While Code Language Models (CLMs) have demonstrated superior performance in software engineering tasks such as code generation and summarization, recent empirical studies reveal a critical privacy vulnerability: these models exhibit unintended memorization of sensitive training data, enabling verbatim reproduction of confidential information when specifically prompted. To address this issue, several approaches, including training data de-duplication and differential privacy augmentation, have been proposed. However, these methods require full-model retraining for deployed CLMs, which incurs substantial computational costs. In this paper, we aim to answer the following research question: Can sensitive information memorized by CLMs be erased effectively and efficiently? We conduct a pioneering investigation into erasing sensitive memorization in CLMs through machine unlearning - a post-hoc modification method that removes specific information from trained models without requiring full retraining. Specifically, we first quantify the memorization risks of sensitive data within CLM training datasets and curate a high-risk dataset of 50,000 sensitive memorized samples as unlearning targets. We study two widely used gradient ascent-based unlearning approaches: the vanilla and constraint-based methods, and introduce CodeEraser, an advanced variant that selectively unlearns sensitive memorized segments in code while preserving the structural integrity and functional correctness of the surrounding code. Extensive experiments on three families of CLMs, i.e., CodeParrot, CodeGen-Mono, and Qwen2.5-Coder, validate the effectiveness and efficiency of CodeEraser in erasing targeted sensitive memorization while maintaining model utility.

Despite the significant progress of deep learning models in multitude of applications, their adaption in planning and policy related areas remains challenging due to the black-box nature of these models. In this work, we develop a set of DeepLogit models that follow a novel sequentially constrained approach in estimating deep learning models for transport policy analysis. In the first step of the proposed approach, we estimate a convolutional neural network (CNN) model with only linear terms, which is equivalent of a linear-in-parameter multinomial logit model. We then estimate other deep learning models by constraining the parameters that need interpretability at the values obtained in the linear-in-parameter CNN model and including higher order terms or by introducing advanced deep learning architectures like Transformers. Our approach can retain the interpretability of the selected parameters, yet provides significantly improved model accuracy than the discrete choice model. We demonstrate our approach on a transit route choice example using real-world transit smart card data from Singapore. This study shows the potential for a unifying approach, where theory-based discrete choice model (DCM) and data-driven AI models can leverage each other's strengths in interpretability and predictive power. With the availability of larger datasets and more complex constructions, such approach can lead to more accurate models using discrete choice models while maintaining its applicability in planning and policy-related areas. Our code is available on https://github.com/jeremyoon/route-choice/ .

Abstract-- Autonomous LLM agents can issue thousands of API calls per hour without human oversight. OAuth 2.0 assumes deterministic clients, but in agentic settings stochastic reasoning, prompt injection, or multi-agent orchestration can silently expand privileges. This paper describes Agentic JWT (A-JWT), a dual-faceted token design that binds each agent action to a cryptographically verifiable user intent and optionally to a workflow step. A-JWT carries an agent's identity as a one-way checksum hash derived from its prompt, tools and configuration and a chained delegation assertion to prove which downstream agent may execute a given task. The design also uses per-agent proof-of-possession keys to prevent replay and in-process impersonation. The paper introduces a new unique authorization grant called'agent_checksum' and adds a lightweight client shim library that self-verifies code at run time, mints intent tokens, tracks workflow steps and derives keys thus enabling secure agent identity and separation even within a single process. We illustrate a comprehensive threat model for agentic applications, implement a Python proof-of-concept, and show functional blocking of scope-violating requests, replay, impersonation, and prompt-injection pathways with sub-millisecond overhead on commodity hardware. The design aligns with ongoing OAuth agent discussions and offers a drop-in path toward zero-trust guarantees for agentic applications. A comprehensive performance and security evaluation with experimental results will appear in our forthcoming journal submission. I. Introduction AI Agents are not a theoretical phenomenon anymore. Large enterprises now use AI agents [1], to possibly execute millions of API calls per hour. Major cloud LLMs now serve hundreds of millions of API requests per day, for example Baidu's ERNIE handles approximately 200 M daily queries, providing the raw horsepower that agent frameworks build on [2], yet those calls still ride on OAuth tokens designed for deterministic clients. A quick peek into the scale of operations and future trends would reveal that the volume of AI Agent activity has grown dramatically, underscoring their operational impact. Baidu's large volume of API calls per day has seen a 4 fold increase in just a few months [2]. A recent cloud survey found OpenAI/Azure AI services are used in 67% of cloud deployments, alongside a rise in self-hosted AI models across 75% of organizations [3].

