The Federal Trade Commission (FTC) has launched an investigation into several major artificial intelligence companies amid a wave of recent rogue artificial intelligence agents -- AI systems that can make decisions and take actions on behalf of users -- from firms and claims by former industry insiders that AI could end humanity within the next decade. The FTC launched the investigation several weeks ago. It was first reported on Wednesday by the New York Post and has since been confirmed by other outlets, including the Washington Post and the Reuters news agency. The probe marks the first enforcement action by a federal agency examining rogue agents. The investigation was launched amid a wave of incidents, first reported in July, of agents going rogue in testing, including when an OpenAI agent broke out of containment during testing, hacking into the AI firm Hugging Face.

Greenland's delegation gets hero's welcome after signing US-Denmark pact Greenland's delegation gets hero's welcome after signing US-Denmark pact Greenland's delegation returned home to a hero's welcome in Nuuk after signing a new pact with the US and Denmark that expands military access to the island, strengthening security and preserving its sovereignty. Share Greenland's delegation gets hero's welcome after signing US-Denmark pact on social media UN funding cuts hit refugees at Cox's Bazar settlement OpenAI CEO: Tech companies don't'have all the answers' on AI policy Sikorski: Russia doesn't have the forces to invade NATO Why have some nations developed while others struggle? Spain's Sanchez warns of far-right threat in UNGA speech Venezuela's Rodriguez pledges elections, path forward in UNGA speech

What is Iran's Pickaxe Mountain? Turkiye lays out'mechanisms' of defence pact with Pakistan, Saudi Arabia Turkiye, Saudi Arabia and Pakistan will group senior ministers, hold joint exercises and deepen defence industry cooperation under a military pact signed between the regional powers last week, Turkiye's defence ministry says. The three countries on August 7 signed the Mecca Joint Defence Agreement on August 7, which stipulates that an armed attack against any member would be regarded as an attack on all, similar to NATO's Article 5 collective defence clause. "Under the accord, strategic political and military mechanisms plan to be established involving the Defence Ministers, Foreign Ministers, and Chiefs of General Staff/Armed Forces Commanders, and that coordination among the three countries will be conducted at the highest level," it said. The ministry added that joint military exercises involving land, naval and air forces, as well as air defence and unmanned systems were planned, in addition to deeper defence industry cooperation.

What is Iran's Pickaxe Mountain? Where does Iran stand on Saudi-Pakistan-Turkiye pact? What are the challenges facing the Mecca agreement? Saudi Arabia and Pakistan have expanded their mutual defence pact to add Turkiye, raising questions about the emergence of a new security architecture at a time when the United States and Iran have yet to reach a final deal to end their war. Under the agreement signed in Mecca on Friday, any external armed attack on either of the three countries will now be considered an attack on all.

Therefore, many existing SLP vectorization schemes are driven by hand-crafted heuristics (Liu et al., 2012;

In this section, we derive a lower bound for the trace of the covariance of the PG estimator in environments with stochastic dynamics. Let us assume that the initial policyπ(ai|si) follows the uniform distribution such thatπ(ai = 1|si) = π(ai = +1|si) = 12 for alli. Its optimal policy fort, πtθf(t|s), should producet x because otherwise it has the risk of ending up with ν reward, which is not an optimum. Since FiGAR-C is unaware of underlying state changes, its best strategy is to shorten the duration ofactions tobemoreresponsive. In VPG, we do not use any technique for variance reduction such asvalue functions and reward-to-go policygradient; hence, the formula for its gradient estimator is identical to Equation (3).

Enterprise environments contain a heterogeneous, rapidly growing collection of internal artifacts related to code, data, and many different tools. Critical information for assessing privacy risk and ensuring regulatory compliance is often embedded across these varied resources, each with their own arcane discovery and extraction techniques. Therefore, large-scale privacy compliance in adherence to governmental regulations requires systems to discern the interconnected nature of diverse artifacts in a common, shared universe. We present Privacy Artifact ConnecT or (PACT), an embeddings-driven graph that links millions of artifacts spanning multiple artifact types generated by a variety of teams and projects. Powered by the state-of-the-art DRAGON embedding model, PACT uses a contrastive learning objective with light fine-tuning to link artifacts via their textual components such as raw metadata, ownership specifics, and compliance context. Experimental results show that PACT's fine-tuned model improves recall@1 from 18% to 53%, the query match rate from 9.6% to 69.7% when paired with a baseline AI agent, and the hitrate@1 from 25.7% to 44.9% for candidate selection in a standard recommender system.

Satisfiability Modulo Theory (SMT) solvers have advanced automated reasoning, solving complex formulas across discrete and continuous domains. Recent progress in propositional model counting motivates extending SMT capabilities toward model counting, especially for hybrid SMT formulas. Existing approaches, like bit-blasting, are limited to discrete variables, highlighting the challenge of counting solutions projected onto the discrete domain in hybrid formulas. We introduce pact, an SMT model counter for hybrid formulas that uses hashing-based approximate model counting to estimate solutions with theoretical guarantees. pact makes a logarithmic number of SMT solver calls relative to the projection variables, leveraging optimized hash functions. pact achieves significant performance improvements over baselines on a large suite of benchmarks. In particular, out of 14,202 instances, pact successfully finished on 603 instances, while Baseline could only finish on 13 instances.

On Sunday, the world's governments made a series of commitments to transform global governance at the United Nations Summit of the Future in New York. The ambitiously named summit was described as a "once-in-a-generation opportunity" to "forge a new global consensus on what our future should look like". Indeed, we are at a critical time when change is urgently needed. The world faces "a moment of historic danger", with increasingly imminent risks – from nuclear war to a planetary emergency, from persistent poverty and widening inequality to the unhindered advancement of artificial intelligence – threatening humanity's very existence. These are global challenges that cannot be solved purely at the national level: The people of the world need – and deserve – better coordinated global action.

With the advent of large foundation model based planning, there is a dire need to ensure their output aligns with the stakeholder's intent. When these models are deployed in the real world, the need for alignment is magnified due to the potential cost to life and infrastructure due to unexpected faliures. Temporal Logic specifications have long provided a way to constrain system behaviors and are a natural fit for these use cases. In this work, we propose a novel approach to factor in signal temporal logic specifications while using autoregressive transformer models for trajectory planning. We also provide a trajectory dataset for pretraining and evaluating foundation models. Our proposed technique acheives 74.3 % higher specification satisfaction over the baselines.