Large Language Models (LLMs) are increasingly tasked with analyzing legal texts and citing relevant statutes, yet their reliability is often compromised by general pre-training that ingests legal texts without specialized focus, obscuring the true depth of their legal knowledge. This paper introduces CLaw, a novel benchmark specifically engineered to meticulously evaluate LLMs on Chinese legal knowledge and its application in reasoning. CLaw comprises two key components: (1) a comprehensive, fine-grained corpus of all 306 Chinese national statutes, segmented to the subparagraph level and incorporating precise historical revision timesteps for rigorous recall evaluation (64,849 entries), and (2) a challenging set of 254 case-based reasoning instances derived from China Supreme Court curated materials to assess the practical application of legal knowledge. Our empirical evaluation reveals that most contemporary LLMs significantly struggle to faithfully reproduce legal provisions. As accurate retrieval and citation of legal provisions form the basis of legal reasoning, this deficiency critically undermines the reliability of their responses. We contend that achieving trustworthy legal reasoning in LLMs requires a robust synergy of accurate knowledge retrieval--potentially enhanced through supervised fine-tuning (SFT) or retrieval-augmented generation (RAG)--and strong general reasoning capabilities. This work provides an essential benchmark and critical insights for advancing domain-specific LLM reasoning, particularly within the complex legal sphere.

The rapid progress of image generative AI has blurred the boundary between synthetic and real images, fueling an arms race between generators and discriminators. This paper investigates the conditions under which discriminators are most disadvantaged in this competition. We analyze two key factors: data dimensionality and data complexity. While increased dimensionality often strengthens the discriminators ability to detect subtle inconsistencies, complexity introduces a more nuanced effect. Using Kolmogorov complexity as a measure of intrinsic dataset structure, we show that both very simple and highly complex datasets reduce the detectability of synthetic images; generators can learn simple datasets almost perfectly, whereas extreme diversity masks imperfections. In contrast, intermediate-complexity datasets create the most favorable conditions for detection, as generators fail to fully capture the distribution and their errors remain visible.

Large language models (LLMs) are a prominent subset of AI, built on advanced neural network architectures that can generate new data, including text, images, and audio. LLMs utilize various technologies to identify patterns in a given set of training data, without requiring explicit instructions about what to look for [ 12, 35 ] . LLMs typically assume that the training data follows a probability distribution, and once they have identified existing patterns, they can generate new instances that are similar to the original data. By drawing from and combining training data, LLMs can create new content that tran scends the initial dataset [1 7 ].

Federally funded research and development centers (FFRDCs) face text-heavy workloads, from policy documents to scientific and engineering papers, that are slow to analyze manually. We show how large language models can accelerate summarization, classification, extraction, and sense-making with only a few input-output examples. To enable use in sensitive government contexts, we apply OnPrem$.$LLM, an open-source framework for secure and flexible application of generative AI. Case studies on defense policy documents and scientific corpora, including the National Defense Authorization Act (NDAA) and National Science Foundation (NSF) Awards, demonstrate how this approach enhances oversight and strategic analysis while maintaining auditability and data sovereignty.

A deep dive into the open literature shows that there are t hree fundamental limitations to current ML approaches, namely blackbox brittleness (which renders models uninterpretable and unreliable under distribution shift [2]), causal blindness (which conflates correlation with causation [3]), and alignment failures (which produce systems optimizing objectives misaligned with human values [4]) . These deficiencies stem from a profound philosophical poverty in how ML conceptualizes knowledge, reasoning, and values. The first fundamental limitation, b lackbox brittleness, manifests when trained models fail on seemingly trivial variations of their training distribution. For example, a vision model that accurately identifies stop signs under normal conditions might misclassify them entirely when small adversarial perturbations are applied [5] . Not surprisingly, t h e same brittleness extends beyond adversarial examples to everyday distribution shifts (e.g., natural language processing models exhibit performance degradation when processing text from different cultural contexts, etc.) [6] .

Here's How to Find Out if You're Eligible In a settlement with the FTC, Amazon will have to pay out over a billion dollars to US customers for "deceptive" sign-up and cancellation processes. Amazon customers with a Prime subscription will soon be able to make claims online for their share of the $1.5 billion the company is being ordered to pay to users in the United States. Amazon now has to "provide $1.5 billion in refunds back to consumers harmed by their deceptive Prime enrollment practices," according to a press release from the FTC. The total settlement with the FTC is $2.5 billion, which includes a $1 billion penalty owed to the government. "There was no admission of guilt in this settlement by the company or any executives," says Alisa Carroll, an Amazon spokesperson, in an email sent to WIRED on Thursday after the decision was released.

