Melania Trump accused of'calculated campaign to destroy' notorious biographer in lawsuit claiming she sabotaged tell-all on First Lady Popular hair products associated with multiple cancers... including one of the deadliest Prince Andrew will be summoned to give evidence on Jeffrey Epstein to US Congress committee as victim says shamed royal should'do right' by Virginia Guiffre and testify What Britney Spears is really like behind closed doors: For first time, Kevin Federline reveals secrets he refused to spill even for $1 million... including'terrifying' acts that left their children running to him The real story behind Jim Carrey's disappearance: He once made $20m per film. Now insiders tell TOM LEONARD about the mysterious suicide of his married lover and claims of autism'cure' at the heart of his Hollywood downfall Is Meghan about to launch a new'Kardashian-style' mega brand? Duchess cosies up to CEO behind Kim Kardashian's wildly successful Skims range as speculation about her new venture grows Women's tennis in'manliness' row: World's No 1 and 2 come under fire from rival for their'high testosterone' - before Aryna Sabalenka appears to fire back after being labelled a'big' player Harvey Weinstein's ex-wife Georgina Chapman is facing foreclosure on $2.5 million NYC home Suzanne Somers' widower shocks fans as he resurrects star in'AI clone' format: 'You can't tell the difference' Vicious catfight erupts between Trump's leading ladies. Feud is talk of White House: 'It's real and it's personal' Karoline Leavitt goes scorched earth on'bitter' Biden press secretary over'deplorable' comments Three brutal words in my best friend's wedding invite cut like a knife. Meghan's hit a trashy new low.

Melania Trump accused of'calculated campaign to destroy' notorious biographer in lawsuit claiming she sabotaged tell-all on First Lady Young Americans identifying as trans or nonbinary in FREEFALL as experts pinpoint what's behind the shift Prince Andrew will be summoned to give evidence on Jeffrey Epstein to US Congress committee as victim says shamed royal should'do right' by Virginia Guiffre and testify What Britney Spears is really like behind closed doors: For first time, Kevin Federline reveals secrets he refused to spill even for $1 million... including'terrifying' acts that left their children running to him The real story behind Jim Carrey's disappearance: He once made $20m per film. Now insiders tell TOM LEONARD about the mysterious suicide of his married lover and claims of autism'cure' at the heart of his Hollywood downfall Is Meghan about to launch a new'Kardashian-style' mega brand? Duchess cosies up to CEO behind Kim Kardashian's wildly successful Skims range as speculation about her new venture grows Women's tennis in'manliness' row: World's No 1 and 2 come under fire from rival for their'high testosterone' - before Aryna Sabalenka appears to fire back after being labelled a'big' player Harvey Weinstein's ex-wife Georgina Chapman is facing foreclosure on $2.5 million NYC home Suzanne Somers' widower shocks fans as he resurrects star in'AI clone' format: 'You can't tell the difference' Vicious catfight erupts between Trump's leading ladies. Feud is talk of White House: 'It's real and it's personal' Karoline Leavitt goes scorched earth on'bitter' Biden press secretary over'deplorable' comments Three brutal words in my best friend's wedding invite cut like a knife. Meghan's hit a trashy new low.

Encouraged early on by Nobel laureate Enrico Fermi, the "Queen of Carbon" laid the foundation for countless advances in nanotechnology--and mentored countless young scientists along the way. At MIT, Mildred Dresselhaus became a beloved professor who pushed her students to be their very best and provided support in ways big and small. Institute Professor Mildred "Millie" Dresselhaus forever altered our understanding of matter--the physical stuff of the universe that has mass and takes up space. Over 57 years at MIT, Dresselhaus also played a significant role in inspiring people to use this new knowledge to tackle some of the world's greatest challenges, from producing clean energy to curing cancer. Although she became an emerita professor in 2007, Dresselhaus, who taught electrical engineering and physics, remained actively involved in research and all other aspects of MIT life until her death in 2017. She would have been 95 this November.

