Look Up Trending Now Good Connection: Uplifting stories for a digital age Creator Playbook Say More Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Switch Off Mashable Voices Mashable Selects Safety Net Versus Gift Ideas For Everyone On Your List All Series'Silo' Season 3's twisty ending, explained Sam Haysom is the General Assignment Editor, UK, for Mashable. He covers entertainment and online culture, and writes horror fiction in his spare time. All products featured here are independently selected by our editors and writers. If you buy something through links on our site, Mashable may earn an affiliate commission. Last week, Season 3 delivered its most shocking episode so far .

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Trump vows Iran will'pay' for killing US soldiers US President Donald Trump says Iran will "pay" for killing United States soldiers after multiple service members were killed in action over the weekend. "Every time Iran kills an American Soldier they will pay for that killing many times over!" Trump said on Monday on social media. "This directive has been passed on to Secretary of War, Pete Hegseth, Chairman of the Joint Chiefs of Staff, Daniel Caine, and every Leader in the Military. The US military also identified two soldiers who were killed in Jordan on Friday while defending against Iranian ballistic missile and drone attacks. Seventeen US troops have been killed since the start of the five-month US-Israel war on Iran. The renewed exchanges of fire, now in their second week, have seen the US target Iranian bridges and electrical facilities as the countries appear to inch closer to a return to full-scale war.

Large language models (LLMs) are often prompted with multi-level directives, such as system instructions and user queries, that imply a hierarchy of authority. Yet models frequently fail to enforce this structure, especially in multi-step reasoning where errors propagate across intermediate steps. Existing methods rely on oracle completions but lack verifiable reward signals or intermediate traces, limiting their applicability. We introduce a unified supervision framework that embeds programmatically verifiable checkers into synthesized instruction-conflict instances. Each instance pairs a compliance directive with a conflicting one, along with an executable verifier that deterministically checks output adherence. This enables alignment without oracle labels or reasoning traces, supporting both instruction-tuned and reasoning models. The framework is instantiated via a synthesis pipeline that includes unittest-based validation, LLM-assisted repair, and a probabilistic analysis of cleaning reliability. Fine-tuning on the resulting data improves instruction hierarchy adherence and boosts safety robustness, generalizing to adversarial safety benchmarks without task-specific supervision. This highlights verifiable supervision as a scalable foundation for robust alignment.

Large language models (LLMs) are often prompted with multi-level directives, such as system instructions and user queries, that imply a hierarchy of authority. Yet models frequently fail to enforce this structure, especially in multi-step reasoning where errors propagate across intermediate steps. Existing methods rely on oracle completions but lack verifiable reward signals or intermediate traces, limiting their applicability. We introduce a unified supervision framework that embeds programmatically verifiable checkers into synthesized instruction-conflict instances. Each instance pairs a compliance directive with a conflicting one, along with an executable verifier that deterministically checks output adherence. This enables alignment without oracle labels or reasoning traces, supporting both instruction-tuned and reasoning models. The framework is instantiated via a synthesis pipeline that includes unit-test-based validation, LLM-assisted repair, and a probabilistic analysis of cleaning reliability. Fine-tuning on the resulting data improves instruction hierarchy adherence and boosts safety robustness, generalizing to adversarial safety benchmarks without task-specific supervision. This highlights verifiable supervision as a scalable foundation for robust alignment.

"Defenders cannot afford to take weeks to patch," one Cybersecurity and Infrastructure Security Agency official warned on Wednesday. With new generations of AI models fueling both rapid software vulnerability discovery and the potential for faster exploitation by malicious hackers, the United States Cybersecurity and Infrastructure Security Agency released a new directive on Wednesday that requires more rapid and efficient software patching by federal civilian agencies. The "binding operational directive" (BOD) lays out a rubric for how quickly bugs must be fixed based on four assessments of urgency, with a turnaround time in critical cases of just three days. Chris Butera, CISA's acting executive assistant director for cybersecurity, told reporters on Wednesday that the goal of the directive is to help agencies prioritize, so they can address the most problematic vulnerabilities first while taking more time to remediate bugs that pose a less-pressing risk. The directive comes as private companies and governments have been scrambling to assess the extent of the cybersecurity reckoning that AI vulnerability and exploit development capabilities could unleash.

Protein-protein interactions (PPIs) govern nearly all cellular processes, yet computational methods for identifying binding partners typically produce ranked predictions without mechanistic justification. This creates a fundamental barrier to adoption because biologists cannot assess whether predictions reflect genuine biochemical insight or spurious correlations. We present \textbf{Protein Thoughts}, a framework that reformulates PPI discovery as an interpretable search problem with explicit reasoning. The system decomposes binding evidence into four biologically meaningful signals: sequence similarity reflecting evolutionary relationships, structural complementarity capturing geometric fit, interface balance, and chemical compatibility encoding residue-level interactions. Rather than collapsing these signals into an opaque score, we preserve their individual contributions through a transparent value function that enables both ranking and auditing. To navigate large candidate spaces efficiently, we introduce hypothesis-guided entropy-regularized Tree-of-Thoughts search. A fine-tuned language model generates search directives from embedding-derived features, classifying candidates as high-priority, exploratory, or skippable. These directives condition a Boltzmann policy that balances exploitation with entropy-driven exploration, while hypothesis-aware pruning prevents premature abandonment of promising candidates. For candidates exhibiting score disagreement, hypothesis-conditioned embedding-space flow matching transports protein embeddings toward the binder manifold. On the SHS148k benchmark, Protein Thoughts achieves mean best-binder rank of 11.2 versus 47.7 for an entropic tree search baseline, a 76% improvement, and for binding prediction the trained value function achieves $91.08 \pm 0.19$ Micro-F1, outperforming existing PPI methods on the same dataset.

A central notion in practical and theoretical machine learning is that of a weak learner, classifiers that achieve better-than-random performance (on any given distribution over data), even by a small margin. Such weak learners form the practical basis for canonical machine learning methods such as boosting.

Recent advances in large language models (LLMs) have significantly accelerated progress in code translation, enabling more accurate and efficient transformation across programming languages. While originally developed for natural language processing, LLMs have shown strong capabilities in modeling programming language syntax and semantics, outperforming traditional rule-based systems in both accuracy and flexibility. These models have streamlined cross-language conversion, reduced development overhead, and accelerated legacy code migration. In this paper, we introduce OMPILOT, a novel domain-specific encoder-decoder transformer tailored for translating C++ code into OpenMP, enabling effective shared-memory parallelization. OMPILOT leverages custom pre-training objectives that incorporate the semantics of parallel constructs and combines both unsupervised and supervised learning strategies to improve code translation robustness. Unlike previous work that focused primarily on loop-level transformations, OMPILOT operates at the function level to capture a wider semantic context. To evaluate our approach, we propose OMPBLEU, a novel composite metric specifically crafted to assess the correctness and quality of OpenMP parallel constructs, addressing limitations in conventional translation metrics.