"Noise" protection can be added to content before it's uploaded online. A new technique developed by Australian researchers could stop unauthorised artificial intelligence (AI) systems learning from photos, artwork and other image-based content. Developed by CSIRO, Australia's national science agency, in partnership with the Cyber Security Cooperative Research Centre (CSCRC) and the University of Chicago, the method subtly alters content to make it unreadable to AI models while remaining unchanged to the human eye. This could help artists, organisations and social media users protect their work and personal data from being used to train AI systems or create deepfakes. For example, a social media user could automatically apply a protective layer to their photos before posting, preventing AI systems from learning facial features for deepfake creation.

Reasoning-augmented search agents such as Search-R1, trained via reinforcement learning with verifiable rewards (RLVR), demonstrate remarkable capabilities in multi-step information retrieval from external knowledge sources. These agents address the limitations of their parametric memory by dynamically gathering relevant facts to address complex reasoning tasks. However, existing approaches suffer from a fundamental architectural limitation: they process search queries strictly sequentially, even when handling inherently parallelizable and logically independent comparisons. This sequential bottleneck significantly constrains computational efficiency, particularly for queries that require multiple entity comparisons. To address this critical limitation, we propose ParallelSearch, a novel reinforcement learning framework that empowers large language models (LLMs) to recognize parallelizable query structures and execute multiple search operations concurrently. Our approach introduces dedicated reward functions that incentivize the identification of independent query components while preserving answer accuracy through jointly considering correctness, query decomposition quality, and parallel execution benefits. Comprehensive experiments demonstrate that ParallelSearch outperforms state-of-the-art baselines by an average performance gain of 2.9% across seven question-answering benchmarks. Notably, on parallelizable questions, our method achieves a 12.7% performance improvement while requiring only 69.6% of the LLM calls compared to sequential approaches.

We introduce MapStory, an LLM-powered animation prototyping tool that generates editable map animation sequences directly from natural language text by leveraging a dual-agent LLM architecture. Given a user written script, MapStory automatically produces a scene breakdown, which decomposes the text into key map animation primitives such as camera movements, visual highlights, and animated elements. Our system includes a researcher agent that accurately queries geospatial information by leveraging an LLM with web search, enabling automatic extraction of relevant regions, paths, and coordinates while allowing users to edit and query for changes or additional information to refine the results. Additionally, users can fine-tune parameters of these primitive blocks through an interactive timeline editor. We detail the system's design and architecture, informed by formative interviews with professional animators and by an analysis of 200 existing map animation videos. Our evaluation, which includes expert interviews (N=5) and a usability study (N=12), demonstrates that MapStory enables users to create map animations with ease, facilitates faster iteration, encourages creative exploration, and lowers barriers to creating map-centric stories.

AI-generated text is proliferating across domains, from creative writing and journalism to marketing content and scientific articles. Models can follow user-provided instructions to generate coherent and grammatically correct outputs but in this work, we study a more fundamental question: how do we evaluate and improve the writing quality of AI-generated text? Writing quality assessment has received less attention from the community, in part because it is fundamentally subjective and requires expertise. We first introduce the Writing Quality Benchmark (WQ) by consolidating five writing-preference datasets into 4,729 writing quality judgments. Our experiments show that most of the competitive baselines, including state-of-the-art LLMs that excel at reasoning tasks, barely outperform random baselines on WQ. We then train specialized Writing Quality Reward Models (WQRM) of various sizes for writing quality assessment that demonstrate strong generalization on four out-of-distribution test sets and 74% accuracy on the WQ benchmark. To further show WQRM's practical benefits during inference, we leverage additional test-time compute to generate and rank multiple candidate revisions, allowing us to select higher-quality outputs from an initial draft. Human evaluation with 9 experienced writers confirm that WQRM-based selection produces writing samples preferred by experts 66% overall, and 72.2% when the reward gap is larger than 1 point. We release our datasets and models to encourage community engagement with writing quality assessment and development of AI writing systems better aligned with human preferences.

Principle Engraving: In the third stage, we fine-tune the original LLM (the base model) on the self-aligned responses, generated by the LLM itself through prompting, while pruning the principles and demonstrations for the fine-tuned model.

