Jon Stewart leads defense of Jimmy Kimmel as late night hosts unite to mock Trump's'censorship' Trump just humiliated Harry and Meghan with two brutal words... but even more embarrassing is the reason they're having to stay silent: MAUREEN CALLAHAN Disturbing full story of singer D4vd's relationship with girl, 13, found dismembered in his Tesla... as creepy messages, songs and links to stars are exposed Utter chaos breaks out backstage at The View over Jimmy Kimmel: Hosts at war and staff in fear... as network bosses impose strict new'rule' The strain shows on Jimmy Kimmel as he emerges for first time after show's shock cancellation Queen Camilla appears to'pull rank' as Kate chats animatedly with Melania during State visit - and ushers Princess back towards William Seth Meyers responds to Jimmy Kimmel cancellation with dose of mockery for Trump: 'A great president, an even better golfer' President of America's biggest university forced to step down over'transgender indoctrination' Starbucks responds after barista refuses to write'Charlie Kirk' on customer's cup due to'policy' Millions under tsunami threat as fallout from monster 7.8 earthquake threatens US Woman, 84, horrified after stomach-churning discovery in Morrisons juice: 'Dead snake' slithered out of carton Two elderly women were left horrified and upset when they found a'dead snake' in a carton of fruit juice--and refuse to believe supermarket bosses' claim that the foot-long gelatinous entity is merely a string of mould. Betty Richards, 84, bought a carton of apple and mango juice from the Armthorpe branch of Morrisons as a treat for her best friend of twenty years, Julie Bircumshaw, also 84. The BBC reports that Ms Bircumshaw noticed some'bits of black' around the opening of the 1L carton, but after tasting the juice, thought it was fine to drink. When Ms Richards popped over to see her friend at home in Doncaster a week later, she was told about the discolouration around the nozzle. She was concerned, and decided to take a closer look at the £1.35 carton--which was almost empty.

Hyabusa2 is already 105.5 million miles on its journey, but new data indicates trouble. Breakthroughs, discoveries, and DIY tips sent every weekday. Japan's Hyabusa2 space probe is currently about 105.5 million miles away, en route to its second asteroid rendezvous . However, revised data collected from a global network of observatories now indicates that the space rock designated as 1998 KY26 will look and behave far differently than astronomers previously theorized--and it may prove disastrous for the tiny explorer. In 2010, the Japanese Aerospace Exploration Agency (JAXA) made history when its Hayabusa probe became the first spacecraft to not only land on and launch from an asteroid (Itokawa), but successfully return to Earth with samples .

The process is similar to how human babies learn that bowls and spoons are used to eat. Breakthroughs, discoveries, and DIY tips sent every weekday. We've long known that dogs are pretty smart. They may "picture" objects in their heads just like us, communicate with button boards, and can even understand fairly complicated words . They also appear to categorize objects by function, understanding how similar types of toys work, even if they don't look alike.

Misinformation in healthcare, from vaccine hesitancy to unproven treatments, poses risks to public health and trust in medical systems. While machine learning and natural language processing have advanced automated fact-checking, validating biomedical claims remains uniquely challenging due to complex terminology, the need for domain expertise, and the critical importance of grounding in scientific evidence. We introduce CER (Combining Evidence and Reasoning), a novel framework for biomedical fact-checking that integrates scientific evidence retrieval, reasoning via large language models, and supervised veracity prediction. By integrating the text-generation capabilities of large language models with advanced retrieval techniques for high-quality biomedical scientific evidence, CER effectively mitigates the risk of hallucinations, ensuring that generated outputs are grounded in verifiable, evidence-based sources. Evaluations on expert-annotated datasets (HealthFC, BioASQ-7b, SciFact) demonstrate state-of-the-art performance and promising cross-dataset generalization. Code and data are released for transparency and reproducibility: https://github.com/PRAISELab-PicusLab/CER

Dialects represent a significant component of human culture and are found across all regions of the world. In Germany, more than 40% of the population speaks a regional dialect (Adler and Hansen, 2022). However, despite cultural importance, individuals speaking dialects often face negative societal stereotypes. We examine whether such stereotypes are mirrored by large language models (LLMs). We draw on the sociolinguistic literature on dialect perception to analyze traits commonly associated with dialect speakers. Based on these traits, we assess the dialect naming bias and dialect usage bias expressed by LLMs in two tasks: an association task and a decision task. To assess a model's dialect usage bias, we construct a novel evaluation corpus that pairs sentences from seven regional German dialects (e.g., Alemannic and Bavarian) with their standard German counterparts. We find that: (1) in the association task, all evaluated LLMs exhibit significant dialect naming and dialect usage bias against German dialect speakers, reflected in negative adjective associations; (2) all models reproduce these dialect naming and dialect usage biases in their decision making; and (3) contrary to prior work showing minimal bias with explicit demographic mentions, we find that explicitly labeling linguistic demographics--German dialect speakers--amplifies bias more than implicit cues like dialect usage.

