TPUSA issues blistering response to Hollywood nepo baby who called Erika Kirk a'sociopath' and urged Trump to'kill' organization Who's The Boss? star Judith Light, 77, has fans concerned with strange poses on red carpet Shock as Home Depot rival closes all 15 of its stores and declares bankruptcy thanks to consumers' reluctance to spend ROBERT HARDMAN: What Trump told me about the King and William. Men everywhere secretly have the same complaint about their sex lives. It's NOT about looks or frequency... Spirit Airlines prepares to shut down as Trump's rescue deal falls apart I'm the REAL Emily from Devil Wears Prada: Anna Wintour's assistant played by Emily Blunt reveals herself... and cutthroat behind-scenes details that the movie did NOT include The Devil Wears Prada 2 review: Searingly silly, ridiculous sequel is a complete disgrace to fashion... and guilty of the biggest sin of all: JANE TIPPETT The ultimate Ozempic survival kit: Experts reveal cheap drugstore remedies and one miracle food every GLP-1 user needs to ease side effects... meaning you can take a HIGHER dose and lose MORE weight Mom stunned to discover she is pregnant with twins just WEEKS after giving birth: 'I was in denial' Alleged JPMorgan sex slave unmasked as crisis sparks drama at America's biggest bank: 'Everyone's wondering what Jamie thinks' Time machines may seem better suited to science fiction than the physics lab, but experts say this futuristic technology could become a reality. Researchers have revealed how time travel could really work by using the laws of quantum physics. While their method won't let you hop back to the time of the dinosaurs, scientists say it could be possible to send messages into the past.

We present a novel framework that integrates Large Language Models (LLMs) into the Git bisect process for semantic fault localization. Traditional bisect assumes deterministic predicates and binary failure states assumptions often violated in modern software development due to flaky tests, nonmonotonic regressions, and semantic divergence from upstream repositories. Our system augments bisect traversal with structured chain of thought reasoning, enabling commit by commit analysis under noisy conditions. We evaluate multiple open source and proprietary LLMs for their suitability and fine tune DeepSeekCoderV2 using QLoRA on a curated dataset of semantically labeled diffs. We adopt a weak supervision workflow to reduce annotation overhead, incorporating human in the loop corrections and self consistency filtering. Experiments across multiple open source projects show a 6.4 point absolute gain in success rate from 74.2 to 80.6 percent, leading to significantly fewer failed traversals and by experiment up to 2x reduction in average bisect time. We conclude with discussions on temporal reasoning, prompt design, and finetuning strategies tailored for commit level behavior analysis.

Kaliane Bradley's The Ministry of Time was (largely) a hit with the New Scientist Book Club One of the wonderful things about science fiction is the broadness of its church, and this was really brought home to me by our two most recent reads. The New Scientist Book Club moved from the hard science fiction spacefaring of Larry Niven's classic Ringworld in May to the near-future-set time travel of Kaliane Bradley's The Ministry of Time for our June read. The former takes its science seriously, diving into the maths and physics of its set-up with gusto; the latter – not so much. Culture editor Alison Flood rounds up the New Scientist Book Club's thoughts on our latest read, the science fiction classic Ringworld by Larry Niven The story of an unnamed civil servant who is given the job of supporting an "expat" from history – Commander Graham Gore, a (real) Victorian polar explorer from 1847 – The Ministry of Time is many things in one: a thriller, a romance, a piece of climate fiction (apparently), a science fiction novel about time. I couldn't put it down and loved all of it – apart, perhaps, from the ending.

The Trump Administration quietly revealed it has futuristic technologies that literally bend time during a speech on'the golden age of American innovation.' The director of the White House Office of Science and Technology Policy, Michael Kratsios, declared that the US currently has the ability to'manipulate time and space' and'leave distance annihilated.' Kratsios made the bold statement on Monday during the Endless Frontiers Retreat, a scientific conference in Texas focused on promoting US technological innovations to maintain global competitiveness. The rest of the director's speech touched on American breakthroughs of the past and undoing Biden-era policies that the Trump Administration claims stifled innovation - adding that the regulatory process on new tech has been a burden since the 1970s. Kratsios actually referenced this again at the end of his speech, saying that Americans will soon have the choice to'craft new technologies and give themselves to scientific discoveries that will bend time and space.'

