language models (LLMs) and leveraging LLM internal representations. We then explore a modeling framework that extends existing attributed text generation methods to LAQuer. We evaluate this framework across two grounded text generation tasks: Multi-document Summarization (MDS) and Long-form Question Answering (LFQA). Our findings show that LAQuer methods significantly reduce the length of the attributed text. Our contributions include: (1) proposing the LAQuer task to enhance attribution usability, (2) suggesting a modeling framework and benchmarking multiple baselines, and (3) proposing a new evaluation setting to promote future research on localized attribution in content-grounded generation.

--This paper presents a real-time transaction monitoring framework that integrates graph-based modeling, narrative field embedding, and generative explanation to support automated financial compliance. The system constructs dynamic transaction graphs, extracts structural and contextual features, and classifies suspicious behavior using a graph neural network. A retrieval-augmented generation module generates natural-language explanations aligned with regulatory clauses for each flagged transaction. Experiments conducted on a simulated stream of financial data show that the proposed method achieves superior results, with 98.2% F1-score, 97.8% precision, and 97.0% recall. Expert evaluation further confirms the quality and interpretability of generated justifications. The findings demonstrate the potential of combining graph intelligence and generative models to support explainable, audit-ready compliance in high-risk financial environments. Graph-based analytics have become essential in financial crime detection due to their ability to represent relationships between clients, transactions, and geographic entities [1].

The rule $\mathrm{Defeated}(x) \leftarrow \mathrm{Attacks}(y,x),\, \neg \, \mathrm{Defeated}(y)$, evaluated under the well-founded semantics (WFS), yields a unique 3-valued (skeptical) solution of an abstract argumentation framework (AF). An argument $x$ is defeated ($\mathrm{OUT}$) if there exists an undefeated argument $y$ that attacks it. For 2-valued (stable) solutions, this is the case iff $y$ is accepted ($\mathrm{IN}$), i.e., if all of $y$'s attackers are defeated. Under WFS, arguments that are neither accepted nor defeated are undecided ($\mathrm{UNDEC}$). As shown in prior work, well-founded solutions (a.k.a. grounded labelings) "explain themselves": The provenance of arguments is given by subgraphs (definable via regular path queries) rooted at the node of interest. This provenance is closely related to winning strategies of a two-player argumentation game. We present a novel approach for extending this provenance to stable AF solutions. Unlike grounded solutions, which can be constructed via a bottom-up alternating fixpoint procedure, stable models often involve non-deterministic choice as part of the search for models. Thus, the provenance of stable solutions is of a different nature, and reflects a more expressive generate & test paradigm. Our approach identifies minimal sets of critical attacks, pinpointing choices and assumptions made by a stable model. These critical attack edges provide additional insights into the provenance of an argument's status, combining well-founded derivation steps with choice steps. Our approach can be understood as a form of diagnosis that finds minimal "repairs" to an AF graph such that the well-founded solution of the repaired graph coincides with the desired stable model of the original AF graph.

This paper presents LEMONADE, a large-scale conflict event dataset comprising 39,786 events across 20 languages and 171 countries, with extensive coverage of region-specific entities. LEMONADE is based on a partially reannotated subset of the Armed Conflict Location & Event Data (ACLED), which has documented global conflict events for over a decade. To address the challenge of aggregating multilingual sources for global event analysis, we introduce abstractive event extraction (AEE) and its subtask, abstractive entity linking (AEL). Unlike conventional span-based event extraction, our approach detects event arguments and entities through holistic document understanding and normalizes them across the multilingual dataset. We evaluate various large language models (LLMs) on these tasks, adapt existing zero-shot event extraction systems, and benchmark supervised models. Additionally, we introduce ZEST, a novel zero-shot retrieval-based system for AEL. Our best zero-shot system achieves an end-to-end F1 score of 58.3%, with LLMs outperforming specialized event extraction models such as GoLLIE. For entity linking, ZEST achieves an F1 score of 45.7%, significantly surpassing OneNet, a state-of-the-art zero-shot baseline that achieves only 23.7%. However, these zero-shot results lag behind the best supervised systems by 20.1% and 37.0% in the end-to-end and AEL tasks, respectively, highlighting the need for further research.

Large Language Models (LLMs) can generate content spanning ideological rhetoric to explicit instructions for violence. However, existing safety evaluations often rely on simplistic binary labels (safe and unsafe), overlooking the nuanced spectrum of risk these outputs pose. To address this, we present XGUARD, a benchmark and evaluation framework designed to assess the severity of extremist content generated by LLMs. XGUARD includes 3,840 red teaming prompts sourced from real world data such as social media and news, covering a broad range of ideologically charged scenarios. Our framework categorizes model responses into five danger levels (0 to 4), enabling a more nuanced analysis of both the frequency and severity of failures. We introduce the interpretable Attack Severity Curve (ASC) to visualize vulnerabilities and compare defense mechanisms across threat intensities. Using XGUARD, we evaluate six popular LLMs and two lightweight defense strategies, revealing key insights into current safety gaps and trade-offs between robustness and expressive freedom. Our work underscores the value of graded safety metrics for building trustworthy LLMs.

