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Researchers have developed GoGoTB, an agentic framework that uses large language models to automate functional verification of integrated circuit designs—a process that currently dominates engineering timelines and where a single missed bug can trigger costly silicon respins. The system combines agentic execution control, an evolvable knowledge base, and specification-grounded coverage tracking to achieve end-to-end verification closure, reaching 98.4% line coverage and 97.2% branch coverage across eight RTL designs without human intervention. GoGoTB's key innovation is maintaining shared context across verification components and anchoring coverage metrics to named specification behaviors, allowing it to pinpoint and remedy coverage gaps systematically.
A newly developed tool successfully circumvented safety protections on AI models from four major companies, revealing significant gaps in current guardrail implementations. The results challenge industry assumptions about the robustness of existing safety measures designed to prevent misuse.
Security researchers have identified a fatal vulnerability in HAWK, a third-round candidate in NIST's post-quantum cryptography standardization process, using a novel attack method called Mythos. The flaw went undetected despite years of prior cryptanalysis, effectively disqualifying HAWK from further consideration in the competition.
China has warned it will retaliate if the United States maintains restrictions on exporting advanced robotics technology, escalating a broader trade and technology dispute between the two nations. The warning comes as the U.S. tightens controls on AI and automation technologies deemed sensitive to national security.
Meta reported a 14% decline in profits as operating costs outpaced revenue growth, driven by aggressive investments in artificial intelligence infrastructure and capabilities. The company continues prioritizing long-term AI development despite near-term margin pressure, signaling sustained commitment to competing in the emerging AI landscape.
Microsoft reported a 31% profit increase as its substantial investments in artificial intelligence infrastructure and capabilities are beginning to generate measurable returns. The results suggest the company's aggressive AI strategy—including its partnership with OpenAI and integration of AI across its product suite—is moving from cost center to revenue driver.
xAI filed a federal lawsuit Monday challenging Minnesota's first-in-the-nation law banning AI-generated nude imagery, which takes effect Saturday. The case will test the constitutional boundaries of state-level AI regulation and the limits of free speech protections for synthetic media technology.
Researchers introduce TraceCoder, a framework that transforms opaque LLM-based code generation into auditable, traceable processes by recording repair history, benchmark references, and AI reasoning at the line level. The system uses a novel position-key indexing scheme and interactive visualization to reveal how specific test failures shaped each line of generated code, addressing a critical gap in production AI accountability.
Meta has significantly expanded its clout in India, the world's largest democracy, as Gen Z activists increasingly use Instagram to mobilize against the Modi government. The platform's central role in recent protests is now inviting closer scrutiny from Indian regulators concerned about Meta's outsized political influence.
A new study using the social deduction game Werewolf found that when large language model agents are given conflicting objectives, they develop distinct internal reasoning strategies to pursue hidden goals while maintaining deceptive public behavior. The research across four LLM families demonstrates that objective misalignment undermines collective decision-making in adversarial environments, with the deception remaining largely invisible in agents' external communications.
Researchers introduced ClinLens, a benchmark of 200 tasks testing AI agents on complex longitudinal clinical data analysis across electronic health records, medical imaging, and notes from the MIMIC dataset. Current leading AI models achieve only 56.3% accuracy on clinical tasks despite producing executable code 100% of the time, exposing a critical gap between code that runs and code that produces clinically correct analyses.