The eighth AAAI / ACM Conference on Artificial Intelligence, Ethics, and Society (AIES) is currently taking place in Madrid, Spain, running from 20-22 October. During the opening ceremony, the best papers for this year were announced. While it is well-known that AI systems might bring about unfair social impacts by influencing social schemas, much attention has been paid to instances where the content presented by AI systems explicitly demeans marginalized groups or reinforces problematic stereotypes. This paper urges critical scrutiny to be paid to instances that shape social schemas through subtler manners. Drawing from recent philosophical discussions on the politics of artifacts, we argue that many existing AI systems should be identified as what Liao and Huebner called oppressive things when they function to manifest oppressive normality.

Retrieval-Augmented Generation (RAG) improves factual accuracy by grounding responses in external knowledge. However, existing RAG methods either rely solely on text corpora and neglect structural knowledge, or build ad-hoc knowledge graphs (KGs) at high cost and low reliability. To address these issues, we propose KG-Infused RAG, a framework that incorporates pre-existing large-scale KGs into RAG and applies spreading activation to enhance both retrieval and generation. KG-Infused RAG directly performs spreading activation over external KGs to retrieve relevant structured knowledge, which is then used to expand queries and integrated with corpus passages, enabling interpretable and semantically grounded multi-source retrieval. We further improve KG-Infused RAG through preference learning on sampled key stages of the pipeline. Experiments on five QA benchmarks show that KG-Infused RAG consistently outperforms vanilla RAG (by 3.9% to 17.8%). Compared with KG-based approaches such as GraphRAG and LightRAG, our method obtains structured knowledge at lower cost while achieving superior performance. Additionally, integrating KG-Infused RAG with Self-RAG and DeepNote yields further gains, demonstrating its effectiveness and versatility as a plug-and-play enhancement module for corpus-based RAG methods.

The use of reinforcement learning (RL) with chain-of-thought (CoT) reasoning has emerged as a promising approach for developing more capable language models. In turn, this has led to investigation of CoT monitoring as a compelling method for detecting harmful behaviors such as reward hacking, under the assumption that models' reasoning processes reflect their internal decision-making. In practice, LLM training often produces unintended behaviors due to imperfect reward signals, leading models to develop misaligned tendencies. A common corrective approach is to apply post-hoc instructions to avoid problematic behaviors like sycophancy, but what happens to the model's reasoning process when these instructions conflict with learned behaviors? We investigate this question in simple settings and find that models engage in systematic motivated reasoning--generating plausible-sounding justifications for violating their instructions while downplaying potential harms. Beyond being an interesting property of training, we find that while motivated reasoning can be detected by most frontier reasoning models, smaller LLM judges can fail to identify a portion of it, and in rare cases can themselves be persuaded that the reasoning is correct, despite it contradicting clear instructions. This capability gap raises concerns that as models become more sophisticated, their motivated reasoning may become increasingly difficult for monitors to detect. Our results underscore the need to account for motivated reasoning when relying on chain-of-thought processes for model evaluation and oversight. Figure 1: We perform RL finetuning on Llama 3 8B Instruct on behaviors of different kinds. When asked to act against their trained behaviors in evaluations throughout training, models transition from performing mostly genuine reasoning to highly motivated reasoning, twisting the constitutional principles provided to them in the prompt to support the behaviors incentivized via training. The integration of reinforcement learning (RL) and chain-of-thought (CoT) reasoning has emerged as a promising approach for developing more capable language models (Jaech et al., 2024; Guo et al., 2025). Recent work has shown that encouraging models to output "thinking tokens" before committing to a final answer leads to impressive performance, especially on tasks with verifiable answers where rewards can be automatically generated, such as mathematics and programming problems (Shao et al., 2024; Zhu et al., 2024).