--As large language models are increasingly deployed in sensitive environments, fingerprinting attacks pose significant privacy and security risks. We present a study of LLM fingerprinting from both offensive and defensive perspectives. Our attack methodology uses reinforcement learning to automatically optimize query selection, achieving better fingerprinting accuracy with only 3 queries compared to randomly selecting 3 queries from the same pool. Our defensive approach employs semantic-preserving output filtering through a secondary LLM to obfuscate model identity while maintaining semantic integrity. The defensive method reduces fingerprinting accuracy across tested models while preserving output quality. These contributions show the potential to improve fingerprinting tools capabilities while providing practical mitigation strategies against fingerprinting attacks. Large language models (LLMs) have become ubiquitous across industries, from customer service chatbots to code generation tools and content creation platforms. As organizations increasingly rely on these models for sensitive applications, the ability to identify which specific model generated a given text--known as LLM fingerprinting--has emerged as a critical security concern.

While everyone else has been talking about Nvidia's GPUs, Lisa Su has discreetly turned AMD into a chipmaking phenom. Su, the leader of AMD, moves fast these days, though I suspect that's always been the case. Her company's chips underpin the artificial intelligence that's changing the world at breakneck speeds. To hear Su and literally everyone else in semiconductors talk about it, the US is in an AI with China--and the rules keep changing . The Trump administration has once again shifted its stance on what kind of chips can and can't be shipped to China, with the latest decree being that the US will take a 15 percent cut of AMD and Nvidia chip sales to China. Meanwhile, on the home front, Su has claimed that AMD's newest AI chips can outperform Nvidia's--part of her strategy to keep eroding Nvidia's dominance in the market. So, yeah: Be ready to keep up. Under Lisa Su, the stalwart American semiconductor company has reasserted itself as a force in the age of AI. "Reasserted" doesn't do it justice: Su took a struggling AMD and executed a 10-year turnaround that has been, as one economist put it, nothing short of remarkable. Since 2014, when Su took over as CEO, AMD's market cap has risen from around $2 billion to nearly $300 billion. Aside from her well-known bona fides, Su herself--what drives her, what inspires her, what irritates her, where her politics lie--is less known. This is what I was hoping to learn when I visited AMD's offices and labs in the hills of Austin, Texas, on a day in late June when the wind seemed to do little more than push heat around. Our conversation kicked off with China, which accounts for nearly a quarter of AMD's business. Su now travels frequently to Washington, DC, to grease the wheels. "We've come to realize that export controls are a bit of a fact of life," she told me, "just given how critical the chips that we make are." In other words, it's precisely because AMD's chips are so darn important--to national security, to national economies--that they're now at the heart of modern statecraft.

Artificial intelligence researchers have proposed various data-driven algorithms to improve the processes that match individuals experiencing homelessness to scarce housing resources. It remains unclear whether and how these algorithms are received or adopted by practitioners and what their corresponding consequences are. Through semi-structured interviews with 13 policymakers in homeless services in Los An-geles, we investigate whether such change-makers are open to the idea of integrating AI into the housing resource matching process, identifying where they see potential gains and drawbacks from such a system in issues of efficiency, fairness, and transparency. Our qualitative analysis indicates that, even when aware of various complicating factors, policymak-ers welcome the idea of an AI matching tool if thoughtfully designed and used in tandem with human decision-makers. Though there is no consensus as to the exact design of such an AI system, insights from policymakers raise open questions and design considerations that can be enlightening for future researchers and practitioners who aim to build responsible algorithmic systems to support decision-making in low-resource scenarios.

Existing empirical methods often yield incomplete forgetting or unintended degradation of unrelated knowledge due to poor localization. In this work, we propose GRIN: a modular and targeted framework for LLM unlearning. GRIN introduces a novel gradient-ratio-based metric to identify parameters most responsible for memorizing forget data. We then perform selective noise injection into these parameters prior to fine-tuning, which improves unlearning performance while maintaining model utility. Finally, we propose new evaluation metrics tailored to the LLM setting and validate our approach on standard benchmarks such as TOFU, WMDP, and SafePKU. Content Warning: This paper contains examples of critically harmful language.

Actress, entrepreneur, and model Christie Brinkley joins'Fox & Friends' to discuss her new memoir "Uptown Girl," which reflects on her early life, marriages, and career in the public eye. Christie Brinkley and her daughter Sailor Brinkley-Cook have plenty in common despite their 44-year difference. The supermodel, 71, recently appeared on Kristin Davis' "Are You a Charlotte?" Both women were shocked by the results. "[Sailor] said, 'Mom, you're right not to go on [dating apps] because the same guys that, you know, said yes to me are saying yes to you,'" the Sports Illustrated Swimsuit model revealed.