Figure 1: STRIVE can conduct zero-shot object navigation in diverse and complex real-world environments by leveraging our novel multi-layer representation and efficient two-stage navigation policy. Abstract-- Vision-Language Models (VLMs) have been increasingly integrated into object navigation tasks for their rich prior knowledge and strong reasoning abilities. However, applying VLMs to navigation presents two key challenges: effectively parsing and structuring complex environment information and determining when and how to query VLMs. T o address these challenges, we propose a novel framework that incrementally constructs a multi-layer environment representation consisting of viewpoints, object nodes, and room nodes during navigation. Viewpoints and object nodes facilitate intra-room exploration and accurate target localization, while room nodes support efficient inter-room planning. Building on this structured representation, we propose a novel two-stage navigation policy, integrating high-level planning guided by VLM reasoning with low-level VLM-assisted exploration to efficiently and reliably locate a goal object. Object navigation is a fundamental task in robotics, where an agent must locate an instance of a given object category in unknown environments. This task is particularly challenging, as it requires the agent to understand complex visual information, reason about spatial relationships, and make decisions based on both current and past observations. Advances in Vision-Language Models (VLMs) [1], [2], [3] have demonstrated strong capabilities in contextual visual understanding and common-sense reasoning. However, existing approaches often face two significant challenges: First, the input to VLMs typically lacks a structured representation of the environment and is often restricted to local observations.

Recent advances in large language models (LLMs) have opened the door to culture-aware language tasks. We introduce the novel problem of adapting wine reviews across Chinese and English, which goes beyond literal translation by incorporating regional taste preferences and culture-specific flavor descriptors. In a case study on cross-cultural wine review adaptation, we compile the first parallel corpus of professional reviews, containing 8k Chinese and 16k Anglophone reviews. We benchmark both neural-machine-translation baselines and state-of-the-art LLMs with automatic metrics and human evaluation. For the latter, we propose three culture-oriented criteria -- Cultural Proximity, Cultural Neutrality, and Cultural Genuineness -- to assess how naturally a translated review resonates with target-culture readers. Our analysis shows that current models struggle to capture cultural nuances, especially in translating wine descriptions across different cultures. This highlights the challenges and limitations of translation models in handling cultural content.

Large Language Models (LLMs) have shown remarkable prowess in text generation, yet producing long-form, factual documents grounded in extensive external knowledge bases remains a significant challenge. Existing "top-down" methods, which first generate a hypothesis or outline and then retrieve evidence, often suffer from a disconnect between the model's plan and the available knowledge, leading to content fragmentation and factual inaccuracies. To address these limitations, we propose a novel "bottom-up," data-driven framework that inverts the conventional generation pipeline. Our approach is predicated on a "Retrieval-First for Knowledge, Clustering for Structure" strategy, which first establishes the "knowledge boundaries" of the source corpus before any generative planning occurs. Specifically, we perform exhaustive iterative retrieval from the knowledge base and then employ an unsupervised clustering algorithm to organize the retrieved documents into distinct "knowledge clusters." These clusters form an objective, data-driven foundation that directly guides the subsequent generation of a hierarchical outline and the final document content. This bottom-up process ensures that the generated text is strictly constrained by and fully traceable to the source material, proactively adapting to the finite scope of the knowledge base and fundamentally mitigating the risk of hallucination. Experimental results on both 14B and 32B parameter models demonstrate that our method achieves performance comparable to or exceeding state-of-the-art baselines, and is expected to demonstrate unique advantages in knowledge-constrained scenarios that demand high fidelity and structural coherence. Our work presents an effective paradigm for generating reliable, structured, long-form documents, paving the way for more robust LLM applications in high-stakes, knowledge-intensive domains.

