Justice Department Says Anthropic Can't Be Trusted With Warfighting Systems In response to Anthropic's lawsuit, the government said it lawfully penalized the company for trying to limit how its Claude AI models could be used by the military. The Trump administration argued in a court filing on Tuesday that it did not violate Anthropic's First Amendment rights by designating the AI developer a supply-chain risk and predicted that the company's lawsuit against the government will fail. "The First Amendment is not a license to unilaterally impose contract terms on the government, and Anthropic cites nothing to support such a radical conclusion," US Department of Justice attorneys wrote. The response was filed in a federal court in San Francisco, one of two venues where Anthropic is challenging the Pentagon's decision to sanction the company with a label that can bar companies from defense contracts over concerns about potential security vulnerabilities. Anthropic argues the Trump administration overstepped its authority in applying the label and preventing the company's technologies from being used inside the department.

Recurrent neural networks are powerful models for processing sequential data, but they are generally plagued by vanishing and exploding gradient problems. Unitary recurrent neural networks (uRNNs), which use unitary recurrence matrices, have recently been proposed as a means to avoid these issues. However, in previous experiments, the recurrence matrices were restricted to be a product of parameterized unitary matrices, and an open question remains: when does such a parameterization fail to represent all unitary matrices, and how does this restricted representational capacity limit what can be learned? To address this question, we propose full-capacity uRNNs that optimize their recurrence matrix over all unitary matrices, leading to significantly improved performance over uRNNs that use a restricted-capacity recurrence matrix. Our contribution consists of two main components. First, we provide a theoretical argument to determine if a unitary parameterization has restricted capacity. Using this argument, we show that a recently proposed unitary parameterization has restricted capacity for hidden state dimension greater than 7. Second,we show how a complete, full-capacity unitary recurrence matrix can be optimized over the differentiable manifold of unitary matrices. The resulting multiplicative gradient step is very simple and does not require gradient clipping or learning rate adaptation. We confirm the utility of our claims by empirically evaluating our new full-capacity uRNNs on both synthetic and natural data, achieving superior performance compared to both LSTMs and the original restricted-capacity uRNNs.

The potential for artificial intelligence to affect election results is a major public concern. Two new papers - with experiments conducted in four countries - demonstrate that chatbots powered by large language models (LLMs) are quite effective at political persuasion, moving opposition voters' preferences by 10 percentage points or more in many cases. The LLMs' persuasiveness comes not from being masters of psychological manipulation, but because they come up with so many claims supporting their arguments for candidates' policy positions. "LLMs can really move people's attitudes towards presidential candidates and policies, and they do it by providing many factual claims that support their side," said David Rand, a senior author on both papers. "But those claims aren't necessarily accurate - and even arguments built on accurate claims can still mislead by omission."

US Lawmakers Move to Kill the FBI's Warrantless Wiretap Access A bipartisan bill would force the FBI to get a warrant to read Americans' messages and ban the federal purchase of commercial data on US residents ahead of a critical April deadline. A bipartisan privacy coalition in the United States Congress introduced legislation on Thursday that would impose a strict warrant requirement on the FBI's backdoor searches of Americans' communications, aligning federal law with a 2025 federal court ruling that found the warrantless practice unconstitutional. The bill, the Government Surveillance Reform Act of 2026, repeals controversial expansions of the government's warrantless wiretapping authority while overhauling key aspects of federal surveillance law--setting up a showdown with the US intelligence community and its congressional allies weeks before a sweeping global spy program sunsets on April 20. Senators Ron Wyden and Mike Lee are leading the legislative push alongside Representatives Warren Davidson and Zoe Lofgren. The measure carries endorsements from civil liberties organizations across the political spectrum.

The proof of infeasibility condition (Theorem 3.2) is provided in Section B. Explanations on conditions derived in Theorem 3.2 are included in Section C. The proof of properties of the proposed model (r)LogSpecT (Proposition 3.4 The truncated Hausdorff distance based proof details of Theorem 4.1 and Corollary 4.4 are Details of L-ADMM and its convergence analysis are in Section F. Additional experiments and discussions on synthetic data are included in Section G. ( m 1) Again, from Farkas' lemma, this implies that the following linear system does not have a solution: Example 3.1 we know δ = 2|h Since the constraint set S is a cone, it follows that for all γ > 0, γ S = S . Opt(C, α) = α Opt(C, 1), which completes the proof. The proof will be conducted by constructing a feasible solution for rLogSpecT. Since the LogSpecT is a convex problem and Slater's condition holds, the KKT conditions We show that it is feasible for rLogSpecT. R, its epigraph is defined as epi f: = {( x, y) | y f ( x) }. Before presenting the proof, we first introduce the following lemma.