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.

Joaquin Oliver was 17 years old when he was shot in the hallway of his high school. An older teenager, expelled some months previously, had opened fire with a high-powered rifle on Valentine's Day in what became America's deadliest high school shooting. Seven years on, Joaquin says he thinks it's important to talk about what happened on that day in Parkland, Florida, "so that we can create a safer future for everyone". But sadly, what happened to Joaquin that day is that he died. The oddly metallic voice speaking to the ex-CNN journalist Jim Acosta in an interview on Substack this week was actually that of a digital ghost: an AI, trained on the teenager's old social media posts at the request of his parents, who are using it to bolster their campaign for tougher gun controls.

Large Language Models (LLMs) have started to demonstrate the ability to persuade humans, yet our understanding of how this dynamic transpires is limited. Recent work has used linear probes, lightweight tools for analyzing model representations, to study various LLM skills such as the ability to model user sentiment and political perspective. Motivated by this, we apply probes to study persuasion dynamics in natural, multi-turn conversations. We leverage insights from cognitive science to train probes on distinct aspects of persuasion: persuasion success, persuadee personality, and persuasion strategy. Despite their simplicity, we show that they capture various aspects of persuasion at both the sample and dataset levels. For instance, probes can identify the point in a conversation where the persuadee was persuaded or where persuasive success generally occurs across the entire dataset. We also show that in addition to being faster than expensive prompting-based approaches, probes can do just as well and even outperform prompting in some settings, such as when uncovering persuasion strategy. This suggests probes as a plausible avenue for studying other complex behaviours such as deception and manipulation, especially in multi-turn settings and large-scale dataset analysis where prompting-based methods would be computationally inefficient.

This paper introduces a comprehensive benchmark for evaluating how Large Language Models (LLMs) respond to linguistic shibboleths: subtle linguistic markers that can inadvertently reveal demographic attributes such as gender, social class, or regional background. Through carefully constructed interview simulations using 100 validated question-response pairs, we demonstrate how LLMs systematically penalize certain linguistic patterns, particularly hedging language, despite equivalent content quality. Our benchmark generates controlled linguistic variations that isolate specific phenomena while maintaining semantic equivalence, which enables the precise measurement of demographic bias in automated evaluation systems. We validate our approach along multiple linguistic dimensions, showing that hedged responses receive 25.6% lower ratings on average, and demonstrate the benchmark's effectiveness in identifying model-specific biases. This work establishes a foundational framework for detecting and measuring linguistic discrimination in AI systems, with broad applications to fairness in automated decision-making contexts.

--Spectral clustering is a leading clustering method. Two of its major shortcomings are the disjoint optimization process and the limited representation capacity. T o address these issues, we propose a deep spectral clustering model (named BootSC), which jointly learns all stages of spectral clustering-- affinity matrix construction, spectral embedding, and k -means clustering--using a single network in an end-to-end manner . Moreover, a semantically-consistent orthogonal re-parameterization technique is introduced to or-thogonalize spectral embeddings, significantly enhancing the discrimination capability. Experimental results indicate that BootSC achieves state-of-the-art clustering performance. For example, it accomplishes a notable 16% NMI improvement over the runner-up method on the challenging ImageNet-Dogs dataset. EEP clustering models aim to detect underlying cluster structures within unlabelled data. To train these models, creating effective and efficient supervision signals is necessary. Inadequate supervision could result in excessive computational costs [1], training instability [2], and degenerate results [3]. Classical deep clustering models [5], [6], [7], [8], [9], [10] commonly adopt cluster assignments obtained by k -means on data representations as training supervision. A major challenge with this k -means-style supervision is that data representations are assumed to follow simple isotropic Gaussian distributions.

Reinforcement learning-based retrieval-augmented generation (RAG) methods enhance the reasoning abilities of large language models (LLMs). However, most rely only on final-answer rewards, overlooking intermediate reasoning quality. This paper analyzes existing RAG reasoning models and identifies three main failure patterns: (1) information insufficiency, meaning the model fails to retrieve adequate support; (2) faulty reasoning, where logical or content-level flaws appear despite sufficient information; and (3) answer-reasoning inconsistency, where a valid reasoning chain leads to a mismatched final answer. We propose TIRESRAG-R1, a novel framework using a think-retrieve-reflect process and a multi-dimensional reward system to improve reasoning and stability. TIRESRAG-R1 introduces: (1) a sufficiency reward to encourage thorough retrieval; (2) a reasoning quality reward to assess the rationality and accuracy of the reasoning chain; and (3) a reflection reward to detect and revise errors. It also employs a difficulty-aware reweighting strategy and training sample filtering to boost performance on complex tasks. Experiments on four multi-hop QA datasets show that TIRESRAG-R1 outperforms prior RAG methods and generalizes well to single-hop tasks. The code and data are available at: https://github.com/probe2/TIRESRAG-R1.

As AI systems become integral to knowledge - intensive work, questions arise not only about their functionality but also their epistemic roles in human - AI interaction. While HCI research has proposed various AI role typologies, it often overlooks how AI resh apes users' roles as knowledge contributors. This study examines how users form epistemic relationships with AI -- how they assess, trust, and collaborate with it in research and teaching contexts. Based on 31 interviews with academics across disciplines, we developed a five - part codebook and identified five relationship types: Instrumental Reliance, Contingent Delegation, Co - agency Collaboration, Authority Displacement, and Epistemic Abstention. These reflect variations in trust, assessment modes, tasks, and human epistemic status. Our findings show that epistemic roles are dynamic and context dependent . We argue for shifting beyond static metaphors of AI toward a more nuanced framework that captures how humans and AI co - construct knowledge, enriching HCI's understanding of the relational and normative dimensions of AI use.