--Smart contracts can implement and automate parts of legal contracts, but ensuring their legal compliance remains challenging. Existing approaches such as formal specification, verification, and model-based development require expertise in both legal and software development domains, as well as extensive manual effort. Given the recent advances of Large Language Models (LLMs) in code generation, we investigate their ability to generate legally compliant smart contracts directly from natural language legal contracts, addressing these challenges. We propose a novel suite of metrics to quantify legal compliance based on modeling both legal and smart contracts as processes and comparing their behaviors. We select four LLMs, generate 20 smart contracts based on five legal contracts, and analyze their legal compliance. We find that while all LLMs generate syntactically correct code, there is significant variance in their legal compliance with larger models generally showing higher levels of compliance. We also evaluate the proposed metrics against properties of software metrics, showing they provide fine-grained distinctions, enable nuanced comparisons, and are applicable across domains for code from any source, LLM or developer . Our results suggest that LLMs can assist in generating starter code for legally compliant smart contracts with strict reviews, and the proposed metrics provide a foundation for automated and self-improving development workflows. Blockchains are increasingly used for multi-party business processes [1], with users making direct agreements via immutable programs known as smart contracts [2]. With the tokenized asset market projected to reach US $16.1 trillion by 2030 [3], businesses are rapidly developing smart contract-based services on blockchain platforms. To avoid significant legal, financial, and reputational risks, these smart contracts must comply with legal constraints that arise from legal contracts between stakeholders [4], [5] and regulatory frameworks in the operating jurisdiction [6]. Manually implementing legally compliant code is a time-consuming and error-prone process, especially for complex multi-party agreements. With recent advancements in large language models (LLMs) such as GPT -4, there has been a growing interest in using generative models to write code.

As large language models (LLMs) grow in power and influence, ensuring their safety and preventing harmful output becomes critical. Automated red teaming serves as a tool to detect security vulnerabilities in LLMs without manual labor. However, most existing methods struggle to balance the effectiveness and diversity of red-team generated attack prompts. To address this challenge, we propose \ourapproach, a novel automated red teaming training framework that utilizes reinforcement learning to explore and generate more effective attack prompts while balancing their diversity. Specifically, it consists of three training stages: (1) Cold Start: The red team model is supervised and fine-tuned on a jailbreak dataset obtained through imitation learning. (2) Warm-up Exploration: The model is trained in jailbreak instruction following and exploration, using diversity and consistency as reward signals. (3) Enhanced Jailbreak: Progressive jailbreak rewards are introduced to gradually enhance the jailbreak performance of the red-team model. Extensive experiments on a variety of LLMs show that \ourapproach effectively balances the diversity and effectiveness of jailbreak prompts compared to existing methods. Our work significantly improves the efficiency of red team exploration and provides a new perspective on automated red teaming.

Recent advances in Large Language Models (LLMs) highlight the need to align their behaviors with human values. A critical, yet understudied, issue is the potential divergence between an LLM's stated preferences (its reported alignment with general principles) and its revealed preferences (inferred from decisions in contextualized scenarios). Such deviations raise fundamental concerns for the interpretability, trustworthiness, reasoning transparency, and ethical deployment of LLMs, particularly in high-stakes applications. This work formally defines and proposes a method to measure this preference deviation. We investigate how LLMs may activate different guiding principles in specific contexts, leading to choices that diverge from previously stated general principles. Our approach involves crafting a rich dataset of well-designed prompts as a series of forced binary choices and presenting them to LLMs. We compare LLM responses to general principle prompts stated preference with LLM responses to contextualized prompts revealed preference, using metrics like KL divergence to quantify the deviation. We repeat the analysis across different categories of preferences and on four mainstream LLMs and find that a minor change in prompt format can often pivot the preferred choice regardless of the preference categories and LLMs in the test. This prevalent phenomenon highlights the lack of understanding and control of the LLM decision-making competence. Our study will be crucial for integrating LLMs into services, especially those that interact directly with humans, where morality, fairness, and social responsibilities are crucial dimensions. Furthermore, identifying or being aware of such deviation will be critically important as LLMs are increasingly envisioned for autonomous agentic tasks where continuous human evaluation of all LLMs' intermediary decision-making steps is impossible.

Narrative frames are a powerful way of conceptualizing and communicating complex, controversial ideas, however automated frame analysis to date has mostly overlooked this framing device. In this paper, we connect elements of narrativity with fundamental aspects of framing, and present a framework which formalizes and operationalizes such aspects. We annotate and release a data set of news articles in the climate change domain, analyze the dominance of narrative frame components across political leanings, and test LLMs in their ability to predict narrative frames and their components. Finally, we apply our framework in an unsupervised way to elicit components of narrative framing in a second domain, the COVID-19 crisis, where our predictions are congruent with prior theoretical work showing the generalizability of our approach.