Large language models (LLMs) show promise for translating natural-language statutes into executable logic, but reliability in legally critical settings remains challenging due to ambiguity and hallucinations. We present an agentic approach for developing legal-critical software, using U.S. federal tax preparation as a case study. The key challenge is test-case generation under the oracle problem, where correct outputs require interpreting law. Building on metamorphic testing, we introduce higher-order metamorphic relations that compare system outputs across structured shifts among similar individuals. Because authoring such relations is tedious and error-prone, we use an LLM-driven, role-based framework to automate test generation and code synthesis. We implement a multi-agent system that translates tax code into executable software and incorporates a metamorphic-testing agent that searches for counterexamples. In experiments, our framework using a smaller model (GPT-4o-mini) achieves a worst-case pass rate of 45%, outperforming frontier models (GPT-4o and Claude 3.5, 9-15%) on complex tax-code tasks. These results support agentic LLM methodologies as a path to robust, trustworthy legal-critical software from natural-language specifications.

The upsurge of policies and guidelines that aim to ensure Artificial Intelligence (AI) systems are safe and trustworthy has led to a fragmented landscape of AI governance. The European Union (EU) is a key actor in the development of such policies and guidelines. Its High-Level Expert Group (HLEG) issued an influential set of guidelines for trustworthy AI, followed in 2024 by the adoption of the EU AI Act. While the EU policies and guidelines are expected to be aligned, they may differ in their scope, areas of emphasis, degrees of normativity, and priorities in relation to AI. To gain a broad understanding of AI governance from the EU perspective, we leverage qualitative thematic analysis approaches to uncover prevalent themes in key EU documents, including the AI Act and the HLEG Ethics Guidelines. We further employ quantitative topic modelling approaches, specifically through the use of the BERTopic model, to enhance the results and increase the document sample to include EU AI policy documents published post-2018. We present a novel perspective on EU policies, tracking the evolution of its approach to addressing AI governance.

Generating regulatorily compliant Suspicious Activity Report (SAR) remains a high-cost, low-scalability bottleneck in Anti-Money Laundering (AML) workflows. While large language models (LLMs) offer promising fluency, they suffer from factual hallucination, limited crime typology alignment, and poor explainability -- posing unacceptable risks in compliance-critical domains. This paper introduces Co-Investigator AI, an agentic framework optimized to produce Suspicious Activity Reports (SARs) significantly faster and with greater accuracy than traditional methods. Drawing inspiration from recent advances in autonomous agent architectures, such as the AI Co-Scientist, our approach integrates specialized agents for planning, crime type detection, external intelligence gathering, and compliance validation. The system features dynamic memory management, an AI-Privacy Guard layer for sensitive data handling, and a real-time validation agent employing the Agent-as-a-Judge paradigm to ensure continuous narrative quality assurance. Human investigators remain firmly in the loop, empowered to review and refine drafts in a collaborative workflow that blends AI efficiency with domain expertise. We demonstrate the versatility of Co-Investigator AI across a range of complex financial crime scenarios, highlighting its ability to streamline SAR drafting, align narratives with regulatory expectations, and enable compliance teams to focus on higher-order analytical work. This approach marks the beginning of a new era in compliance reporting -- bringing the transformative benefits of AI agents to the core of regulatory processes and paving the way for scalable, reliable, and transparent SAR generation.

A House Oversight Committee hearing produced a flood of bizarre claims about cloud seeding, chemtrails, and solar geoengineering. Proven, human-driven changes to the weather were dismissed. "What this whole debate comes down to is who controls the skies," Republican representative Marjorie Taylor Greene of Georgia told the audience at a House Oversight Committee hearing on Tuesday. "Do we believe in God and that he has dominion over his perfect creation of planet Earth? Do we believe that he has given us everything we need to survive as a civilization since the beginning of time? Or do you believe in man's claim of authority over the weather, based on scientists that have only been alive for decades and weren't here to witness the climate changes since the beginning of time?"

Silicon Valley startup Tensor plans to sell the world's first personal robocar, allowing consumers to own self-driving cars by 2026.

Steven Pinker's new book shows how he's become a contradictory figure Steven Pinker's new book When Everyone Knows That Everyone Knows makes a compelling case for common knowledge. Steven Pinker argues that "cancel culture" is a form of censorship Steven Pinker's new book perfectly encapsulates what a contradictory figure he has become. Much of it is a clear, fascinating explanation of a major psychological phenomenon . But then he starts telling you what he thinks about current affairs. Pinker is a psychologist at Harvard University who has written a string of popular science books. Some, like Words and Rules, are rooted in his own research and are a good read.

OpenAI will restrict how ChatGPT responds to a user it suspects is under 18. OpenAI will restrict how ChatGPT responds to a user it suspects is under 18. Sam Altman said if there is doubt the system will default to the under-18 experience putting'safety ahead of privacy and freedom for teens' OpenAI will restrict how ChatGPT responds to a user it suspects is under 18, unless that user passes the company's age estimation technology or provides ID, after legal action from the family of a 16-year-old who killed himself in April after months of conversations with the chatbot. OpenAI was prioritising "safety ahead of privacy and freedom for teens", chief executive Sam Altman said in a blog post on Tuesday, stating "minors need significant protection". The company said that the way ChatGPT responds to a 15-year-old should look different to the way it responds to an adult.