House Oversight chair James Comer claims Bill Clinton and Hillary Clinton agreed to depositions in the Epstein investigation after facing bipartisan contempt votes that surprised them.

As large language models become increasingly capable, there is growing concern that they may develop reasoning processes that are encoded or hidden from human oversight. To investigate whether current interpretability techniques can penetrate such encoded reasoning, we construct a controlled testbed by fine-tuning a reasoning model (DeepSeek-R1-Distill-Llama-70B) to perform chain-of-thought reasoning in ROT-13 encryption while maintaining intelligible English outputs. We evaluate mechanistic interpretability methods--in particular, logit lens analysis--on their ability to decode the model's hidden reasoning process using only internal activations. We show that logit lens can effectively translate encoded reasoning, with accuracy peaking in intermediate-to-late layers. Finally, we develop a fully unsupervised decoding pipeline that combines logit lens with automated paraphrasing, achieving substantial accuracy in reconstructing complete reasoning transcripts from internal model representations. These findings suggest that current mechanistic interpretability techniques may be more robust to simple forms of encoded reasoning than previously understood. Our work provides an initial framework for evaluating interpretability methods against models that reason in non-human-readable formats, contributing to the broader challenge of maintaining oversight over increasingly capable AI systems.

Various studies have attempted to remove sensitive or private knowledge from a language model to prevent its unauthorized exposure. However, prior studies have overlooked the complex and interconnected nature of knowledge, where related knowledge must be carefully examined. Specifically, they have failed to evaluate whether an unlearning method faithfully erases interconnected knowledge that should be removed, retaining knowledge that appears relevant but exists in a completely different context. To resolve this problem, we first define a new concept called superficial unlearning, which refers to the phenomenon where an unlearning method either fails to erase the interconnected knowledge it should remove or unintentionally erases irrelevant knowledge. Based on the definition, we introduce a new benchmark, FaithUn, to analyze and evaluate the faithfulness of unlearning in real-world knowledge QA settings. Furthermore, we propose a novel unlearning method, KLUE, which updates only knowledge-related neurons to achieve faithful unlearning. KLUE identifies knowledge neurons using an explainability method and updates only those neurons using selected unforgotten samples. Experimental results demonstrate that widely-used unlearning methods fail to ensure faithful unlearning, while our method shows significant effectiveness in real-world QA unlearning.

Many tasks related to Computational Social Science and Web Content Analysis involve classifying pieces of text based on the claims they contain. State-of-the-art approaches usually involve fine-tuning models on large annotated datasets, which are costly to produce. In light of this, we propose and release a qualitative and versatile few-shot learning methodology as a common paradigm for any claim-based textual classification task. This methodology involves defining the classes as arbitrarily sophisticated taxonomies of claims, and using Natural Language Inference models to obtain the textual entailment between these and a corpus of interest. The performance of these models is then boosted by annotating a minimal sample of data points, dynamically sampled using the well-established statistical heuristic of Probabilistic Bisection. We illustrate this methodology in the context of three tasks: climate change contrarianism detection, topic/stance classification and depression-relates symptoms detection.

This paper presents a question-answering approach to extract document-level event-argument structures. We automatically ask and answer questions for each argument type an event may have. Questions are generated using manually defined templates and generative transformers. Template-based questions are generated using predefined role-specific wh-words and event triggers from the context document. Transformer-based questions are generated using large language models trained to formulate questions based on a passage and the expected answer. Additionally, we develop novel data augmentation strategies specialized in inter-sentential event-argument relations. We use a simple span-swapping technique, coreference resolution, and large language models to augment the training instances. Our approach enables transfer learning without any corpora-specific modifications and yields competitive results with the RAMS dataset. It outperforms previous work, and it is especially beneficial to extract arguments that appear in different sentences than the event trigger. We also present detailed quantitative and qualitative analyses shedding light on the most common errors made by our best model.

Former Secretary of State Hillary Clinton took a swipe at voters "upset" by the forthcoming rematch between President Biden and former President Trump during her appearance on "The Tonight Show." Hillary Clinton reacted to a recent ruling in Arizona, which bans abortion in nearly all circumstances, calling it "cruelty" during an interview with Kelly Clarkson and encouraging Americans to vote in a way that would "make life better" for the largest number of people. "I feared it would happen but I hoped it wouldn't happen. Now here we are in the middle of this very difficult period for women in about half the states in our country, who cannot get the care that they need. And the old law in Arizona is without exceptions and the danger to women's lives as well as to our right to make our own decisions about our bodies and ourselves is so profound," Clinton said during the interview with Clarkson on "The Kelly Clarkson Show."

ESPN personality and OutKick's Clay Travis talk about who the pundit will vote for in the 2024 presidential election. ESPN star Stephen A. Smith snapped back at former Democrat presidential nominee Hillary Clinton, who told voters to "get over yourselves" when asked about Americans dreading a Trump-Biden rematch this November. Clinton made her declaration in an appearance on Monday's "The Tonight Show." She suggested it wasn't a hard choice to make for voters because "one is old, and effective, and compassionate, has a heart and really cares about people. And one is old and has been charged with 91 felonies."

Welcome to Fox News' Politics newsletter with the latest political news from Washington D.C. and updates from the 2024 campaign trail. Both Former President Trump and Hunter Biden have accused the Justice Department of bringing politically biased charges. Trump, ahead of campaign stops in the battleground states of Michigan and Wisconsin, claimed Biden has "orchestrated" every lawsuit and indictment against him with the help of the Justice Department. "Please remember, ALL of these Lawsuits, Charges, and Indictments that have been brought against me have been orchestrated and coordinated by Crooked Joe Biden, the White House, and the DOJ, as an ATTACK ON CROOKED'S POLITICAL OPPONENT, ME," Trump posted on his Truth Social account Tuesday morning. Similarly, Hunter Biden's attorney blasted the decision by a federal judge who refused to dismiss tax charges against the first son, saying they will continue to fight the "abnormal way" Special Counsel David Weiss has handled the case.

Luke Bryan speaks during the signing of the ELVIS Act to Protect Voice & amp; Likeness in Age of AI event at Robert's Western World on March 21, 2024, in Nashville, Tennessee. 'AMAZING PRECEDENT': Luke Bryan is celebrating new protections from artificial intelligence for musicians in Nashville. Luke Bryan has high praise for the Tennessee state government over its new AI regulation law. ELECTION THREAT: Former Secretary of State Hillary Clinton described herself as a victim of election disinformation during a panel discussion on Thursday, and warned that the advancement of artificial intelligence (AI) will make her experience "look primitive." LEVEL UP: Google has developed an artificial intelligence system that can play video games like a human and take orders from players and could eventually even have real-world implications down the line.

Former Secretary of State Hillary Clinton described herself as a victim of election disinformation during a panel discussion on Thursday, and warned that the advancement of artificial intelligence (AI) will make her experience "look primitive." Clinton was taking part in a Columbia University event titled, "AI's Impact on the 2024 Global Elections." She discussed her own experience in 2016 when she lost to former President Donald Trump, pointing out that the internet was populated with memes, fake content and conspiracies about her in the lead-up to Election Day. "I don't think any of us understood it. I did not understand it. I can tell you, my campaign did not understand it. Their, you know, the so-called'Dark Web' was filled with these kinds of memes and stories and videos of all sorts…portraying me in all kinds of… less than flattering ways," Clinton said.