Large Language Model (LLM) hallucination is a significant barrier to their reliable deployment. Current methods like Retrieval-Augmented Generation (RAG) are often reactive. We introduce **Dynamic Self-reinforcing Calibration for Hallucination Suppression (DSCC-HS)**, a novel, proactive framework that intervenes during autoregressive decoding. Inspired by dual-process cognitive theory, DSCC-HS uses a compact proxy model, trained in adversarial roles as a Factual Alignment Proxy (FAP) and a Hallucination Detection Proxy (HDP). During inference, these proxies dynamically steer a large target model by injecting a real-time steering vector, which is the difference between FAP and HDP logits, at each decoding step. This plug-and-play approach requires no modification to the target model. Our experiments on TruthfulQA and BioGEN show DSCC-HS achieves state-of-the-art performance. On TruthfulQA, it reached a 99.2% Factual Consistency Rate (FCR). On the long-form BioGEN benchmark, it attained the highest FActScore of 46.50. These results validate DSCC-HS as a principled and efficient solution for enhancing LLM factuality.

Large language models (LLMs) often struggle with context fidelity, producing inconsistent answers when responding to questions based on provided information. Existing approaches either rely on expensive supervised fine-tuning to generate evidence post-answer or train models to perform web searches without necessarily improving utilization of the given context. We propose CARE, a novel native retrieval-augmented reasoning framework that teaches LLMs to explicitly integrate in-context evidence within their reasoning process with the model's own retrieval capabilities. Our method requires limited labeled evidence data while significantly enhancing both retrieval accuracy and answer generation performance through strategically retrieved in-context tokens in the reasoning chain. Extensive experiments on multiple real-world and counterfactual QA benchmarks demonstrate that our approach substantially outperforms supervised fine-tuning, traditional retrieval-augmented generation methods, and external retrieval solutions. This work represents a fundamental advancement in making LLMs more accurate, reliable, and efficient for knowledge-intensive tasks.

Although significant progress has been made in many tasks within the field of Natural Language Processing (NLP), Controlled Text Generation (CTG) continues to face numerous challenges, particularly in achieving fine-grained conditional control over generation. Additionally, in real scenario and online applications, cost considerations, scalability, domain knowledge learning and more precise control are required, presenting more challenge for CTG. This paper introduces a novel and scalable framework, AgentCTG, which aims to enhance precise and complex control over the text generation by simulating the control and regulation mechanisms in multi-agent workflows. We explore various collaboration methods among different agents and introduce an auto-prompt module to further enhance the generation effectiveness. AgentCTG achieves state-of-the-art results on multiple public datasets. To validate its effectiveness in practical applications, we propose a new challenging Character-Driven Rewriting task, which aims to convert the original text into new text that conform to specific character profiles and simultaneously preserve the domain knowledge. When applied to online navigation with role-playing, our approach significantly enhances the driving experience through improved content delivery. By optimizing the generation of contextually relevant text, we enable a more immersive interaction within online communities, fostering greater personalization and user engagement.

Breakthroughs, discoveries, and DIY tips sent every weekday. While it's not helping fly the Millennium Falcon, marine biologists have discovered a new type of deep-sea coral in the western Pacific Ocean that bears a striking similarity to a certain beloved character. A new scientific analysis of the species initially documented almost two decades ago indicates that is its own species, with long, hairlike branches that live up to its namesake Wookie. Ten years later, another example was documented close to the Mariana Trench . But it would take a few more years before University of Hawai'i ecologist Les Watling noticed the strange, ethereal sight while reviewing research from some of his colleagues.

Silicon Valley startup Tensor plans to sell the world's first personal robocar, allowing consumers to own self-driving cars by 2026.