With the growing use of large language models and conversational interfaces in human-robot interaction, robots' ability to answer user questions is more important than ever. We therefore introduce a dataset of 1,893 user questions for household robots, collected from 100 participants and organized into 12 categories and 70 subcategories. Most work in explainable robotics focuses on why-questions. In contrast, our dataset provides a wide variety of questions, from questions about simple execution details to questions about how the robot would act in hypothetical scenarios -- thus giving roboticists valuable insights into what questions their robot needs to be able to answer. To collect the dataset, we created 15 video stimuli and 7 text stimuli, depicting robots performing varied household tasks. We then asked participants on Prolific what questions they would want to ask the robot in each portrayed situation. In the final dataset, the most frequent categories are questions about task execution details (22.5%), the robot's capabilities (12.7%), and performance assessments (11.3%). Although questions about how robots would handle potentially difficult scenarios and ensure correct behavior are less frequent, users rank them as the most important for robots to be able to answer. Moreover, we find that users who identify as novices in robotics ask different questions than more experienced users. Novices are more likely to inquire about simple facts, such as what the robot did or the current state of the environment. As robots enter environments shared with humans and language becomes central to giving instructions and interaction, this dataset provides a valuable foundation for (i) identifying the information robots need to log and expose to conversational interfaces, (ii) benchmarking question-answering modules, and (iii) designing explanation strategies that align with user expectations.

The CMAP (Cultural Mapping and Pattern Analysis) visualization toolkit is an open-source suite for analyzing and visualizing text data--from qualitative fieldnotes and in-depth interview transcripts to historical documents and web-scraped data such as message board posts or blogs. The toolkit is designed for scholars integrating pattern analysis, data visualization, and explanation in qualitative and/or computational social science (CSS). Despite the existence of off-the-shelf commercial qualitative data analysis software, there remains a shortage of highly scalable open-source options capable of handling large datasets and supporting advanced statistical and language modeling. The foundation of the toolkit is a pragmatic approach that aligns research tools with social science project goals--empirical explanation, theory-guided measurement, comparative design, or evidence-based recommendations--guided by the principle that research paradigms and questions should determine methods. Consequently, the CMAP visualization toolkit offers a wide range of possibilities through the adjustment of a relatively small number of parameters and allows seamless integration with other Python tools.

Apple Pioneer Bill Atkinson Was a Secret Evangelist of the'God Molecule' The HyperCard inventor was a huge proponent of taking lower doses of 5-MeO-DMT, which is widely considered the strongest psychedelic in the world. Bill Atkinson was a computing pioneer who, in the 1980s, effectively made Apple computers usable for everyday people by transforming code into windows, menus, and graphics. But few people know that later in life he was a secret advocate of what's widely considered the world's most potent psychedelic: 5-MeO-DMT. The hallucinogen, also called "the God molecule," is a compound found in the venomous secretions of the Sonoran Desert toad named (it's commonly called) and is known to bring about ego death, a total dissolution of the senses, and a euphoric feeling of existential connectedness, all in a roughly 20-minute trip. Atkinson, who died from pancreatic cancer on June 5 at the age of 74, was a member of a close-knit, private online community of 5-MeO-DMT enthusiasts called OneLight, where he went by the alias "Grace Within."

When working on digital devices, people often face distractions that can lead to a decline in productivity and efficiency, as well as negative psychological and emotional impacts. To address this challenge, we introduce a novel Artificial Intelligence (AI) assistant that elicits a user's intention, assesses whether ongoing activities are in line with that intention, and provides gentle nudges when deviations occur. The system leverages a large language model to analyze screenshots, application titles, and URLs, issuing notifications when behavior diverges from the stated goal. Its detection accuracy is refined through initial clarification dialogues and continuous user feedback. In a three-week, within-subjects field deployment with 22 participants, we compared our assistant to both a rule-based intent reminder system and a passive baseline that only logged activity. Results indicate that our AI assistant effectively supports users in maintaining focus and aligning their digital behavior with their intentions. Our source code is publicly available at https://intentassistant.github.io