Large Language Models (LLMs) are prone to generating plausible yet incorrect responses, known as hallucinations. Effectively detecting hallucinations is therefore crucial for the safe deployment of LLMs. Recent research has linked hallucinations to model uncertainty, suggesting that hallucinations can be detected by measuring dispersion over answer distributions obtained from a set of samples drawn from a model. While drawing from the distribution over tokens defined by the model is a natural way to obtain samples, in this work, we argue that it is sub-optimal for the purpose of detecting hallucinations. We show that detection can be improved significantly by taking into account model uncertainty in the Bayesian sense. To this end, we propose a very simple and efficient approach that perturbs an appropriate subset of model parameters, or equivalently hidden unit activations, during sampling. We demonstrate its effectiveness across a wide range of datasets and model architectures.

Large language models (LLMs) often produce incorrect or outdated information, necessitating efficient and precise knowledge updates. Current model editing methods, however, struggle with long-form knowledge in diverse formats, such as poetry, code snippets, and mathematical derivations. These limitations arise from their reliance on editing a single token's hidden state, a limitation we term "efficacy barrier". To solve this, we propose AnyEdit, a new autoregressive editing paradigm. It decomposes long-form knowledge into sequential chunks and iteratively edits the key token in each chunk, ensuring consistent and accurate outputs. Theoretically, we ground AnyEdit in the Chain Rule of Mutual Information, showing its ability to update any knowledge within LLMs. Empirically, it outperforms strong baselines by 21.5% on benchmarks including UnKEBench, AKEW, and our new EditEverything dataset for long-form diverse-formatted knowledge. Additionally, AnyEdit serves as a plug-and-play framework, enabling current editing methods to update knowledge with arbitrary length and format, significantly advancing the scope and practicality of LLM knowledge editing.

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Mirror Me's Black Panther ran about 100 meters in under 10 seconds. A Chinese team has unveiled a groundbreaking quadruped robot that is pushing the boundaries of robotics and speed. The Black Panther 2.0, developed by Zhejiang University's humanoid innovation institute in collaboration with the Hangzhou-based startup Mirror Me, has achieved a remarkable feat by running approximately 100 meters in under 10 seconds. The design of the Black Panther 2.0 draws inspiration from various animals, resulting in a highly efficient biomechanical structure. Its carbon-fiber shins are modeled after jerboa desert rodents, increasing stiffness by an impressive 135% while only adding 16% to its weight.

Rep. Brandon Gill, R- Tex., got into a heated exchange with CNN host Pamela Brown over the Trump administration's crackdown on government spending, specifically for public broadcasting at PBS and NPR. By 1970, both PBS and NPR sprang forth from the CBP, and Americans were treated to the "News Hour," "Sesame Street," British comedies and science programming at a time when there were only three networks, cable TV was strictly for the boondocks, and VCRs were science fiction. A big part of the reason that programming was limited was that production costs for broadcasting were incredibly high. In David Grzybowski's book, 'The Big Story,' he cites Philadelphia news anchor Larry Kane talking about how hard it was during the 1979 Three Mile Island nuclear scare to just get a live TV shot from Harrisburg to Philly: "I know we had a live microwave, but the microwaves didn't go that far. I think we sought some satellite time. The satellite times in those days were 5,000 a minute."

OnePlus has been a bit up and down since it merged with Oppo back in 2021. It gained greater access to powerful components and partnerships with brands like Hasselblad, while its software and product lineup took a few steps back before finding its stride again. But now, three generations after the merger, OnePlus' latest flagship phone -- the OnePlus 13 -- feels like a fantastic return to form. In some areas, the company is even pushing the limits of hardware and gadget design in ways that rivals from Samsung and Google aren't. And with a starting price of 900, OnePlus has managed to undercut its closest competitor too, which makes this phone a great choice for anyone who cares more about getting hardware upgrades than fancy new AI tricks.

Sylvester Stallone is the latest celebrity embracing artificial intelligence. The "Rocky" star invested, along with several others, in Largo.ai, an AI-driven analytics platform for film, TV and advertising, raising 7.5 million in financing for the company. "We are very happy to have a cinema legend like Sylvester Stallone supporting our journey. Stallone's story in cinema and the disruption that he created is truly very inspiring for any entrepreneur like me and his support in the new era of cinema with AI by being a partner in Largo.ai is truly encouraging for us," Largo.ai CEO and co-founder Sami Arpa told Fox News Digital in a statement.

Hasbro Entertainment and Legendary Entertainment have joined forces to bring Magic: The Gathering to the big and small screens. The pair have signed a licensing deal to create "a live-action feature film and television universe" inspired by the card game. First up will be a movie, with other media to follow, but that's all that's been revealed so far. Longtime MTG fans might feel skeptical about this announcement, because this isn't the first time the intellectual property has been promised some kind of film or television adaptation. The card game's Fandom wiki page lists many of the proposed movie projects over the years.

Retrieval-augmented generation (RAG) has emerged as a promising technology for addressing hallucination issues in the responses generated by large language models (LLMs). Existing studies on RAG primarily focus on applying semantic-based approaches to retrieve isolated relevant chunks, which ignore their intrinsic relationships. In this paper, we propose a novel Knowledge Graph-Guided Retrieval Augmented Generation (KG$^2$RAG) framework that utilizes knowledge graphs (KGs) to provide fact-level relationships between chunks, improving the diversity and coherence of the retrieved results. Specifically, after performing a semantic-based retrieval to provide seed chunks, KG$^2$RAG employs a KG-guided chunk expansion process and a KG-based chunk organization process to deliver relevant and important knowledge in well-organized paragraphs. Extensive experiments conducted on the HotpotQA dataset and its variants demonstrate the advantages of KG$^2$RAG compared to existing RAG-based approaches, in terms of both response quality and retrieval quality.