Amazon Will Pay $2.5 Billion to Settle FTC Suit That Alleged'Dark Patterns' in Prime Sign-Ups Amazon will pay both the Federal Trade Commission and consumers directly to settle a lawsuit alleging that it used manipulative and deceptive tactics to encourage sign-ups for Prime. Amazon has agreed to pay $2.5 billion to settle a lawsuit filed by the Federal Trade Commission, which alleged that the company has "knowingly duped" millions of people into enrolling in its Amazon Prime membership program by using what the FTC has described as " dark patterns, " or, manipulative, coercive, or deceptive user-interface designs." The settlement claimed that Amazon "obtains consumers' billing information before it discloses all material terms for an Amazon Prime subscription," and in doing so, was in violation of the Restore Online Shoppers' Confidence Act, which was signed into law in 2010 to prevent the use of deception to prompt or encourage online purchases. The $2.5 billion payment includes $1 billion that has to be paid to the FTC, and $1.5 billion that will go directly to consumers who unknowingly signed up for Prime, or tried and failed to cancel their Prime subscriptions due to Amazon's online interface, between June 23, 2019 and June 23, 2025. Individual consumers can get compensated up to $51 each. In a statement released by the FTC on Tuesday, agency chairman Andrew Ferguson said that the settlement "made history and secured a record-breaking, monumental win for the millions of Americans who are tired of deceptive subscriptions that feel impossible to cancel." "Today, we are putting billions of dollars back into Americans' pockets, and making sure Amazon never does this again," Ferguson said. Amazon spokesperson Alisa Carroll tells WIRED that there was "no admission of guilt in this settlement by the company or any executives.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Jillian Lauren, a best-selling author who is married to the bassist from the rock band Weezer, appears in court at the Clara Shortridge Foltz Criminal Justice Center in Los Angeles on May 13, 2025. This is read by an automated voice. Please report any issues or inconsistencies here . Jillian Lauren was arrested after authorities said she brandished a gun and fired toward police officers.

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Las Vegas man raped by mother as a child is told by judge that he's legal father to brother believed to be his biological son The five'worst cities to be a woman' in America... and they're all in deep red states D4vd's mansion is mysteriously EMPTIED by movers... as disturbing theory emerges about why he still hasn't been arrested after dismembered girl was found in his Tesla There's a sinister Establishment'plot' to undermine Prince William and Kate and bring back Harry and Meghan. I refuse to be part of it... and today I'm exposing what's going on: RICHARD EDEN Pastor and wife were living WITH their son who'kept four people PRISONER in basement dungeon'... as survivor's family reveal what'evil monster' did to victims Why your star sign has been WRONG your whole life: As astronomers reveal the zodiac is more than 2,500 years out of date, use our graphic to find out what your horoscope really is - and why it's changed I woke up one day with tinnitus. It ruined my life... but this is how I got rid of the agony for good - and why I believe we are treating this insidious condition completely wrong: Expert and audiologist DR GLADYS SANDA Star Trek icon William Shatner, 94, jokes about his DEATH as he breaks silence after'medical emergency' Amazon agrees to record $2.5b settlement over claims it tricked customers Jessica Alba is being humiliated by her ex-husband chasing lookalikes half her age! How I look like this at 45, by DAVID GANDY: His 45-minute routine, exactly what he eats, the cheap supplement he takes every day, what makes a man... and how to find the perfect pair of pants Livvy Dunne leaves fans in disbelief as'gross' photo goes viral She shows off huge new'Queen' ring with gem'associated with the goddess Diana' Megyn Kelly's brutal takedown of left-wing student who said Trump rhetoric was to blame for Charlie Kirk murder Top journalist slams entitlement of Ben Affleck and Jennifer Gardner's woke daughter after UN speech demanding mask mandates The threat of artificial intelligence wiping us out is so grave governments must bomb any labs suspected of developing it, by two top academics who've studied the threat for 25 years... and spell out in terrifying detail our bloody end This is the sad truth about people like Violet Affleck who are still wearing masks. I've seen it in so many patients at my practice... and this is what's really going on: DR MAX PEMBERTON It's time to rewrite the family tree, as scientists have revealed that our species is even older than we thought.