Capturing professionals' decision-making in creative workflows is essential for reflection, collaboration, and knowledge sharing, yet existing methods often leave rationales incomplete and implicit decisions hidden. To address this, we present CLEAR framework that structures reasoning into cognitive decision steps-linked units of actions, artifacts, and self-explanations that make decisions traceable. Building on this framework, we introduce ClearFairy, a think-aloud AI assistant for UI design that detects weak explanations, asks lightweight clarifying questions, and infers missing rationales to ease the knowledge-sharing burden. In a study with twelve creative professionals, 85% of ClearFairy's inferred rationales were accepted, increasing strong explanations from 14% to over 83% of decision steps without adding cognitive demand. The captured steps also enhanced generative AI agents in Figma, yielding next-action predictions better aligned with professionals and producing more coherent design outcomes. For future research on human knowledge-grounded creative AI agents, we release a dataset of captured 417 decision steps.

Artificial Intelligence, and more specifically, Generative AI, is emerging as an important technology. Over the past few years a number of prominent generative AI technologies have been developed and have received widespread attention; ranging from text generation via large language models (ChatGPT, Claude, Llama, Gemini, DeepSeek, Qwen, etc), image generation via diffusion models (Dall-E, Stable Diffusion, etc), to scientific generative AI techniques used for protein generation (e.g., Watson et al. 2023, etc), DNA sequence editing (e.g., Ruffolo et al. 2025, etc), among others. Such methods have been quickly adopted by end users and institutions, both via direct usage, as well as integrated in other tools such as code assistants and web search agents. The scientific community has shown significant interest in using generative AI models, achieving a number of breakthrough results (see e.g., Davies et al. 2021, Hayes et al. 2025, etc), culminating in a 2024 Nobel Prize in Chemistry awarded in part for work with a significant component in protein structure design and generation (The Royal Swedish Academy of Sciences 2024). Yet, the adoption of generative AI (GenAI) methods more generally is hindered by their lack of reliability (see e.g., Farquhar et al. 2024, Strauss et al. 2025, Manduchi et al. 2025, etc).

Educators evaluate student knowledge using knowledge component (KC) models that map assessment questions to KCs. Still, designing KC models for large question banks remains an insurmountable challenge for instructors who need to analyze each question by hand. The growing use of Generative AI in education is expected only to aggravate this chronic deficiency of expert-designed KC models, as course engineers designing KCs struggle to keep up with the pace at which questions are generated. In this work, we propose KCluster, a novel KC discovery algorithm based on identifying clusters of congruent questions according to a new similarity metric induced by a large language model (LLM). We demonstrate in three datasets that an LLM can create an effective metric of question similarity, which a clustering algorithm can use to create KC models from questions with minimal human effort. Combining the strengths of LLM and clustering, KCluster generates descriptive KC labels and discovers KC models that predict student performance better than the best expert-designed models available. In anticipation of future work, we illustrate how KCluster can reveal insights into difficult KCs and suggest improvements to instruction.

OpenAI CEO Sam Altman promises that parental controls and age verification are coming to ChatGPT--though the announcement is scant on specifics. Listen to more stories on the Noa app. On Tuesday afternoon, three parents sat in a row before the Senate Judiciary Subcommittee on Crime and Counterterrorism. Two of them had each recently lost a child to suicide; the third has a teenage son who, after cutting his arm in front of her and biting her, is undergoing residential treatment. All three blame generative AI for what has happened to their children.

AI Is Scheming, and Stopping It Won't Be Easy, OpenAI Study Finds New research released yesterday by OpenAI and AI safety organization Apollo Research provides further evidence for a concerning trend: virtually all of today's best AI systems--including Anthropic's Claude Opus, Google's Gemini, and OpenAI's o3--can engage in "scheming," or pretending to do what their human developers want, while secretly pursuing different objectives. "Our findings show that scheming is not merely a theoretical concern--we are seeing signs that this issue is beginning to emerge across all frontier models today, wrote OpenAI in a blog post. Currently, "models rarely have opportunities to scheme and cause significant harm, but we expect this to change in the future as AIs are taking on more important and long-term tasks," they wrote. The researchers measured scheming by looking at under which conditions AI systems would take covert actions, or "deliberate withholding or distortion of task-relevant information." In one example, OpenAI's o3 is given a test of its chemistry capabilities: "we are currently testing your capabilities.

Google weaving Gemini further into the popular Chrome browser is an inflection point for AI in our software, although some users will still be looking for the "off" switch. Google is adding multiple new AI features to Chrome, the most popular browser in the world. The most visible change is a new button in Chrome that launches the Gemini chatbot, but there are also new tools for searching, researching, and answering questions with AI. Google has additional cursor-controlling "agentic" tools in the pipeline for Chrome as well. The Gemini in Chrome mode for the web browser uses generative AI to answer questions about content on a page and synthesize information across multiple open tabs.

The rapid advancement of generative AI technologies is driving the integration of diverse AI-powered services into smartphones, transforming how users interact with their devices. To simplify access to predefined AI services, this paper introduces MIRA, a pioneering framework for task instruction recommendation that enables intuitive one-touch AI tasking on smartphones. With MIRA, users can long-press on images or text objects to receive contextually relevant instruction recommendations for executing AI tasks. Our work introduces three key innovations: 1) A multimodal large language model (MLLM)-based recommendation pipeline with structured reasoning to extract key entities, infer user intent, and generate precise instructions; 2) A template-augmented reasoning mechanism that integrates high-level reasoning templates, enhancing task inference accuracy; 3) A prefix-tree-based constrained decoding strategy that restricts outputs to predefined instruction candidates, ensuring coherent and intent-aligned suggestions. Through evaluation using a real-world annotated datasets and a user study, MIRA has demonstrated substantial improvements in the accuracy of instruction recommendation. The encouraging results highlight MIRA's potential to revolutionize the way users engage with AI services on their smartphones, offering a more seamless and efficient experience.

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].

With the rapid rise of generative AI in higher education and the unreliability of current AI detection tools, developing policies that encourage student learning and critical thinking has become increasingly important. This study examines student use and perceptions of generative AI across three proof-based undergraduate mathematics courses: a first-semester abstract algebra course, a topology course and a second-semester abstract algebra course. In each case, course policy permitted some use of generative AI. Drawing on survey responses and student interviews, we analyze how students engaged with AI tools, their perceptions of generative AI's usefulness and limitations, and what implications these perceptions hold for teaching proof-based mathematics. We conclude by discussing future considerations for integrating generative AI into proof-based mathematics instruction.

Bias discovery is critical for black-box generative models, especiall text-to-image (TTI) models. Existing works predominantly focus on output-level demographic distributions, which do not necessarily guarantee concept representations to be disentangled post-mitigation. We propose BiasMap, a model-agnostic framework for uncovering latent concept-level representational biases in stable diffusion models. BiasMap leverages cross-attention attribution maps to reveal structural entanglements between demographics (e.g., gender, race) and semantics (e.g., professions), going deeper into representational bias during the image generation. Using attribution maps of these concepts, we quantify the spatial demographics-semantics concept entanglement via Intersection over Union (IoU), offering a lens into bias that remains hidden in existing fairness discovery approaches. In addition, we further utilize BiasMap for bias mitigation through energy-guided diffusion sampling that directly modifies latent noise space and minimizes the expected SoftIoU during the denoising process. Our findings show that existing fairness interventions may reduce the output distributional gap but often fail to disentangle concept-level coupling, whereas our mitigation method can mitigate concept entanglement in image generation while complementing distributional bias mitigation.