Microsoft and Nvidia unveiled plans to invest up to $45 billion in the UK during US President Donald Trump's state visit. Microsoft and Nvidia have unveiled plans to invest up to $45 billion dollars into the UK economy, in a move that will bolster the building of more data centers as well as research and development into artificial intelligence . The investment comes as US president Donald Trump travels to Britain, where he is expected to announce a US-UK tech deal alongside UK prime minister Keir Starmer. As part of the agreement, Microsoft has committed to invest $30 billion in AI infrastructure over the next four years. The company claims this is the largest financial commitment it has ever made in the UK and will make up more than two thirds of the total investment announced into the UK this week, timed to Trump's visit.

CEO Sam Altman announced an age-prediction system and new parental controls in a blog post on Tuesday. OpenAI announced new teen safety features for ChatGPT on Tuesday as part of an ongoing effort to respond to concerns about how minors engage with chatbots . The company is building an age-prediction system that identifies if a user is under 18 years old and routes them to an " age-appropriate " system that blocks graphic sexual content. If the system detects that the user is considering suicide or self-harm, it will contact the user's parents. In cases of imminent danger, if a user's parents are unreachable, the system may contact the authorities.

Inside Anthropic's Big Washington Push Welcome back to In the Loop, new twice-weekly newsletter about AI. If you're reading this in your browser, why not subscribe to have the next one delivered straight to your inbox? The AI industry has descended upon Washington. The industry recently pledged up to $200 million toward new super PACs aimed at influencing upcoming elections. And on Monday, I attended an event that epitomized this swell of capital and effort: The Anthropic Futures Forum.

AI tools including Perplexity and Open AI's GPT-4 often provide one-sided answers to contentious questions, and don't back up their arguments with reliable sources How well-supported are the claims made by AI tools? Generative AI tools, and the deep research agents and search engines powered by them, frequently make unsupported and biased claims that aren't backed up by the sources they cite. That's according to an analysis which found that about one-third of answers provided by the AI tools aren't backed up by reliable sources. For OpenAI's GPT 4.5, the figure was even higher, at 47 per cent. Alongside this, they put five deep research agents through their paces: GPT-5's Deep Research feature, Bing Chat's Think Deeper option and deep research tools offered by You.com, Google Gemini and Perplexity.

As long as there has been AI, there have been people sounding alarms about what it might do to us: rogue superintelligence, mass unemployment, or environmental ruin. But another threat entirely--that of kids forming unhealthy bonds with AI--is pulling AI safety out of the academic fringe and into regulators' crosshairs. This has been bubbling for a while. Two high-profile lawsuits filed in the last year, against Character.AI and OpenAI, allege that their models contributed to the suicides of two teenagers. A study published in July, found that 72% of teenagers have used AI for companionship. And stories about "AI psychosis" have highlighted how endless conversations with chatbots can lead people down delusional spirals.

Gary Rivlin joins Elizabeth Spiers to discuss his book on Silicon Valley's race to capitalize on AI. Please enable javascript to get your Slate Plus feeds. If you can't access your feeds, please contact customer support. Check your phone for a link to finish setting up your feed. Please enter a valid phone number.

The Cloudflare CEO joined to talk about standing up to content scraping, the internet's potential futures, and his company's relationship to Trump. Matthew Prince may not be a household name, but the world most certainly knows his work. Prince is the cofounder and CEO of Cloudflare . Launched in 2010, the internet infrastructure company has found itself increasingly in the position of serving as the web's bodyguard. It filters out bad traffic, keeps sites safe, and stops them from crashing when too many people visit. Its tools defend against DDoS attacks. In 2017, Cloudflare made headlines when it dropped white supremacist site The Daily Stormer . Cloudflare's severing of ties with The Daily Stormer marked a momentous shift, one that came after years of claiming a neutral stance. Prince continues to evolve the way Cloudflare works. In July, the company rolled out a new tool tasked with blocking unauthorized AI scraping. It effectively creates a pay-per-crawl model requiring AI platforms to shell out money if they want access to a site's content. On this episode of, I talked to Prince about publishing, the old internet, and how his ideal version of the future web means that OpenAI just might become the Netflix of content. KATIE DRUMMOND: Good to have you here, Matthew. You should have been warned ahead of time, but you probably weren't.