From H. G. Wells's The Time Machine to Christopher Nolan's Interstellar, the possibility of travelling through time has fascinated people for centuries. But, although it sounds like pure science fiction, physicists now believe that time travel really is possible. In fact, scientists say that people have already done it. But, before you start to plan your trip to ancient Rome, the experts caution that real time travel is nothing like what you see in the movies. It might seem obvious, but here on Earth, we all move through time at a speed of one second per second.

Alignment in artificial intelligence pursues the consistency between model responses and human preferences as well as values. In practice, the multifaceted nature of human preferences inadvertently introduces what is known as the "alignment tax" -a compromise where enhancements in alignment within one objective (e.g.,harmlessness) can diminish performance in others (e.g.,helpfulness). However, existing alignment techniques are mostly unidirectional, leading to suboptimal trade-offs and poor flexibility over various objectives. To navigate this challenge, we argue the prominence of grounding LLMs with evident preferences. We introduce controllable preference optimization (CPO), which explicitly specifies preference scores for different objectives, thereby guiding the model to generate responses that meet the requirements. Our experimental analysis reveals that the aligned models can provide responses that match various preferences among the "3H" (helpfulness, honesty, harmlessness) desiderata. Furthermore, by introducing diverse data and alignment goals, we surpass baseline methods in aligning with single objectives, hence mitigating the impact of the alignment tax and achieving Pareto improvements in multi-objective alignment.

Data contamination, i.e., the presence of test data from downstream tasks in the training data of large language models (LLMs), is a potential major issue in measuring LLMs' real effectiveness on other tasks. We propose a straightforward yet effective method for identifying data contamination within LLMs. At its core, our approach starts by identifying potential contamination at the instance level; using this information, our approach then assesses wider contamination at the partition level. To estimate contamination of individual instances, we employ "guided instruction:" a prompt consisting of the dataset name, partition type, and the random-length initial segment of a reference instance, asking the LLM to complete it. An instance is flagged as contaminated if the LLM's output either exactly or nearly matches the latter segment of the reference. To understand if an entire partition is contaminated, we propose two ideas. The first idea marks a dataset partition as contaminated if the average overlap score with the reference instances (as measured by ROUGE-L or BLEURT) is statistically significantly better with the completions from guided instruction compared to a "general instruction" that does not include the dataset and partition name. The second idea marks a dataset partition as contaminated if a classifier based on GPT-4 with few-shot in-context learning prompt marks multiple generated completions as exact/near-exact matches of the corresponding reference instances. Our best method achieves an accuracy between 92% and 100% in detecting if an LLM is contaminated with seven datasets, containing train and test/validation partitions, when contrasted with manual evaluation by human experts. Further, our findings indicate that GPT-4 is contaminated with AG News, WNLI, and XSum datasets.

We're often told to "be here now." Yet the mind is rarely tethered in place. We take mental trips to our past, revisiting what happened yesterday or when we were children, or we project into an imagined future: tomorrow's dinner date, the trajectory of our career at age 50. Rather than a diversion from the norm of mindful presence, this tendency to internally visit other time lines, called "mental time travel," is common; young adults, for example, think about their future an average of 59 times a day. Psychologists have suggested that this ability to time travel from the confines of our own heads is a fundamental aspect of what it means to be human.

Netflix has an excellent international library, including German sci-fi gem Dark -- one of the best series on Netflix full stop. This adult animated anthology series spans a range of genres, with plenty of episodes hitting the Black Mirror comparison button. Robots in a post-apocalyptic city, farmers piloting mech suits and a space mission gone wrong all pop up in the first season. While the episodes can be hit and miss (some have been criticized for their treatment of women), you'll find plenty of thought-provoking and impressive animation. This apocalyptic sci-fi from Belgium will probably turn you off from flying any time soon.

"Eternal Threads" can be a fun puzzle to play with once you have all the pieces together. It encourages experimenting with different possible outcomes. With some combinations of choices, a character may survive the fire, but their life is irrevocably changed for the worse because of what they did or didn't do beforehand. But it takes a long time to get to that point. The full playtime is around 10 hours, and by the time you've absorbed enough context through cutscenes to understand how certain events impact the timeline, the game's in its final hours. It's a shame too, because the narrative's final act does take an interesting turn, but my patience was already exhausted by that point.